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Investigation of protein induction in vascular-targeted strategies.
COLE, Laura Margaret.
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COLE, Laura Margaret. (2013). Investigation of protein induction in vascular-targeted strategies. Doctoral, Sheffield Hallam University (United Kingdom)..
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Investigation of Protein Induction in Vascular-targetedStrategies
Laura Margaret Cole
A thesis submitted in partial fulfilment of the requirements of Sheffield Hailam University
for the degree of Doctor of Philosophy
In collaboration with the University of Sheffield
February 2013
ACKNOWLEDGEMENTS
First and foremost, I would like to express my gratitude and appreciation of my principal supervisor Professor Malcolm R Clench, not only fo r his support and encouragement throughout my studies but for giving me many opportunities to extend my knowledge of
analytical Mass Spectrometry through conferences and instilling faith in me during the
presentation of my work. I am now a 'convert to Mass Spectrometry'!
I would also like to thank Dr Vikki A Carolan for her expertise and advice received during the
writing of my thesis and comments on the structure of the many abstracts and posters I have
emailed her over the last four years.
Special thanks and recognition is owed to the collaboration with the University of Sheffield. The
guidance by Professor Gillian M Tozer has been invaluable. I would also like to express my
gratitude to Professor Martyn N Paley and Professor Nicola J Brown for their input and support
during the many Imaging update meetings.
I would like to say thank you to Dr Chris Sutton at the Institute of Cancer Therapeutics in
Bradford for his patience and training during my time there.
For help with training and assistance on a variety of new techniques I am truly grateful to Dr Marie Claude Djidja, Emmanuelle Claude, Dr Adam Dowle, Dr Samira Kazan, Dr Florian Wulfert,
Kevin Blake, Rachel Daniels, Jenny Globe and M att Fisher.
I gratefully acknowledge Dr Simona Francese and Dr David Smith who not only have been
helpful minds of information during my PhD but great conference travel cohorts too.
I would like to declare a special thank you to colleagues past and present for their friendship
and support during my PhD who include Dr Paul J Trim, Leesa Ferguson, Dr Kate Phillips, Dr Louise Vickers, Eva llles-Toth, Dr Philippa J Hart, Bryn Flinders, Robert Bradshaw, Chris Mitchell
and Afnan Batubara, some of which are within the Mass Spectrometry group here at the BMRC.
A huge acknowledgement is extended to the 'coffee club ladies'; Dr Claire M Bradford, Dr Helenne Nyamwaro and Dr Rachel E Doherty who not only introduced me to cherry scones but also shared much laughter and discussion!
A very special thank you to my family and parents Tony and Laura who as always, support me
in everything that I have ever set out to achieve.
Last, but certainly not the least, much love and endless thanks to my husband Wayne and
daughter Alicia for support and encouragement words just cannot describe.
Abstract
The aims of the study reported in this thesis were to develop and utilise mass spectrometry
imaging techniques (MALDI-MSI), in combination with conventional proteomic methodologies,
to investigate protein induction in vascular-targeted strategies.
Proteins thought to be involved in tumourigenesis and drug treatment resistance were
observed along with the responses from proteins identified via the techniques used, in this
global analysis study. MALDI-MSI, LC-ESI-MS/MS, LC-MALDI-MS/MS with iTRAQ labelling and
immunohistochemistry intended to provide cross validation of the effects post administration
of vascular disrupting agent CA-4-P. Two mouse fibrosarcoma models (expressing VEGF120/ VEGF188 isoforms only) following treatment with the tubulin-binding tumour vascular disrupting agent, combretastatin A-4-phosphate (CA-4-P) have been studied.
The gross haemorrhagic pharmacological response elicited by CA-4-P was visible by MALDI-MSI throughout the fibrosarcoma 120 time course. The latter encouraged the prospect that other proteins could potentially be observed induced via a dose response relationship. The
haemoglobin time course using the resistant 188 tumour model gave quite different results to
those previously seen in the MALDI-MSI of the fibrosarcoma 120 data set. The first indication
of the 'switch back to tissue viability' concept was revealed.
The experimental work using LC-ESI-MS/MS revealed many proteins connected with necrosis, apoptosis, cell structural reorganisation, polymerisation, tumour survival and stress induced
molecular chaperones. The inverse correlation of structural proteins, haemoglobin and heat shock molecular chaperones gave the required validation and identification to relate these
responses to those seen in MALDI-MSI. The relationship pathways generated by using STRING9.0 proteomic network software gave an invaluable insight into the activity of the active
tumour milieu and provided a means of linking the identified proteins to their functional partners. Protein-protein interactions could be observed to help interpretation of the MALDI-
MSI, LC-ESI-MS/MS and iTRAQ LC-ESI-MS/MS response graphs.
Overall, the dose relationships observed in the iTRAQ data by the proteins involved in
haemorrhaging, structural remodelling, were in good agreement with the other techniques
employed here.
It could be said that MALDI-MSI could potentially forge a place in the workflow of clinical diagnostics. Targeted approaches for the observation of disease biomarkers could be visualised
using MALDI-MSI and serve as a complimentary technique to standard clinical imaging. A
novel method reported here using a multi-peptide recombinant standard could prove an
important diagnostic tool for the analysis of patient biopsies and tissue micro-arrays. The
exciting prospect is the diversity of a multi-peptide recombinant standard, an artificial construct that can be engineered to include any prospective biomarkers for both research and
diagnostic screening applications.
Table of ContentsTable of Contents.......................................................................................................................................1
List of Figures.............................................................................................................................................. 6
Chapter 1 ................................................................................................................................................ 6
Chapter 2 ................................................................................................................. 7
Chapter 3 ................................................................................................................................................ 8
Chapter 4 .............................................................................................................................................. 10
Chapter 5 .............................................................................................................................................. 11
Abbreviations........................................................................................................................................... 13
Chapter 1 ...................................................................................................................................................17
General Introduction...............................................................................................................................17
1.1 Cancer............................................................................................................................................. 18
1.1.1. Statistical information, incidence/ prevalence............................................................... 18
1.1.2 Tumour types........................................................................................................................ 22
1.1.3 Overview of current anti-cancer therapies.......................................................................23
1.1.4 Vascular targeted strategies................................................................................................ 30
1.1.5 Vascular disrupting agents.................................................................................................. 33
1.1.6 Biomarker Discovery............................................................................................................ 39
1.1.7 Considering Vascular disrupting agents for anti-cancer therapeutics..........................41
1.2 The Role of Mass Spectrometry in Biomarker Discovery....................................................... 41
1.2.1. Overview of mass spectrometry as an analytical tool.................................................... 41
1.2.2. Basic principles of mass spectrometry............................................................................. 42
1.2.3. Ionisation techniques and Mass analysers.......................................................................46
1.3 Mass spectrometry instrumentation........................................................................................ 49
1.3.1 LC-MS.................................................................................................................................... 49
1.3.2. Electrospray ionisation (ESI)............................................................................................... 51
1.3.3. Matrix assisited laser desorption ionisation (MALDI)................................ 52
1.3.4. Quadrupole Time of flight mass spectrometry (QqToF)................................................54
1.3.5. Synapt HDMS - travelling wave ion mobility separation............................................... 56
1.3.6. MALDI-mass spectrometry imaging.................................................................................. 59
1.3.7. Quantitative Proteomics............................ 60
1.3.8. Aims of the study.................................................................................................................. 63
References............................................................................................... 66
2.1 Introduction.................................................................................................................................75
2.2 Materials and Samples................................................................................................................ 76
2.2.1 Chemicals and Materials..................................................................................................... 76
2.2.2 Tissue samples.......................................................................................................................77
2.2.3. Experimental groups...........................................................................................................77
2.2.4 Tissue preparation................................................................................................................ 77
2.2.5 In situ tissue digestion and trypsin deposition................................................................. 77
2.3Methods and instrumentation.................................................................................................... 78
2.3.1 Matrix deposition................................................................................................................. 78
2.3.2 Instrumentation....................................................................................................................78
2.3.3 Haematoxylin and eosin staining....................................................................................... 79
2.3.4 Data pre-processing using SpecAlign and Marker View software................................ 79
2.3.5 Statistical analysis..................................................................................................................79
2.3.6 Data pre-processing usingWaters MassLynx™ Software and MATLAB®...................... 79
2.4 Results and Discussion.............................................................................................................. 80
2.4.1 Initial MALDI profiling and imaging of in situ tissue tryptic digests in mouse
fibrosarcoma 120 models.............................................................................................................. 80
2.4.2 Haematoxylin and Eosin staining of fibrosarcoma 120 tissue........................................83
2.4.3 Initial imaging of proteins according to theoretical digests using MALDI-MSI ......86
2.4.4 Principle component analysis-discriminant analysis (PCA-DA).................................. 88
2.4.5. Employment of Ion Mobility MALDI-Mass Spectrometry to study//? situ tryptic
digestion in fibrosarcoma 120 models........................................................................................ 91
2.4.6 Rho GTPases: a target to help understand resistance to CA-4-P?................................ 99
2.4.7.Statistical analysis of mouse fibrosarcoma 120 tumour tissue using MATLAB® 107
2.4.8 Region of interest (ROI) study for the analysis of tumour rim and necrotic regions
within fibrosarcoma 120 tissue................................................................................................... 112
2.4.9 Concluding Remarks........................................................................................................... 118
References.......................................................................................................................................... 120
3.1 Introduction................................................................................................................................. 124
3.2 Materials and Samples...............................................................................................................126
3.2.1 Chemicals and Materials....................................................................................................126
3.2.2 Tissue samples..................................................................................................................... 126
3.2.3. Experimental groups......................................................................................................... 126
3.2.4 Tissue preparation...............................................................................................................126
3.2.5 In situ tissue digestion and trypsin deposition............................................................... 127
3.3 Methods and instrumentation..................................................................................................127
2
3.3.1 Matrix deposition............................................................................................................... 127
3.3.2 Instrumentation..................................................................................................................127
3.3.3 Haematoxylin and eosin staining.....................................................................................128
3.3.4 Statistical analysis............................................................................................................... 129
3.3.5 Data pre-processing using Waters MassLynx™ Software and MATLAB®...................129
3.3.6Protein network analysis.................................................................................................... 129
3.3.7 Immunohistochemical staining........................................................................................ 129
3.3.8 Protein precipitation and digestion................................................................................ 130
3.3.9 Protein estimation - BCA assay....................................................................................... 131
3.4 Results and Discussion...........................................................................................................132
3.4.1 MALDI-MSI of fibrosarcoma 188 CA-4-P treated tumour tissue................................ 132
3.4.2 Statistical analysis of mouse fibrosarcoma 188 tumour tissue using MATLAB® 150
3.4.3 Label free LC-ESI -MS/MS for the identification of proteins in a CA-4-P treated
fibrosarcoma 188 mouse model............................................................................................... 159
3.4.4 Protein relationship mapping of fibrosarcoma 188 LC-ESI-MS/MS data using String9.0................................................................................................................................................... 182
3.4.5 Concluding remarks........................................................................................................... 186
References......................................................................................................................................... 188
4.1 Introduction................................................................................................................................ 194
4.2 Materials and Samples..............................................................................................................195
4.2.1 Chemicals and Materials...................................................................................................195
4.2.2 Tissue samples....................................................................................................................195
4.2.3. Experimental groups..................................................................................................... 196
4.2.4 Tissue preparation............................................................................................................. 196
4.2.5 Tissue homogenisation and precipitation of protein................................................... 196
4.2.6 Protein Digestion................................................................................................................197
4.2.7 Preparation of samples for column loading.................................................................. 197
4.2.8 C18 bond-elute preparation.............................................................................................197
4.2.9 Sample re-suspension.......................................................................................................198
4.2.10 iTRAQ labelling.................................................................................................................198
4.2.11 SCX..................................................................................................................................... 199
4.3Methods and instrumentation................................................................................................. 199
4.3.1 Sample fractionation and matrix deposition................................................................. 199
4.3.2 Instrumentation................................................................................................................ 199
4.3.3 Data pre-processing..........................................................................................................200
4.3.4 Statistical analysis.............................................................................................................. 201
3
4.3.5 Protein network analysis................................................................................................... 201
4.4 Results and discussion - Fibrosarcoma 120............................................................................ 202
4.4.1. HPLC UV profiles of fibrosarcoma 120 tumour fractions............................................ 202
4.4.2 Frequency of unique peptides identified........................................................................203
4.4.3. Analysis using excel...........................................................................................................203
4.4.4. Analysis with Scaffold Q + .................................................................................................207
4.4.5 Protein relationship mapping of fibrosarcoma 120 data using STRING 9.0.............. 218
4.5 Results and discussion - Fibrosarcoma 188............................................................................ 224
4.5.1. HPLC UV profiles of fibrosarcoma 188 tumour fractions............................................ 224
4.5.2 Frequency of unique peptides identified........................................................................225
4.5.3. Analysis using excel........................................................................................................... 225
4.5.4. Analysis with Scaffold Q + ................................................................................................. 231
4.5.5 Protein relationship mapping of Fibrosarcoma 188 data using STRING 9 .0 ............. 242
4.5.6 Protein dose response relationship analysis using String 9 .0 ......................................247
4.5.7 Relation of MALDI-MSI to iTRAQ proteomic response.................................................256
4.5.8 Concluding Remarks........................................................................................................... 259
References..........................................................................................................................................260
5.1 Introduction................................................................................................................................. 266
5.2 Materials and Methods............................................................................................................. 267
5.2.1 Peptide sequences chosen for a recombinant protein standard based on MALDI-MSI and conventional proteomic analysis of fibrosarcoma 120 and 188 tissue.........................267
5.2.2 Bacterial culture..................................................................................................................268
5.2.3 Plasmid preparation........................................................................................................... 269
5.2.4 E. coli- transformation........................................................................................................269
5.2.5 Protein Purification............................................................................................................ 269
5.4.6 Digestion...............................................................................................................................270
5.4.7 MALDI-MSI - Chemicals and Materials............................................................................ 271
5.4.8 Methods and instrumentation..........................................................................................272
5.3 Results and Discussion.............................................................................................................. 273
5.3.1 MALDI-IMS-MS Peptide mass fingerprint of the digested recombinant DNA construct ............................................................................................................................. 273
5.3.2 MALDI-IMS-MSI of fibrosarcoma 188 tumour tissue, Control and post CA-4-P
treatm ent.......................................................................................................................................275
5.3.3 Recombinant MALDI-IMS-MSI visualised through HDI 1.1 software..........................280
5.3.4 Cross validation of Plectin and HSP-90 response in fibrosarcoma 188 tissue
employing a multimodal proteomic strategy............................................................................292
4
5.4 Concluding Remarks................................................................................................................295
References..........................................................................................................................................297
Appendices.............................................................................................................................................304
Appendix 1: Publications................................................................................................................. 305
Appendix 2: Oral presentations..................................................................................................... 306
Appendix 3: Poster presentations..................................................................................................307
5
List of FiguresChapter 1Figure 1.1: Percentage of all deaths due to cancer in the different regions of the world (WHO)
Regions of the World............................................................................................................................... 19Figure 1. 2: Cancer incidence worldwide map.....................................................................................20Figure 1.3 : The 20 Most Common Causes of Cancer Death, UK..................................................... 21
Figure 1.4 : Diagrammatical representation of the possible routes of cell division..................... 22Figure 1. 5 : Proposal of adding CSC specific predictive tests to therapy regimes for potentialincrease in tumour control probability.................................................................................................26Figure 1. 6 : Images to illustrate the substandard tumour vasculature.......................................... 33Figure 1. 7 : Chemical structures of vascular disrupting agents....................................................... 34Figure 1. 8 : Proposed mechanism of action of Combretastatin A-4-3-0-phosphate...................35Figure 1. 9 : Fluorescent staining of Human umbilical vein endothelial cells, Control and postCA4P administration................................................................................................................................ 36Figure 1 .10 : CA-4-P and its effect on hypoxia induction and vascular shutdown........................37Figure 1 .1 1 : Exploitation of tumour vasculature by antiangiogenic treatment and vasculardisrupting agents......................................................................................................................................37Figure 1.12 : Therapeutic targeting of the hallmarks of cancer...................................................... 40Figure 1 .13:The cells of the tumour microenvironment...................................................................40Figure 1 .14 : Basic principles of a mass spectrometer...................................................................... 43Figure 1.15 : Basic ionisation techniques............................................................................................ 46Figure 1.16 : Mass analysers used in mass spectrometers...............................................................47Figure 1.17 : Protein identification methods......................................................................................48Figure 1.18 : Schematic representation of a liquid chromatography mass spectrometer.........50Figure 1.19 : Schematic of Electrospray ionisation............................................................................ 51Figure 1. 20 : Ionisation using MALDI....................................................................................................52Figure 1. 21: Diagrammatical representation of a tandem QqTOF mass spectrometer........... 55Figure 1. 22 : Schematic representation of a ToF modulator............................................................56Figure 1. 23 : SYNAPT™ G2 HDMS system............................................................................................57Figure 1. 24 : Stacked ring ion guide.....................................................................................................57Figure 1. 25 : Travelling wave ion guide- schematic representation................................................58Figure 1. 26 : MALDI-MSI workflow diagram...................................................................................... 60Figure 1. 27 :The concept of area peptide measurement coupled with a specific time
domain,visualisedthrough Progenesis LC- MS software....................................................................61
Figure 1. 28 : Simple workflow using Isobaric Tags for Relative and Absolute Quantitation(iTRAQ).......................................................................................................................................................62Figure 1. 29 : VEGF isoform expression and exon structure..............................................................64
6
Chapter 2Figure 2.1: Representative peptide mass fingerprint (PMF) arising from an on-tissue digest of aVEGF120 tumour 24 h after dosing with 100 mg/kg i.p, CA-4-P...................................................... 80Figure 2. 2: Peptide mass fingerprints obtained from the fibrosarcom 120 tumour tissue
sections treated with saline or 100 mg/kg i.p..................................................................................... 81
Figure 2. 3: MALDI-MS images for the distribution of m/z 1274throughout a fibrosarcoma 120timecourse................................................................................................................................................ 82Figure 2 .4 : Haematoxylin and eosin stained sections...................................................................... 84Figure 2. 5: Higher magnification images of the H&E staining of the tumour rim in the VEGF120tumour sections....................................................................................................................................... 85Figure 2. 6: MALDI-MSI of peptides from tumour 5:1 (24hrs CA-4-P).............................................86Figure 2. 7: PCA-DA of fibrosarcoma 120 tumour in situ tryptic digests.........................................88Figure 2. 8: Representative peptide mass fingerprint (PMF) arising from an on-tissue digest of aVEGF120 tumour 6 h after dosing with 100 mg/kg i.p, CA-4-P.........................................................92Figure 2. 9: IMS-MS/MS data obtained from the peak observed in Figure 2.8 at m/z 1819....... 93Figure 2 .10 a: A Drift scope plot of a tumour section 24 h post CA-4-P showing the ion mobilityseparation as a function of drift time................................................................................................... 94
Figure 2.11: The distribution of two known peptides in tumour 5_6, i.e. a tumour 24 h aftertreatment with CA-4-P........................................................................................................................... 97Figure 2.12: Overlaid MALDI MSI images showing differing spatial distribution and coregistration of peptides...........................................................................................................................98Figure 2.13: Major Functions of Rho GTPases in vitro.................................................................... 100Figure 2.14: Peptide mass fingerprints obtained from the fibrosarcom 120 tumour tissue30min post CA-4-P(100 mg/kg i.p.) with peak of interst at m/z 844...................... 101Figure 2.15: IMS-MS/MS data obtained from the peak observed in Figure 2.14 at m/z 844.5. 102Figure 2.16: Mascot score histogram detailing the results in the identification of N-chimaerin. 102
Figure 2.17: Zoomed in Driftscope plot showing ion of interest, arrow indicating the peakoriginally selected.................................................................................................................................. 103
Figure 2.18: PMF's throughout a fibrosarcoma 120 time course with putative peptide of interest N-chimaerin visible in early time points .Control (saline), 0.5 h post CA-4-P, 6 h postCA-4-P and 24 h postCA-4-P................................................................................................................. 104Figure 2.19: MALDI-MSI throughout a fibrosarcoma 120 time course of the putative peptideRho GTPase activating protein 2 m/z 844 (183-189, RLTSLVR).......................................................105Figure 2. 20 : PCA of fibrosarcoma 120 tumour tissue in situ tryptic digests............................... 108Figure 2. 21: Partial least squares discriminant analysis (PLSDA) regression vector plot comparing MALDI "on-tissue" digest data from samples of fibrosarcoma 120 Control/Saline
and 30 min post combretastatin-4-phosphate (CA-4-P) (a vascular disrupting agent), treatmenttumour samples......................................................................................................................................110Figure 2. 22: HDI software displaying MALDI-MSI from a fibrosarcoma tumour section 24h postCA-4-P.......................................................................................................................................................113Figure 2. 23: PLSDA regression vector plot showing fibrosarcoma 120 Control necrotic regions
versus fibrosarcoma 120 necrotic regions post 24h CA-4-P............................................................114
7
Figure 2. 24: PLSDA regression vector plot showing fibrosarcoma 120 Control rim regionsversus spectra from rim regions post 24h CA-4-P............................................................................ 115Figure 2.25: VIP scores for the Control fibrosarcoma 120 tumour rim regions...........................116Figure 2.26: VIP scores for the 24h post CA-4-P fibrosarcoma 120 tumour rim regions........... 117
Chapter 3Figure 3.1: Diagrammatical cross section of the region present at the end of growing longbones showing the areas involved in epiphysis and metaphysis................................................... 124Figure 3. 2: Screen shot of protein delocalisation in MALDI-MSI, visualised using BioMapimaging software................................................................................................................................... 133
Figure 3. 3: Screen shot of an on tissue tryptic digest using MALDI-MSI, visualised using BioMap
imaging software................................................................................................................................... 135
Figure 3.4: PMF's of a fibrosarcoma 188 time course study. Spectra shown are Control, 0.5h,6h, 24h and 72h post CA-4-P treatment.............................................................................................137Figure 3. 5: Zoomed in region of fibrosarcoma 188 PMF's. The blue arrows indicate the gross
differences visible in the 24h and 72h time points, m/z 1416 being Hb and an unknown speciesat m/z 1577............................................................................................................................................. 138Figure 3.6: Comparison control tissue peptide mass fingerprints from fibrosarcoma 120 and188 on tissue digests............................................................................................................................. 139Figure 3. 7: MALDI-MSI multi image of a fibrosarcoma 188 time course..................................... 141Figure 3. 8: Fibrosarcoma 188 MALDI-MSI multi image showing spatial distribution of HSP-90.MALDI images showing spatial distribution of HSP-90 alpha at m/z 1168................................... 143Figure 3. 9: Immunohistochemical staining of a fibrosarcoma 188 time course.........................144
Figure 3.10: Immunohistochemical staining of HSP-90 in Fibrosarcoma 188 tissue..................145Figure 3.11: SYNAPT G2 MALDI imaging plate indicating tissue image regions..........................147Figure 3 .1 2 :40pm MALDI-MSI of 24h post CA-4-P fibrosarcoma 188 tumour tissue 148Figure 3 .1 3 :40pm MALDI-MSI of m/z 944.5 in fibrosarcoma 188 tumour tissue..................... 148Figure 3 .1 4 :40pm MALDI-MSI of a on tissue trytic digest and corresponding H and Ehistological section................................................................................................................................ 149Figure 3.15: 30 pm MALDI-MSI of a Control fibrosarcoma 188 tumour on tissue tryptic digest.................................................................................................................................................................. 149
Figure 3.16: PCA score plot of fibrosarcoma 188 tumour tissue in situ tryptic digests.............150Figure 3.17: PCA loadings plot fibrosarcoma 188 tumour tissue in situ tryptic digests............ 151Figure 3.18: Partial least squares discriminant analysis (PLSDA) regression vector plot comparing MALDI "on-tissue" digest data from samples of fibrosarcoma 188 Control and 0.5hpost combretastatin-4-phosphate (CA-4-P), treatment tumour samples....................................152Figure 3.19: Partial least squares discriminant analysis (PLSDA) regression vector plot for thecomparison of Control and 6h post CA-4-P fibrosarcoma 188 on tissue digests.........................153Figure 3. 20: Partial least squares discriminant analysis (PLSDA) regression vector plot for thecomparison of Control and 24h post CA-4-P fibrosarcoma 188 on tissue digests...................... 154Figure 3. 21: Partial least squares discriminant analysis (PLSDA) regression vector plot for thecomparison of Control and 72h post CA-4-P fibrosarcoma 188 on tissue digests.......................155Figure 3. 22: VIP scores for the 0.5h post CA-4-P fibrosarcoma 188 on tissue digest................ 156
8
Figure 3. 23: VIP scores for the 6h post CA-4-P fibrosarcoma 188 on tissue digest....................156Figure 3. 24: VIP scores for the 24h post CA-4-P fibrosarcoma 188 on tissue digest................. 157Figure 3. 25: VIP scores for the 72h post CA-4-P fibrosarcoma 188 on tissue digest................. 157Figure 3. 26: Scaffold proteomic tool software used for LC-ESI-MS/MS analysis....................... 160Figure 3. 27: Graph A - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................161Figure 3. 28: Graph B - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................162Figure 3. 29: Graph C - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3....................................................... 163Figure 3. 30: Graph D - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................164
Figure 3. 31: Graph E - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................165Figure 3. 32: Graph F - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................166Figure 3. 33: Graph G - Fibrosarcoma 188 treatment time course results, exported fromScaffold 3.................................................................................................................................................167
Figure 3. 34: Fibrosarcoma 188 time course results post CA-4-P treatment using LC-ESI-MS/MS. 168Figure 3. 35: MS/MS spectrum and normalised intensity graph of Alpha-2-macroglobulin infibrosarcoma 188 LC-ESI-MS/MS results............................................................................................. 170
Figure 3. 36: Immunohistochemical staining of macrophage cell surface marker F4/80 usingfibrosarcoma 188 tissue lOx................................................................................................................ 172Figure 3. 37: MS/MS spectrum and normalised intensity graph of Plectin in fibrosarcoma 188
LC-ESI-MS/MS results. Insert displays the normalised spectral counts for Plectin throughout thetime course post CA-4-P........................................................................................................................174Figure 3. 38: Fibrosarcoma 188 MALDI-MSI multi image showing spatial distribution of Plectin.MALDI images showing spatial distribution of Plectin at m/z 977.................................................174Figure 3. 39: Immunohistochemical staining of Plectin in Fibrosarcoma 188 tissue post CA-4-Ptreatment................................................................................................................................................ 175Figure 3.40: Optical scans of anti-Plectin and HSP-90 Immunohistochemical staining infibrosarcoma 188 serial sections, 72h post CA-4-P treatm ent...................................................... 177Figure 3.41: Example MS/MS spectrum and normalised intensity graph of IQGAP1 infibrosarcoma 188 LC-ESI-MS/MS results............................................................................................ 178Figure 3.42: Example MS/MS spectrum and normalised intensity graph of Tenascin C infibrosarcoma 188 LC-ESI-MS/MS results............................................................................................ 179Figure 3.43: Example MS/MS spectrum and normalised intensity graph of Alpha-enolase infibrosarcoma 188 LC-ESI-MS/MS results............................................................................................ 180Figure 3.44: Example MS/MS spectrum and normalised intensity graph of Transforming
growth factor-beta-induced protein ig-h3 (Tgfbi) in fibrosarcoma 188 LC-ESI-MS/MS results. 181
Figure 3.45: LC-ESI-MS/MS proteins of interest from fibrosarcoma 188, visualised throughproteomic pathway software STRING 9.0.......................................................................................... 182
Figure 3.46: Proteomic network from LC-ESI-MS/MS fibrosarcoma 188 data............................ 184
9
Chapter 4Figure 4 .1: The principles of multiplex tagging chemistry............................................................ 194Figure 4.2: UV profiles from the fibrosarcoma 120 fractions after SCX...................................... 202Figure 4. 3: Frequency of detection of unique peptides by LC-MALDI-MS/MS......................... 203Figure 4 .4 : iTRAQ results from fibrosarcoma 120 fixed filtered search......................................204Figure 4. 5: iTRAQ ratio response relative to the control (113), normalised to an average of 1.
................................................................................ 205
Figure 4. 6: Scaffold proteomic software used for iTRAQ analysis................................................ 208Figure 4 .7: Scaffold proteomic software with the employment of 'Q+' function for iTRAQ ratioanalysis....................................................................................................................................................209Figure 4. 8: iTRAQ fold change ratio using fibrosarcoma 120 tumour digest samples.............. 210Figure 4. 9: Vimentin peptides identified, response bar graph and related MS/MS spectrumfrom iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120 tissue................................................................ 212Figure 4.10: Haemoglobin subunit beta-1 peptides identified, response bar graph and relatedMS/MS spectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120 tissue................................ 213Figure 4.11: Peroxiredoxin-1 (Macrophage 23kDa stress protein) peptides identified, response
bar graph and related MS/MS spectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120tissue...................................................................................................... 214Figure 4.12: Tubulin beta-5 chain peptides identified, response bar graph and related MS/MSspectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120 tissue.............................................. 215Figure 4 .13: Annexin A5 peptides identified, response bar graph and related MS/MS spectrumfrom iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120 tissue................................................................ 216Figure 4.14: Keratin -1 (type II skeletal 1) peptides identified, response bar graph and relatedMS/MS spectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 120 tissue................................ 217Figure 4.15: Fixed modification search showing proteins from iTRAQ using Fibrosarcoma 120tumours, visualised by STRING 9.0 (Confidence view).....................................................................219Figure 4.16: Fixed modification search of iTRAQ fibrosarcoma 120 proteomic data, compliedusing STRING 9.0, evidence view.........................................................................................................220Figure 4.17: Zoomed in region of a fixed modification search, iTRAQ fibrosarcoma 120 samples. 221
Figure 4.18: Zoomed in region of a fixed modification search, iTRAQ fibrosarcoma 120 samples. 222
Figure 4.19: UV profiles from the fibrosarcoma 188 fractions after SCX..................................... 224Figure 4. 20: Frequency of detection of unique peptides by LC-MALDI-MS/MS........................225Figure 4. 21: iTRAQ results from fibrosarcoma 188 fixed filtered search.................................... 226Figure 4. 22: a iTRAQ ratio response relative to the control (113), normalised to an average of 1...........................................................................................................................................................................227
Figure 4. 23: Scaffold proteomic software used for iTRAQ analysis.............................................232Figure 4. 24: Scaffold proteomic software with the employment of 'Q+' function for iTRAQ
ratio analysis........................................................................................................................................... 233Figure 4. 25: iTRAQ fold change ratio using fibrosarcoma 188 tumour digest samples............ 234Figure 4. 26: Vimentin peptides identified, response bar graph and related MS/MS spectrumfrom iTRAQ LC-MALDI-MS/MS Fibrosarcoma 188 tissue.................................................................237Figure 4. 27: Haemoglobin subunit beta-1 peptides identified, response bar graph and related
MS/MS spectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 188 tissue .................... 238
10
Figure 4. 28: Pyruvate kinase isozymes M 1/M 2 peptides identified, response bar graph andrelated MS/MS spectrum from iTRAQ LC-MALDI-MS/MS Fibrosarcoma 188 tissue.................. 239Figure 4. 29: Tenascin peptides identified, response bar graph and related MS/MS spectrumfrom iTRAQ LC-MALDI-MS/MS Fibrosarcoma 188 tissue................................................................ 240Figure 4. 30: Fibronectin peptides identified, response bar graph and related MS/MS spectrumfrom iTRAQ LC-MALDI-MS/MS Fibrosarcoma 188 tissue................................................................ 241
Figure 4. 31: iTRAQ Fixed modification search using Fibrosarcoma 188 tumours, visualised bySTRING 9.0 (Confidence view)............................................................................................................. 243Figure 4. 32: iTRAQ Fixed modification search using Fibrosarcoma 188 tumours, visualised bySTRING 9.0 (Evidence view)..................................................................................................................244Figure 4. 33: Zoomed in region of a fixed modification search, iTRAQ fibrosarcoma 188 samples,visualised through STRING 9.0.............................................................................................................245Figure 4. 34: iTRAQ fibrosarcoma 188 zoomed in region of a fixed modification search,visualised through STRING 9.0.............................................................................................................246Figure 4. 35: iTRAQ fibrosarcoma 188 zoomed in region of a fixed modification search,visualised through STRING 9.0.............................................................................................................247Figure 4. 36: iTRAQ proteomic response results using a Fibrosarcoma 120 treatmenttimecourse.............................................................................................................................................. 248Figure 4. 37: Fibrosarcoma 120 proteomic response using String 9.0.......................................... 250Figure 4. 38: iTRAQ combinations of fibrosarcoma 120 increased and decreased proteins.... 251
Figure 4. 39: iTRAQ proteomic response results using a Fibrosarcoma 188 treatment timecourse...................................................................................................................................................... 252Figure 4.40: iTRAQ proteomic response results using a 24h post CA-4-P Fibrosarcoma 188treatment................................................................................................................................................ 253Figure 4.41: iTRAQ proteomic response results using a 0.5h post CA-4-P Fibrosarcoma 188treatment................................................................................................................................................ 254Figure 4.42: iTRAQ combinations of fibrosarcoma 188 increased and decreased proteins. ...255
Figure 4.43: Bar graph showing protein response relative to the control (113) normalised to anaverage of 1, using fibrosarcoma 188 tissue..................................................................................... 256Figure 4.44: MALDI-MSI multiple Fibrosarcoma 188 sample on tissue digest of Plectin at m/z977............................................................................................................................................................257Figure 4.45: MALDI-MSI multiple Fibrosarcoma 188 sample on tissue digest of Hb subunit alpha at m/z 1416.................................................................................................................................. 258
Chapter 5Figure 5.1: pETBIue-1 vector and sequence used for the synthesis of the recombinant proteinstandard.................................................................................................................................................. 268Figure 5. 2: The final sequence of the recombinant standard........................................................ 268Figure 5. 3: PMF of a novel recombinant standard for MALDI-IMS-MSI validation.................... 274Figure 5.4: MALDI-MSI of on tissue tryptic digests and digested recombinant standard in 72h
treated tissue..........................................................................................................................................275Figure 5. 5: MALDI-MS images for the distribution peptides in fibrosarcoma 188 72h tissue,employing a DNA artificial construct for image validation..............................................................276Figure 5. 6: MALDI-MS images displaying peptides distribution in fibrosarcoma 188 6h treated
tissue, employing a DNA artificial construct for image validation. The spatial distribution of peptides; Actin (m/z 1198) with arrow indicating position of the spotted recombinant standard,
11
Histone HB (m/z 1032)showing the absence of the standard and CHCA peak at 1066 to showinverse image correlation.....................................................................................................................277Figure 5. 7: MALDI-MS images for the distribution peptides in fibrosarcoma 188 Control tissue,
employing a DNA artificial construct for image validation............................................................. 278Figure 5. 8: HDI visualisation of Actin at m/z 1198.7. Arrow indicates the ion of interestseparated by drift time and yellow square denotes the ROI for exportation..............................281Figure 5. 9: HDI visualisation of m/z 1198.6 to display the differentiation in tissue and standardimage due to separation in drift time compared to m/z 1198.7....................................................283Figure 5.10: HDI visualisation of HSP-90a at m/z 1168.5. Arrows indicates the ion of interestseparated by drift time, and yellow ROI square is shown..............................................................284Figure 5.11: HSP-90 a at m/z 1168.5 after exportation to Mass Lynx software from theselected ROI in HDI 1.1......................................................................................................................... 285Figure 5.12: HDI visualisation of Histone H3 at m/z 1032.7. The arrow indicates the ion ofinterest separated by drift time and no signal apparent in the standard.....................................286Figure 5.13: HDI visualisation of Actin at m/z 1198.7. The arrow indicates the ion of interestseparated by drift time......................................................................................................................... 287Figure 5.14: HDI visualisation of Histone 2A at m/z 944.5. The arrow indicates the ion ofinterest separated by drift time.......................................................................................................... 288Figure 5.15: HDI software with image correlation filter for Plectin m/z 1032 displaying fellowHistones...................................................................................................................................................290Figure 5.16: HDI software with image correlation filter for Plectin m/z 1350 showing nodiscrimination between other peaks............................................................. 291Figure 5.17: MALDI-MSI of fibrosarcoma 188 on tissue tryptic digests for the spatialdistribution of Plectin at m/z 1385......................................................................................................292Figure 5.18: MALDI-MSI and conventional proteomic techniques to asses a dose relationship inPlectin...................................................................................... 293Figure 5.19: MALDI-MSI and conventional proteomic techniques to asses a dose relationship inHSP-90..................................................................................................................................................... 294Figure 5. 20: Issues and factors to consider when performing on tissue digestion for MALDI imaging experiments............................................................................................................................. 296
List of Tables
Table 1: Cancer incidence throughout the regions of the w orld.................................................... 18Table 2: Current anti-cancer treatments..............................................................................................29Table 3: Categorisation of vascular targeted therapies.....................................................................30
Table 4 : Example of drugs within each category................................................................................31
Table 5 : Inhibiting the process of angiogenesis.................................................................................32Table 6 : Mass spectrometers for proteomics.................................................................................... 45Table 7 : Common matrices used in UV- MALDI..................................................................................54Table 8: Identities and Mascot scores from the IMS-MS/MS analyses of peptide signals...........91Table 9: Twelve peptides from target proteins chosen for positive identification..................... 267
12
AbbreviationsACN: acetonitrile
Aldoartl: Fructose-bisphosphate aldolase A
AL022784: alpha-enolase 1 (Enol)
ANI: aniline
APCI: atmospheric pressure chemical ionisation
APPI: atmospheric pressure photoionisation
BCA: bicinchoninic acid assay
Calr: Calreticulin
CHCA: a-cyano-4-hydroxycinnamic acid
CHCI3: chloroform
CID: collision-induced dissociation
CA-4-P: combretastatin A-4 -3-0 phosphate
CSC: cancer stem cell
dH20: deionised water
DMXAA: 5,6-Di-methylxanthenone-4-acetic acid
Eeflal: Elongation factor 1-alpha 1
ESI: electrospray ionisation
ESI-LC-MS: electrospray ionisation-liquid chromatograpgy-mass spectrometry
ESI-LC-MS/MS: electrospray ionisation-liquid chromatograpgy-tandem mass spectrometry
EtOH: ethanol
FFPE: formalin fixed paraffin embedded
GAPDH: Glyceraldehyde-3-phosphate dehydrogenase
13
Gpil: Glucose-6-phosphate isomerase
GRP-78: glucose- regulated protein 78 (Hspa5)
HE: haematoxylin and eosin
HIF-1 a: hypoxia- inducible factor 1 alpha
HPLC: high performance liquid chromatography
HSP: heat shock protein
IHC: immunohistochemistry
IMS: ion mobility separation
IQGAP1: Ras GTPase-activating-like protein
iTRAQ: isobaric tag for relative absolute quantitation
KTR1: Keratin-l(type II skeletal-1)
LC: liquid chromatography
LC-MALDI: liquid chromatography-matrix assisted laser desorption ionisation
LC-MALDI-MS/MS: liquid chromatography-matrix assisted laser desorption ionisation-tandem
mass spectrometry
LC-MS/MS: liquid chromatography -mass spectrometry tandem mass spectrometry
Lgalsl: Galectin-1
MALDI: matrix assisted laser desorption ionisation
MALDI-MS: matrix assisted laser desorption ionisation-mass spectrometry
MALDI-IMS-MS: matrix assisted laser desorption ionisation-ion mobility separation-mass
spectrometry
MALDI-IMS-MSI: matrix assisted laser desorption ionisation-ion mobility separation-mass
spectrometry imaging
MALDI-MSI: matrix assisted laser desorption ionisation- mass spectrometry imaging
MALDi-ToF: matrix assisted laser desorption ionisation-time of flight
Mcm5: DNA replication licensing factor MCM5
MS: mass spectrometry
m/z: mass- to -charge ratio
MeOH: methanol
MudPIT: multidimensional protein identification technology
N2 laser: nitrogen laser
Nd: YAG: neodymium-doped yttrium aluminium garnet
Nd:YV04: yttrium ortho-vanadate
NH4HC03: ammonium bicarbonate
NHS: N-hydroxysuccinimide
OcGIc: Octyl-a/p-glucoside
PCA: principle component analysis
PCA-DA: principle component analysis-discriminant analysis
PLSDA: partial least squares discriminant analysis
PMF: peptide mass fingerprint
PRDX1: Peroxiredoxin-1
Q: quadrupole
QqTOF: quadrupole time of flight
ROI: region of interest
S100a6: Calcylin
SMA: smooth muscle actin
TAA: tumour associated antigen
TFA: trifluoroacetic acid
Tgfbi: Transforming growth factor-beta-induced protein ig-h3
15
Tnc: Tenascin C
TOF: time of flight
TOF MS: time of flight mass spectrometry
Tpil: Triosephosphate isomerase
TSP: thermospray
TWIGS: travelling wave ion guides
VDA: vascular disrupting agent
VEGF: vascular endothelial growth factor
VEGFR: vascular endothelial growth factor receptor
VIP: variable importance in projection
VTS: vascular targeted strategy
VTT: vascular targeted therapy
16
Chapter 1General Introduction
17
1.1 Cancer
1.1.1. Statistical information, incidence/ prevalenceAt the time that this account was written the latest official current world population estimate
was ~7,094,391,160. This approximation was made based on population figures on the
27thJanuary 2013 (Worldometers 2013). Demographic predications state that by 2028 the
number of our planet's inhabitants will exceed 8 billion (Cancer Research UK 2007).
The current population is described as 'aging', the relevance of this from a cancer perspective
is that the disease has been known to predominantly affect the older generations (Cancer
Research UK 2007). Worldwide life expectancy is increasing, people are living longer and the
average lifetime expectancy at birth is said to be 65yrs possibly escalating to 76yrs by the year
2050. Throughout the world there are still ~12.7 million people who are annually diagnosed
with cancer with estimations likely to increase to ~26 million by 2030 (World Cancer Research
Fund 2010). The UK has the 22nd highest overall cancer rate in the world.
Africa 715,571 6
Asia 6,092,359 48
Europe 3,208,882 25
Latin America & 906,008 7Caribbean
North America 1,603,870 13
Oceania 135,864 1
Developed regions 5,555,281 44
Developingregions 7,107,273 56
World 12,662,554 100
Table 1 : Cancer incidence throughout the regions o f the world (Adapted from Cancer Research UK 2011).
It is evident from Table 1 that Asia has the largest proportion of new cases diagnosed but this
is understood to be a reflection of the magnitude of population size within that particular
continent.
18
On a global scale the mortality rate is such that in 2008 there were an estimated 7.6 million
deaths as a result of cancer, the bar graph below shows the proportion of deaths due to cancer
in the regions of the world (World Health organisation 2012).
25
Western Europe Amencas World South-East Eastern Africa Pacific Asia Mediterranean
Area
Figure 1 .1 : Percentage of all deaths due to cancer in the different regions o f the world (WHO) Regions of the World, 2008 Estimates (Cancer Research UK 2007).
The following map (Figure 1.2) entitled 'Cancer Incidence Worldwide' (Cancer Research UK
2011) represents the most frequently diagnosed cancers within the areas shown in 2008. The
cancer types which account for the largest proportion of cases diagnosed are found to be; lung,
breast, bowel, stomach and prostate.
19
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It is stated that 47% of all cancer related death are from lung, bowel, breast and prostate
cancers (Cancer Research UK 2011). Figure 1.3 displays the 20 most common causes of death
from cancer in the UK.
Male ■ Female
Lung Bowel* Breast
Prostate Pancreas
Oesophagus
Stomach Bladder
Leukaemia Non-Hodgkin Lymphoma
Ovary Kidney
Brain and Central Nervous SystemLiver
Other Digestive Organs Myeloma
Mesothelioma Malignant Melanoma
Oral Uterus
Other Sites**
0 5.000 10,000
Number of Deaths
15,000 20,000
Figure 1.3 : The 20 Most Common Causes of Cancer Death, UK, 2010 (Cancer Research UK 2011).
21
1.1.2 Tumour typesThere are over 200 different kinds of cancer which can be classified by cell of origin (Cancer
Research UK 2009). Tumours are generally classified as benign or malignant to illustrate that
not all are cancerous (Medline Plus 2010). Malignant is the term used to describe an abnormal
neoplasm which is cancerous and benign is a non-cancerous growth. The cells which constitute
the cancerous tumour types have metastatic capabilities and are able to reach other regions of
the body to form secondary tumours whilst benign tumour types do not metastasize.
Figure 1 .4 : Diagrammatical representation o f the possible routes o f cell division (National Cancer Institute 2009).
The cancer cell has the ability to elicit morphological changes as depicted in Figure 1.4., to
ultimately disregard the usual signals for cell suicide or apoptosis.
It is possible to divide solid tumours into 3 broad categories these being; carcinomas which
originate from altered epithelial cells which function as linings of a body surface region i.e. skin,
gut, cervix, mouth, lung, sarcomas occur in connective tissue i.e. bone, muscle, cartilage and
lymphomas/ leukaemia which describes cancers that arise from lymphoid organs (lymph nodes,
spleen) and cells from bone marrow (http://www.chemotherapy.com/ 2011).
Loss of Normal Growth Control
Normal cell division
CeB SujckV or ApoptoiH
Cancer cell division
Flrct Second mutation mutation
TTWd Fourth Of mutation later mutation
Uncontrolled growth
22
1.1.3 Overview of current anti-cancer therapiesPre-treatment clinical assessment of patient tumours is generally based on tumour size,
through histological staining, tumour grade and is regularly coupled with the employment of
various imaging techniques (Koch et al 2010). The tumour grading is a system that is used as a
reference in order to classify cancer cells. Analysis of the abnormalities exhibited by cancer
cells are visualised by microscopy, here the aggressiveness of the tumour can be determined.
Although factors used for tumour grading vary in cancer type the cellular architecture and
growth patterns are studied (National Cancer Institute 2009). The outcome of these
evaluations will then determine the appropriate treatment route.
An example of a grading system commonly used by Doctors is the TNM system. This grading
system is an accepted method by the International Union against Cancer (UICC) and the
American Joint Committee on Cancer (AJCC) (National Cancer Institute 2009). In brief the
rationale behind the TNM method is that the T refers to the extent of the tumour, the 'N'
amount of colonisation in the lymph nodes and 'M ' the presence of metastasis. A number is
assigned to the T /N /M indicative of size and metastatic status. The TNM system is further
annotated, details are as follows:
Primary Tumours (T); TX - Primary tumour cannot be evaluated, TO - no evidence of primary
tumour, Tis - Carcinoma in situ and T l, T2, T3, T4 refer to the size/ severity of the primary
tumour.
Regional Lymph Nodes (N); NX-regional lymph nodes cannot be evaluated, NO - no regional
lymph node involvement and N l, N2, N3 depicts the involvement of regional lymph nodes and
to what extent.
Distant Metastasis (M) - MX distant metastasis cannot be evaluated, M0 - no distant
metastasis and M l distant metastasis is present.
Further categorisations of the above TNM combinations are then graded as follows;
Stage 0 - Carcinoma in situ, Stage I, Stage II, and Stage III Higher are indicative of the extent
ofcancer: Larger tumour size and/or spread of the cancer beyond the organ in which it first
developed to nearby lymph nodes and/or organs adjacent to the location of the primary
tumour.Stage IV - the cancer has metastasised to another organ(s).
The above information regarding the TNM system was taken from (National Cancer Institute
2009, http://www.cancer.gov/cancertopics/factsheet/detection/staging).
23
Currently the main course of action for the treatment of tumours is by surgical removal,
chemotherapy, radiotherapy, systemic adjuvant treatments and in recent times some agents
have been used to achieve a more targeted approach (Koch et al 2010).
A brief account follows which considers the history of how both chemotherapy and
radiotherapy have been implemented as anticancer therapies within the clinical setting.
Secondly, a compilation anticancer therapies in table format aims to summarise the range of
treatments available with an overview of how they are administered, anti-cancer mechanisms
and possible side effects.
It is documented that work by German scientist Paul Ehrlich lead to the adoption of the widely
used term 'chemotherapy' (DeVita and Chu 2008). During turn of the 20th century, the focus
of his work was the discovery and advancement of drugs to combat infectious disease.
Amongst Ehrlich's achievement was the use of animal models to enable assessment of
potential anticancer agents.
With reference to a cancer chemotherapy timeline by DeVita and Chu 2008, it took
approximately 40 years to develop a suitable experiment model (DeVita and Chu 2008).
Although this period included the revolutionary success of transplantable tumour rodent
models by New Yorker George Clowes a surgeon/ researcher from the Roswell Park Memorial
Institute.
With regard to early anticancer therapeutics, 1908 saw the use of arsenicals which had been
originally used to eradicate Syphilis (DeVita and Chu 2008). 'Fowlers solution1 (arsenicals) as it
was known, was used to treat leukaemias and apparently a common practice well into the
1930's (Papac 2001).
Optimism arising from these early triumphs in cancer therapy was dampened following work
investigating the use of nitrogen mustard (1943) to eradicate lymphomas. (DeVita and Chu
2008). Reports stated that patients suffering from lymphomas experienced only short-lived
remission and thus began a decline of confidence in the concept of 'a drug to cure cancer'.
It was not until the opening of the Cancer Chemotherapy National Service Centre (CCNSC) in
1955 that a possible future for cancer drug development was confirmed (DeVita and Chu 2008).
This change of heart was said to be encouraged by the positive response to the drug
methotrexate in children with acute leukaemia. Since the 1960s, the development of anti
cancer drugs has been aided by many advances. Experimental models which employ
xenografts and novel cell culture methods have been used. Adjuvent therapy (chemotherapy,
radiation therapy, hormone therapy, targeted therapy, or biological therapy) given in addition
24
to the primary treatment was developed and investments into molecular biology have been
made. From the beginning of the 21st century, there seems to have been a different approach
in anticancer therapeutics emerging (DeVita and Chu 2008). Completion of sequencing of the
genome appears to have led to a more targeted approach to cancer treatment. Through
advancements in chemotherapy a major decrease in mortality is seen by 2007, the latter
considering knowledge of prevention and improvements in diagnostics.
Radiotherapy/ Radiation Oncology are terms which are commonly associated with cancer
treatment with an estimated 50% of patients receiving this form of anticancer therapy for both
palliative and therapeutic means (Delaney et al 2005). In combination with other forms of
management, radiation therapy features in approximately 40% of those patients who are
cured of their disease (Shariq et al 2009).
After the German physicist Wilhelm Roentgen discovered x-rays in 1895, the concept of using
x-rays as a therapeutic aid was not long to follow, assisted greatly through work on radioactive
elements (radium, polonium) by Marie Curie and husband Pierre (Connell and Heilman 2009).
There have been numerous clinical, technological and biological advances in radiotherapy
since Roentgen's breakthrough over 115 years ago. Examples of key clinical advances are Henri
Coutard using fractionated external beam radiotherapy (XRT) in 1928 to cure head/ neck
malignancies, the use of the proton beam (1961) and the high precision accuracy of
stereotactic body radiotherapy (1995) (Connell and Heilman 2009).
A large proportion of patients, however, still exhibit metastasising cancers even after
treatments such as combination radiotherapy. (Koch et al 2010). One explanation could be the
inadequate targeting of cancer stem cells (CSCs), i.e. CSCs could be sheltered by hypoxic
tumour environments enabling them 'radio-resistant'. Therefore increased doses of radiation
could be concentrated to these specific resistant areas. The following proposal (Figure 1.5) by
Koch et al (2010) suggested how CSC-predictive test regime and radiotherapy could be
implemented.
25
Current therapy
Entity I Histology Tumour volume Tumour stage Metabolic imaging (PET)
CancerFuture therapypatient
(Clinical) predictors(Clinical) predictors
Entity I Histology Tumour volume Tumour stage
• Metabolic imaging (PET)
Additional CSC-specific predictive tests
CSC densityCSC distributionCSC sensitivity to therapyImaging of CSC niches I enriching micromilieuSignature (DNA, RNA, protein etc.) of CSCs
Estimation of response to therapy
Decision for empirical class solution
r.......
±Estimation of response to therapy
CSC-based design of individual therapy within class solution
Frequently incomplete eradication of CSCs Improved eradication of CSCs
Frequent tumour recurrences
El 80
--------Higher cure rates ____________
& 0
0
@
| £ BOo I’ GO
| | 40
K & 20i O
T e r*
m Tumour eradicated
O Non-CSC
0 C S C
Figure 1. 5 : Proposal of adding CSC specific predictive tests to therapy regimes fo r potential increase in tumour control probability (Koch et a 12010).
Table 2 gives a list of current anti-cancer therapies, administration routes, mechanism and
possible side effects related to the particular therapy.
26
Anti-cancer Therapy Administration Route Anti-cancer Action Examples of Possible
Side Effects
Biological therapy Orally, in jected,
in travenous,
in tra d e rm a l/
in tram uscu la r
Targets th e im m une
system, re ta rd a tio n o f
cancer cells, e.g. IL-2,
In te rfe ro n alpha,
Rashes, nausea, flu like
sym ptom s, hypotension
Chemotherapy Orally, in jected, Targets cells o f high Hair loss, rashes,
in travenous, in tra - p ro life ra tive na tu re d ia rrhoea , nausea/
a rte ria lly , in su ffla tio n a lte ring DNA synthesis/ vom iting , d igestion
fun c tion , in te rfe rence issues, fa tigue
w ith cell cycle e.g.
a lky la ting
agents(C isplatin),
an tim e tab o lite s
(m e tho trexa te ),
a n titu m o r an tib io tics
(B leom ycin),
anthracyclines
(Idarubic in), p lan t
alkalo ids (V inblastine),
taxanes (Paclitaxel),
m onoclona l an tibod ies
(Bevacizumab),
Radiation Therapy Patient exposure to E xp lo ita tion o f rap id Hair loss, rashes.
source o f rad ia tion , d iv id ing cell types, d ia rrhoea , nausea/
localised beam exposure o f rad ia tion to vom iting , d igestion
tu m o u r regions issues, fa tigue ,
ne u trop en ia
Targeted Therapies O rally (small m olecu le Target high Nausea/ vom iting ,
inh ib ito rs ), p ro life ra tive cells, rashes, d ia rrhoea , skin
In travenously Inh ib ito rs o f d isco lo ra tion ,
(m onoclona l angiogenesis, ty ros ine dyspepsia, flu c tu a tio n s
an tibodies), kinase rece p to r in b lood pressure
in h ib ito rs , to im pede
tu m o u r en la rgem ent
and survival
27
Proton Beam Therapy Patient exposure to
source o f rad ia tion ,
h igh ly specific localised
beam
Based on s tab ility o f
p ro tons enabling 3D
m an ipu la tion o f beam.
Highly specific to
tu m o u r region w ith
m in im al exposure to
norm al tissue
Hair loss, rashes,
d ia rrhoea , nausea/
vom iting , d igestion
issues, fa tigue ,
neu tropen ia
Surgical Oncology Perform ed by Surgeon
p re /p o s t chem othe rapy
or rad io the rapy if
m alignan t
Surgical rem oval o f
tu m o u r tissue plus
som e hea lthy tissue
fro m periphery,
transp la n ta tio n o f bone
m arrow
Risk o f in fe c tion , ha ir
loss, by 'p rep a ra tive
reg im en ' (bone m arro w
transp la n t
surgery)d iarrhoea ,
nausea/ vom iting ,
fa tigue
Gene Therapy A fte r transduc tion cells
are re in troduced in to
pa tien t by in travenous
in fusion, insu ffla tion ,
d irec tly in to tu m o u r if
accessible
Cancer cells th a t have
taken up transduced
cells are said to have
increased suscep tib ility
to chem othe rapy and
o th e r anticancer
tre a tm e n ts
Long te rm side effects
unknow n, f lu -like
sym ptom s,
hypo tens ion , vom iting ,
nausea
Hormone Therapy O rally, in jec tion ,
surgical in te rve n tio n
In te rfe rence w ith
ho rm one cell surface
receptors to block
activa tion o f a
Flu-like sym ptom s,
w e igh t gain, th rom b os is
in rare cases, a lopecia,
m u s c le / jo in t pain
pa rticu la r horm one
th o u g h t to encourage
tu m o u r g ro w th , e.g.
Tam oxifen - a n ti
oestrogen, Fulvestrant
- oestrogen recep to r
an tagon ist
Vaccine Therapies Presently tria lled on Aim to induce tu m o u r Long te rm side e ffec ts
pa tien ts w ith advanced im m unogen ic ity unknow n, flu - like
illness, no t ye t am ongst sym ptom s, vo m itin g ,
the m ainstay o f nausea
28
anticancer trea tm e n ts ,
the search fo r novel
tu m o u r associated
antigens is underw ay
Complementary / Various rou tes
Alternative Medicine ad m in is tra tion
o f Num erous categories Advice requ ired fo r the
some w h ich include; p a tien t to consider
sp iritua l healers, poss ib ility o f cross
d ie ta ry in te rven tion , rea c tiv ity w ith
herbal m edicines conven tion means o f
psychological m ethods, tre a tm e n t
cu ltu ra l techniques
based on tra d itio n
Table 2: Current anti-cancer treatments (Oncolink 2010., National Cancer Institute 2009).
29
1.1.4 Vascular targeted strategiesTargeting the tumour vasculature is a promising anticancer strategy and drugs of this nature
are in various stages of clinical trials. Here, the types of vascular -targeted therapies (VTTs) will
be discussed looking at pitfalls, limitations and future aspirations regarding this kind of
approach to anticancer medicine. Firstly a brief overview will describe the targets and
mechanisms of VTTs focusing then on the vascular disrupting agent Combretastatin A-4-3-0-
phosphate (CA-4-P/ ZybrestatTM/ Fosbretabulin).
The importance of angiogenesis in the development and rejuvenation of tumour tissue was
initially suggested by Folkman in 1971 (Folkman 1971). In vivo experiments using rodent
models carried out by Denekamp, showed that blocking and interference of the tumour blood
supply resulted in deterioration of tumour tissue (Denekamp 1990). Denekamp then proposed
the tumour endothelium as a therapy target by exploiting its high proliferative fragile nature to
ultimately interfere and/or destroy the tumour vasculature.
Vascular targeted agents can be divided in to two main categories (Table 3) i.e. those which
inhibit formation of new blood vessels (antiangiogenic) and the vascular disrupting agents
which take advantage of the substandard micro-architecture of the tumour vasculature
(Siemann et al 2005). Examples of VTTs and targets are shown in Table 4.
Vascular-targeted therapy
Antiangiogenic agents Vascular disrupting agents
Stop new vessel fo rm a tio n (neovascularisa tion) D isrupt a rch itec tu re o f tu m o u r b lood vessels
Continual tre a tm e n t Acute tre a tm e n t
Early stage - asym ptom atic metastases Established disease, e ffec tive against large
tu m o u r masses, cause cen tra l necrosis
Table 3: Categorisation of vascular targeted therapies (Siemann 2005).
30
Vascular-targeted strategies
Antiangiogenic Vascular disrupting
Target
VEGF/VEGFR 1-3
inhibition
Tie-2/angiopoietin
Av03 integrin
Endogenous inhibitors
VE-cadherin
Unknown
Example
Bevacizumab, VEGF-
Trap, ZD6474
A422885.66
V itaxin , c ileng itide
Ang iosta tin
E4G10
Thalidom ide
Target
Tubulin
Cytokine induction
Ab Toxin
Av|33 integrin
Unknown
Example
C-A-4-P, AVE8062A
DMXAA
VEGF-gelonin
Gene the rapy , pep tide
ta rge ting
PDT
Table 4 : Example o f drugs within each category (Siemann 2005).
The design hypothesis of antiangiogenic agents is to hamper the cascade of biochemical events
that result in new vessel development (Siemann 2005). Amidst the complex array of signals
and receptors which govern the latter process, vascular endothelial growth factor (VEGF) is
thought to be an important target for impeding antiangiogenesis. An example of anti
angiogenic therapy is Bevacizumab/ Avastin, this uses monoclonal antibodies to target VEGF or
its' receptors, as does ZD6474 a tyrosine kinase inhibitor. (Wedge et al 2002). One treatment
success story of Bevacizumab was when it was used in fluorouracil based combination
chemotherapy to treat colorectal cancer. (Hurwitz et al 2004).
31
Table 5 illustrates the stages of angiogenesis and goals of the 'antiangiogenic 'approach.
(Siemann et al 2005).
The steps towards
Angiogenesis
Antiangiogenisisapproach
Endothialc«llproliferation
Angiogenic switch
Upre gulation of factors and docking of
endothelialcell receptors which promote
angiogenesis
Matrix metalloproteinase activation and
degradation of basement membrane
* \
Endothelial cell migration and tube formation
t ......
Inhibition of proangioge me factors
k I II III III II I IMI
Inhibition of stimulus amplification of
e ndoge nous suppre ssors of angiogenesis
Receptor antagonists signal transduction
inhibitors
Ihibition of matrix breakdown
f ............
Inhibition of tube formation
Table 5 : Inhibiting the process o f angiogenesis (Siemann et al 2005).
32
1.1.5 Vascular disrupting agentsVascular disrupting agents (VDAs) exploit the characteristic tumour vasculature (Figure 1.6)
which is notoriously sinusoidal and leaky, and is hence susceptible to vascular disruption.
(Tozer et al 2008). Shortly after administration of VDAs major shutdown occurs of the tumour
vascular network.
Healthy vasculature, m ature blood
vessels, smooth muscle and pericyte
support
Tum our vasculature, im m ature blood
vessels, lack o f support by smooth
muscle and pericytes
Figure 1. 6 : Images to illustrate the substandard tumour vasculature (Oxigene 2010).
Flavanoids (e.g. DMXAA) and tubulin-binding agents (e.g. CA-4-P) are VDAs, both are designed
to disrupt tumour blood vessels but work in different ways as the chemical structures suggest
in Figure 1.7.
33
CA-4-P (Fosbretabulin)
HoCO
O— P— ONa
ONaOCH
H
DMXAA (Vadimezan)
O
CH, c h 2c o o h
CA-l-P (Oxi4503)
H3CO.
h 3CO
OCH
ONa 0 \ | ONa
I, 0
o/ /
'O — P — ONa\
OCH3 0NaAVE8062 (Ombrabulin)
H3CO.
H3C 0 ‘
OCHNHF.HCI
OCH
Figure 1. 7 : Chemical structures o f vascular disrupting agents (Kanthou and Tozer 2007).
The mechanism of 5,6-dimethylxanthnone-4-acetic acid (DMXAA/ ASA404) is still not yet fully
understood but it is thought to operate via the induction of TNF-a. (Thorpe et al 2003).
Through the action of DMXAA it is suggested that TNF-a is retained within the tumour
microenvironment thus stimulating additional amounts of TNF-a amongst other cytokines.
The tumour blood vessels are weakened with the subsequent changes in morphology leading
to haemorrhagic necrosis (Kanthou and Tozer 2007). In Phase I clinical trials DMXAA showed
the drug had a well tolerated anti-tumour action, Phase II involved combination with34
conventional chemotherapy agents with positive outcomes when used in non-small cell lung
cancer patients. DMXAA then moved into Phase III clinical trials which unfortunately did not
produce the same effects as in its early trial success (Buchanan 2012).
Microtubule depolymerising agents have a destructive effect on the architectural integrity of
3D capillary composition (Bayless and Davies 2004). The following diagrammatical
representation (Figure 1.8) from a paper by Kanthou and Tozer (2007) details the proposed
mechanism of action of a microtubule depolymerising agent (e.g. Combretastatin A-4-3-0-
phosphate).
H IV '
A< m \ IV
Untreated endothelial cells
• intact micronjbulss• non-contractile cells• tight VE-cadherin junctions• low permeability
• low RhoA-GTPase activity• low Rho kinase activity
Microtubules
— Actin stress fibers
%% Cell surface actin blebs
VE-cadherin junctions
VDA-treated endothelial cells
• disrupted microtubules• contractile cells• actin stress fibers• strong fecal adhesions• disrupted VE-cadherin junctions• high permeability
• high RhoA-GTPase activity• high Rho kinase activity• high SAPK-2'p3d activity• high ERK activity?
VDA-treated endothelial cells
• disrupted microtubules• conlractile-blebbrng cells• actin-lined surface blebs• (Disassembled focal adhesions• lost VE-cadherm junctions• high permeability
• necrotic celts
• high RhoA-GTPase activity• high Rho kinase activity• high SAPK-2/p38 activity• low ERK activity?
Dug Dacmvry Today S‘rat*yes
Figure 1. 8 : Proposed mechanism of action o f Combretastatin A-4-3-0-phosphate (Kanthou and Tozer 2007).
It is suggested that increased permeability post CA-4-P administration is a major factor in the
disruption of endothelial organisation; this is evident from Figure 1.8 (Kanthou and Tozer
2007). Interference in vascular integrity due to abnormality in the cytoskeleton, subsequently
leads to stress fibre formation and endothelial cells as a result become more rounded
('blebbing'). Haemorrhagic necrosis follows exacerbating the already hypoxic regions within
the tumour microenvironment.
35
The remodelling of the actin cytoskeleton and formation of actin stress fibres shown in Figure
1.8 is clearly demonstrated in the Figure 1.9 adapted from Kanthou and Tozer 2002 and has
also been shown by Siemann (2010).
(b)
(d )
Figure 1. 9 : Fluorescent staining o f Human umbilical vein endothelial cells, Control and post CA4P administration (Kanthou and Tozer 2002). Figure 1.9 shows dual staining for filamentous actin (Texas Red Phalloidin (red)) and beta tubulin (green): (a) control actin, (b) control tubulin, (c) CA4P (1 uM for 30 min) actin, (d) CA4P (1 uM for 30 min) tubulin.
In vivo studies using CA-4-P have clearly shown how the effects of the drug can induce vascular
shutdown resulting in increased hypoxia. (Tozer et al 2008). Studies on mouse models (Figure
1.10) looking at hypoxic responses in mammary carcinomas after 50mg/kg CA-4-P, indicated
increased pimonidazole staining compared to non treated. Similarly, dorsal skin flap
experiments in mice (Figure 1.10) displayed the de-vascularising properties of CA-4-P
HOmg/kg.
(a)
(c)
36
»*«n
Figure 1 .1 0 : CA-4-P and its effect on hypoxia induction and vascular shutdown (Tozer et al 2008). Panel A shows pimonidazole staining o f the control untreated tumour at the top with the bottom being 1 hour after CA-4-P, panel B is the untreated image of a rat bearing P22 sarcoma directly below is the tumour 1 hour 40 min post CA-4-P administration in vivo using a window chamber method for analysis o f treatments effects.
Abnormal tumor vascular network with poor perfusion, high interstitial fluid pressure and areas of hypoxia
Tumor-VDAs
Blind ends
Abnormal bulges
Direct endothelial cell effects Vascular disruption
Remaining rin of viable cells that can be targeted with chemotherapeutic agents and radiation
Necrotic central core
Decreased nascent Vessel number
Anti-VEGF effectsInitial vascular normalization 1Anti-Angiogenic
Agents
I p a l i i[ ’.vSi I VS f j Improved blcod flow'
loading to improved delivery of
chemotherapeuticagents
Figure 1 .1 1 : Exploitation of tumour vasculature by antiangiogenic treatment and vascular disrupting agents (Siemann 2010).
To summarise the different actions of both VDAs and antiangiogenic treatments a diagram
(Figure 1.11) by Siemann (2010) highlights the preclinical effects of each type.
With all new drugs/ therapies in development there are numerouscomplications to overcome
and consider i.e. the therapeutic window with the balance between safe dosage and the onset37
of adverse effects. The phrase 'tumour evasion' frequents many article titles and appears to be
a key issue in the advancement of this type of anti-tumour medicine.
Zhi-Chao and Jie (2008) present valid points within their review on tumour evasion in a section
entitled "Hypoxia: Two sides of the coin". They state that disruption by vascular targeted
treatment to increase hypoxia severing the lifeline of the tumour, can indeed have undesirable
consequences. It is suggested that there is a requirement to unravel some 'cause-effect'
connection. Hypoxia-inducible factors (HIF) have been known to correlate with the up-
regulation of VEGF, involved in angiogenesis regulation. So could VTTs amplify the angiogenic
process after the creation of an increased hypoxic tumour environment? Hypothetically
speaking this could even help the metastatic process ensuing tumour invasion. (Abdollahi and
Folkman 2010).
Evasion of the hosts' immune system is one feat that the tumour is capable of and there is a
school of thought that claims there could be a similar evasion mechanism in place against VTTs.
The heterogeneous nature of the tumour mass could well contain a cell population deemed
oblivious to vascular targeted strategies. (Zhi-Chao and Jie 2008). The latter is supported by
the hypothesis of four possible vascular phenotypes in existence; normal pre-existing, tumour
phenotypic vessels, neo-vasculature which is normal and abnormal neo-vasculature. This
could explain why some cells remain unaffected including the well documented viable tumour
rim. (Hinnen and Eskens 2007).
The viable tumour rim being a speculative resistance factor is also thought to achieve evasion
through its positioning, adjacent to the non-diseased tissue. (Tozer et al 2008). Consequently,
seizing oxygen and essential nutrients as it resides in this prime location.
The explanations why resistance issues exist against VTTs appears to be complex and multi
factorial. That said progress is being made through various combination therapies. Xenograft
experimental models displayed increased quantities of VEGF and basic fibroblast growth factor
following doses of CA-4-P, with angiogenic circulating progenitor cells in murine studies. (Tozer
et al 2008) Promising responses have been reported by using VDAs and anti-angiogenic VEGFR
tyrosine kinase inhibitor treatments in combination therapy.
38
1.1.6 Biomarker DiscoveryWhen considering biomarker discovery as it relates to oncology many complexities exist. There
are countless biochemical pathways with numerous prospective targets, with more layers of
complexity added by the multifaceted characteristics of the cancer cell. A cell unchallenged by
host immunogenicity, that has mastered tissue invasion and anti-cancer drug evasion.
Nevertheless a good place to start is given bythe 'hallmarks' of cancer created by Hanahan and
Weinberg (2000).
Whilst the 'hallmarks' of cancer reveal the attributes and capabilities of this lethal cell type, a
further proposal by Lord and Ashworth (2010) relates the possible drug targets to each
characteristic of the cancer cell to provide inspiration for biomarker exploration. (Lord and
Ashworth 2010). A diagram in the article by Lord and Ashworth (2010) supplies a wealth of
information on the latter subject however, one consideration to be made is the possible
upshot from molecular interactions and when choosing one trait to target such as 'sustained
angiogenesis' or 'proteotoxic stress'.
This concern is echoed by Lord and Ashworth (2010) as they discuss how in order to decipher
cancer mechanistics, medicine should bear in mind how drugs disturb whole biochemical
networks as opposed to solitary pathways. A concept which has been termed, "drugging the
undruggable".
Over a decade after their original paper, Hanahan and Weinburg (2011) proposed an updated
version of 'The Hallmarks of Cancer' entitled 'Hallmarks of Cancer: The Next Generation'. The
idea of two additional hallmarks of cancer were put forward these being the "reprogramming
of energy metabolism"and "evading immune destruction".
Figure 1.12 displays the original hallmarks with the addition of 'deregulating cellular
energetics' this referring to reprogramming of cellular metabolism in favour of proliferation
and 'avoiding immune destruction' which describes the ability to fend off advances from
macrophages, natural killer cells and T/ B lymphocytes (Hanahan and Weinburg 2011). The
article proposes two enabling characteristics, genome instability and mutation and tumour-
promoting inflammation. Both of which are said to be key orchestrators in the original and
emerging characteristics.
Hanahan and Weinburg also included therapeutic suggestions to hinder a particular cancer
hallmark as shown in Figure 1.8.
39
Aerobic glycolysis inhibitors
( Proapoptotic BH3 mimetics
PARPinhibitors
C ) CEGFRinhibitors
Cyclin-dependent kinase inhibitors
Sustainingproliferative
signaling
Evadinggrowth
suppressorsImmune activating anti-CTLA4 mAb
Avoidingimmune
destruction
C Enablingreplicativeimmortality
TelomeraseInhibitors
Tumor promoting
inflammation
Inducingangiogenesis
Selective anti inflammatory drugs
Activating invasion & metastasis
) cInhibitors of VEGF signaling
Inhibitors of HGF/c-Met
Deregulating cellular
energetics.
Resistinqcell
death
Genome instability &
mutation
V *
Figure 1 .1 2 : Therapeutic targeting o f the hallmarks o f cancer (Hanahan and Weinberg 2011).
Cancer-Associated Fibroblast , (CAF)~
Endothelial Cell (EC) 1
Cancer Stem Cell (CSC)
r - Cancer Cell (CC)
v w n r immunePericyte (PC)' inflammatory Cells
' :V (ICs)
Local & Bone marrow- derived Stromal Stem & Progenitor Cells
Invasive Cancer Cell
sat'
Core of Primary Tumor microenvironment
Invasive Tumor microenvironment
Metastatic Tumor microenvironment
Figure 1 .13: The cells o f the tumour microenvironment. (Hanahan and Weinberg 2011).
Advances in the knowledge of the individual cancer cells that make up the solid tum our have
enabled appreciation of their influential drive on the tumour microenvironment. Figure 1.13
40
depicts the varied cell types and sub types. Each cell has a specialised role and collectively
supports the plasticity required for tumourigenesis (Hanahan and Weinberg 2011).
Interestingly it is known that the inflammatory immune cells present within this cellular
assemblage, possess both pro-oncogenic and anti-tumour activity. The latter is thought to aid
future tumour seeding during the metastatic process.
1.1.7 Considering Vascular disrupting agents for anti-cancer therapeuticsIt is known that one vessel is primarily responsible for supplying the tumour cell population
with oxygen and nutrients for growth, development and excretion of waste metabolites.
(Thorpe et al 2003). Disruption of this vital sustenance by vascular disrupting agents causes
rapid shutdown of blood flow to and from the tumour.
Unlike some conventional cancer therapies which focus on cell killing VDAs create damage via
morphological changes to produce drastic effects on the tumour microenvironment. Drug
deliverance is simple due to endothelial positioning near the bloodstream.
In addition to the above, another reason to support the use of vascular targeted therapy is that
the diploid target cells are thought not to be as drug resistant due to low risk of mutations. The
drug has good clinical measurability being blood flow, in most cases short-term exposure of a
VDA creates sufficient hypoxic conditions. To conclude, intermittent dosage with regular
treatments may be adequate to provide a synergistic effect, not true in the case of
antiangiogenesis agents. (Thorpe et al 2003).
1.2 The Role of Mass Spectrometry in Biomarker Discovery
1.2.1. Overview of mass spectrometry as an analytical toolMass spectrometry can be applied to numerous analytical subject areas from the food industry
to forensic applications to complement various diagnostic and prognostic techniques (Griffiths
2008). Techniques using mass spectrometry have supported the advancement of proteomics,
metabolomics, lipidomics and atomic physics to forge a place within the clinical setting.
Proteomics is a platform for potential biomarker discovery and mass spectrometry is widely
used as a discovery tool (Anderson 2005). Advances in mass spectrometry instrumentation
have resulted in high mass accuracy and vast improvements in sensitivity and specificity (Al-
Shahib 2012). The forward momentum of mass spectrometry has led to the analysis of
diseased and non-diseased tissues supported by bioinformatics.
41
Recent experimental work by Rower et al (2011) showed development in the early diagnosis
of invasive ductal breast carcinoma through protein signatures and structural analysis. Such
progress in the identification of disease biomarkers could not only benefit patients directly but
support future drug design and development.
1.2.2. Basic principles of mass spectrometryThe mass spectrometer uses an ion source (liquid-phase/ solid-state) to transfer the analyte
into gas phase ions which then travel to the mass analyser to be sorted according to mass-to-
charge ratio (m/z), finally a detector accounts for the relative abundance of each ion at a
particular m/z (Hoffmann and Stroobant 2007). Instrumental calibration and use of analytical
standards is also vital to produce accurate mass measurements whilst using the many different
mass spectrometric techniques. External calibration is performed before sample analysis
where a known selected compound is used for spectral comparison against a reference list
where assignment of exact masses can then be made. Internal calibration describes a
calibration method where a reference compound is introduced into the mass spectrometer
during sample analysis. This technique compensates for any possible changes in conditions in
the analytical instrumentation (Hoffmann and Stroobant 2007).
Figure 1.14 is a representation of the basic principles of the mass spectrometer.
With regard to proteomics there are a variety of mass spectrometers available some of which
are described in more detail in later.
The following table (Table 6) adapted from Han et al (2008) details commonly used
instruments, applications and performance indicated by mass analyser type.
42
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1.2.3. Ionisation techniques and Mass analysersIn physical chemistry there are four main ways to produce gas phase ions from a molecule
interest which are; spray ionisation, desorption ionisation, chemical ionisation and electron
ionisation (Cooks 2010). Figure 1.15 shows these ionisation techniques schematically.
(A) (B)SampSe
Capilary
SprayingGas
Energy Beam
(C) (D) © v/
o r m —
M — * M H ++
H hvM|
XQ '
Figure 1.15 : Basic ionisation techniques. (Pol et al 2010). (A) spray ionisation, (B) desorption ionisation, (C) chemical ionisation and (D) electron ionisation.
Examples of spray ionisation are electrospray and nanospray, these techniques require the
sample to be in solution and regularly coupled to an LC system (Pol et al 2010). Secondary ion
mass spectrometry (SIMS), desorption electrospray ionisation (DESI) and matrix assisted laser
desorption ionisation (MALDI) are all methods using desorption ionisation from a solid surface
(Pol et al 2010). The desorption methods previously mentioned are used for image acquisition.
Chemical ionisation and electron ionisation use techniques involving gas-phase ion-molecule
reactions at atmospheric pressure, atmospheric pressure chemical ionisation (APCI) and
electron impact are such techniques. APCI can be applied to polar and relatively non-polar
molecular analysis with electron ionisation is useful for organic compounds.
Figure 1.16 displays current mass analysers at use in the analytical laboratory.
46
(A)Entrance
Slit QuadrupoleRods
| j
(B)
0„
ExitSlit
/3
— j ILinear
Detector
ReflectorDetector
\ Reflector
(Ion Mirror)
(D)
(E)
CentralElectrode
Electrode
EntranceEndcap Endcap
Electrode
(C) Magnet
Detector DetectorElectrode Electrode
TrappingPlates
Plates
SupercondutiveMagnet
Excitation
Figure 1 . 1 6 : Mass analysers used in mass spectrometers, (a) Quadrupole, (b) Time-of-flight (TOF), (c) Magnetic sector, (d) Ion trap, (e) Orbitrap, (f) Ion cyclotron resonance. (Pol et al 2010,. Trim et al 2012).
47
1.2.4. Proteomics using mass spectrom etry
Currently mass spectrometers allow interpretation of complex protein mixtures that contain
post translational modifications (PTMs) through fragmentation techniques (Hoffmann and
Stroobant 2007). Tandem mass spectrometry (MS/MS) describes the selection of a precursor
ion which then undergoes collision induced dissociation (CID) by an inert gas to generate a
product ion spectrum, b and y ions are generated due to shattering of the peptide C-N
backbone (Hoffmann and Stroobant 2007). To illustrate the stages of protein identification in
more detail the flowchart shown in Figure 1.17 adapted from Han et al (2008) segregates the
various methods into either bottom-up or top-down proteomic methods (Han et al 2008).
Proteins
(Shotgun approach) digestion
Protein separation Protein fractionation separation
Digestion ^Onlinemultidimensional separation (nD) LC- MS
Online (ID) LC-MS
(PMF) Target precursor
ion selection
Peptides
Peptide precursor mass measurement
Offline static MSPeptide mass measurement
Online LC-MS
Peptide identification
Protein ID from MS
Single protein or less complex mixture Peptide mixture
Data dependant MS/MS
Single protein or less complex mixture
Bottomup
Topdown
Data dependant MS/MS
Protein ID and characterisation from peptide MS/MS
Protein ID and characterisation from intact protein MSr
Protein precursor mass measurement
Figure 1 . 1 7 : Protein identification methods (Han et al 2008).
An article by Djidja et al (2009) regarding the profiling and imaging of glucose-regulated
protein 78 kDa (Grp78) contains an clear example of peptide identification from the peptide
mass fingerprint (PMF) of a tryptic digest, a bottom-up method. Here in-situ tryptic digestion
was performed directly onto tissue sections, the resulting PMF allowed selection of precursor
48
ion for MS/MS. Peptide sequences/ MS/MS spectra are then entered into algorithmic
bioinformatics programmes i.e. MASCOT that can result in protein identification through
searches performed against a protein database such as UniProt KB/Swiss-Prot.
Interpretation of comparative proteomics data in itself presents a huge challenge and
Hodgkinson et al (2011) published an antibody microarray proteomic study based on the
theory proposed by Petrak et al (2008) that such experiments possibly result in 'repeatedly
identified differentially expressed proteins' (RIDEPs). Petrak et al (2008) devised a list of 15
'RIDEPs' that were commonly observable in human and rodent samples. Amongst the RIDEPs
were heat-shock protein 27, peroxiredoxins, annexins and enolase 1 with proteins such as
zyxin and smad4 found by Hodgkinson et al (2011). The latter could provide a good place to
start in any shotgun proteomics experiment, especially assessment of a treatment response
time course and how these proteins appear to be differentially expressed.
Frequently MS/MS alone is not sufficient enough to provide a good clear fragmentation
spectrum from the parent ion. Especially when analysing the high abundance of ions
generated from an on tissue digest. The novel concept of ion mobility-mass spectrometry
which describes the further separation of isobaric ions indeed facilitates the task of precursor
ion selection. (McLean et al 2005). Ion mobility separation (IMS) can be applied throughout MS
acquisition and then again during MS/MS enabling a 'cleaner' peptide mass fingerprint with
improved subsequent precursor fragmentation. IMS will be discussed further later on.
To conclude this section, the vast amount of data currently now attainable in proteomics
experiments is remarkable. Dealing with the computational capacity required to process and
analyse these data will remain a challenge. However this is imperative to contribute to and
advance proteomic analysis.
1.3 Mass spectrometry instrumentation
1.3.1 LC-MSThe diagram in Figure 1.18 shows how the analytical technique of high performance liquid
chromatography (HPLC) can be coupled to a mass spectrometer a combination generally
referred to as LC-MS. The nature of this method requires the analyte to be in solution for
injection onto a column packed with coated silica particles (stationary phase) (University of
Bristol 2005). Varying the concentration of the mobile phase passing over the column will then
determine what is eluted in to the mass spectrometer for analysis.49
The sample can either be collected in fractions (Figure 1.18) or sent to the mass spectrometer
via an interface: Electrospray ionisation (ESI), atmospheric pressure chemical ionisation (APCI),
thermospray (TSP) and atmospheric pressure photoionisation (APPI) have been or are
currently used (Hoffmann and Stroobant 2007).
Column selector valveAutosampler
Mass spectrometerColumns□
UV detector
Fraction collector
Flow splitter
HPLC pumps
Figure 1 . 1 8 : Schematic representation o f a liquid chromatography mass spectrometer. (The LC/MS Unit 2009)
LC-MS/MS is regularly used in clinical chemistry for its universal capacity for the detection and
quantification of small molecules (Jong et al 2011). The technique of LC-MS/MS has proven to
be invaluable in the diagnosis of pheochromocytoma, a tumour that can develop in the centre
of the adrenal gland (Lagerstedt et al 2004). Renowned for its specificity and sensitivity LC-MS
has the ability to measure plasma free metabolites metanephrine and normetanephrine
occurring in nmol/L, known markers for pheochromocytoma.
50
1.3.2. Electrospray ionisation (ESI)Electrospray ionisation mass spectrometry (ESI-MS) is a highly sensitive soft ionisation
technique capable of analysing low volume, non-volatile samples (Ho et al 2003).
During flow through the sample (in solution) is nebulised under atmospheric pressure via an
electrical potential which has been applied to the needle (Gates 2004). Overtime as the
multiply charged droplet travels towards the sample cone solvent evaporation occurs resulting
in shrinkage. This is due to the 'Raleigh effect' which describes an event where the surface
tension can no longer retain the charge (Figure 1.19). The droplet containing the analyte is
then ripped apart by 'Coulombic explosion'.
Cone<counterelec?ro<fo)
Spfay needle tip mullply charged droplet
sowentx evaporaton
analytemolecule
•Coulonnbic''^ explosion + +
fie “Rayleigh' limit is reached
muHply charged droplet
analyts
Power Supply
University o f0 B B R IS T O L
Figure 1 .1 9 : Schematic o f Electrospray ionisation (Gates P 2004).
The multiply charged droplet is expelled from the needle, overtime solvent evaporation occurs
resulting in shrinkage of the droplet leading to "Coulombic explosion". There are two models
of ion generation in ESI, the ion evaporation model (IEM) and the charged residue model
(CRM) (Wand and Cole 2000). The IEM was first introduced in 1976 by Iribarne and Thomson
and due to an increasing electrical field present on the surface of the droplet, solvent-ion
clusters are discharged (Wand and Cole 2000). In the case of the CRM, continuous droplet
fission through continuous solvent evaporation results in the formation of gas-phase ions.
51
1.3.3. Matrix assisited laser desorption ionisation (MALDI)Matrix assisted laser desorption ionisation (MALDI) is a soft ionisation technique which is
widely used for analysis of non-volatile compounds in many applications including proteomics,
lipidomics, synthetic polymers and the study of small molecules.
The matrix plays a key role in the successful ionisation of the analyte. After deposition of the
matrix solution onto the sample, formation of matrix co-crystals occurs after drying/
evaporation which form a protective coat from the ablation of the laser (Hoffmann and
laser
analyte/matrix spo: be3IT
i:
analyteions
cation
sampe plate
matnxtons
//
extractiongrid
w To mass spectrometer
University o fQ B BRISTOL
\focussing
lensStroobant 2007). Under vacuum the laser is pulsed onto the matrix embedded sample and
although not completely understood, it is thought that the energy is transferred from the
surface of excitation of the matrix crystals to the analyte. The transfer of energy from the laser
to the matrix then matrix to the analyte is a two step process resulting in the ionisation of both
enabling the sample molecules to remain structurally intact. It is reported that gas phase ion
proton transfer is the most accepted explanation for MALDI matrix ionisation (Breuher et
ol1999). There are also other proposals for this mechanism these being; excited state proton
transfer, gas phase photoionisation, ion-molecule reactions and desorption of preformed ions
(Hoffmann and Stroobant 2007). Figure 1.20 aims to depict ionisation principles of MALDI.
Figure 1. 2 0 : Ionisation using MALDI. (Gates 2004). The protonated cloud o f ions is shown above the analyte/matrix co-crystallisation.
The formation of Cations with the analyte takes place in the following way;
[M+X]+ (X= H, Na, K etc.) (Gates 2004).
52
Selection of a MALDI matrix requires tailoring to the specific analyte chosen and wave length
of the laser (Hoffmann and Stroobant 2007).
The matrix needs to exhibit stability under vacuum, be soluble in selected solvent, form
adequate co-crystals with the analyte and absorb the laser wavelength (Sigma Aldrich 2001).
Examples of common matrices used and the associated applications are listed in Table 7.
The MALDI source consists of a pulsed laser (varying pulse widths) to produce packets of ions
to be introduced into the time of flight (ToF) analyser (Hoffmann and Stroobant 2007).
Nitrogen lasers were the initial choice for MALDI and are relatively inexpensive compared to
otheralternatives, although reports claim that irregularity of the beam can lead to 'hot spots'
typically due to the heterogeneous nature of the N2 laser. (Trim et ol 2010). However the short
lifespan of the N2 laser is an issue when selecting this laser type for MALDI imaging. Therefore
these have been largely superseded by the use of ND:YAG solid state lasers. These solid state
lasers are a common choice for MALDI-MSI due to their high repetition rate and extended
lifespan (Trim et ol 2012). It is stated that up to 1 kHz having been employed using ND:YAG
compared to 20 Hz on a standard N2 laser and ~ lx l0 9 shots compared to 2x107-6x107 shots
respectively.
Trim et al (2010) proposed 2 other alternatives these being; Nd:YLF (yttrium lithium fluoride)
and Nd:YV04 (yttrium ortho-vanadate). The latter paper continues to investigate the Nd:YV04
laser and details experiments to progress the appearance of the beam using mechanical
oscillation to reduce laser 'speckle'. Results with employment of beam enhancement displayed
improved spectra with increased intensity.
Proteins/ peptides a - C yano-4-hydroxycinnam ic acid (CHCA), 2,5-
D ihydroxybenzo ic acid (DHB), 3 ,5 -D im e thoxy-4 -
hyd roxycinnam ic acid (sinapic) (SA_
Lipids D ith rano l (DIT)
Inorganic molecules T rans-2 -(3 -(4 -te rt-B u ty lphe ny l)-2 m ethy l-2 -
p ro pe ny lied ene )m a lo no n itr ile (DCTB)
Organic molecules 2,5 -D ihydroxybenzoic acid (DHB)
53
Carbohydrates 2,5 -D ihydroxybenzoic acid (DHB), a - Cyano-4-
hydroxycinnam ic acid (CHCA),
T rihyd roxyace tophenone (THAP)
Oligonucleotides T rihyd roxyace tophenone (THAP), 3-
H ydroxyp ico lin ic acid (HPA)
Synthetic polymers 2,5-D ihydroxybenzoic acid (DHB), D ith rano l (DIT),
T rans-3-indoleacrylic acid (IAA)
Table 7 : Common matrices used in UV- MALDI.(Hoffmann and Stroobant 2007).
1.3.4. Quadrupole Time of flight mass spectrometry (QqToF)The term QqToF refers to a hybrid mass spectrometer which the employs a quadrupole, (an
arrangement of four metal rods with applied potentials; +(U+Vcos(wt)) and -(U+Vcos(cot))
where fixed U and alternating Vcos(cot) potentials can be applied to selected ions for MS/MS.
The quadrupole arrangement used in a typical QqTOF arrangement is shown in Figure 1.21,
where Q refers to the mass resolving quad and q describes a hexapole collision cell which can
further dissociate precursor ions which are then separated again by another quadrupole or
time of flight mass analyser. (Chernushevich et ol 2001). The diagram (Figure 1.21) shows Q0
which functions as a "collisional dampening" r.f. quad to focus ions prior entry into the Q1
mass filter and Q2 collision cell. In some cases instruments can feature Q0 (Figure 1.21) and Q2
both as hexapoles. The schematic diagram above also illustrates the location of the ToF mass
analyser and other components within the Q-ToF instrumentation.
54
Electrospray QO
Ion„ M odu la to r
-CL /■Kid** TorrId ni rorrj M C P
D e tec to r
Accelerating Column
TurboPump
Shield (“Liner" l
Figure 1. 2 1 : Diagrammatical representation o f a tandem QqTOF mass spectrometer. (Chernushevich et a12001).
If only mass spectra in ToF MS mode is required the Q1 quad operates in r.f. mode to simply
provide a means of transmission for the ions generated which then continue to the ToF mass
analyser. (Chernushevich et al 2001). Q2 parameters are maintained (<10 electron volts (eV))
as such to not induce fragmentation.
In MS/MS mode the precursor ion of interest is selected via the mass filtering Q1 which then
reaches the Q2 and undergoes collision induced dissociation (CID) with an inert gas i.e.
nitrogen/ argon. Subsequent acceleration then occurs of ion fragments and corresponding
parent ions to the ion modulator (see Figure 1.21) in the formation of a continuous beam
focused by ion optics. The pulsation of ions via an electric field at discreet bursts admits ions
into the ToF mass analyser (Figure 1.22) in a way governed by the ion’s characteristic trajectory.
55
60
C i alinfi —I r—I * u M n
IIII
l“ l In f r a c t io n - * pulse
Detector
Collision cell Q2
c = a 11!!5. .5t T i B H
i)
T roF
Figure 1. 22 : Schematic representation of a ToF modulator (Chernushevich et al 2001).
1.3.5. Synapt HDMS - travelling wave ion mobility separation.Briefly mentioned earlier in section 1.2.4. (Proteomics using mass spectrometry), is the
revolutionary concept of ion mobility separation (IMS) coupled with mass spectrometry. This
novel concept allows visualisation into another dimension of spectral data.IMS has structural
elucidation capabilities and the capacity to provide a further separation method of isobaric
ions. (Pringle et al 2007).
The theory of ion mobility describes the separation of bundles of ions under a static electric
field with the use of a 'background' gas (Pringle et al 2007,. Clemmer and Jarrold 1997,.
Valentine et al 1997). A novel idea which uses molecular separation by mass, shape (cross
sectional area) and chiral spin. Work reported in this thesiswas performed usingSYNAPT™
G1/G2HDMS systems (Waters Corp., Milford, MA) (Figure 1.23). A series of 3 travelling wave
stacked ring ion guides (Figure 1.24) constitute the ion mobility segment of the Synapt series
instruments where packets of ions are allowed to accumulate from previous IMS then released
to the ion guide for further mobility separation.
56
Ionizationsource
A <
Time-of-f light mass analyzer
Tri Wave High-fieldpusher
Ion detection system
r - s . . T-wave 1 ion guide
..........
Ion mobility separationQuadrupole. Ion
mirror
Transfer
mHelium cell
sDual-stage reflectron
Figure 1. 23 : SYNAPT™ G2 HDMS system (Waters Corp., Milford, MA). (Waters 2012).The Tri Wave ion mobility cell is shown where selected ions of interest are separated dependant on size, cross sectional area and chiral spin. The mass analyser is set to dual reflectron mode to aid the focusing of high energy ions to achieve high mass resolution.
In
Ion*
O ut
Figure 1. 24 : Stacked ring ion guide (Pringle et al 2007).
57
Travelling wave ion guides (TWIGs) which carry the ions through are the result of a temporary
dc voltage applied to the RF of opposite electrodes in constant succession throughout the
instrument. (Pringle et al 2007). The motion of the ions is likened to the "surfing o f ions on the
wove” . (Giles et al 2004). Figure 1.25 illustrates the travelling wave ion guide, the driving
force for the movement of ions.
K nit* Hectrode Pan
l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
Time
■ f f f f
r e
1
n *
1 's
■
D C Pulsc ,ons
Figure 1. 2 5 : Travelling wave ion guide- schematic representation. (Pringle et al 2007).
The principle of ion mobility separation is based on the collision cross-section (Q) as the ions
traverse through a field of inert gas, this can be expressed in the following equation. The
values relate to gas-phase collision cross-section (Clemmer and Jarrold 1997):
Q = -------16 N
Where N is the background gas number density, ze is the ionic charge, p the reduced mass of
the ion and the drift gas molecules, kb Boltzmann's constant, T gas temperature, A^is the
mobility to 273.2 K and 760 Torr.
58
1.3.6. MALDI-mass spectrometry imagingMatrix assisted laser desorption ionisation - mass spectrometry imaging (MALDI-MSI) is an
advanced analytical tool that allows molecular profiling and imaging of many classes of
compounds including; proteins, peptides, lipids, drugs and many other molecules directly from
tissue sections. The use of this technique for the study of biological tissue was first described
by Caprioli et al. in 1997 and it has been improved and adapted for use in many other studies
(Caprioli et al 1997., Chaurand et al 2006., Chaurand et al 2003., Stoeckli et al 2007,. Burrell et
al 2007,. Djidja et al 2008,. Trim et al 2008,. Fournier et al 2008).
MALDI-MSI allows the acquisition of multiple single mass spectra across the tissue section at a
spatial resolution predefined by the operator (typically 20-200 pm) (Figure 1.27). These mass
spectra are then combined together in order to generate molecular maps or images which
represent the distribution and the relative abundance and/or intensity of a specific ion signal
detected within the tissue section. MALDI-MSI has been shown to be a powerful technique for
direct protein analysis within tissue sections and in tumour tissue samples, it has been used for
discrimination between tumour and non tumour regions with no requirement for predefined
targets (Schwartz et al 2004,. Chaurand et al 2001,. Lemaire et al 2007,. Schwamborn et al
2007,. Lemaire et al 2007). A recent and exciting development in the technique is the use of
"on-tissue" tryptic digestion in order to achieve direct identification of proteins within a tissue
section (Lemaire et al 2007,. Shimma et al 2006,. Groseclose et al 2007).
Such molecular profiling and imaging could be described as a bottom-up shotgun approach to
protein identification, performed directly on cryo-sectioned tissue samples, rather than
through protein extraction methods. The chance to visualise the position of peptides within an
image generated by MALDI-MSI could indeed advance our knowledge in cancer research
studies. Also being able to observe co-localisation of peptides and possibly relate them to
disease states can provide complementary information regarding cancerous tissues.
A 2009 article by Djidja e ta l mentioned earlier, described the profiling and imaging of glucose-
regulated protein 78 kDa (BiP/Grp78) in pancreatic tumour sections. As described within the
article, the MALDI images were the first of their kind, with the discussion emphasising the low
abundance of this particular heat shock protein. However its importance is not to be
underestimated in the aggressiveness, progression and drug resistant nature of tumours. The
paper then goes on to describe how the combination of ion mobility and MALDI helped to
selectively target specific proteins for imaging. This technique has been called Matrix-Assisted
Laser Desorption-lonisation-lon Mobility Separation-Mass Spectrometry Imaging (MALDI IMS-
59
MSI) and its advantages for imaging of specific proteins has also been described by others
(McDonnell et al 2010).
deposition of matrix solution samples local peptide/protein content
formation of pcptide'proteins doped matrix crystats
Tissue section placed onto a conductive glass slide for combined imaging mass spectrometry and histological analysis.
discrete droplets spray
uniform coalingdiscrete arraymatrix
deposition »
mass analysis measures local peptides'protoin conten*Datacube of position
correlated spectraUV laser
MS analysis of discrete array
MS anatys>s of array within uniform coating
MS analysis of all positions
* V
X
Figure 1. 2 6 : MALDI-MSI workflow diagram (McDonnell et al 2010).
In addition to the matrix deposition shown above in Figure 1.26 displaying a discreet
arrangement of matrix/ reagent droplets, other methods include manual or automated
spraying to achieve a finer more homogenous coating for co-crystallisation.
Examples of commercially available sample preparation for enzyme and/or matrix deposition
are; Chemical Inkjet Printer by Shimadzu a sprayer using vibration vaporisation, Suncollect by
SunChrom an automated spraying system, Portrait™ 630 Multispotter by Labcyte which works
using high precision acoustic ejection from a reservoir source. (McDonnell et al 2010).
1.3.7. Quantitiative ProteomicsA number of methods have been proposed for quantitative protoemics.which include both
label and label free quantitation.
1.3.7.1 Label free quantitationImmunoassays and immunoblots are methods which are widely used, however development
and optimisation of protein specific antibodies can be considered both time expensive and
time consuming (Tate et al 2012). Cross reactivity can also be one of the main pitfalls of
immune targeted approaches. It can be said that the latter includes some of the reasoning
behind a shift towards mass spectrometry.
60
One of the most basic label free methods of quantitation in proteomics is spectral counting.
Shotgun proteomics (as shown in Figure 1.17) describes an experiment where MS/MS results
are summed for each peptide and comparison of summed events for each peptide can then be
analysed throughout a time course of results (Tate et ol 2012). More detailed analysis of
spectral counting can involve i.e. counting of unique peptides before and after normalisation.
Measurement of the area generated from peptide signal intensity in the MSI scan is another
quantitative label free approach (Tate et ol 2012). Quantitative information is acquired via an
extracted ion chromatogram. This value from the ion of interest is taken at a specific region in
time. This information is then used to associate the LC elution peak with the selected mass of
interest. Processing of these data involves the alignment of MSI chromatographic peaks
throughout the samples/ time course samples analysed, the areas are then profiled.
Figure 1.27 shows an example using Progenesis, one of the proteomic software packages that
can be used to analyse area measurements as previously described.
Control Treated
Figure 1. 27 :The concept of area peptide measurement coupled with a specific time domain,visualisedthrough Progenesis LC~MS software. (http://www.nonlinear.com/products/progenesis/lc-ms/overview/)
61
1.3.7.2 Labelling fo r proteomic quantitationIsobaric Tags for Relative and Absolute Quantitation (iTRAQ) have been applied to numerous
mass spectrometry studies (Song et a/2008,. Ross et ol 2004,. Gygi et ol 1999). iTRAQ enables
samples to be labelled and then combined to perform 4 plex / 8 plex experiments to analyse
tryptic digests from cells/ tissue (Shilov et al 2007). Each isobaric tag is comprised of a reporter
group, balance group and a peptide reactive group. This proteomic bottom up approach
requires tandem MS to give relative absolute quantitation information using reporter groups
from 113 - 121. A control is included as a reference in the combined labelled samples and
quantitation ratios are then relative to the control. MS/MS spectra can then be submitted to
protein search software which can result in identification of the labelled peptides.
An example of an iTRAQ workflow is shown in Figure 1.28.
Tw\*trr**r* K TfM$r*fw8 TwMfrwtC Tf*»cnwit D
Protein extraction followed by enzymatic digestion
Combine labeled digests
Analysis by LC-MS/MS
iTRAQ label rr R A 0114 (TRAQ11S I ITRAQ 116 HRA0117
QUANTIFICATION FROM ITRAQ
REPORTER ONS
1PEPTIDE SEQUENCE IDENTIFICATION FROM
PEPTIDE BACKBONE FRAGMENT IONS __________________A__________________
m/z
Figure 1. 28 : Simple workflow using Isobaric Tags fo r Relative and Absolute Quantitation (iTRAQ). ( Broad Institute 2013 ).Relative quantitation can be measured by observation o f the isobaric MS/MS fragments in the region o f the spectra that correspond to a particular labelled specimen.
62
1.3.8. Aims of the studyThe aims of the study reported in this thesis are to develop and utilise mass spectrometry
imaging techniques (MALDI-MSI), in combination with conventional proteomic methodologies,
to investigate protein induction in vascular-targeted strategies.
Here MALDI-MSI intended to compliment conventional proteomics to provide information
relating the spatial distribution of protein/ peptides throughout time course experiments. Both
CA-4-P susceptible (fibrosarcoma 120) and resistant (fibrosarcoma 188) tumour models were
used to perform profiling and imaging experiments.
The tumour models used within this study originate from a mouse embryonic fibrosarcoma cell
line and had been genetically engineered to either express solely the vascular endothelial
growth factor (VEGF) 120 or 188 isoforms. Vasculature derived from the VEGF 120 growth
factor characteristically favours cell proliferation but produces an erratic disorganised network
of blood vessels with 'blind ends' lending itself to haemorrhagic disruption by CA-4-P. Tumour
blood capillaries in the fibrosarcoma 188 model are less substandard and as a consequence
mimic a more normal vascular arrangement (Tozer et al 2008). Tozer et al (2008) found that
VEGF 188 tumour vasculature had increased pericyte support hence adding to CA-4-P
resistance.
Nine different VEGF isoforms have currently been discovered in humans, with VEGF189,
VEGF165 and VEGF121 isoforms being the most documented and VEGF120, VEGF164 and
VEGF188 in mice ( Tozer et al 2008,. Harris et al 2012). VEGF mRNA is transcribed from eight
exons and splice variants result in the various isoforms mentioned previously. Each isoform is
known to have its own distinctive vascular modelling and angiogenic configuring capability.
Each VEGF isoform is thought to govern angiogenesis in relation to whether it is matrix bound
or not and yet little is known with regards to VEGF biological / mechanistic function (Harris et
al 2012). VEGF120 does not possess a heparin-binding site and is described as being 'readily
diffusible'. VEGF188 on the other hand is bound to proteoglycans, components of the
extracellular matrix or at the cell surface (Figure 1.29) (Tozer et al 2008).
In tumours VEGF is associated with tumour growth and metastasis, increased levels of VEGF is
said to correlate with poor patient survival.
63
VEGF-188
VEGF-164
DiffusibleVEGF-120
Exons
Matrix-bound
Heparin binding site
Neuropiiin-binding site
Dimerization and VEGF-R binding
sites
Bstd control 188 164 120 std control 188 164 120
500400300
200200
100 100Cell lines Tumor extracts
Figure 1.29: (A) The exon structure of the mouse VEGF isoforms showing the differences / similarities between exons and nature of each isoform whether diffusible or matrix bound. (B) The reverse transcription-PCR of cDNA showing the bands representative of each VEGF isoform. Traces of bands relating to other isoforms are present in the control but still align with the specific isoform bands (Tozer et al 2008).
PMFs were acquired from in-situ tissue tryptic digestion and proteins thought to be involved in
tumourigenesis and drug treatment resistance were observed and targets which could be
linked with hypoxic conditions of the tumour microenvironment.
Multivariate Statistical analysis aimed to observe grouping and classification within data time
courses post CA-4-P administration.
The techniques used; MALDI-MSI, ESI-LC-MS/MS, LC-MALDI-MS/MS with iTRAQ labelling
intended to provide cross validation of the effects post administration of vascular disrupting
agent CA-4-P.
MALDI imaging of proteins related to tumour biology and the stress response could forge a
place in the clinical setting providing alternative tool in the field of cancer research.
64
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73
Chapter 2Protein induction in a CA-4-P
susceptible tumour model usingMALDI-MSI
74
2.1 IntroductionThe discovery of biomarkers that can be used to determine success of cancer therapy or
resistance to it, at an early stage, presents a very complex problem. There are countless
biochemical pathways to be considered and the multifaceted characteristics of the cancer cell
have also to be taken into account; a cell that can largely evade the host's immune responses,
has mastered tissue invasion and anti-cancer drug resistance.
One promising class of anti-cancer drug currently under developmentis the vascular disrupting
agents (VDAs) (Kanthou and Tozer 2007). VDAs cause rapid, selective and sustained shutdown
of tumour blood flow. This produces drastic effects on the tumour microenvironment. Certain
aspects of blood flow modification are due to the direct effects of the drug on the endothelial
cells that line the luminal surface of blood vessels.The article by Kanthou and Tozer describes a
proposed mechanism of action for CA-4-P (Kanthou and Tozer 2007). It is suggested that
increased permeability after the administration of CA-4-P is one major factor in the disruption
of endothelial organisation. Interference in vascular integrity due to abnormality in the
cytoskeleton, subsequently leads to stress fibre formation and endothelial cells become more
rounded ('blebbing'). Haemorrhagic necrosis follows exacerbating the already hypoxic regions
within the tumour microenvironment.
As mentioned in the introductory chapter, the remodelling and formation of actin stress fibres
is clearly demonstrated using immunofluorescence by Siemann (Siemann 2010). The images
contained in this article are good examples of the "rounding" of endothelial cells and
"condensation" of the microtubules post CA-4-P treatment.
Drug delivery of VDAs is relatively simple due to endothelial positioning near the bloodstream.
The diploid target cells are thought not to be directly drug-resistant due to low risk of
mutations (Thorpe et al 2003).
VDAs have relatively good clinical measurability, i.e. bloodflow and in most cases short-term
exposure to a VDA creates sufficient hypoxic conditions to induce necrosis. However, the use
of VDAs to increase hypoxia can also have undesirable consequences (Zhi-chao and Jie 2008),
(Tozer et al (2008) . Hypoxia-inducible factors (HIF), which are prevalent under hypoxic
conditions, cause up-regulation of pro-angiogenic factors such as VEGF. VDA smight amplify
the angiogenic process after the creation of an increased hypoxic tumour environment.
Hypothetically speaking this could even help the metastatic process ensuring tumour invasion
(Zhi-chao and Jie 2008).
75
In addition to acquired resistance to treatment, the heterogeneous tumour mass could well
contain a cell population oblivious to vascular targeted strategies, i.e. innate resistance
(Thorpe et al 2003). Support for this comes from the hypothesis that there are four possible
micro-vascular phenotypes in existence i.e. normal pre-existing, tumourphenotypic vessels,
normal neo-vasculature and abnormal (pathological) neo-vasculature. It may explain why
some tumour regions remain unaffected by VDA treatment, including the well documented
viable tumour rim (Abdollahi and Folkman 2010). The viable tumour rim is also thought to
achieve evasion through its positioning, adjacent to the non-diseased tissue, which is well
supplied with oxygen andessential nutrients (Kanthou and Tozer 2009). Resistance
mechanisms to VDAs are clearly complex and multifactorial but increased knowledge in this
area could provide strategiesfor future combination therapies.
The largest group of VDAs are tubulin binding, microtubule-depolymerising drugs such as
combretastatin A-4-3-0-phosphate (CA-4-P/Fosbretabulin/ZybrestatTM), which is currently in
late stage clinical trials. Combination of CA-4-P or a related compound, Oxi4503 (CA-l-P), with
anti-angiogenic therapysuch as VEGF receptor tyrosine kinase inhibitors has already shown
promise and novel concepts aimed at targeting circulating angiogenic endothelial progenitor
cells have also been proposed. This area has been recently reviewed by Siemann (Siemann and
Horsman 2009).
The experimental work reported here, describes a study, by MALDI MSI and MALDI IMS-MSI,
of the proteins induced in a mouse transplanted fibrosarcoma model (VEGF120 tumours), at a
number of time points, following treatment with CA-4-P. Imaging of peptide signals after in
situ tissue tryptic digestion was carried out using MALDI-MS and MALDI IMS-MSI to observe
their spatial distribution. MALDI-IMS-MS/MS of the peptide signals was performed to identify
proteins involved in the pharmacodynamic responses to treatment.
2.2 Materials and Samples
2.2.1 Chemicals and Materialsa-Cyano-4-hydroxycinnamic acid (CHCA), aniline (ANI), ethanol (EtOH), chloroform (CHCI3),
acetonitrile (ACN), octyl-a/b-glucoside (OcGIc), tri-fluoroacetic acid (TFA), ammonium
bicarbonate, haematoxylin, eosin, xylene and DPX mountant were from Sigma-Aldrich (Dorset,
76
UK). Modified sequence grade trypsin (20 pg lyophilised) was obtained from Promega
(Southampton, UK).
2.2.2 Tissue samplesSevere combined immunodeficiency (SCID) mice were injected sub-cutaneously in the flank
with a 50 pi tumour cell suspension containing 1 x 106 cells in serum-free medium.The cells
employed in this study were from the mouse fibrosarcoma cell line, VEGF120. This has been
engineered to express only the VEGF 120 isoform. Tumours were allowed to grow to
approximately 500 mm3, before CA-4-P treatment (a single dose of 100 mg/kg i.p). The dosage
for CA-4-P are consistent with animal studies performed at clinically relevant doses (Galbraith
2003., Prise 2002). Treatment was either Saline/ Control, Mice were killed and tumours excised
at various times after treatment. In the case of Oh CA-4-P, the tumour was excised
immediately (within 2mins) after dispatchment. All animal work carried out documented
herein was performed by Dr. J. E. Bluff, Tumour Microcirculation Group, University of Sheffield,
UK. Samples were provided as frozen excised tumours and stored at -80°C.
2.2.3. Experimental groupsControls (no treatment, saline i.p), n = 6 (labelled tu m o u rl_ l - tumour 1_6), C-A4-P (0 h after
treatment), n = 6, (labelled tumour2_l - tumour 2_6), C-A4-P (0.5 h after treatment), n =
6,(labelled tumour 3_1 - tumour 3_6), C-A4-P (6 h after treatment),n = 6, (labelled tumour 4_1
- tumour 4_6), C-A4-P (24 h after treatment)n = 6, (labelled tumour 5_1 - tumour 5_6).
2.2.4 Tissue preparationFrozen tissue sections were cut to give approximately 10pm sections, using a Leica CM3050
cryostat (Leica Microsystems, MiltonKeynes, UK) (Djidja et al 2009). The sections were then
freeze thaw mounted on poly-lysine glass slides. Mounted slides were either used
immediately or stored in an airtight tube at -80 °C for subsequent use.
2.2.5 In s itu tissue digestion and trypsin depositionThe triplicate tissue samples to undergo MALDI-MS/MSI were washed initially with 70% and
then 90% ethanol for 1 min then left to dry, subsequently slides were immersed in chloroform
for 10 s. Prior to matrix application,
in situ tissue digestion was performed with trypsin solution prepared (from lyophilised trypsin)
at 20 pg/ml by addition of 50 mM ammonium bicarbonate (NH4HC03) pH 8, containing 0.5%
octyl-a/b-glucoside (OcGIc) based on a protocol by Djidja et al (2009).77
Two automated systems were used for trypsin application. The "Suncollect" (SunChrom,
Friedrichsdorf, Germany) automatic pneumatic sprayer was used to spray trypsin in a series of
layers. The Portrait™ 630 Multispotter (Labcyte, Sunnyville CA) was used to apply trypsin in a
200-300 pm array of spots. The sections for MALDI-MS and MALDIMSI were incubated
overnight in a humidity chamber containing H20 50%: methanol 50% overnight at 37 °C and
5% C02.
2.3Methods and instrumentation
2.3.1 Matrix depositionThe matrix, a-cyano-4-hydroxycinnamic acid (CHCA) and aniline in acetonitri!e:water:TFA
(1:1:0.1 by volume), was applied using either the Suncollect (at 5 mg/ml) or the Portrait™ 630
Multispotter (at 10 mg/ml), as described above. Identical coordinate settings were used as
with the trypsin deposition, to ensure sample uniformity. Equimolar amounts of aniline were
added to the CHCA solution, i.e. 1 mlof 5 mg/ml CHCA solution contained 2.4 pi of aniline.
These matrix deposition parameters were based on methods from Djidja et ol (2009).
2.3.2 InstrumentationPeptide mass fingerprints and images were acquired by MALDI-MS/MSI using a Q-Star Pulsar i
hybrid quadrupole time-of-flight mass spectrometer (Applied Biosystems/MDS Sciex, Concorde,
Ontario, Canada) fitted with a variable repetition rate Nd:YV04 laser set at 5 kHz. Image
acquisition was performed using raster imaging mode at 150 Im spatial resolution, Biomap
3.7.5.5 software (http://www.maldi-msi.org/) was used for image generation. Instrument
calibration was performed using standards consisting of a mixture of 217 polyethylene glycol
(Sigma-Aldrich, Gillingham, UK) ranging between m/z 100 to 218 3000 Da prior to MALD1-IMS-
MSI analysis.To enable simple visual comparison between images all data were normalised to
m/z 877 (a peak arising from the aCHCA matrix) and intensity scales in the BioMap software
were all set to the same value. MALDI- IMS/MS, MALDI- IMS/MSI and MALDI- IMS-MS/MS
were performed using a HDMS SYNAPT™ G1/G2 system (Waters Corporation, Manchester, UK)
and Drift scope 2.1 software (Waters Corporation, UK). In order to achieve good quality
MS/MS spectra, they were acquired manually moving the laser position and adjusting the
collision energy to achieve good signal to noise for product ions across the full m/z range of
the spectrum. Collision energies were adjusted from 70 to 100 eV during acquisition and
acquisition times were generally of the order of 5-10 s per spectrum. MS/MS spectra were
uploaded to perform a Mascot (Matrix Science, London, UK) search which used the UniProt
database in order to generate a sequence match (see Table 1 for results).
78
2.3.3 Haematoxylin and eosin stainingSlides were immersed in haematoxylin for 1 min, rinsed in tap water until the water ran clear,
immersed in 1% eosin for 30 s and rinsed in tap water until the water ran clear. They were
then dehydrated as follows: 50% ethanol for 2 min, 70% ethanol for 2 min, 80% ethanol for 2
min, 95% ethanol for 2 min and 4 changes of xylene applied to each slide for 1 min at a time.
Finally, they were mounted with DPX mountant and left to dry in the fumehood overnight.
2.3.4 Data pre-processing using SpecAlign and Marker View softwareData lists were exported from Analyst QS software (Applied Biosystems/MDS Sciex, Concorde,
Ontario, Canada) as text files, then imported into SpecAlign (Oxford, UK) to undergo the
following data processing; baseline subtraction, smooth, denoise, normalise TIC, remove
negative, generate average spectrum, processing spectral alignment, PAFFT correlation
method max shift 20. Files were then exported as text files for import into Marker View
software 1.2 (Applied Biosystems/MDS Sciex, Concorde, Ontario, Canada).
2.3.5 Statistical analysisPCA-DA was performed using Marker View software 1.2 (Applied Biosystems/MDS Sciex,
Concorde, Ontario, Canada). Post SpecAlign text files were imported and data was transposed
into table format. A minimum intensity of 0.1 was selected, with a maximum number of peaks
of 20,000. Monoisotopic peaks selected by Marker View were used in the supervised PCA-DA.
PCA and PLSDA was performed using MATLAB®(Matrix Laboratory) (MathWorks, Inc., Natick,
MA 486USA).The PCA-DA/ PCA/ PLSDA data that follows is representative of the fibrosarcoma
120 data where 6 biological replicates per time point with 6 technical replicates for each,
(control - 24 hours). PLSDA Data for ROI study using HDI software (Waters Corporation,
Manchester, UK) was analysed using one tumour per time point with 9 technical replicates per
ROI. Variable Importance in Projection (VIP) scores were used from PLSDA regression
modelling to determine the importance of variables within the data time course.
2.3.6 Data pre-processing usingWaters MassLynx™ Software and MATLAB®MS results are imported into MATLAB® in .txt or .csv format either post "SpecAlign" or after
application of "automatic peak detection" if using the instrument data processing software
(Waters MassLynx™ Software). Normalisation (2-Norm) and mean centre were selected,
"contiguous blocks" was used for cross validation.
79
2.4 Results and Discussion
2.4.1 In itial MALDI profiling and imaging of in situ tissue tryptic digests in mouse fibrosarcoma 120 modelsInitial profiling and imaging experimental work was performed using an Applied Biosystems Q-
Star Pulsar i Quadrupole ToF Mass Spectrometer. Preliminary peptide mass fingerprints
(PMF's) (Figure 2.1) where acquired to provide insights into typical high abundant potential
peptides in order to observe any gross pharmacological responses post CA-4-P administration.
+TGF MS: 42 MSA scans from Sanple 3 (190210_5_6_centre) of 5_6.wiff a=3.57031177631956190e-004, t0=5.60672130395284330e+001, Smoothed, Baseline...
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Figure 2 .1 : Representative peptide mass fingerprint (PMF) arising from an on-tissue digest of a VEGF120 tumour 24 h after dosing with 100 mg/kg i.p, CA-4-P. Prior to matrix application in situ tissue digestion was performed with trypsin solution prepared (from lyophilised trypsin) at 20 pg/m l by addition o f 50 m M ammonium bicarbonate (NH4HC03) pH 8.12, containing 0.5% octyl-a/b-glucoside (OcGIc). The matrix employed was a-cyano-4- hydroxycinnamic acid (CHCA, 5 mg/ml) and aniline in acetonitrile:water:TFA (1:1:0.1 by volume).
80
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CA-4-P, (c) 0.5 h post CA-4-P, (d) 6 h post CA-4-P and (e) 24 h post CA-4-P.The increase in the
relative intensity of m/z 1819.3 along with other readily identifiable haemoglobin peptides (e.g.
m/z 1529.7HbAa 18-32 (IGGHGAEYGAEALER), m/z 1274.3 HbAb 52-41(LLVVPWTQR)) can be
seen. This increase in tissue haemoglobin is as would be expected considering the vascular
damaging propertiesof CA-4-P. Due to disruption of the 3D capillary architecture, endothelial
cell necrosis, apoptosis and leakage of blood cells into tumour tissues is inevitable. This is
clearly illustrated by Figure 2.3, where MALDI-MS images for the distribution of m/z 1274
(HbAb 52-41) at each time point are shown along with photographs of the corresponding
haematoxylin and eosin stained sections. It is debateable in the Oh treatment how much of the
drug reaches the tumour, however there is an apparent signal in Figure 2.3 corrresponding to
m/z 1274. Further drug distribution studies would clarify presence of CA-4-P and its
metabolites.
Figure 2. 3: MALDI-MS images fo r the distribution of m /z 1274 throughout a fibrosarcoma 120 timecourse. (HbAb 52-41) in (a) control (saline), (b) 0 h post CA-4-P (c) 0.5 h post CA-4-P, (d) 6 h post CA-4-P and (e) 24 h postCA-4-P, along with photographs of the corresponding haematoxylin and eosin stained sections.
24 hourS CA-4-P
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necrosis
82
2.4.2 Haematoxylin and Eosin staining of fibrosarcoma 120 tissueHaematoxylin and eosin (H&E) staining of each fibrosarcoma 120 serial section was performed
after imaging for identification of viable and necrotic tissue and to make observations of the
tumour rim.
Examples of viable and necrotic tissue are shown in Figure 2.4: (a) control saline-treated
tumour which is essentially viable, (b) control tumour with a small necrotic region, (c) 0 h CA-4-
P showing viable tissue, (d) 0 h CA-4-P showing viable and necrotic regions, (e) 0.5 h after CA-
4-P showing viable tissue, (f) 0.5 h after CA-4-P showing viable and increasing necrotic regions,
(g) 6 h after CA-4-P showing partially viable regions, (h) 6 h after CA-4-P with haemorrhage and
necrosis and (i) 24 h after CA-4-P treatment showing total necrosis.
Higher magnification images of the H&E staining of the tumour rim in the VEGF120 tumour
sections are shown in Figure 2.5: (a) control/ saline, (b) 0 h CA-4-P, (c) 0.5 h after CA-4-P, (d) 6
h after CA- 4-P and (e) 24 h after CA-4-P. The H&E tissue sections indeed reflect the
heterogeneous nature of the tumour especially considering the control with no CA-4-P (Figure
2.5 a), however the effect of the vascular disrupting agent is still apparent and the 0.5 h image
(Figure 2.5 c) suggests a good example of the viable tumour resistant rim.
In all images, viable tumour cells have densely packed nuclei which appear as regions of
"organised cells" which stain blue in H&E staining whereas regions of necrosis, due to cell lysis
tend to show as a disorganised region with less blue staining. In these data, haemorrhaging
also gives a pink colour to the tissue e.g. Figure 2.4h.
83
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Figure 2. 5: Higher magnification images of the H&E staining o f the tumour rim in the VEGF120 tumour sections. Control (saline), (b) 0 h post CA-4-P, (c) 0.5 h post CA-4-P, (d)6 h post CA-4-P and (e) 24 h post CA-4-P. The 0.5 h image displays a good example o f the viable tumour resistant rim.
85
2.4.3 In itial imaging of proteins according to theoretical digests using M A L D I-
MS1Proteins which could be of relevance to tumour biology and the stress response were
tentatively identified according to theoretical digests (Li et al 2011). The following preliminary
MALDI images were observed to aid focus on potential future protein targets to consider in
this dose response relationship study.
CHCA matrix peak m/z 877
r■k
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(a) m/z 1887 Grp78
%*
(d) VEGF 120 m/z 1485
(b) m/z 1749 HIF-1a0 .9 6
(e) VEGF A m/z 1850
Figure 2. 6: MALDI-MSI o f peptides from tumour 5:1 (24hrs CA-4-P). CHCA matrix peak m /z 877 used for normalisation ,(a) m /z 1887 Grp78, (b) m /z 1749 HIF-la, (c) TNF-a, (d) m /z 1485.7 VEGF 120, (e) m /z 1850.8 VEGF A (188), according to theoretical digests and (f) tumour 5:1 histological section after H8cE staining with magnified insert showing necrotic tissue.
86
The ion signals shown in Figure 2.6 were generated according to predicted theoretical tryptic
digests. Although at this stage protein the identifications were not yet confirmed, it was
fascinating to notice the differing spatial distribution of the potential peptides thus reflecting
the heterogeneous nature of tumour tissue.
The possible detection and spatial distribution of proteins like glucose-regulated protein 78/
Binding immunoglobulin protein (Grp-78/ BiP) (Figure 2.6a) could reveal vital information of
how CA-4P can effect tumorigenesis thus inducing other pathological stimuli.
The image tentatively identified as belonging to VEGF A (188 isoform using UniProKB database
(mouse) with 2 missed cleavages) in Figure 2.6 e is quite contradictory to the mouse model
employed which had been genetically engineered to solely express the VEGF 120 isoform. The
latter could be due to immune cell infiltration.
It was found however, that through further interrogation of peptide sequence databases i.e.
Blast (http://blast.ncbi.nlm.nih.gov/) and reference to the article by Tischer et al (1991) that
the peptide sequence given regarding the VEGF 120 ion at m/z 1485 is incorrect. The latter
discovery came about whilst checking whether or not the sequence giving the ion signal
(Figure 2.6 e) at m/z 1850 really matched part of the VEGF 188 protein sequence to support
the theory of possible immune cell infiltration. It appeared that none of the VEGF 120 peptide
'RWQKIADELGNR' (2 missed cleavages) giving m/z 1485 provided by UniProKB theoretical
digest is detectable in the original VEGF nucleotide sequences (Tisher et al 1991). The
sequence in question when submitted to Blast database gave a result suggesting that the
peptide could be a zinc finger protein. The VEGF 188 peptide 'FKSWSVHCEPCSERR' (although 2
missed cleavages) giving m/z 1850, matched with the nucleotide sequences detailed in
UniProKB, Blast and original VEGF nucleotide sequences presented in Tischer et al (1991). Part
of the VEGF 188 peptide sequence coincided with nucleotide sequences present in the VEGF
exon 6, a region of the VEGF gene known exclusively to VEGF 188 (Figure 1.29), hence
supporting the presence of VEGF 188 in the MALDI-MSI (Figure 6 e).
87
2.4.4 Principle component analysis - discriminant analysis (PCA-DA)Multivariate analysis was chosen to statistically analyse the complex data sets which arise from
on tissue PMF's. PCA-DA was performed to try and correlate peptide inductionwith dose
response and to provide a method of further analysingthe PMF data.
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Figure 2. 7: PCA-DA of fibrosarcoma 120 tumour in situ tryptic digests.(a) The scores plot showing groupings and variability between tumour time point spectra, Tumour 1-5 indicates Saline/Control - 24h post CA-4-P (b) the loadings plot displaying the separation and spatial distribution o f m /z values in relation to score plot positions, (c) illustrates the variability of two haemoglobin peptides within each tumour section. The red arrow on (b) indicates m /z 1274 (indicative o f haemoglobin b chain) which is associated with the tumour 4 set (6 h post CA-4-P).
88
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89
Figure 2.7 shows the results of PCA-DA of VEGF120 in situ tryptic digests: (a) the scores plot
showing groupings and variability between tumour time point spectra, (b) the loadings plot
displays the separation and spatial distribution of m/z values in relation to score plot positions,
(c) illustrates the variability of two haemoglobin peptides within each tumour section and an
unknown at m/z 901. The simple response present from the haemoglobin peptides was
regarded as a standard for future time course results to aid validation of dose response
relationships. The red arrow on Figure 2.7(b) indicates m/z 1274 (indicative of haemoglobin (3
chain) which is associated with the tumour 4 set (6 h after CA-4-P). In Figure 2.7(b) the green
data points are peaks identified as monoisotopic by the Marker View 1.2 software and blue
data points are peaks which have not been assigned as monoisotopic. These are termed
"default" by the software.
For the chemometric investigation of the proteomics data reported here, multivariate analysis
was deemed essential because of the large data sets thousands of signals can arise from just a
single pixel due to the acquisition of full-range mass spectra (McCombie et al 2005).
Comparisons of spectral signals are required between both biological and technical replicates
throughout a time course experiment and multivariate analysis allows a better visualisation of
the "vast data clouds". PCA-DA was chosen to provide preliminary insights into ion signals
generated by in situ tryptic digestion using MALDI-MSI with the hypothesis that the samples
should show separation of unique peptides due to known individual treatment time points.
The scores plot in figure 2.7a provides clear separation of tumour treatment groups 3, 4 and 5
which refer to 0.5 h, 6 h and 24 h post CA-4-P respectively. The earlier time points being
tumour 1 (control/ saline) and 2 tumour (0 h) are not visible but can been seen if the region
indicated is expanded. The peaks that appear on the loadings plot (Figure 2.7b) seem to be
representative of high abundant peptides i.e. histones (m/z 944, m/z 1032, m/z 1325) and
haemoglobin (m/z 1274, m/z 1416) which relate to the occurrence of haemorrhagic necrosis,
DNA damage and hypoxic conditions within the tissue.
90
2.4.5. Employment of Ion Mobility MALDI-Mass Spectrometry to stud yin situ tryptic digestion in fibrosarcoma 120 modelsIon mobility enabled MALDI profiling and imaging describes a further separation method of
isobaric species thus opening up another dimension to spectral analysis.
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Haem oglob in P01942 2858.4 VADALASAAGHLDDLPGALSALSDLHAHK 24 >20subun it alpha +21.9819 AT D14 C a tion:M g [II] (DE)
[+21.9694], Ca tion:Na (DE) [+21.9819]2836.4 VADALASAAGHLDDLPGALSALSDLHAHK 87 >201819.8 TYFPHFDVSHGSAQVK 106 >361529.7 IGGHGAEYGAEALER 98 >181416.7 GGHGAEYGAEALER 87 >43
H is tone H4 P62806 1325.7 DNIQGITKPAIR 76 >14
Haem oglob in P02088 1302.6 VNSDEVGGEALGR 79 >38beta chain 1161.6 LVVYPWTQR 29 >20
1048.5 VVYPWTQR 32 >20
H is tone H2A Q8CGP5 1300.6 NDEELNKLLGR 33 >21Type 1-F
Ac tin , aortic P62737 1198.7 AVFPSIVGRPR 30 >13sm ooth 976.4 AGFAGDDAPR 64 >19muscle
H is tone H3 P68433 1032.6 YRPGTVALR 11 >10715.4 DIQLAR 19 >17
H is tone H2A Q8CGP5 944.5 AGLQFPVGR 24 >19Type 1-F
Rho GTPase- Q91V57 844.5 RLTSLVR 21 >18ac tiva tingp ro te in 2 / N-ch im aerin
Table 8: Identities and Mascot scores from the IMS-MS/MS analyses of peptide signals showing Mascot score o f mouse protein identifications and corresponding threshold score at 95% confidence level.
91
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A representative peptide mass fingerprint obtained from a tumour from an animal sacrificed 6
h after administration of CA-4-P (hereafter referred to as 6 h CA-4-P) using the Synapt
instrument, is shown in Figure 2.8. These data illustrate the vast number of peptides that arise
from an in situ tissue tryptic digest, as previously reported (Schwamborn et a/2007.,Le maire et
al 2007., Shimma et al 2006., Groseclose 2007).
LC_1OG302_W 35 (1.061) S611.40.00). Srt (Mn. aOOOl Cm (3 JO) TOf MSMS 1819 35UD*too-
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Figure 2. 9: IMS-MS/MS data obtained from the peak observed in Figure 2.8 at m /z 1819.3. This was identified by Mascot search as arising from Mouse HbAa 42-57 (TYFPHFDVSHGSAQVK).
Figure 2.9 shows IMS-MS/MS data obtained from the peak at m/z 1819.8. This was identified
by Mascot search as arising from Mouse HbAa 42-57 (TYFPHFDVSHGSAQVK) (Table 1). These
data are also a further indication of the advantages of IMS for the identification of "on-tissue"
generated tryptic peptides. The complexity of the obtained data sets is shown in Figure 2.10a,
where ions arising from doubly charged species and singly charged species are clearly
separated by the use of ion mobility and further those singly charged ions arising from
peptides, matrix adducts and lipids are also separated. Use of IMS separation prior to MS/MS
yielded good quality MS/MS spectra for peptides that produced a high and significant Mascot
score.
93
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Figure 2.10 c: Drift scope plot shows ion mobility separation of ion of interest haemoglobin subunit alpha m/z 1819.8 and C13 as shown on mass directly below.
The additional specificity afforded by ion mobility that allows the selection of individual ion
species can clearly be seen in Figure 2.10c . The white arrow indicates the ion of interest (m/z
1819.8) and the ion signal directly above depicts the isotope C13 which can be seen on the
mass spectum below the plot.
Figure 2.11: The distribution o f two known peptides in tumour 5_6, i.e. a tumour 24 h after treatment with CA-4-P (a) m /z 1416 from haemoglobin a chain depicting central necrotic haemorrhage and (b) m /z 1198 corresponding to actin.
The MALDI MSI images shown in Figure 2.11 show the distribution of two known peptides in
tumour 5_6, i.e. a tumour 24 h after treatment with CA-4-P: (a) m/z 1416 from haemoglobin a
chain depicting central necrotic haemorrhage and (b) m/z 1198 corresponding to actin. The
inverse nature of the ir distribution is striking.
97
Figure 2 .12: Overlaid MALDI MSI images showing differing spatial distribution and coregistration of peptides, (a) m /z 944 Histone 2A in red and m /z 1274 Hb in solid green, (b) m /z 1819 Hb in red and m /z 944 Histone 2A in solid green.
Figure 2.12 shows examples of overlaid images to show differing spatial distribution and co
registration of peptides using MALDI MSI: (a) m/z 944 histone 2A in red and m/z 1274 Hb in
solid green, (b) m/z 1819 Hb in red (from rainbow) and m/z 944 histone 2A in solid green. The
Mascot scores for the IMS-MS/MS analyses of these peptides are given in Table 8.
98
2.4.6 Rho GTPases: a target to help understand resistance to CA-4-P?The Rho GTPasesare proteins known to be involved in cell permeability and actin/cytoskeleton
dynamics. (Modolo et al 2012., Aznar and Lacal 2001., Aznar et al 2004., Schmitz et al 2002).
Influence on extra cellular matrix (ECM) integrity, apoptosis, cell adhesion and proliferation,
makes them potential targets in the comprehension of anti-tumour drug resistance.
A review by Vega and Ridley (2008) discusses the role of Rho GTPases in cancer cell biology
and describes them as "molecular switches" which continuously revert from the active and
inactive forms which are GTP-bound and GDP-bound respectively. In the activated state they
can bind to many effectors which subsequently elicit a cascade of events leading to the
processes mentioned previously.
Interestingly with relevance to the work reported here, some Rho proteins promote
neovascularisation which could be as a result of association with angiogenic factors and drug
resistance effectors (Vega and Ridley 2008).
Over a decade ago work by Kozma et al (1996) suggested that N-chimaerin (Rho GTPase
activating protein 2) is implicated in morphological changes of the cytoskeleton when
associated with cancer related protein Racl and cytoskeleton signalling protein Cdc42Hs
(Kozma 1996). It is stated that n-Chimaerin induces the formation of the actin-based structures
lamellipodia (actin projections) and filopodia in migratory mammalian cells and have pro-
oncogenic properties. It has been suggested that Rho GTPases have strong influence not only
on tumourigenesis but the migratory functionality of the cancer cell (Figure 2.13), therefore
key players in the metastatic process (Karlsson et al 2009).
A potential combination therapy for colorectal cancer patients is administration of
bevacizumab and cetuximab to target vascular endothelial growth factor (VEGF) and epidermal
growth factor receptor (EGFR) (Leve and Morgado-Di'az 2012). It is known that in gastric
cancer VEGF interacts with Racl whereas EGFR acts as a stimulatory factor of Rac to encourage
the migratory process of colonic cells. Therefore the therapeutic likelihood of inhibiting cancer
progression is evident. In an article by Leve and Morgado-Di'az (2012) it is suggested that it
could be highly beneficial for the future of anti-cancer therapy to identify inhibitors of the Rho
GTPase-associated functions.
99
Tumor cell
Cell cycle
Epithelial cell polarity
Rho A, Racl
Cdc4^
Angiogenesispromoting
factors
MigrationMesenchymal: Racl Amoeboid: Rho A D i recti onal ity: Cdc42
Figure 2 .13: Major Functions o f Rho GTPases in vitro ( Karlsson et al 2009).
A putative peptide assigned to the Rho protein N-chimaerin as mentioned earlier (Rho GTPase-
activating protein 2) was identified in the follow ing MALDI-IMS-MS/MS experimental work
(Table 8). Mascot search results produced the peptide 183-189 (RLTSLVR) for the ion at m/z
844.5288.
The peak of interest is indicated in the PMF from fibrosarcoma 120 tissue 30min post CA-4-P
administration (Figure 2.14). The corresponding MALDI image is featured showing the spatial
distribution of the selected ion at m/z 844. The peptide appears to be located mostly around
the periphery of the tumour possibly indicative of its involvement in resistant to CA-4-P. The
IMS-MS/MS data obtained from the peak observed in Figure 2.14 at m/z 844.5 is shown in
Figure 2.15, the mascot score histogram for the said results is featured in Figure 2.16.
100
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Figure 2 .15: IMS-MS/MS data obtained from the peak observed in Figure 2.14 at m /z 844.5. This was identified by Mascot search as arising from Mouse N-chimaerin (Rho GTPase activating protein 2) 183-189 (RLTSLVR), the insert features the matched MS/MS fragmentation pattern.
{Science} M a s c o t S e a r c h R e s u l t s
OsarEmailSearch title MS data file Database Taxonomy Timestamp Protein hits
Laura Cole ImooleSmy.shu.ac.ok
a l l . p k lSwissProt 2012_10 (538259 sequences; 191113170 residues)Mus. (16624 sequences)16 Nov 2012 at 11:25:09 GMTH2A1F_MOOSE Histcne H2A type 1-F OS=Hus musculus GN=Histlh2af FE=1 SV: CHIN MOOSE N-chimaerin 05=Mus muscuius GM=Chnl FE=1 5V=2
M ascot Score Histogram
Ions score is -10*Log(P); where P is the probability that the observed match is a random event Individual ions scores > 18 indicate identity or extensive homology’ (p<0.05).Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits
Protein Score
Peptide Summary Report
Figure 2 .16 : Mascot score histogram detailing the results in the identification of N-chimaerin.
102
57
20.0 40.0 60.0 80.0 100.0 120.0 140.0 1600 180.0Drift Ttrte (Bins)
(O.M.202)(l4».:oO.M))(«M.IH_2>M.tO) (845:55043)
Figure 2 .17 : Zoomed in Driftscope plot showing ion of interest, arrow indicating the peak originally selected.
It could be said that there are many overlapping signals in the tryptic peptide MS/MS spectrum
shown in Figure 2.15 where both the arginine y l ion (m/z 175) and evidence of potential
hydoxy fatty acids (m/z 187) are visible. This is also evident in the Driftscope plot (Figure 2.17)
where the arrows point to the actual peak that was selected for MS/MS fragmentation. The
dominant peak to the left of the indicated peak in Figure 2.17 would be part of the MS/MS
spectrum, despite this the peptide was matched and identified (Figure 2.15) having 1 missed
cleavage from the fragments present in the data.
103
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104
The apparent absence/ low abundance of the ion at m/z 844 ( putative peptide, Rho GTPase
activating protein 2) in the later time points post CA-4-P administration, could be indicative of
how the drug effects the Rho protein's usual involvement in tumour progression and neo
genesis of the tumour vasculature. The latter response is evident in the follow ing MALDI-MSI
(Figure 2.19) of the ion found at m/z 844.5 corresponding to N-chimaerin.
LC_100720_3_ 1 _M $01 J)02J301 _max j j i v (MEAN 844 530-84*St1OJ. 100 720_ I _5_MS0 t_3G2_M *. {MEAN £4a -yj-AM W0|
1312T0 ;MEAN $4,3 j
Figure 2 .19: MALDI-MSI throughout a fibrosarcoma 120 time course o f the putative peptide Rho GTPase activating protein 2 m /z 844 (183-189, RLTSLVR).(a) control (saline), (b) 0.5 h post CA-4-P, (c) 6 h post CA-4-P and (d)24 h postCA-4-P, the intensity scales are all set a t 80 to aid observation of response.
As mentioned previously in this chapter the spatial distribution of this putative protein appears
to be localised around the periphery of the tumour sections in the Control/ saline and 30min
105
post CA-4-P (Figure 2.19). The images mirror the spectra in Figure 2.18 with the peak of
interest decreasing in abundance as the time course progresses.
106
2.4.7.StatisticaI analysis of mouse fibrosarcoma 120 tumour tissue using MATLAB®Principle component analysis (PCA)is an unsupervised test to produce unbiased clustering of a
data set, once confident that there were some differences emerging due to none
discrimination of data then partial least squares discriminant analysis (PLSDA) was then
chosen to provide a classification model.
MATLAB® (Matrix Laboratory) (MathWorks, Inc., Natick, MA 486USA) is used in many areas as
a computational statistical tool for mass spectrometry multivariate data analysis, and also via
the Bioinfomatics Toolbox" provides tools for spectral alignment and pre-processing.
Spectral data from both profiling and imaging experiments can be imported into MATLAB® in
order to build predictive models. This allows the user a powerful means of understanding
complex data sets. In addition to script writing using the usual MATLAB® command format, use
of the Eigenvector PLS_Toolbox (http://www.eigenvector.com) enables access to a number of
advanced statistical tools for the analysis of mass spectrometry (MS) data (Eigenvector
Research Inc 2012). Recognition of trends within a data time course experiment can be
detected via decomposition mathematical modelling i.e. principle component analysis (PCA).
Classification studies of MS spectra were performed using PLSDA, where regression vector
plots can illustrate presence of ions unique to a particular sample. MS results are imported
into MATLAB® in .txt or .csv format either post "SpecAlign" or after application of "automatic
peak detection" if using the instrument data processingsoftware (Waters MassLynx™
Software). There are various pre-processing options available to aid linearization of variables
within MS data with normalisation (2-Norm) and mean centre being frequently selected. 2-
Norm refers to the sum of the squared value of all variables of the sample (Eigenvector
Research Inc. 2012). During building of a PCA/PLSDA model the choice of cross validation is
crucial to determine complexity of the data i.e. to consider data splits samples and/or latent
variables/principle components. An example for a time course experiment would be selection
of "contiguous blocks" which would look for assessing predictability between repeat blocks of
spectra to circumvent the "replicate sample trap" which could lead to overly optimistic fitting
of data.
In MATLAB® complex data clouds can be modelled with ease via the Eigenvector PLS_Toolbox
allowing a deeper perception of variability throughout time course experiments.
107
Score PlotSamples/Scores Plot oftest.txt
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1276 4193 Variables/Loadings Plot fortest.txt
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Figure 2. 2 0 : PCA of fibrosarcoma 120 tumour tissue in situ tryptic digests. The score plot (a) shows groupings between the tumour time points, the loading plot (b) contains the peak information which spatial relate to the groupings seen in (a) with dominant Hb peaks.
Figure 2.20a although not aesthetically pleasing as the PCA-DA model featured earlier in Figure
2.7 shows similar groupings shared by the earlier time points w ith separate regions assigned
108
for the 6h and 24h post CA-4-P. The Hb peaks are clearly visible in the later time points of the
corresponding loadings plot (Figure 2.20b). This now, seemingly a characteristic of the CA-4-P
susceptible fibrosarcoma model.
The conclusions that can be drawn from these data are fairly limited as the very intense
haemoglobin and histone peaks appear to dominate the statistics. It could be argued that
biomarker discovery requires supervised discriminant analysis i.e. PLSDA, opposed to non
discriminant analysis. The rationale behind this idea is that non discriminant analysis could
mask expected trends, an example of this scenario would be a treatment time course study.
The treatment added throughout a time course experiment is a known factor that is expected
to influence the data generated. Pre-grouping the data before building the statistical model
could aid the separation of relevant peaks of interest.
PLSDA aimed to provide classification between tumour time points. The PLSDA data that follow
are representative of the fibrosarcoma 120 data set as described in 2.3.5 Statistical analysis
section.
An example of the clarity of the data possible from analysis using MATLAB® is shown in Figure
2.21. Here, the regression vector plots from PLSDA of "on-tissue" digests of fibrosarcoma 120
Control/Saline and 30 min post combretastatin-4-phosphate (CA-4-P) (a vascular disrupting
agent), treatment tumour samples show the increased abundance of peaks at m/z 944 and
1032. This allows classification between data obtained from the control/saline and 30 min post
treatment tumour samples based on the induction of Histone 2A and Histone H3. The latter
could possibly be a reflection of DNA damage necrosis and hypoxia as a result of CA-4-P
administration as discussed in a previous section.
109
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peptide information.
2.4.8 Region of interest (ROI) study for the analysis of tumour rim and necrotic regions within fibrosarcoma 120 tissue.To enable further examination of tryptic digest spectra from MALDI images acquired using the
fibrosarcoma 120 tumour set a ROI study was performed. ROI's were exported from the
tumour rim and necrotic regions using the H&E tissue viable and necrotic areas for image co
registration. Straightforward selection of 'central necrosis' and Viable rim' regions was not
possible as the complex patterns of necrotic infiltration required a mixture of microscope and
whole scanned image analysis.
Peptide induction was observed via PLSDA and Variable Importance in Projection (VIP) scores
were used from PLSDA regression modelling to determine the importance of variables within
the data time course.
HDI software (Waters Corporation, Manchester, UK) was used to view the MALDI-MSI for the
following statistics. ROI's were selected according to areas either being necrotic or directly on
the tumour rim for export to WatersMassLynx™ Software. After using the automatic peak
detection processing option the text files were then imported intoMATLAB® where predictive
statistical models were built.
112
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The zoomed insert in Figure 2.24 shows a clear separation between control and 24h post CA-4-
P.
To examine this data further the follow ing VIP scores show the variables w ithin the
fibrosarcoma 120 ROI study that are deemed most important.
Rhoprote in
Variables/Loadings Plot forNecrotic_rim_V120_20000_peaks.txt150
1028.5925
1844.5084 VIP scores for Control tum our rim
H2A944.5335
100
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Figure 2.25: VIP scores for the Control fibrosarcoma 120 tumour rim regions. The Rho protein N-chimaerin is indicated at m/z 844, Histone 2A at m/z 944 and actin aortic smooth muscle at m/z 1198. These data is in good agreement with the increased ion signal for m/z 844.5 displayed in earlier results for this protein.
116
Variables/Loadings Plot for Necrotic_rim_V120_20000_peaks.txt
VIP scores for 24 hours post CA-4-P tumour rim
944.5335
H2A
Rhoprotein
>-
0oHb Decreased
abundanceof Rho GTPase activating protein 2
: 1530.7177
945.5380
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Figure 2.26: VIP scores for the 24h post CA-4-P fibrosarcoma 120 tumour rim regions. The Rho protein N-chimaerin is indicated at m/z 844, Histone 2A at m/z 944 and actin aortic smooth muscle at m/z 1198 and a Hb peaks at m/z 1529. The above results reflect the decrease in m/z 844.5 evident in results shown previously.
The VIP score plots provide an interesting compliment to the data shown in the PLSDA
regression vector plots. Figure 2.25 illustrates the ion at m/z 844.5 as a key peptide in the rim
region, a response echoed in the peptide's spatial distribution in Figure 2.19a and 2.19b.
In contrast to this Figure 2.26 displays the contrary showing Hb and markers of necrosis as the
key players influencing variability, with marked decrease of importance for N-chimaerin.
117
2.4.9 Concluding RemarksThe potential for investigations into protein induction following drug treatment strategies is
phenomenal. Currently one major challenge is the management and analysis of the large
amounts of data generated. Considering each of the peptide mass fingerprints (Figures 2.2 and
2.8), on first inspection these seem to be very noisy, however this could be a signature of a
successful tryptic digestion. Therefore the many low-abundance peptide signals contained in
the apparent noise could house evidence of significant proteins such as heat shock proteins in
addition to the expected and readily visible abundant peptide signals (actin, histones etc.). Due
to the nature of these results one has to question whether or not each peak is worthy of
further scrutiny, or should the vast amount of peaks be simply treated as noise/interference.
The use of ion mobility separation and its associated data extraction software certainly
appears to ease this dilemma making many more signals visible for subsequent examination by
MS/MS. In this work the suspected pharmacological response of haemorrhaging to treatment
with CA-4-P was investigated (Figure 2.3). The MALDI-MS images generated from m/z 1274 in
Figure 2.3 show a clear increase in Hb with time after treatment with CA-4-P. The latter is then
confirmed in Figure 2.4 in the H&E sections. At 6 h after CA-4-P treatment, the tumour in
Figure 2.4 was partially necrotic and Hb was evident in both necrotic and viable tumour
regions. The tumour excised at 24 h was almost completely necrotic. The increase in
haemoglobin is also clearly visible in Figure 2.7(c), the time course plot extracted from the
PCA-DA data, where again the increase in m/z 1274 and m/z 1820 can be clearly observed.
Many other peptides were observed in this work, including some tentatively identified as
arising from VEGF isoforms other than VEGF120 Figure 2.6e. This mostly likely could be due to
infiltration of immune cells (McDonnell et al 2012) especially after further investigation into
the VEGF 188 peptide 'FKSWSVHCEPCSERR' which confirmed that it was present in the exon 6
region known exclusively to VEGF 188. This influx and subsequent induction of certain
chemokines is a studied response to tumour vascular targeted therapy. In the case of the VEGF
120 signal at m/z 1485, it was found to be vital to cross check protein database outputs and
refer back to the original publication detailing the sequencing of that particular gene to be sure
analysis of the correct peptide sequence information.
In PCA-DA of complex data sets, the scores plot (in this case Figure 2.7a) represents the
variance of the original variables, i.e. the obtained sample groups. The loadings plot (in this
case Figure 2.7b) describes the variable behaviour and differences between the observed
groups. The PCA-DA of tryptic digest spectra from fibrosarcoma 120 tumour tissue reported
here shows good grouping of the tumour time points (Figure 2.7a). Confirmation of the
118
increase in haemoglobin due to CA-4-P treatment is given by the association of haemoglobin
ions with the 24 h tumour in the loadings plot (Figure 2.7b) and this is shown further in Figure
2.7(c). Further analysis of this large data was required. The aim of this was to examine whether
changes in signals arising from proteins of lower abundance provided an insight into other
pharmacodynamic responses to the treatment.
After the discovery via Mascot search of protein N-chimaerin (Figure 2.16) (Rho GTPase
activating protein 2), spectra in Figure 2.18 and MALDI images in Figure 2.19 depicted the
proteins decreased expression over time and peripheral spatial distribution in the early time
points. The latter could be due to interference by CA-4-P. Driftscope plots (Figure 2.17) helped
to build evidence of its response throughout the time course of experiments. Statistical
analysis using MATLAB® confirmed the earlier findings from this chapter namely reflecting
haemorrhaging, necrosis and N-chimaerin responses.
MALDI MSI has been used to study the effect of treatment with CA-4-P, a vascular disrupting
agent in late stage clinical trials, on mouse VEGF120 tumours. A strategy incorporating "on-
tissue" tryptic digestion, MS/MS identification peptides and ion mobility separation to improve
specificity was employed. By taking tumour samples at a number of time points after
treatment, gross effects were clearly visible through changes in the expression of certain
peptides. These were identified as arising from haemoglobin and indicated the disruption of
the tumour vasculature. It was hoped that the use of PCA-DA, PCA and PLSDA would reveal
more subtle changes taking place in the tumour samples however at present these are masked
by the dominance of the changes in the haemoglobin signals and other proteins of high
abundance.
The MALDI-IMS-MS/MS reported here was performed manually by direct peptide/ protein
profiling. The numerous signals from the PMF's generated most likely contain many peaks
which have been post translationally modified thus hampering peptide identifications. The
MS/MS spectrum in Figure 2.15 is an example of this, clearly containing overlapping MS/MS
fragmentation patterns. Therefore the experimental work reported in the following chapters
which describes similar experiments conducted using a CA-4-P resistant mouse model explores
not only MALDI-MSI but the use of conventional proteomic techniques to compliment and aid
interpretation of the MALDI-MSI data.
119
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122
Chapter 3Protein induction post CA-4-P
administration in a resistant fibrosarcoma 188 mouse model
123
3.1 IntroductionIn this Chapter data from the analysis of a mouse fibrosarcoma model tumour genetically
engineered to solely express the insoluble matrix bound VEGF 188 isoform is described. In
contrast to the soluble VEGF 120 isoform type that were the focus of Chapter 2, which have
been found to be susceptible to VDA , the VEGF188 isoform type have been reported to be
resistant to treatment with VDA (Tozer et al 2008) . In bone development, VEGF 120 and
VEGF 188 both have d ifferent roles in vascularisation (Maers et al 2004). VEGF 188 is amongst
the matrix bound isoforms thought to be involved in vascularisation of the metaphyseal bone
regions, whereas the soluble VEGF components are concerned w ith the epiphyseal area. It is
stated that the various isoforms of VEGF all play unique roles in the proliferation and
chondrocyte survival in a hypoxic environment (Maers et al 2004).
Capitalfemoral
epiphysis
Epiphysis
Growth plate
Metaphysis ' MMG 2mi5
Figure 3.1: Diagrammatical cross section of the region present at the end of growing long bones showing the areas involved in epiphysis and metaphysis. (Ortho paediatrics group (2012) (http://www.orthopediatrics.com/docs/Guides/slipped_CFE.html)
The term fibrosarcoma describes a malignant fibrous mesenchymal neoplasm which is said to
be found in the femur, humerus, jaw bone and is sometimes present in organs/ soft tissues
(Encyclopaedia Britannica Inc 2013),. Angiero et al 2007).
In tumours VEGF is known to be a potent angiogenic stimulator and levels are found to be
elevated in both the tumour cells and stroma (Harris et al 2012, Saharinen et al 2011). The
tumour microenvironment is said to promote the recruitment of inflammatory cells due to
chemoattractants. These actions are said to result in such cells becoming a major component
124
of the actual tumour mass (Saharinenet al 2011). Pro- and/or antitumorigenic functions can
result as a consequence of immune cell infiltration which is largely tissue/ tumour dependant.
Tumour associated myeloid cells (TAMs) are known to secrete numerous factors which favour
angiogenesis some of which include; VEGFs, TNF-a, interleukin (IL)-lb, matrix
metalloproteinases, PDGFB, basic fibroblastgrowth factor.
In the data to follow using fibrosarcoma 188 tumours, a further time point of 72h post CA-4-P
was added based on the more resistant nature of this matrix bound VEGF isoform model
(Tozer et al 2008). The rationale for this decision was to try to observe any response which
could be indicative of drug resistance and a potential switch back to tumour viability.
The use of CA-4-P treatment could enhance knowledge of the proteins induced in tumours
expressing either fibrosarcoma 188/ 120 isoforms under hypoxic conditions. The following
data aims to provide proteomic comparisons throughout a treatment time course using
fibrosarcoma 188 models, and allow observational differences to me made between the
expression of proteins in the resistant fibrosarcoma 188 and susceptible 120 tumour models.
125
3.2 Materials and Samples
3.2.1 Chemicals and Materialsa-Cyano-4-hydroxycinnamic acid (CHCA), aniline (ANI), ethanol (EtOH), chloroform (CHCI3),
acetonitrile (ACN), octyl-a/b-glucoside (OcGIc), tri-fluoroacetic acid (TFA), ammonium
bicarbonate, haematoxylin, eosin, xylene and DPX mountant were from Sigma- Aldrich
(Dorset, UK). Modified sequence grade trypsin (20 pg lyophilised) was obtained from Promega
(Southampton, UK).
3.2.2 Tissue samplesMice were injected sub-cutaneously in the flank with a 50 pi tumour cell suspension containing
1 x 106 cells in serum-free medium. The cells employed in this study were from the mouse
fibrosarcoma cell line, VEGF188. This has been engineered to express only the VEGF188
isoform. Tumours were allowed to grow to approximately 500 mm3, before CA-4-P treatment
(a single dose of 100 mg/kg i.p). The dosage for CA-4-P are consistent with animal studies
performed at clinically relevant doses (Galbraith 2003., Prise 2002). Mice were sacrificedand
tumours excised at various times after treatment. All animal work carried out documented
herein was performed by Dr. J. E. Bluff, Tumour Microcirculation Group, University of Sheffield,
UK. Samples were provided as frozen excised tumours and stored at -80°C.
3.2.3. Experimental groupsControls (no treatment, saline i.p), n = 4 (labelled tumour 6_1 - tumour 6_4), C-A4-P (0.5 h
after treatment), n = 5, (labelled tumour 7_1 - tumour 7_5), C-A4-P (6 h after treatment), n =
5, (labelled tumour 8_1 - tumour 8_5), C-A4-P (24 h after treatment), n = 5, (labelled tumour
9_1 - tumour 9_5), C-A4-P (72 h after treatment) n = 4, (labelled tumour 10_1 - tumour 10_4).
3.2.4 Tissue preparationFrozen tissue sections were cut to give approximately 10pm sections, using a Leica CM3050
cryostat (Leica Microsystems, Milton Keynes, UK) (Djidja et al 2009). The sections were then
freeze thaw mounted on poly-lysine glass slides. Mounted slides were either used
immediatelyor stored in an airtight tube at -80 °C for subsequent use.
126
3.2.5 In situ tissue digestion and trypsin depositionThe tissue samples in triplicate to undergo MALDI-MS/MSI were washed initially with 70% and
then 90% ethanol for 1 min then left to dry, subsequently slides were immersed in chloroform
for 10 s. Prior to matrix application, in situ tissue digestion was performed with trypsin solution
prepared (from lyophilised trypsin) at 20 pg/ml by addition of 50 mM ammonium bicarbonate
(NH4HCO3) pH 8, containing 0.5%octyl-a/b-glucoside (OcGIc) based on a protocol by Djidja et al
(2009).
The "Suncollect" (SunChrom, Friedrichsdorf, Germany) automatic pneumatic sprayer was used
to spray trypsin in a series of layers. The sections for MALDI-MS and MALDI- MSI were
incubated overnight in a humidity chamber containing H20 50%: methanol 50% overnight at
37°C and 5% C02.
3.3 Methods and instrumentation
3.3.1 Matrix depositionThe matrix, a-cyano-4-hydroxycinnamic acid (CHCA) and aniline in acetonitrile:water:TFA
(r.l:0.1 by volume), was applied using either the Suncollect (at 5 mg/ml) or the Portrait™ 630
Multispotter (at 10 mg/ml), as described above. Identical coordinate settings were used as
with the trypsin deposition, to ensure sample uniformity. Equimolar amounts of aniline were
added to the CHCA solution, i.e. 1 mlof 5 mg/ml CHCA solution contained 2.4 pi of aniline.
These matrix deposition parameters were based on methods from Djidja et al (2009).
3.3.2 InstrumentationMALDI- IMS/MS, MALDI- IMS/MSI and MALDI- IMS-MS/MS were performed using a HDMS
SYNAPT™ G2 system (Waters Corporation, Manchester, UK) and Drift scope 2.1 software
(Waters Corporation, UK). Instrument calibration was performed using standards consisting of
a mixture of 217 polyethylene glycol (Sigma-Aldrich, Gillingham, UK) ranging between m/z 100
to 218 3000 Da prior to MALDI-IMS-MSI analysis. In order to achieve good quality MS/MS
spectra, they were acquired manually moving the laser position andadjusting the collision
energy to achieve good signal to noise for product ions across the full m/z range of the
spectrum.Collision energies were adjusted from 70 to 100 eV during acquisition and
acquisition times were generally of the order of 5-10 s per spectrum. MS/MS spectra were
uploaded to perform a Mascot (Matrix Science, London, UK) search which used the UniProt
database in order to generate a sequence match.Image acquisition was performed using raster
imaging mode at 30-100 pm spatial resolution, Biomap 3.7.5.5 software (http://www.maldi-
127
msi.org/) was used for image generation. To enable simple visual comparison between images
all data were normalised to m/z 877/ m/z 1066 (peaks arising from the aCHCA matrix).
The LC-ESI-MS/MS analysis of prepared tissue digests was performed by Adam Dowle at the
Proteomics Technology Facility, Department of Biology (Area 15), University of York. LC-ESI-
MS/MS analyses were performedwith a Bruker nanoESI source fitted with a steel needle using
Ion spray voltage of 1500V. MS/MS was acquired using the following Auto MSMS settings: MS:
0.5 s (acquisition of survey spectrum), MS/MS (CID with N2 as collision gas): ion acquisition
range: m/z 300-1,500,0.1 s acquisition for precursor intensities above 100,000 counts, for
signals of lower intensities down to 1,000 counts acquisition time increased linear to Is, the
collision energy and isolation width settings were automatically calculated using the
AutoMSMS fragmentation table:, 5 precursor ions, absolute threshold 1,000 counts, preferred
charge states: 2 - 4 , singly charged ions excluded. 1 MS/MS spectrum was acquired for each
precursor and former target ions were excluded for 30 s. The data obtained from the
University of York was in the MASCOT .data file format. The .dat files selected used the
following modification searches and parameters using the MASCOT Mus (Musculus) protein
database:
Fixed modifiation: Carbamidomethyl (C) and variable modifications: Acetyl (Protein N-term), Gln->pyro-Glu (N-term Q),Glu->pyro-Glu (N-term E),Oxidation (M).
Massvalues: Monoisotopic, protein Mass : Unrestricted, peptide Mass Tolerance : ± 10 ppm, fragment Mass Tolerance: ± 0.1 Da and max Missed Cleavages : 1
These data were then processed using Scaffold 3 proteomic software tool for visualisation and
analysis of the LC-ESI-MS/MS data (http://www.proteomesoftware.com/). The data files (.dat)
produced from MASCOT which corresponded to each digested analysed, were uploaded
individually. Analysis with X! Tandem was selected in order to improve protein identifications
with searching through an additional database.
3.3.3 Haematoxylin and eosin stainingSlides were immersed in haematoxylin for 1 min, rinsed in tap water until the water ran clear,
immersed in 1% eosin for 30 s and rinsed in tap water until the water ran clear. They were
then dehydrated as follows: 50% ethanol for 2 min, 70% ethanol for 2 min, 80% ethanol for 2
min, 95% ethanol for 2 min and 4 changes of xylene applied to each slide for 1 min at a time.
Finally, they were mounted with DPX mountant and left to dry in the fumehood overnight.
128
3.3.4 Statistical analysisPCA and PLSDA was performed using MATLAB® (Matrix Laboratory) (MathWorks, Inc., Natick,
MA 486 USA). The PCA/ PLSDA data that follows is representative of the fibrosarcoma 188 data
where either n= 4/5 biological replicates per time point with up to 6 technical replicates for
each, (control - 72 hours). PLSDA Data for ROI study using HDI software (Waters Corporation,
Manchester, UK) was analysed using one tumour per time point with 9 technical replicates per
ROI. Variable Importance in Projection (VIP) scores were used from PLSDA regression
modelling to determine the importance of variables within the data time course.
3.3.5 Data pre-processing using Waters MassLynx™ Software and MATLAB®MS results wereimported into MATLAB® in .txt or .csv format after application of "automatic
peak detection" using the instrument data processing software (Waters MassLynx™ Software).
Normalisation (2-Norm) and mean centre were selected, "contiguous blocks" was used for
cross validation.
3.3.6Protein network analysisAccession lists generated by results from the LC-ESI-MS/MS Mascot searches were imported
into the STRING 9.0 database (from Mus (Musculus) MASCOT protein database). Observations
of the relationships were made between the proteins indentified throughout a fibrosarcoma
188 time course. The sample data was used to build predictive proteomic pathways and study
the predicted functional partners of known protein-protein interactions (http://string-db.org/).
3.3.7 Immunohistochemical staining
3.3.7.1 Chemicals a nd M a te ria lsMethanol, Acetone, Hydrogen peroxide solution 30% wt., Xylene, Ethanol, Gill's Haematoxylin
and DPX mountant were all purchased from Sigma Aldrich UK. Phosphate buffer saline tablets
(Dulbecco 'A' Tablets) were from Oxoid Ltd. Normal goat serum, ImmEdge hydrophobic barrier
pen, 10X Casein solution, Avidin/Biotin horseradish peroxidise complex blocking kit, ABC
solution kit, Diaminobenzidine (DAB) substrate kit were from Vector Laboratories Ltd UK. HSP-
90 (abl3494,16F1) and Plectin (ab32528, E398P) antibodies were from Abeam, Cambridge UK.
Macrophage cell surface marker F4/80 (MCA497GA, CI:A3-1) antibody was from AbD Serotec.
129
3.3.7.2 Im m unoh is tochem ica l m ethodsMounted frozen tissue sections were allowed to equilibrate to R.T for 5 min. Slides were then
fixed in ice cold acetone for 20 min then rinsed in PBS. Endogenous peroxidases were blocked
using 30% H202 and Methanol. After rinsing in PBS, tissues were blocked for lhr using Goat
sera containing 10% Casein. After PBS washes, Primary Ab was added and left O.N at 4°C. To
aid optimisation of immunohistochemistry, Ab's were first diluted using dilutions ranging from
1:50 - 1:1600 depending on Ab and manufacturer's guidelines. Primary antibody dilutions
were; 1:400 for both Plectin and F4/80 and 1:2000 for HSP-90. Isotype IgG controls were used
for F4/80 and for Plectin and HSP-90 primary antibody was just omitted a negative control.
After overnight incubation and subsequent PBS washes, Secondary Ab was added diluted
(1:200) in 2% Goat sera and left for an incubation time of lh at R.T. After this period, ABC
solution was added after rinsing in PBS and left to incubate for 45min at R.T. After PBS
washing, DAB solution was applied and left to allow development of the staining. Slides were
rinsed in tap water prior to immersion in Gill's Haematoxylin for 2min. After slide dehydration
using 70% -100% EtOH tissue was immersed in 2 changes of Xylene for 5 min each. Slides were
mounted using DPX mountant.
3.3.8 Protein precipitation and digestion
3.3.8.1 Chemicals and m a te ria lsChloroform (CHCI3), methanol (MeOH), acetonitrile (ACN), hydrochloric acid (HCI), tri-
fluoroacetic acid (TFA), ammonium bicarbonate, ,DL-Dithiothreitol solution, lodoacetamide,
urea, potassium di-hydrogen phosphate (KH2P04), TRIzol were from Sigma-Aldrich (Dorset,
UK). Modified sequence grade trypsin (20 pglyophilised) was obtained from Promega
(Southampton, UK,).
3.3.8.2Tissue hom ogen isa tion and p re c ip ita t io n o f p ro te inThe fibrosarcoma 188 tumour tissue was homogenised in 800pl of TRIzol solution using a
micro-homogeniser (Kline and Wu 2009). Homogenised solution was centrifuged (1,500 rpm)
for 5 min to pellet out nuclei/ unbroken cells. Post-nuclear supernatant was then centrifuged
(14,000 rpm) for 30 min. Resulting supernatant was discarded and cellular membrane pellet
was retained for protein precipitation. 200pl of MeOH and 50pl of CHCI3 were then added to
each protein pellet sample. After vortexing, 150pl HPLC H20 was added with further vortexing.
After centrifugation (14,000 rpm) for 2 min, the bottom CHCI3 layer was removed. A further
50pl CHCI3 was added, removal of the bottom CHCI3 layer was repeated following
130
centrifugation (14,000 rpm) for 2 min. Subsequent removal of the H20 layer resulted in the
remaining protein precipitate layer. CHCI3 (50pl) and MeOH (150pl) were added directly to the
protein precipitate, after vortexing solution was centrifuged (14,000 rpm) for 2 min.
Supernatant was removed and protein pellet was then allowed to air dry for 2 min.
3.3.8.3 P ro te in D igestionlOOpI of 0.1% RapiGest in 50mM NH4HC03 buffer (pH 7.8) was added to the air dried protein
pellet. Each sample pellet/ solution was consecutively vortexed, incubated at -80°C (~ lhr) and
heated to 70°C (1 min) until solubilised. Once fully solubilised the sample was heated to 100°C
(2 min) and then left to reach room temperature. Each sample was reduced with DTT (final
concentration 5mM) for 30 min at 60°C the left to reach room temperature. Solutions were
then alkylated with iodoacetamide (final concentration 15mM) in the dark for 30 min at room
temperature. Sequence grade modified trypsin was added (20pg/ml) to 80pg of protein
following the BCA Protein Assay (see 3.3.9) used for protein determination. In-solution digests
were incubated over night at 37°C with shaking.
3.3 .8A P repa ra tion o f sam ples f o r co lum n lo ad ingFibrosarcoma 188; HCL (final concentration lOOmM) was added to the overnight digest,
solution was then incubated for a further 45 min at 37°C. After this step the sample was then
centrifuged (14,000 rpm, 4°C) for 10 min, the supernatant removed for analysis. This
remaining solution was lyophilised and stored at -80°C until further use.
3.3.9 Protein estimation - BCA assay
3.3.9.1 Chemicals and m a te ria lsBCA protein assay reagent (bicinchoninic acid), copper (II) sulfate pentahydrate 4% solution,
protein standard solution, 1.0 mg/mL bovine serum albumin (BSA) were from Sigma Aldrich
UK. RapiGest detergent solution was purchased from Waters (UK).
3.3.9.2BCA m ethodSolubilised protein pellets were thawed, vortexed and centrifuged ready for the BCA assay.
BSA standards were prepared within an analytical range of 0-4 mg/ml. BCA reagent was added
to BCA standards and tumour tissue solution samples and left to incubate at R/T for 30min.
BCA standards were measured in triplicate and Samples were measured in duplicate in a 96
well plate using a Wallac plate reader (spectrophotometer) at 570nm.
131
3.4 Results and Discussion
3.4.1 MALDI-MSI of fibrosarcoma 188 CA-4-P treated tumour tissueThe MALDI spectra from both fibrosarcoma 188 and 120 on tissue digests contain numerous
peaks, the challenge so far has been exploration and identification of the low abundant species
present.
One of the main aims at this point of the project was to develop a system of techniques that
could be used in collaboration with each other in order to co-validate proteomic responses
observed within a treatment time course.
Proteins of interest possibly induced as a result of CA-4-P treatment were firstly visualised by
MALDI-MSI as putative protein targets. These responses therefore could then be supported by
immunohistochemical staining. Similarly protein responses observed employing conventional
proteomic techniques i.e. Liquid Chromatographic methods coupled with ESI or MALDI, were
then used to relate back to MALDI-MSI.
132
3.4.1.1 Trypsin application fo r the avoidance of protein delocalisationOne of the main potential pitfalls of in situ tissue tryptic digestion is protein delocalisation. A
factor which when occurs eliminates the analysis of spatial protein distribution. To ensure
good tryptic digest MALDI-MSI, a homogeneous coating is essential during trypsin application.
Other issues to consider would be the tissue type, section size and to ensure a suitable flow
rate is employed when using a pneumatic sprayer for trypsin deposition. Too much trypsin
sprayed per cycle could lead to 'over wetting' of the tissue and subsequent protein
delocalisation. The follow ing images were as a result of initial trypsin application optimisation
involving flow rates, cycles and gas pressure in order to avoid any issues previously mentioned.
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133
digest where trypsin applied using a pneumatic sprayer has caused delocalisation of proteins as a result of an incorrect spraying speed. The trypsin therefore; has not had sufficient drying time and over wetting of the tissue has occurred. The cross hairs which map the ion signals in the top MALDI-MSI in Figure 3.2, show lots of tryptic peptide signals outside of the tissue, indicative that the spatial distribution of the proteins have changed. The signals given from the cross hairs in the bottom image in Figure 2 show a substandard tryptic spectrum as a result.
The MALD-MSI featured in Figure 3.3 can be regarded as an exemplar of on tissue tryptic
digestion. The characteristic contrast of discreet CHCA matrix clusters seen in the left hand
MALDI-MSI, compared to the tryptic PMF evident from the cross hair mapping on the tissue
image shown to the right.
The most suitable parameters for spraying trypsin were found to be the employment of a
series of 5 layers at a flow rate of 2.5pl/ minute. To avoid protein delocalisation and ensure
sample spray uniformity the regions set for trypsin application allowed enough drying time
between each spray pass. A fine spray was found to be essential with a view to maintain
protein positioning within the tissue and the nitrogen gas pressure used was ~15 psi for
spraying trypsin.
For matrix application, the same principles were applied but the gas pressure was decreased to
~10 psi. The flow rate was adjusted to 4pl/ minute to take into account of solvent evaporation
and aid sample coverage and co-crystallisation.
134
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135
The spectra in Figure 3.4 are typical representations of fibrosarcoma 188 peptide mass
fingerprints (PMF) throughout the CA-4-P treatment time course studied.
The spectra within the time course of Figure 3.4 all appear to be quite similar upon first
inspection. The zoomed in regions of the fibrosarcoma 188 time course now shown in Figure
3.5 shows subtle differences of the appearance of Hb at m/z 1416 and appearance of an
unknown at m/z 1577. The high abundant Hb peaks (m/z 1274, m/z 1302, m/z 1529 and m/z
1819) which are evident in the spectra from Figures 2.1 and 2.2 in Chapter 2, do not dominate
the fibrosarcoma 188 spectra. This could be due to the CA-4-P resistant nature of this tumour
mouse model.
136
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model, fibrosarcoma 120 and 188. The peak tentatively relating to the Rho GTPase protein
discussed in Chapter 2 can be seen in the fibrosarcoma 120 spectra (indicated by an arrow),
but not at the same intensity in the spectra corresponding to fibrosarcoma 188. Similarly, the
same can be deduced for the ion at m/z 1325 relating to Histone H4 seen in the 188.
Although further work is required to determine the relevance of increased Histone H4
intensity seen here, there have been many studies on the analysis of Histone H4 acetylation/
deacetylationwith the premise of histone deacetylase inhibitors as potential anti-cancer
treatment (Advani et al 2010). It is stated that expression of Histone acelylation in cancers can
influence patient prognosis. Reports claim that increased levels of acetylated Histone H4 in
patients with esophageal squamous cell cancer can correlate with an encouraging clinical
outcome (Toh et al 2007).
Ultimately, histones are said to be key in gene regulation (Grunstein 1997). In the 188 Control
spectra seen here (Figure 3.6), the prominent Histone H4 could be a characteristic of this more
drug resistant fibrosarcoma 188 tissue.
140
3.4.1.3 MALDI-MSI and immunohistochemistry to analyse a fibrosarcom a 188 treatm ent courseInitial MALDI-MSI using the fibrosarcoma 188 tissue was carried out at a resolution of 100pm
employing a spot to spot laser pattern with the SYPNAT G2 HDMS system (Waters, UK). The
gross haemorrhagic pharmacological response that already has been shown in Chapter 2 using
the fibrosarcoma 120 tumours was considered a good place to start when validating responses
seen by other ions. Figure 3.7 shows a multi time point MALDI image of a fibrosarcoma 188
time course ranging from Control - 72 hours post CA-4-P. The MALDI images were taken from
a slide containing multiple samples.
Switch back to viable tissue.
Corresponding to Hb m/z 1819
24 hours CA-4-P
Control/saline
72 hours CA-4-P
0.5 hours CA-4-P
50711 _V188_combined_678910_ma* div (MEAN: 1819.29-1819.49;
6 hours CA- 4-P
Figure 3. 7: MALDI-MSI multi image of a fibrosarcoma 188 time course. The images show the spatial distribution of a peptide of haemoglobin subunit alpha at m/z 1819 throughout a treatment time course from Control-7 2 hours post CA-4-P administration.
The MALDI-MSI shown in Figure 3.7 of the matrix bound VEGF 188 model shows a different
response to that seen of a Fib peptide in Figure 2.3 (Chapter 2). The haemorrhagic response is
observed much later in the 24h tissue opposed to the 6h sample in the fibrosarcoma 120
MALDI-MSI. In contrast to to the evidence of complete necrosis and vascular disruption in the
last time point of the fibrosarcoma 120 (Figure 2.3 - 24h), here the 72h time point exhibits a
very different response. There appears to be evidence of a switch back to viable tissue in this
more resistant tumour model.
141
Heat shock protein 90 (HSP-90) is ubiquitous in all normally functioning cells and serves to
prevent the misfolding of proteins by helping to retain the correct structural format of the
protein (Neckers 2007). The characteristic tumour microenvironment renders cancer cells to
anoxia, alterations in pH and attack via pharmacological anticancer agents. HSP-90 has been
suggested as a potential target protein for anticancer therapy due to the dependence on
structural conformity by the cancer cell (Den and Lu 2012).
It is known that inhibition of HSP-90 results in the degradation of the HSP-90 stabilised protein
(client protein) via the proteasome.
Elevated levels of HSP-90 in breast cancer patients have been found to correlate with poor
patient survival due to the conserving effect of HSP-90 on human epidermal growth factor
receptor 2 (Jameel et al 1992). It is also stated that HSP-90 is highly expressed in solid tumours
and it is suggested that HSP-90 plays a key role in the evasion of apoptosis thus promoting
tumour cell survival (Den and Lu 2012).
There are over 20 known clinical trials taking place which currently involve the use of HSP-90
inhibitors (Den and Lu 2012). A review article by Den and Lu (2012) poses the question of
which tumour type is the most suitable for HSP-90 inhibition study and whether or not the
anticancer target should be the HSP-90 'client protein' i.e. epidermal growth factor receptor in
non-small cell lung cancer/ human epidermal growth factor receptor 2 in breast cancer.
The MALDI images in Figure 3.8 display the spatial distribution and relative abundance of an
ion at m/z 1168 corresponding to HSP-90. The optical scans shown in Figure 3.6 show the
immunohistochemical staining of HSP-90.
142
Corresponding toHsp 90 m/z 1168 % -
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Switch back to viable tissue.
72 hours CA-4-P
250711 V IS3 combined 678910 man diw (MEAN: 1153.29-1163.49) 6 hours CA-4-P-,
Figure 3. 8: Fibrosarcoma 188 MALDI-MSI multi image showing spatial distribution of HSP-90. MALDI images showing spatial distribution of HSP-90 alpha at m/z 1168.
The presence of HSP-90 is shown throughout the fibrosarcoma 188 time course. The MALDI
image corresponding to 24hr post CA-4-P is indicative of complete HSP-90 saturation. This
response is matched by HSP-90 immunohistochemical staining seen in Figure 3.8.
The possible 'switch bock to viable tissue' is also exhibited by the 72h time point in Figure 3.8.
This potential evidence of tumourigenesis can also be seen in the immunohistochemical
equivalent in Figure 3.9.
143
Control/Saline
•v
$
0.5 hours post CA-4-P
\
24 hours post CA-4- P - complete Hsp90 saturation
Figure 3. 9: Immunohistochemical staining of a fibrosarcoma 188 time course. Evidence of complete HSP-90 saturation can be observed in the 24h optical scan with the indication of tumour tissue rejuvenation in the 72h tumour time point.
72 hours post CA-4-P
Switch back to viable tissue.
The microscopic images in Figure 3.10 were captured in brightfield 10X to enable closer
inspection of the immunohistochemical staining seen here in Figure 3.9.
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Figure 3.10; (a) displays the negative Control for HSP-90the time point used was 24h post CA-4-P, (b) HSP-90 staining using fibrosarcoma 188 Control tissue/ (c) HSP-90 staining ofO.Sh post CA-4-P, (d) 6h post CA-4-P,(e) HSP-90 staining of24h post CA-4-P showing HSP-90 tissue saturation status and (f) HSP-90 of the saturated necrotic central region and viable tissue regional indicated by the bracket.
The HSP-90 immunohistochemical staining in Figure 3.10 was captured at 10X magnification to
allow closer inspection of the HSP-90 optical scans previously shown. The apparent saturation
of HSP-90 in the 24h time point visible in the MALDI-MSI and in Figure 3.9 can be seen in
Figure 3.10(e). The lOx image of HSP-90 showing the regional switch back to viability can be
clearly observed in Figure 3.10(f), the viable tumour cells are highlighted by the bracket.
146
3.4.1.4 High resolution MALDI-MSI using fibrosarcom a 188 tum our tissueThe MALDI-MSI featured here employed high resolution imaging with a view to focus down on
the tumour rim regions.
Tissue was imaged using vertical sliced regions which encompassed both the tumour rim and
central area.
Figure 3.11: SYNAPT G2 MALDI imaging plate indicating tissue image regions. The regions selected for MALDI-MSI analysis have been superimposed over the histological tissue sections.
The 40pm resolution image show in Figure 3.12 shows the spatial distribution of m/z 844.5
which corresponded to the putative Rho GTPase activating 2 protein mentioned earlier in
Chapter 2. Although not a dominant peak within the fibrosarcoma 188 spectra, the spatial
distribution of this ion is shown to be localised around the periphery of the tumour tissue in
the follow ing MALDI-MSI of a 72hr treatment time point.
147
"2 Ins CA-4 Ps iZ •
m/z 844.5 Rho GTP- ase activating protein
290711 8 8 _ 4 0 ijm _ 1 0_ I j t »a ■ j i i v (M E AN 84 4 510 -8 44 65 0 )
Figure 3 .1 2 :40pm MALDI-MSI of24h post CA-4-P fibrosarcoma 188 tumour tissue. Peripheral localisation of n-Chimaerin at m/z 844.5.
"2 lu t C’A -I p
(C)
2 9 0 7 1 (MEAN 944 470 9*4f.l0'j SL 1/1
Figure 3 .1 3 :40pm MALDI-MSI of m/z 944.5 in fibrosarcoma 188 tumour tissue. The differing spatial distribution of an ion is shown here corresponding to Histone 2A.
An image from the same acquisition shown in Figure 3.12 is displayed in Figure 3.13. The ion at
m/z 944.5 (Histone 2A) is located in the necrotic region of the tissue, emphasising the
distinction in spatial distribution between m/z 844.5 and 944.5. The necrotic region is
indicated by the H and E in Figure 3.14.
148
40umHistone2AXm/z944.5
• i*7k v ? ’ * 7*
Vi r ■ *
290711 '■'183 40urn 10 1
Figure 3.14: 40pm MALDI-MSI of a on tissue try tic digest and corresponding H and E histological section. A necrotic region is seen here to correlate with the spatial distribution of Histone 2A.
k Jk - «
LC_6_1_080911_30um_max (MEAN:1 198.74-119* LC_6_1 _0S0911 _3Dum jn5^_diV;^EAN:344.430-844.54Ci) SL 1/1
Figure 3.15: 30 pm MALDI-MSI of a Control fibrosarcoma 188 tumour on tissue tryptic digest. The spatial distribution of Actin (a) and Rho GTpase activating protein 2 (b) is shown in the 30pm MALDI-MSI.
30pm images (Figure 3.15) were performed to investigate the tumour rim and although images
appear slightly 'grainy', distribution of known peptides could still be visualised. This 'grainy'
appearance of the MALDI-MSI here could be an artefact of 'over sampling' due to the laser
spot size measuring ~60-70pm teamed w ith the 30pm high resolution laser spot to spot
pattern.
149
3.4.2 Statistical analysis of mouse fibrosarcoma 188 tumour tissue using MATLAB®Initial statistical analysis on the fibrosarcoma 188 tryptic digest spectra was performed using
Principle component analysis (PCA) via MATLAB® through the Eigen Vector tool box, to
produce an unbiased clustering of spectra acquired from MALDI-MSI of the fibrosarcomal88
data. As discussed in Chapter 2, there is a requirement for an element of discrimination in a
statistics model for known time course data. This considered, a preliminary PCA test was done
to predict potential groupings between time points. This PCA consisted of 9 technical
replicates from MALDI images acquired in triplicate to include an example of each CA-4-P
treatment time point. The results are shown in Figures 3.16 and 3.17.
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Figure 3.16: PCA score plot of fibrosarcoma 188 tumour tissue in situ tryptic digests. The score plot in Figure 3.16 shows the potential groupings between the tumour time points in this fibrosarcoma 188 study. From this preliminary PCA test there does appear to be a separation between tumour treatment groups with an apparent overlap observed here in the Control (6:4) and 0.5h post CA-4-P (7:2)
150
VariaHes/Loadngs HotforV188_1example_n_3_9_nepeats.txt
•861.32^2
•878.^420
•862.5570 *1348,8272
•757.4354 V o/A ,„ . . . ,(a ) • 1326.9014
J J&k*7856*,* ’ g ^*|| t *844.4536
*1276.9177 * aacn
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_Vi$.jea(f,2j?^,, { * *i4fe8qp6', ' * 1 41851.1325 * *873.4352
•1199.to8o* . 1476.9374
(b)
(c)
/
-0.1 0 0.1 Loadngs on PC 1(31.37%)
Figure 3.17: PCA loadings plot fibrosarcoma 188 tumour tissue in situ tryptic digests, (a) corresponds to a peak relating to N-Chimaerin mentioned earlier, (b) Histone H4 and (c) Histone 2A.
The loadings plot contains the peak information which spatial relate to the groupings seen in
Figure 3.17. The loadings plot seen here does not appear to exhibit the Hb dominance that was
visible in the PCA loadings plot featured in Figure 2.20, Chapter 2.
The region in Figure 3.17 that spatially relates to the 72h post CA-4-P tissue contains many
unknown peaks between the mass range of 700-800 Da. Further confirmation is required to
elucidate peak identifications here as to whether the peaks are indicative of the viable tissue
switch discussed previously, or due to matrix/ lipid peak signals.
PLSDA was then carried out on a larger data set then the VIP scores for each time point were
then analysed. For the PLSDA analysis, 3 biological repeats per tumour time point were used
via MALDI-MSI acquisitions and 6 technical spectral repeats were taken from each biological
replicate.
PLSDA models were built to compare the treatment time points against the Control tumour
tissue.
151
Variables/Loading Rot for Table from MarkeAiewfo M M LABV188.txt
10326512
1033.6576
0.5 hours post CA-4-P1198.7821
0.5119976Z
1481.CM> 1850.13.7197 ! 1490.0318£ot3<D>O)®tr
n 823.91211275.11- 1530.79621048.1
1499.14726
-0.5909.2135
Control874.5519
873.5749
-1.5500 1000 1500 2000
Variable
Figure 3. 18: Partial least squares discriminant analysis (PLSDA) regression vector plot comparing MALDI "on-tissue" digest data from samples of fibrosarcoma 188 Control and 0.5h post combretastatin-4-phosphate (CA-4-P), treatment tumour samples.The increased abundance of Actin at m/z 1198.7 in the 0.5h post treatment sample can be clearly seen.
As previously mentioned, there are numerous peaks in the low mass range seen here in the
regression vector plot especially in the Control tissue. The peptide corresponding to Actin is
seen to be increased in the 0.5h treatment. A similar treatment response was reported by
Kanthou and Tozer (2002) (discussed in the Introduction) with fluorescent staining of Human
umbilical vein endothelial cells, Control and post CA4P administration. The results from this
study showed an increase of filamentous Actin overtime after treatment.
The follow ing regression vector plot generated using PLSDA is between Control and 6h post
CA-4-P treatment.
152
PI ot for Table from M afreruew fro MATLAB V18& txt342.5?%)
1032.6512
1491.0247756.!
1348.85111.02930.6 .4259
1246.75197 1527.97976 hours post CA-4-P1033.6576
0.4 1349.!971
1113.6 1570.10.2:
1594.88$2007.1926
'2056.•18259! '2257.2742Q: -0.2 1584.89091232.7853
'2131.9541
-0.4
1481.1817-0.6
ControlI5E0 6
909.: 135
900 1000 1500 2000
Figure 3.19: Partial least squares discriminant analysis (PLSDA) regression vector plot for the comparison of Control and 6h post CA-4-P fibrosarcoma 188 on tissue digests. The bracket designates areas which illustrate an abundance of shared peaks.
The results from Figure 3.19 display copious amounts of shared peaks, especially in the region
indicated by the bracket andit is quite d ifficult to distinguish trends between the two time
points.
The main response considered could be the increase in Histone H3 at m/z 1032, also present in
Figure 3.18.
Due to the substandard tumour vascular and high proliferative state, increased hypoxia is
typical of the tumour microenvironment. It is thought that the latter triggers hypoxia-inducible
factor 1 (HIF-1) which is considered a key transcription factor along with, nuclear factor kappa-
light-chain-enhancer of activated B cells (NF-kB), activator protein 1, tumour protein 53 (p53),
and c-Myc (Kenneth and Rocha 2008). The outcome via certain genes from the transcriptional
activation of these factors would be anti-apoptotic, pro-angiogenic, an increase in glycolysis
and support in the formation of metastasis (Zhou et al 2010).
The methylation of genes; Histone H3 lysine 4 (H3K4), H3K36, and H3K79 are said to be
transcriptionally active and a study by Zhou et al (2010) concluded that under hypoxic
conditions, H3K4me3 was found to be increased in human lung carcinoma A549 cells.
153
This link between the increase in Hypoxia and Histone H3 could be an explanation for the m/z
1032 peak observed in the regression vector plots of the fibrosarcoma 188 tumour tissue. The
increase in Histone H3 could therefore be linked to drug resistance.
Variables/Loadings Plot for Table frcm Markeruewfno MATLAB V188.txt
756.!
24 hours post CA-4-P385725.!
0.61032.6512
734|5783
1757.5405 |26.|395
7355728 804.2S
.3468 (784.5604
0.41417.75521944.5722
j 966.5237 1033.6576
i1054.6116(967.5228 j|
34.4833 988.5-456 1 _ „„„„E L X l II . 1055.6260 3 5 4 9 t l .999,3772; . fi11<
1861.0293CM>- ,1274.7575 1439.8243
i1198.7821
o(U>S’
1275.7490 >1348.8511
3> 128317i)78 1349.86041440.84391220.72!1.4651
'1143.6562 ,1500.7638'1240.7552 1325.8464714.ct 6 909.2135
1.5519,.5749
-0.2
52491.1725-0.4
1.5741
Controli.5775-0.6
100 200 300 400 500 600 700 800 900 1000
Figure 3. 20: Partial least squares discriminant analysis (PLSDA) regression vector plot for the comparison of Control and 24h post CA-4-P fibrosarcoma 188 on tissue digests.The blue arrows are indicative of Histone 2A (m/z 944), Histone H3 (m/z 1032), Actin (m/z 1198) and the red arrows correspond to Hb peaks; m/z 1274, m/z 1416 and m/z 1529.
Overtime in the 24h treatment (Figure 3.20) PLSDA the appearance of the Hb peaks can be
seen in this CA-4-P resistant tumour model. The Actin peak is still increased in relation to the
Control vector plot with the same true for Histone H3. Histone 2A is now also observed,
possibly reflecting the occurrence of cellular necrosis and DNA damage as a result of
Combretastatin administration.
154
Vaiables/Loadings Plot forTablefrom MarkerVewfro MATLAB V188.txt-725:5385
0.8 756.5454
3.62750.672 hours post CA-4-P1033.6576 1416.7760
0.4 1417.7552876.991?
105461161440.1
0.21954.1092
1792.00311516.90231181.7222
K -0.2 1335.7940574.!
1325.1844.6042
-0.4
824.5775
Control-0.65590
798.5131-0.8
7934361
500 1000 1500 2000Variable
Figure 3. 21: Partial least squares discriminant analysis (PLSDA) regression vector plot for the comparison of Control and 72h post CA-4-P fibrosarcoma 188 on tissue digests.Histone H3 (m/z 1032) and Haemoglobin subunit alpha (m/z 1416) are highlighted by arrows.
The Hb peaks are still observable in Figure 3.18 from the 72h treatment sample. The Histone
H3 ion could be regarded as a dominant factor again resulting in suppression of the other
peaks. One explanation for this could be the requirement for tumour rejuvenation/
proliferation as signified by the viable tissue regions seen in earlier MALDI-MSI and HSP-90
immunohistochemistry.
155
VIP scores were then observed which considered 0.5h, 6h, 24h and 72h time points based on
the PLSDA predictive models in Figures 3.18-3.21. Inspection of these data aimed to observe
the variables w ithin the fibrosarcoma 188 study that are considered most important to that
time point.
Variables/Loadings Plot forTablefrom Markervewfro MATLAB V188.txt120
100
VIP scores for 0.5h post CA-4-P
80
60
>>29 944.5722
40
20874.J 5V
0Variable
Figure 3. 22: VIP scores for the 0.5h post CA-4-P fibrosarcoma 188 on tissue digest.
Variables/Loadings Plot for Table from Markerview fro MATLAB V188.txt180
140 VIP scores for 6h post CA-4-P
120
11325.8464100
80
1032.6512
60
17)3'40
1199.762
1490.031820
0Variable
Figure 3. 23: VIP scores for the 6h post CA-4-P fibrosarcoma 188 on tissue digest.
156
Variables/Loadings Plot for Table from Markerview fro MATLAB V188.txt200
180
VIP scores for 24h post CA-4-P
160
140
120
100
80
60
40
l| 876.9919
20
0Variable
Figure 3. 24: VIP scores for the 24h post CA-4-P fibrosarcoma 188 on tissue digest.
Variables/Loadings Plot for Table from Markerview fro MATLAB V188.txt120
VIP scores for 72h post CA-4-P
100
80
60
40
20
0!2000
Variable
Figure 3. 25: VIP scores for the 72h post CA-4-P fibrosarcoma 188 on tissue digest.
The VIP scores featured in Figures 3.22-3.25 highlight Actin, Histone 2A, Histone H3 ,Histone
H4 and Hb as some of the important targets, albeit with differing intensities. The main trend
gained from these VIP score data gives the impression of transcriptionally active tumour
tissues. Throughout the treatment time course structural modifications are apparent via the
Actin and Hb peaks present teamed w ith increases in hypoxia related Histone H3 (m/z 1032).
157
Histone H4 (m/z 1325) is deemed to have a key position in the time points apart from in the
24h samples which could be indicative of CA-4-P interference before the regenerative tumour
action commences, seen in the 72h treatment.
158
3.4.3 Label free LC-ESI -MS/MS for the identification of proteins in a CA-4-P treated fibrosarcoma 188 mouse modelRecognition of proteins via label free MS/MS peptide identification describes the widely used
technique of Bottom Up shotgun proteomics. As mentioned earlier (see Introduction), this
methods uses enzymatic digestion of proteins prior to data-dependent mass spectrometric
analysis (Old et al 2005). MS/MS fragmentation spectra are matched against a proteomic
database in order to achieve a unique, rank 1 peptide identification.
The method reported here is based on multidimensional protein identification technology
(MudPIT) involving reversed-phase (RP) HPLC (Kline and Wu 2009,. McCormack et al 1997,.
Link et al 1999,. Wolters et al 2001).
Label free LC-ESI-MS/MS was employed to study protein response throughout the
fibrosarcoma 188 treatment time course. Control, 0.5h, 6h, 24h and 72h post CA-4-P
treatments were used for analysis.
Scaffold 3 proteomics software tool was used for the analysis of the following Label free LC-
ESI-MS/MS. Time course protein responses were plotted using normalised spectral counting
calculated and presented via Scaffold 3. Gene ontology pie charts were included in the
experimental output from Scaffold to aid understanding of biological function of the protein
indentified.
Figure 3.26 displays a screenshot from the fibrosarcoma 188 Label free LC-ESI-MS/MS data list.
After importing of data, 201 proteins were listed which included those which have a minimum
of 2 peptides assigned per protein, 99% minimum protein and 90% minimum peptides.
The data options for exportation to excel are; protein identification probability, percentage of
total spectra, number of assigned spectra, number of unique peptides, number of unique
spectra, percentage coverage, un-weighted spectrum count and quantitative value.
The Control/ CA-4-P protein response bar graphs to follow use the quantitative value based on
normalised spectrum counts.
Figures 3.27 - 3.33 show the entire Scaffold 3 protein list and corresponding normalised
spectrum counts that were generated. Figure 3.34 features a response bar graph of selected
proteins of interest.
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: Fi
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188
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4-P
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The rationale for selection of the proteins included in Figure 3.34 was from proteins that either
had a high percentage of co-variance throughout the time course (as seen in Scaffold 3),
provided a known validating response or may be of relevance to the stress response and could
be characteristic of tumour biology.
The increases in Figure 3.34 from both Haemoglobin subunit alpha and Haemoglobin subunit
beta-2 exhibit the distinctive gross pharmacological response from administration of a vascular
disrupting agent.
Disruption in the architectural integrity of the vasculature is indicated by decreases in
structural Tubulin beta-5 chain and Tubulin alpha-lB chain compared to the Control tumour
tissue.
Actin, cytoplasmic l(Beta-actin) and Actin, alpha skeletal muscle precursor (Alpha-actin-1)
appear to show different trends throughout the LC- ESI-MS/MS data here in Figure 3.34.
It is known that the reorganisation and/or disruption of the cytoskeleton results in stress fibre
formation (Kanthou and Tozer 2007). Administration of CA-4-P elicits such a response on the
tumour cells thought to be induced by stress activated apoptosis.
Overtime the tumour appears to 'retaliate' favouring actin polymerisation after a brief
decrease of Beta-actin in the 6h time point with increased levels evident in the 24h and 72h
time points.
An overall decrease of alpha skeletal muscle precursor (Alpha-actin-1) towards 72h post CA-4-
P is visible from Figure 3.34. This decline could be due to disruption by CA-4-P, further shotgun
proteomics and immunohistochemical studies could ascertain the trend in the decreased seen
here ofAlpha-actin-1. An article by Hemingway et al (2012) reports of a study looking at the
expression of Alpha isoform of smooth muscle actin (SMA) in primary, benign and malignant
bone tumours. It was found that SMA was differentially expressed in the primary tumours and
although findings were inconclusive, interestingly SMA expressing pericytes (peri-vascular
cells) present in bone marrow/ were also mentioned as a source of SMA levels.
The concept of progenitor cells, infiltrating immune cells or indeed a full stromal cell
population could be regarded as a key factor to consider when analysing protein dose
response relationships.
169
v22100% 2,507.40 AMU, +3 H (Parent Error. 0.42 ppm)
•L —(—s ~ I— L —H C+57—|-A
E— l- P - f — K — l - L H - L -. —I—T —hA -f-V —(—D • L H -v H -s - t— Q—f
-A -f-A - l- P — K — i-A -
IJ —K — ' A + A “■v —HAH—t —|— l — Ha
75%-1.50
?lpha-2-macroglobulin*sc«>
c« 50%- > i «
y18 y2025%-
y21y17y14
y,130%
2500 500 750 25001250 1500 22501750 2000m/z
Figure 3. 35: MS/MS spectrum and normalised intensity graph of Alpha-2-macroglobulin in fibrosarcoma 188 LC-ESI-MS/MS results.lnsert displays the normalised spectral counts for Alpha-2-macroglobulin throughout the time course post CA-4-P.
Alpha-2-macroglobulin is a carrier protein and is known to have strong associations w ith
growth factors and cytokines i.e. basic fibroblast growth factor, Interleukin 1- (3 (IL-1(3) and
transforming growth factor beta (as seen in Figure 3.34) (Feige et al 1996,. Wellcome trust
Sanger Institute 2012). Alpha-2-macroglobulin is also known to be synthesised locally in tissues
by infiltrating macrophages.
The dose response relationship of this protein from the LC-ESI-MS/MS results can be observed
in Figure 3.35 via the Scaffold 3 viewer proteomics tool. Levels here appear to be undetected
by this technique in the early fibrosarcoma 188 treated time points but a sudden increase is
evident from the 6h treated sample, a surge in the 24h time point with the 72h representing a
drop in expression.
With aim to study this response further, immunohistochemical staining was performed on
F4/80, a macrophage cell surface marker to see whether or not this response could be studied
indirectly as a reflection of infiltrating immune cells in response to CA-4-P treatment.
170
m E B S m
400 \im
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171
Figure 3. 36: Immunohistochemical staining o f macrophage cell surface marker FA/80 using fibrosarcoma 188 tissue lOx. (a) negative FA/80 isotype control using 72h post CA-A-P fibrosarcoma tissue - central region, (b)negative FA/80 isotype control using 72h post CA-A-P fibrosarcoma tissue - rim region, (c) FA/80 staining of Control - central region, (d) FA/80 staining o f Control - rim region, (e) FA/80 staining o f 0.5h post CA-A-P - central region, (f) FA/80 staining of0.5h post CA-A-P - rim region, (g) FA/80 staining of6h post CA-A-P - central region, (h) FA/80 staining o f 6h post CA-A-P - rim region, (i) FA/80 staining o f 2Ah post CA-A-P - central region with bracketed region showing macrophage population within the necrotic tissue, (j) FA/80 staining of 2Ah post CA-A-P - rim region, (k) FA/80 staining o f 72h post CA-A- P - central region, the arrows indicate evidence o f macrophages still present and an example of a necrotic region is labelled with N (I) FA/80 staining of 72h post CA-A-P - rim region, the bracket indicates a macrophage population with the V labelling an example o f viable tissue.
There appears to be few macrophages present mainly around the periphery of the tumour in
the Control tissue (Figure 3.36 c and d) as indicated by the arrow with a meagre presence
visible w ithin the central region shown. Similar staining is evident in the 0.5h treatment (Figure
3.36 e and f) central and rim areas. The latter could explain the undetected presence of Alpha-
2-macroglobulin in the equivalent Scaffold 3 LC-ESI-MS/MS results.
An army of macrophages are now seen to be infiltrating the 6h post CA-4-P rim and central
tissue regions in Figure 3.36 (g) and (h) with populations still present in the 24h treatment
fibrosarcoma tissue.
172
v>v •■
400 pm
The isotype controls for the immunohistochemical staining of F4/80 are shown in Figure
3.36(a)-(b) using the same tissue from 72h images in 3.36 (k) and (I). There appears to be
macrophages still present in the necrotic and viable tissue as indicated.
The engulfing of cellular debris due to the phagocytic nature of the macrophage could explain
the decrease at 72h following the surge at 24h. The steep increase could be as a result of the
widespread necrosis observed at 24h post CA-4-P and influx of debris clearing immune cells
overtime between 6h/24h time points henceAlpha-2-macroglobulin accumulation in the
tissue. The decreased levels of Alpha-2-macroglobulin at 72h could be indicative of an efflux in
macrophage cell populations.
The tumour associated macrophages (TAM's), known to stimulate angiogenesis undoubtedly
play a major role in drug resistance (Welford et al 2011,. Ey et al 2006,. Bingle et al 2002). The
complex tumour microenvironment can result in the promotion of anti-tumour T cells yet
conversely studies have shown that biological events within the tumour can 're-programme'
TAM's to possess pro-oncogenic, assisting tumour growth and progression. The F4/80 staining
in the 72h could suggest a possible pro-tumourigenic action via the macrophage population
considering their presence in the viable tissue region.
The HSP-90 beta levels in the label free quantitation graph (Figure 3.34) show a steady
increase in response to CA-4-P administration. The HSP-90 alpha however is in close
agreement with the earlier MALDI-MSI in Figure 3.8 and HSP-90 immunohistochemical staining
in Figure 3.9 mirroring the 24h HSP-90 saturation prior to a slight decrease in the 72h due to
the switch back to viable tissue concept.
Plectin (Figure 3.34) was found to have a high value of percentage co-variance (170%)
throughout the sample time course so this effect was investigated inorder to relate the
response to the biological action oftumour tissue. The graph of Fibrosarcoma 188 time course
results post CA-4-P treatment using LC-ESI-MS/MS (Figure 3.34), indicates a marked
abundance of Plectin in the Control then levels dramatically decrease overtime.
Figure 3.37 displays an example of a Plectin MS/MS spectra visualised through Scaffold 3
software.
173
vio100% 1,529.74 4JWU, *2 H (Parent Error: 1.9 ppm)
80%
Plectin'•2 6 0% -
40%
20%
0%0 200 400 500 800 1000 1200 1400
mlz
Figure 3. 37: MS/MS spectrum and normalised intensity graph o f Plectin in fibrosarcoma 188 LC-ESI-MS/MS results. Insert displays the normalised spectral counts fo r Plectin throughout the time course post CA-4-P.
Plectin m/z977 Control/saline
24 hours CA-4-P
72 hours CA-4-P
0.5 hours CA-4-P
6 hours CA-4-R250711 V I38 combined 678810 max iMEAN 377.>20-377. ?• 7CD
Figure 3. 38: Fibrosarcoma 188 MALDI-MSI multi image showing spatial distribution of Plectin. MALDI images showing spatial distribution of Plectin at m /z 977.
The MALDI-MSI shown here in Figure 3.38 are in good agreement with the LC-ESI-MS/MS label
free quantitative results w ith an apparent abundance of Plectin in the Control with the
reduction in levels overtime post CA-4-P.
174
Immunohistochemical studies using anti-Plectin were performed to visualise this response in
histological tumour sections.
400 fjm
Figure 3. 39: Immunohistochemical staining of Plectin in Fibrosarcoma 188 tissue post CA-4-P treatment.Figure 3.39- (a) anti-Plectin staining of fibrosarcoma 188 Control tissue, (b) anti- Plectin staining o f fibrosarcoma 188 tissue 0.5h post CA-4-P, (c) anti-Plectin staining of fibrosarcoma 188 tissue 6h post CA-4-P, (d) anti-Plectin staining of fibrosarcoma 188 tissue 24h post CA-4-P, (e) anti-Plectin staining o f fibrosarcoma 188 tissue 72h post CA-4-P, the 'V' is indicative of the viable tissue stained here using anti-Plectin and the 'N' relates to the necrotic area which remained unstained and (f) the negative control using fibrosarcoma 188 tissue 72h post CA-4-P.
175
The anti-Plectin staining in Figure 3.39 (a) suggests that Plectin is highly expressed in the
Control tissue which correlates with both MALDI-MSI and LC-ESI-MS/MS data seen earlier
(Figure 3.38 and 3.37 respectively). In addition to this, there is also evidence of a reduction in
Plectin expression as the time course progresses as seen in Figures 3.39 (b)-(e).
From the anti-Plectin immunohistochemistry it is also apparent that Plectin is distributed in
the viable tissue regions.
Plectin is decribed as a 'cytolinker' and has a key role in the stabilisation of the cytoskeleton via
the formation of a mesh-like scaffold (Liu et al 2011,. Wiche 1998). An article by Katada et al
(2012) suggested that Plectin could be a 'novel prognostic marker' for head and neck
squamous cell carcinoma. The article concluded that Plectin had a strong influence on tumour
survival and metastatic processes and further work would elucidate how Plectin mediates
cancer migration and invasion. Plectin is amongst the proteins said to be increased in tumour
tissue and studies have found elevated levels of in colorectal cancer, prostate cancer and
pancreatic ductal adenocarcinoma (Winter and Wiche 2013,. Nagel et al 1995,. Lee et al 2004,.
Katada e ta l 2012).
The response reported here using CA-4-P treated fibrosarcoma 188 tissue suggested that
Combretastatin greatly interferes with Plectin expression. In addition to this, the decrease in
Plectin expression overtime could be an indication of apoptosis and/or necrosis within the
tumour tissue. It is known that initiator caspase 8 translocates to Plectin after apoptotic
induction leading to remodelling of actin/ microfilaments (Stegh et al 2000).
Remodelling and degradation of proteins occurs via the intrinsic and extrinsic apoptotic
pathways (Elmore 2007). Executioner caspases (i.e. caspases 3, 6, 7) supersede initiator
caspases (caspases 2, 8, 9 ,10) to then play a role in protein degradation.
Gelsolin, a known substrate for executioner caspase 3 will then go on (after itself being cleaved
by caspase 3) to cleave Actin to essentially destroy the architectural integrity of the
cytoskeleton (Elmore 2007).
Interestingly, from the immunohistochemistry reported here there is strong evidence of an
inverse correlation between the HSP-90 and Plectin. This enabled visualisation of regions
associated with tumour stress response versus those exhibiting tumour survival, growth and
progression. This inverse correlation is displayed in Figure 3.40.
176
Plectin HSP-90
Figure 3. 40: Optical scans o f anti-Plectin and HSP-90 Immunohistochemical staining in fibrosarcoma 188 serial sections, 72h post CA-4-P treatment.The necrotic (N) and viable (V) tissue regions are indicated.
A clear inverse correlation is displayed between the viable Plectin stained tissue and the
necrotic HSP-90 stained regions in Figure 3.40.
In Chapter 2 a Rho GTPase activating protein 2 was identifiable from the MALDI-MSI-MS/MS of
fibrosarcoma 120 tissue, here in the fibrosarcoma 188 samples Ras GTPase-activating-like
protein (IQGAP1) has been matched.
IQGAP1 is said to be a multimodal scaffolding protein and is implemented in actin dynamics,
tubulin multimerisation, cell motility and migration via numerous signalling pathways
(Malarkannanet ol 2012). There are that studies report that increased expression of IQGAP1
resulted in disruption of cell-cell junctions thus promoting a migratory, invasive phenotype
(Mataraza et al 2003). In Figure 3.34 IQGAP1 is undetected in the Control but visible from the
graph in the 0.5h from which point levels decrease in an 'L' trend through to the 72h
treatment, suggesting possible CA-4-P pharmacological action.
An example of an MS/MS spectrum corresponding to IQGAP1 and normalised spectrum time
course graph is featured in Figure 3.41.
177
1IQGAP1
_____ _______________ I .
Biological Sample
1 Control ■ 2 30 Minute Sam ple ■ 3 6 H our S am ple 4 24 H our S am ple ■ 5 72 H our Sam ple
100% J.95 AMU, +2 r y ^ e n t Error: 3.1 pprfe)
7 5%
£</>C*> y13+lcz 5 0 % ' >1■cc
y 1 6
0%0 250 500 750 1000 1500 17501250
mfz
Figure 3. 41: Example MS/MS spectrum and normalised intensity graph o f IQGAP1 in fibrosarcoma 188 LC-ESI-MS/MS results. Insert displays the normalised spectral counts for IQGAP1 throughout the time course post CA-4-P.
It can be said that the glycoprotein Tenascin (C), has similar functions to those of IQGAP1
having involvement in tumour survival and the outgrowth of circulating cancer cells (Oskarsson
et al 2011).
The levels seen in Figure 3.34 show a marked increase in the 0.5h compared to the Control
with decreases evident in the later time points to follow, a similar response to that observed
from IQGAP1.
An example MS/MS spectra with intensity graph is shown in Figure 3.42.
178
10
30 Tenascin C20
10Mr 11 1 1
^
Biological Sample
1 Control ■ 2 30 Minute S am ple * 3 6 Hour S am p le 4 24 Hour S am ple * 5 72 Hour S am ple
100% 1.050.50 AMU, *2 H (Parent Error: 5.7 ppm)
80% -
s eo%
b6
40%
321.M 5, '* f20%
0%0 200 400 600 1000800
m/z
Figure 3. 42: Example MS/MS spectrum and normalised intensity graph o f Tenascin C in fibrosarcoma 188 LC-ESI-MS/MS results. Insert displays the normalised spectral counts fo r Tenascin C throughout the time course post CA-4-P.
The action of CA-4-P does appear to have an inhibitory effect on Tenascin C however there is
literature to suggest that its fellow glycoprotein, Tenascin W could prove to be a more suitable
marker in solid tumours (Brellier et al 2012). This article reports that the expression of
Tenascin W is more specific in tumours compared to healthy tissues therefore facilitating the
grading/ scoring of diseased tissue. Future studies involving Mass spectrometric techniques
could help to ascertain this claim.
179
Alpha-enolase
Biological Sam ple
1 Control ■ ? 3(1 Minute S am nle * 3 fi Hnur S a m p l e A 74 Hnur S am nle n s 7? Hnnr Sarnnle I
b8100% I.032.06 AMU. +3 H (Parent Error 3.7 ppm)
-V — |-G H— D -
- D - » - G ± r V - ■g H a - f — s
8 0% -
(/>c 6 0%
c«>=s■5 4 0 % ' DC
1,923.97y1120%
0%250 5000 750 1000 20001250 1500 1750
m/z
Figure 3. 43: Example MS/MS spectrum and normalised intensity graph o f Alpha-enolase in fibrosarcoma 188 LC-ESI-MS/MS results. Insert displays the normalised spectral counts for Alpha-enolase throughout the time course post CA-4-P.
Alpha-enolase is thought to be implemented in many processes. In addition to its role as a
glycolytic enzyme it is known to have transcriptional capacity and molecular chaperone
capabilities (Pancholi 2001,. Kang et al 2008). The overall reaction to CA-4-P treatment by
Alpha-enolase is a steady increase from Control - 72h. Alpha-enolase is regarded as a tumour
associated antigen (TAA) and when elevated levels are present in tumour tissue activation of
immune responses occur in patients w ith cancer (Capello et al 2011). Increased expression of
Alpha-enolase is also indicative of aggressive tumour progression.
The response seen here by Alpha-enolase in Figures 3.34 and 3.43 could be a reflection of this
CA-4-P resistant tumour type which is now shown to have regenerative capability.
Transforming growth factor-beta-induced protein ig-h3 (Tgfbi) is featured in the follow ing
Figure (3.41).
180
Tgfbi
Biological Sam ple
1 C on tro l ■ 2 30 M inute S am p le ■ 3 6 H our S am p le 4 24 H our S a m p le ■ 5 72 H o u r S am p le
100% 1,406.70 AMU, +2 H (Parent E rror 2.5 ppm)
8 0%
“ 6 0% -
b3
4 0%
b4 a5
20%b5
0%0 200 400 600 800 1000 14001200
m/z
Figure 3. 44: Example MS/MS spectrum and normalised intensity graph o f Transforming growth factor-beta-induced protein ig-h3 (Tgfbi) in fibrosarcoma 188 LC-ESI-MS/MS results. Insert displays the normalised spectral counts fo r Tgfbi throughout the time course post CA- 4-P.
From the LC-ESI-MS/MS time course results in Figure 3.34/ 3.44, Tgfbi is shown to demonstrate
a 'L' shape trendline similar to that seen from Plectin (Figure 3.34) w ith high levels in the
Control compared to the samples post treatment.
The latter response is a trend similar to the results documented in an article by Li et al (2012)
entitled ‘The role o f TGFBI in mesothelioma and breast cancer: association with tumor
suppression'. It is stated that Tgfbi is a known tumour suppressor and expression of TGFBI is
reported to be greatly reduced in many tumour cell lines (Kim et al 2000,. Zhao et al 2004,.
Shao et al 2006). However a dual role is suggested for Tgfbi which proposes an anti-
tumourigenic function in early tumour growth and as a pro-oncogenic supporter in late stage
tumours.
It is hard to ascertain whether the response seen here is solely by Tgfbi in this resistant tumour
type or as a result of a synergistic effect from Tgfbi-CA-4-P interaction.
181
3.4.4 Protein relationship mapping of fibrosarcoma 188 LC-ESI-MS/MS data using String 9.0Visualisation of the complex protein pathways and networks that govern tumour biological
function could help the understanding of protein dose response relationships.
The follow ing protein-protein interactions visualised by String 9.0, are depicted by spatial
positioning and linear connections w ith thicker lines representing stronger associations
between proteins.
HspaS
Hsp90aalHba x
Plecl
¥
Tnc
Hsp90abl
Tubain
Hbb b2
Iqoapl
TubbS
AL0227BJ1
Figure 3. 45: LC-ESI-MS/MS proteins o f interest from fibrosarcoma 188, visualised through proteomic pathway software STRING 9.0.
The String 9.0 association network in Figure 3.45 consisted of the proteins chosen in the LC-
ESI-MS/MS proteins of interest graph (Figure 3.34).
182
The focal pathway association seen here (Figure 3.45) within this group of proteins is the
linkage between the heat shock proteins; HSP-90a, HSP-90P, GRP-78 (Hspa5) and Actin,
cytoplasmic 1 and Ras GTPase-activating-like protein (IQGAP1).
The stronger association within this pathway runs directly from HSP-90a through to IQGAP1
encompassing Actin, cytoplasmic 1. The latter is characteristic of an active tumour micro
environment incorporating the morphological changes exhibited by architectural remodelling
of Actin due to CA-4-Padministration.
Although not directly linked Plectin and Tenascin display close positioning to IQGAP1 all of
which as previously mentioned, are known to be involved with promoting tumour survival,
cellular migration and metastatic invasion.
A strong link is visible between fellow tubulin proteins and interestingly AL022784 (Alpha-
enolase) is in close proximity to carrier protein Alpha-2-macroglobulin (A2m). Close positioning
of AL022784 to macrophage related protein A2m is indicative of allergic stress response which
remains true to the pharmacological intervention here. Haemoglobin subunit beta positioned
above (A2m and AL022784) supports the haemorrhagic theory.
Tumour suppressing adhesion protein Transforming growth factor-beta-induced protein ig-h3
(Tgfbi), is joined in this structural reorganisational milieu, as mentioned earlier the protein has
involvement in cell-collagen interactions.
The full pathway from the LC-ESI-MS/MS results recognised by String 9.0 is featured in Figure
3.46.
183
d) ® ®
The proteomic network association tool String 9.0 is featured in more detail in Chapter 4 in the
iTRAQ proteomic analysis section.
185
3.4.5 Concluding remarksThe data reported here in this Chapter appears to be suggestive of an active tumour
microenvironment possibly relating to this resistant nature of this fibrosarcoma 188 model.
The VIP scores (Figures 3.22-3.25) based on the PLSDA results gave the impression of
transcriptionally active tumour tissues. Structural modifications were apparent via the Actin
and Hb peaks present teamed with increases in hypoxia related Histone H3. As mentioned
earlier, Histone H4 (m/z 1325) is believed to have a chief position in all time points apart from
in the 24h samples. Further work would be required to confirm if this is due to CA-4-P
interference before the regenerative tumour action commences, seen in the 72h treatment.
Collectively the results from the LC-ESI-MS/MS comprise of proteins connected with necrosis,
cell structural reorganisation, polymerisation, tumour survival and stress induced molecular
chaperones. The inverse correlation of molecular chaperone HSP-90 and survival promoting
Plectin is evidence of this showing the distinct regional differences in the MALDI-MSI,
immunohistochemistry and LC-ESI-MS/MS results.
Overall the levels of the proteins involved in heat shock response i.e. GRP-78, HSP-90(3, HSP-70
are increased overtime, but interestingly HSP-90a displays the reduction in the 72h treatment,
possibly highlighted the switch back to tumour viability. Mentioned previously in the
introductory chapter regarding the concept of 'repeatedly identified differentially expressed
proteins' (RIDEPs). A few were also seen in the full list of proteins identified which included
HSP-70, enolase protein and peroxiredoxin.
Future studies could also consider the potential pharmacological interactions with the tumour
suppressor Tgfbi, to clarify whether a dual role is undertaken, dependant on dose response
relationships.
Combination anticancer therapy is a known route of choice in the treatment of patients. The
future of disease diagnostics could include MALDI imaging as a diagnostic tool in combination
with conventional imaging and proteomics. A novel and exciting approach was reported by
Balog et al (2010) which described how mass spectrometry could be incorporated directly into
the surgical theatres to identify tumour tissue boundaries and proximal metastases via Rapid
Evaporative Ionization Mass Spectrometry. The latter subsequently substituting the wait for
histological confirmation.
Label free LC-ESI-MS/MS shotgun proteomicsis a powerful technique with the potential to
identify thousands of proteins. Like all methodologies in science it can be said that
optimisation is key when performing shotgun proteomics. Further optimisation not only during
186
sample preparation but throughout data-dependent acquisition mode could prove invaluable
to advance proteome analysis and coverage (Kalli and Hess 2012).
187
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192
Chapter 4LC-MALDI-MS/MS using iTRAQ labelling to investigate protein
induction in CA-4-P treated fibrosarcoma 120 and 188 mouse
models
193
4.1 IntroductionIt is known that potential disease biomarkers are of relative low abundance thus making data
validation a challenge (Ye et al 2010). The 'matrix suppression effect' is considered to
exacerbate analysis of low abundant species (Knochenmuss and Zenobi 2003). An article by
Knochenmuss and Zenobi (2003) states that in MALDI, matrix ions can be suppressed if
sufficient analyte is present. In on tissue tryptic digestion, this is seldom the scenario for ions
of interest. Suppression from high abundant Hb/ histones within MALDI-MS spectra is another
example of ion suppression. Such ion suppression by high abundant Hb and histone species
was evident in Figure2.1 in Chapter 2.
iTRAQ labelling coupled with LC-MALDI is a technique which aims to provide relative absolute
quantitation of fractionated samples to give protein response information using the reporter
ions generated (Chen et al 2012). Up to 8 samples can be combined for analysis and the
method is described as a robust labelling technique implemented by N-hydroxysuccinimide
(NHS) ester chemistry (Evans et al 2012, Noirel et al 2011). Sensitivity is improved as the mass
spectra displays summed peptide ion signals from the isobaric labelled sample combination.
The principles of iTRAQ multiplex chemistry are shown in Figure 4.1 (Ross et al 2004).
A Isobaric Tag Total mass = 145
A .Reporter Group mass
114-117 (Retains Charge)
Amine specific peptide * reactive group (NHS)
PEPTIDE
Balance Group Mass 31-28 (Neutral loss)
c11141—| 31 [—NHS
m/z114 (+1) 13C
m/z 115 (+2) 13C2
m/z 116 (+3) 13C2 «N
m/z 117 (+4) 13C3 15N
ISq 18Q
18Q
13C
(+3)
(+2)
(+1)
(+0)
+ P E P T ID E
115 - 30 —WHS + P E P T ID E MS/MS
116 - 29 -NHS + P E P T ID E
NHS + P E P T ID E
30 l-NH-P E P T ID E29 l-NH-PEPTIDE
-NH-PEPT DE
114
115
116
117
Reporter-Balance-Peptide INTACT - 4 samples Identical m/z
-Peptide fragments EQUAL -Reporter ions DIFFERENT
Figure 4 .1 : The principles of multiplex tagging chemistry. (Ross et al 2004). A, illustrates the 3 main components of the isobaric tag molecule; reporter, balancer and reactive group. B, shows the formation of an amide linkage created with a peptide amine, CID induces fragmentation here similarly to that of the peptide backbone. C, depicts the individually labelled peptides which when combined cannot be resolved by MS intact analysis, upon CID
194
the reporter ions emerge as separate masses retaining the b-/y- ion information. The intensity of the corresponding reporter ions determines the concentration of the peptides.
The technique of iTRAQ is a recognised method widely used throughout Oncology (Sutton et al
2010). Ruppen et al (2010) studyingKiSS-1 (a metastasis suppressor gene) in bladder cancer
reported the use of iTRAQ to help predict the involvement of proteins in biological
disease.iTRAQ analysis was performed on protein extracts from transfected (vector containing
KiSS-1 gene) bladder cancer cells (EJ138). iTRAQ comparisons were done of the transfected,
mock, and empty vector-exposed cells. A recent paper by McMahon et al (2012) employed
iTRAQ to studyglobal changes in protein expression in a regional analysis experiment using
multicell tumour spheroids. The article also used immunohistochemistry, Western blotting and
enzyme assays to complement the iTRAQ findings. The aim of the work reported in this
Chapter was to support/validate the results found using MALDI-MSI, LC-ESI-MS/MS and
immunohistochemistry described in Chapter 3.
4.2 Materials and Samples
4.2.1 Chemicals and Materialsa-Cyano-4-hydroxycinnamic acid (CHCA), aniline (ANI), ethanol(EtOH), chloroform (CHCI3),
methanol (MeOH), acetonitrile (ACN), hydrochloric acid (HCI), tri-fluoroacetic acid (TFA),
ammonium bicarbonate, BCA protein assay reagent (bicinchoninic acid), copper (II) sulfate
pentahydrate 4% solution, protein standard solution, 1.0 mg/mL bovine serum albumin
(BSA),DL-Dithiothreitol solution, lodoacetamide, urea, potassium di-hydrogen phosphate
(KH2P04), TRIzol were from Sigma-Aldrich (Dorset, UK). Modified sequence grade trypsin (20
pglyophilised) was obtained from Promega (Southampton, UK,).Modified sequence grade
trypsin (lmglyophilised) was obtained from Roche (Roche Products Limited Pharmaceuticals,
UK). iTRAQ reagents (AB Sciex, Warrington, UK). Isolute C18 RP LC column (Kinesis Ltd., St.
Neots, UK)./?op/Gest SF Surfactant was from Waters (UK). Peptide Calibration Standard II
(Bruker Daltonics, Bremen, Germany).
4.2.2 Tissue samplesMice were injected sub-cutaneously in the flank with a 50 pi tumour cell suspension containing
1 x 106 cells in serum-free medium.The cells employed in this study were from the mouse
195
fibrosarcoma cell lines, VEGF120 and VEGF188. Thesehave been engineered toexpress either
the VEGF120 or VEGF188 isoforms only. Tumours were allowed to growto approximately 500
mm3, before CA-4-P treatment (a single doseof 100 mg/kg i.p). The dosage for CA-4-P are
consistent with animal studies performed at clinically relevant doses (Galbraith 2003., Prise
2002). Mice were killed and tumours excised at varioustimes after treatment. All animal work
carried out documented herein was performed by Dr. J. E. Bluff, Tumour Microcirculation
Group, University of Sheffield, UK. Samples were provided as frozen excised tumours and
stored at -80°C.
4.2.3. Experimental groupsFibrosarcoma 120 tumour samples: Controls (no treatment, saline i.p), n = 6 (labelled
tumourlJL - tumour 1_6), C-A4-P (0 h after treatment), n = 6, (labelled tumour2_l - tumour
2_6), C-A4-P (0.5 h after treatment), n = 6,(labelled tumour 3_1 - tumour 3_6), C-A4-P (6 h
after treatment),n = 6, (labelled tumour 4_1 - tumour 4_6), C-A4-P (24 h after treatment)n = 6,
(labelled tumour 5_1 - tumour 5_6).
Fibrosarcoma 188 tumour samples: Controls (no treatment, saline i.p), n = 4 (labelled tumour
6_1 - tumour 6_4), C-A4-P (0.5 h after treatment), n = 5, (labelled tumour 7_1 - tumour 7_5),
C-A4-P (6 h after treatment), n = 5, (labelled tumour 8_1 - tumour 8_5), C-A4-P (24 h after
treatment), n = 5, (labelled tumour 9_1 - tumour 9_5), C-A4-P (72 h after treatment) n = 4,
(labelled tumour 10_1 - tumour 10_4).
4.2.4 Tissue preparationThe following preparation was performed on sections from fibrosarcoma 120 and 188 tissue
with one example used from each tumour treatment time point. Frozen tissue sectionswere
cut to give approximately 10 x lOpmsections per sample for digestion, using a Leica CM3050
cryostat (Leica Microsystems, MiltonKeynes, UK) (Djidja et al 2009). Sample sections were
stored in airtight tubes at -80°C until further use.
4.2.5 Tissue homogenisation and precipitation of proteinThe tissue as described in 4.2.4 was homogenised in 800pl of TRIzol solution using a micro-
homogeniser (Kline and Wu 2009). Homogenised solution was centrifuged (1,500 rpm) for 5
min to pellet out nuclei/ unbroken cells. Post-nuclear supernatant was then centrifuged
(14,000 rpm) for 30 min. Resulting supernatant was discarded and cellular membrane pellet
was retained for protein precipitation. 200pl of MeOH and 50pl of CHCI3were then added to
each protein pellet sample. After vortexing, 150pl HPLC H20 was added with further vortexing.
196
After centrifugation (14,000 rpm) for 2 min, the bottom CHCI3 layer was removed. A further
50|il CHCI3 was added, removal of the bottom CHCI3 layer was repeated following
centrifugation (14,000 rpm) for 2 min. Subsequent removal of the H20 layer resulted in the
remaining protein precipitate layer. CHCI3 (50pl) and MeOH (150pl) were added directly to the
protein precipitate, after vortexing solution was centrifuged (14,000 rpm) for 2 min.
Supernatant was removed and protein pellet was then allowed to air dry for 2 min.
4.2.6 Protein DigestionlOOpI of 0.1% RapiGest in 50mM NH4HCO3 buffer (pH 7.8) was added to the air dried protein
pellet. Each sample pellet/ solution was consecutively vortexed, incubated at -80°C (~ lhr) and
heated to 70°C (1 min) until solubilised. Once fully solubilised the sample was heated to 100°C
(2 min) and then left to reach room temperature. Each sample was reduced with DTT (final
concentration 5mM) for 30 min at 60°C the left to reach room temperature. Solutions were
then alkylated with iodoacetamide (final concentration 15mM) in the dark for 30 min at room
temperature. The fibrosarcoma 120 samples were then lyophilised and re-suspended in 5pl of
8M urea in 400mM NH4HCO3 buffer prior to tryptic digestion. Sequence grade modified trypsin
was added (20pg/ml or lmg/ml) to80pg (fibrosarcoma 188) / lOOpg of protein (fibrosarcoma
120), a BCA Protein Assay Reagent (bicinchoninic acid) was used for protein determination. In
solution digests were incubated over night at 37°C with shaking.
4.2.7 Preparation of samples for column loadingFibrosarcoma 120; after overnight digestion the solution was centrifuged (1,500 rpm) for 1 min
and reaction stopped by placing samples on ice where 10pl of 10% TFA was added. Each
sample for analysis was de-salted using C18 Bond-elute columns and subsequently lyophilised
until required.
Fibrosarcoma 188; HCL (final concentration lOOmM) was added to the overnight digest,
solution was then incubated for a further 45 min at 37°C. After this step the sample was then
centrifuged (14,000 rpm, 4°C) for 10 min, the supernatant removed for analysis. This
remaining solution was lyophilised and stored at -80°C until further use.
4.2.8 C18 bond-elute preparationThe C18 columns were wetted with 1ml MeOH, equilibrated with (x2) 1ml solvent A (2% ACN,
0.05% TFA), after the addition of the sample two wash steps were performed with (x2) 1ml
solvent A (2% ACN, 0.05% TFA) and peptides were collected in a fresh tube after 1ml solvent B
(80% ACN, 0.05% TFA).
197
4.2.9 Sample re-suspensionEach lyophilised sample was re-suspended in 10pl of TEAB, 0.1% SDS, vortexed and centrifuged
(14,500 rpm) for 1 min at room temperature.
4.2.10 iTRAQ labellingThe iTRAQ reagents (AB Sciex, Warrington, UK) were removed from -20°C storage and allowed
to equilibrate to room temperature. After brief centrifugation(bench top), 40pl of isopropanol
was added to each iTRAQ vial, vortexing and brief centrifugation followed. Transfer of each
individual iTRAQ vial to each corresponding sample tube was as follows:
8-plex Fibrosarcoma 120; (a)Control - sample 1 - iTRAQ 113, (b)Control- sample 2 - iTRAQ
114, (c)0h post CA-4-P - sample 3 - iTRAQ 115, (d)0.5h post CA-4-P - sample 4 - iTRAQ 116,
(e) 0.5h post CA-4-P - sample 5 - iTRAQ 117, (f)6h post CA-4-P - sample 6 - iTRAQ 118, (g)6h
post CA-4-P - sample 7 - iTRAQ 119, (h)24h post CA-4-P - sample 8 - iTRAQ 121.
All iTRAQ ratios were calculated relative to the control labelled 113.
(a)Control - sample 1 - iTRAQ 113, (c)0h post CA-4-P - sample 3 - iTRAQ 115, (d)0.5h post CA-
4-P - sample 4 - iTRAQ 116,(f)6h post CA-4-P - sample 6 - iTRAQ 118,(h)24h post CA-4-P -
sample 8 - iTRAQ 121 combinations are shown here for comparison to fibrosarcoma 188.
5-plex Fibrosarcoma 188;(a)Control - sample 1 - iTRAQ 113, (b)0.5h post CA-4-P - sample 2 -
iTRAQ 114, (c)6h post CA-4-P - sample 3 - iTRAQ 115, (d) sample 4 - iTRAQ 116, (e)24h post
CA-4-P - sample 5 -iTRAQ 117.
All iTRAQ ratios were calculated relative to the control labelled 113.
To ensure maximum iTRAQ reagent transfer a further lOpI of isopropanol was added to each
iTRAQ reagent tube to then vortex and centrifuge. The contents were transferred to the
corresponding iTRAQ/ sample contents. Each sample with iTRAQ reagent was then thoroughly
vortexed and centrifuged (1 min) (NB - no turbidity observed). The pH was tested for each
sample and adjusted to pH 7.0 - 10.0 with 1M TEAB (maintain organic concentration of 60%).
50pl of HPLC H20 was added to each reaction, vortex followed by bench top centrifugation.
The corresponding samples were all combined into 1 reaction tube. A further 25pl of HPLC H20
was added to the combined reaction tube before lyophilisation (50°C aqueous and then stored
overnight at 4°C).
198
4.2.11 SCXThe SCX column was washed/wetted using 1ml HPLC grade H20 (assisted by a syringe)
followed by 1ml (x2) of loading buffer. The combined sample was re-suspended in 1ml loading
buffer (25% ACN: 75% HPLC H20) and pH checked to adjust to approximately pH 2.5-3 with
10% TFA. The sample was added to the SCX column by passive hydrostatic pressure and the
flow through collected (FT1). A further 1ml of was added directly to the SCX column and flow
through collected (FT2). For the elution of peptides varying concentrations of 500pl 60mM -
lOOOmM KCL were added to the column resulting in fractions El - E6. Fractions were de-salted
with C18 Isolute columns. Prior to de-salting 1.5ml HPLC solvent A was added (2% ACN, 0.05%
TFA) to the fractions. After de-salting the samples were lyophilised and stored at -20°C until
further use. As required, each eluate was re-suspended in 13pl of 10% ACN.
4.3Methods and instrumentation
4.3.1 Sample fractionation and matrix depositionThe Bruker PROTEINEER fc LC-MALDI Fraction Collector (Bruker Daltoniks, GmbH Bremen,
Germany) was used to deposit the LC separated peptide fractions in a combination of CHCA
matrix and peptide solution. Fractions were automatically spotted onto MALDI AnchorChip
targets. The matrix, a-cyano-4-hydroxycinnamic acid (CHCA) was used, in EtOH: Acetone (2:1),
lOOmM ammonium phosphate, 10% TFA.
4.3.2 InstrumentationLiquid chromatography was performed with LC Packings/Dionex Ultimate 3000 capillary HPLC
system (Dionex, Camberley, Surrey, UK). Samples were injected onto a C18, 300pm x 5mm,
5pm diameter, 100A PepMap pre-column (LC Packings, Sunnyvale, CA) on an LC Packings
UltiMate 3000 (McMahon et al 2012,. Montgomery et al 2012). The sample was washed with
mobile phase A for 3.5 minutes at a flow rate of 300 nl/min on the pre-column before being
switched onto a C18, 75pm x 15cm, 3pm diameter, 100A PepMap column (LC Packings)
equilibrated with mobile phase A at a flow rate of 300 nl/min. Mobile phase B (80%
acetonitrile, 0.05% TFA) was increased to 10% and then increased with a linear gradient to
either 30% - 40% over 105 minutes. The fractions after HPLC were coeluted with 1.2 pL of
saturated a-cyano-4-hydroxy cinnamic acid (CHCA) matrix solution. Peptide Calibration
Standard II (Bruker Daltonics, Bremen, Germany) was as listed; Angiotensin I, Angiotensin II,
Substrate P, Bombesin, ACTH clip 1-17, ACTH clip 18-39, Somatostatin 28, Bradykinin
fragment 1-7 and Renin Substrate Tetradecapeptide porcine. The calibrant covered a mass of
199
range 700-3200 Da. Peptide mass fingerprints and MALDI-MS/MS were acquired by using a
MALDITOF/ TOF UltraFlex II instrument (Bruker Daltonics, Bremen, Germany) with a 200 Hz
smartbeam laser (>250 pJ/pulse) in reflectron positive ion mode. WarpLC software (vl.2) was
used in automated mode which comprised of data acquisition (FlexControl version 3.0) and
peak detection (FlexAnalysis version 3.0, using the Snap peak detection algorithm), initially in
MS mode screening LC fractions between 700 and 3000 Da to generate a list of peptides.
External calibration was carried out for each during MS analysis. For Each peptide MS/MS
analysis was performed using LIFT mode (FlexControl 3.0). Mass lists (FlexAnalysis 3.0, using
TopHat baseline subtraction, Savitzky- Golay smoothing and Snap peak detection algorithms)
from each MS/MS spectrum were compiled into a batch file for exportation to perform protein
database searching.
4.3.3 Data pre-processingMascot software version 2.2 (Matrix Science, U.K.) was used for protein identification
searching, comparisons of the MS fragmentation data were made against the Swiss-Prot 2010
Mus (Musculus) protein database. The Mascot search parameters included used Mudpit
scoring with the following post translational modification parameters for both tumour types;
Fixed modifications : Carbamidomethyl (C) (fixed)
Fixed modifications : Carbamidomethyl (C) with a filter of protein score> 28 (fixed filtered)
Variable modifications : Carbamidomethyl (C), Acetyl (Protein N-term),Gln->pyro-Glu (N-term
Q),Glu->pyro-Glu (N-term E),Oxidation (M) (variable - fixed)
Variable modifications : Carbamidomethyl (C), Acetyl (Protein N-term),Gln->pyro-Glu (N-term
Q),Glu->pyro-Glu (N-term E),Oxidation (M) with a filter of protein score> 28 (variable filtered -
fixed)
The confidence threshold selected was 95% (p < 0.05) for MS/MS data mining. Proteinscape
software v2.1 (Bruker Daltonics, Bremen, Germany) was used to collate the total 7 LC-MALDI
runs in eacg case (fibrosarcoma 120/fibrosarcoma 188) into a single nonredundant protein list
(comprising at least one unique peptide and in the filtered search cases with a Mascot score of
>28) (McMahon et al 2012,. Montgomery et al 2012). The protein lists generated by
ProteinScape was manually filtered to flag proteins iTRAQ labelled/ un-labelled, peptides that
were not ranked first were discarded.
200
4.3.4 Statistical analysisExcel spreadsheets were generated via exportation of data lists generated by ProteinScape for
both fibrosarcoma 120 and fibrosarcoma 188 tumours analysed. The ratios were all quantified
relative to the control at time zero and normalised to the average of 1. Thus allowing the study
of quantitative information from the iTRAQ reporter ions throughout the time course of
samples, post CA-4-P administration. Scaffold 3Q+ was used for additional visualisation and
analysis of the iTRAQ data (http://www.proteomesoftware.com/). The data files (.dat)
produced from ProteinScape which corresponded to each fraction analysed, were uploaded
individually selecting 'iTRAQ 8-plex' and 'MudPit Experiment (combine samples)'. The .dat files
selected were using a fixed modification search. Analysis with X! Tandem was selected in order
to improve protein identifications with searching through an additional database.
4.3.5 Protein network analysisAccession lists generated by ProteinScape were imported into the STRING 9.0 database to
observe the relationships between the proteins indentified and study the predicted functional
partners of known protein interactions (http://string-db.org/).
201
4.4 Results and discussion - Fibrosarcoma 120
4.4.1. HPLC UV profiles of fibrosarcoma 120 tumour fractionsVEGF 12 D E I V E G F 123r :
VEG F12D E3 VEGF 12D,
VEGF 120 UV[ 280 . 0nm>
U V | 2 1 5 . 0 n mt
I ii toy iliw |M««»
,'EGF 12D F-
Figure 4.2: UV profiles from the fibrosarcoma 120 fractions after SCX. (a) shows the UV profile from the first eluant (E l) after 60mM KCI following the flow through (FT) fraction from strong cation exchange, (b) 120mM KCI (E2), (c) 180mM KCI (E3), (d) 300m M KCI (E4), (e) 500mM KCI (E5), (f) lOOOmM KCI (E6) and (g) (FT) after addition o f the combined iTRAQ labelled sample. The FT UV profile (Figure 4.2 (g)) does not show many peptide peaks which could be a sign o f good binding of the sample to the CSX column. It appears from the UV profiles that the most o f the peptides are present in fractions E3-E6. For these HPLC runs the gradient was reduced to 30% to maximise peptide elution from the column.
202
4.4.2 Frequency of unique peptides identified
Number of unique peptides identified andcorrespondingfrequency
120
1 00 —
Q.aao<u-QE3Z
60 —
40
20
-W -52 6 81 3 4 7 9 10 11 12
Frequency
Figure 4. 3: Frequency of detection of unique peptides by LC-MALDI-MS/MS. The graph shows the range o f unique peptides detected, for example 112 unique peptides were only seen once with only 1 peptide having 12 unique peptides assigned to it.
4.4.3. Analysis using excelSpreadsheets were prepared which featured exported lists generated from ProteinScape.
Figure 4.4 displays a screen shot of an excel spreadsheet used for protein analysis and ratio
comparison. This relevance of this data output was to observe the full protein list supplied by
MASCOT (using the 'mus' search database) prior to importing into Scaffold proteomic tool
software using a simple colour coded thresholds shown in Figure 4.4. The thresholds were
determined for each column by using the standard deviation (SD) value. To depict down
regulation values lower that the SD were coloured red, up regulation was determined by
values of 1 + SD and values that fell between the thresholds were coloured yellow.
203
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The results featured in Figure 4.5 are from the complete excel spreadsheet in Figure 4.4.
The iTRAQ ratio response relative to the control (113) shown in Figure 4.5, displays various
outcomes throughout the tumour time course post CA-4-P. Due to the CA-4-P susceptible
nature of the fibrosarcoma 120 model the dominant factors apparent from the bar graph
(Figure 4.5) appear to be from the Hb peptides Haemoglobin subunit alpha, Haemoglobin
subunit beta-1 and Haemoglobin subunit beta-2.
Keratin-1 (KRT1) also produced an increased response compared to the control tumour tissue
and it is difficult to explain the increase seen here in the Oh time point. Further work is
therefore required to validate such a response so soon after treatment. Interestingly, an
article by Tang et al (2012) proposes Keratin 1 as acis-diamminedichloroplatinum-resistant
(cDDP) protein innasopharyngeal carcinoma cell lines. The data from this Nasopharyngeal
carcinoma study concluded that the expression of KRT1 was related to the issue of drug
resistant in this particular cancer and suggested KRT1 as a potential biomarker and key player
in the mechanism of multidrug resistance in Nasopharyngeal carcinoma (Tang et al 2012).
It is known that the type I and II keratins (polypeptides of the non-hair category) are co
expressed during the differentiation of tumours, consist of either acidic or basic proteins and
are found mostly in epithelial cells (Karantza 2011). KTR1 is present in normal endothelia,
benign and malignant tumour tissue (Remotti et al 2001). It is also suggested that KRT1 has a
key role in the architectural integrity of the cytoskeleton in tumour cells (Reichelt et al 2004).
The increased response found in Figure 4.5 (0h-24h post CA-4-P) relative to the control
tumour tissue could be a evidence of the need to maintain epithelial cellular architecture as
KRT1 levels are still elevated in the later time points (Karantza 2011).
In contrast to the latter, type 3 mesenchymal intermediate filament Vimentin, is shown to be
greatly decreased in response to CA-4-P administration (Figure 4.5). Vimentin is expressed in
normal mesenchymal tissue and considered to have involvement in migratory, adhesive and
cell survival mechanisms (Lahat et al 2010).
Vimentin is said to play a role in mesenchymal transition in cancerous tissues with a study by
Handra-luca et al (2011) reporting a three-fold higher expression of vimentin in pancreatic
neoplasms. Increased expression of Vimentin is not seen in the time points following the
control, possibly suggesting an inhibitory action due to CA-4-P. However due to the results
here (Figure 4.5) being n=l then further repeats would clarify the response from Vimentin.
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is commonly used as a housekeeping
gene in quantitative experimental studies (protein/ RNA analysis) where expression levels are
used for normalisation purposes (Barber et al 2005). GAPDH levels therefore are renowned for
consistency throughout various tissues strengthening its use as an internal standard. Reports
by Said et al (2009) revealed that under hypoxic conditions GAPDH in hepatocellular
206
carcinoma, human lung adenocarcinoma epithelia and colon cancer cell lines (in vitro) remains
unregulated by hypoxia. The article concludes that a GAPDH-hypoxia study is not a suitable
strategy for future anti-cancer therapeutics in hepatocellular carcinoma, human lung or human
colon cancer. The GAPDH results in Figure 4.5 are in opposition to this hypothesis. A
considerable increase in the 0.5h post CA-4-P time point is evident whilst the Oh post CA-4-P
remains consistent with the control tissue. Unfortunately the protein remained undetected in
the later time points therefore making it impossible to deduce the complete response
throughout the time course. The other assumption could be that the absence of GAPDH in the
6h and 24h (post CA-4-P) samples is due to the effects of CA-4-P induced hypoxia.
With a view to validate the uncharacteristic alteration in GAPDH response in the later time
fibrosarcoma time points, the responses exhibited by Hb could be considered. GAPDH showed
a marked increase relative to the control as a result of treatment with the vascular disrupting
agent CA-4-P. Peptides relating to GAPDH were absent from the 6hand 24h samples. The
internal standard in the case of this iTRAQ experiment validating the GAPDH reaction post CA-
4-P could be the characteristic haemorrhagic response evident from Haemoglobin subunit
alpha, Haemoglobin subunit beta-1 and Haemoglobin subunit beta-2.
The protein lists reported here were generated using ProteinScape applying fixed modification
Carbamidomethyl (C), and filteredusing a Mascot score >28 which yielded 29 protein
identifications.
The protein lists generated using the alternative modification parameters; fixed, variable
filtered and variable gave the following proteomics outcomes:
fixed - 99 protein identifications, variable filtered - 31 protein identifications, variable - 352
protein identifications.
4.4.4. Analysis w ith Scaffold Q+A screen shot of a protein list generated in Scaffold proteomic software from the LC-MALDI-
MS/MS results is shown in Figure 4.6. The corresponding list generated after applying iTRAQ
quantitative 3Q+ using the parameters above in displayed in Figure 4.7.
207
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The fold change ratios which can be seen in Figure 4.7 are displayed in Figure 4.8.
The search parameters applied by Scaffold proteomic software using the .dat file from
fibrosarcoma fixed search gave information for 17 proteins (Figure 4.8). The subsequent data
shows examples of protein sequences found and spectra used for protein identification and
relative quantitation by Scaffold 3 Q+ using LC-MALDI-MS/MS. The following proteins detailed
are; Vimentin, Hb subunit beta, Peroxiredoxin-1 (Macrophage 23kDa stress protein), Tubulin
beta-5 chain, Annexin A5 and Keratin -1 (type II skeletal-1).
Vimentin (Figure 4.9), Hb subunit beta (Figure 4.10), Tubulin beta-5 chain (Figure 4.12) and
Keratin -1 (type II skeletal-1) (Figure 4.14) are portrayed similarly to the excel analysis in Figure
4.5 as previously discussed. The employment of an unfiltered fixed modification search
(proteins > 10 Mascot score) yielded Annexin A5 and the missing peptides for 6h and 24h post
CA-4-P corresponding to Peroxiredoxin-1 (Macrophage 23kDa stress protein). The Log2 of
normalised intensity in Figure 4.11 shows an initial decrease of Peroxiredoxin-1 with levels
recovering during the 6h and 24h time points. The latter matches the iTRAQ ratio initial
response of Peroxiredoxin-1 shown in Figure 4.5 evident in the Control and Oh time points.
Peroxiredoxin-1 is thought to be involved in hypoxia/ reoxygenation process and levels appear
elevated in a number of cancers (Park et al 2007). A recent study by Kalinina et al (2012)
looking at cisplatin resistance formation in cancer cells using peroxiredoxin genes included
PRDX1.It was found that elevated levels of PRDX1 was found in 3 cisplatin resistant cell lines.
Over expression of the PRDXlgene is said to act as a protective mechanism against H20 2
induced apoptosis (Kalinina et al 2012). Could this be an explanation for the regenerative
response seen here by Peroxiredoxin-1?
The biological role of Annexin A5 has yet to be fully determined however it is commonly used
as a marker of apoptosis (Rand et al 2012). This is due to the favourable binding affinity of
Annexin A5 to phosphatidylserine which is expressed on the cell surface of cells undergoing
programmed cell death. Expression levels of Annexin A5 are known to be increased in vascular
endothelium cells. Interestingly the results in Figure 4.13 show an increase in Annexin A5 as
the time course progresses fitting in with the increase in necrotic tissue postulated by CA-4-P
administration.
211
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4.4.5 Protein relationship mapping of fibrosarcoma 120 data using STRING 9.0Observation of protein relationship maps facilitates the analysis of complex proteomic data
sets. Understanding known protein-protein interactions could provide an insight into potential
biomarkers. The proteomic pathway software STRING 9.0 proposed known protein pathways
and networks with an additional predicted functional partners feature (http://string-db.org/).
The following proteomic networks generated by STRING 9.0 will feature zoomed in sections of
the original networks shown in Figures 4.15 and 4.16 to ease interpretation. Evidence and
confidence views are included which depict evidence of association and strength of association
respectively. To relate to the fixed unfiltered search list imported into Scaffold 3 Q+ the same
list will be used for compilation of the following protein relationship maps.
218
219
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Figure 4 .17: Zoomed in region o f a fixed modification search, iTRAQ fibrosarcoma 120 samples.
There is a link suggested in Figure 4.17 between Peroxiredoxin-1 (Prdxl) and prolyl 4-
hydroxylase beta (P4hb). Considering Prdxl potential involvement in reoxygenantion and anti-
apoptosis, a link with P4hb fits. P4hb function includes the inhibition of aggregated malfolded
proteins (http://string-db.org/). P4hb protein then has strong associations w ith Hspa5 (78 kDa
glucose-regulated protein) and Hsp90bl. Hsp90bl was mentioned earlier in Chapter 3 along
with its relevance to tumour biology and the stress response. Calreticulin (Calr) is also included
to have strong associations w ith P4hb. Trace levels of Calr are found on normal lung cells but
highly expressed in lung cancer cells (Liu et al 2012). Alpha Enolase 1 (Enol, 8 peptides found,
collective Mascot score of 241) can be seen in the zoomed region in Figure 4.17, it also
features in iTRAQ ratio response graph (Figure 4.5) and the iTRAQ fold change graph (Figure
4.8). Both graphs display an early increase in the Oh and 0.5h post CA-4-P with levels
decreasing towards Control levels in the 6h and 24h samples. Enol is known to be involved in
tumourigenesis, proliferation and cancer cell metastasis due to its role as a plasminogen
receptor (Capello et al 2011). It could be suggested that its sudden decrease in the later time
points maybe as a result of increased necrosis by CA-4-P affecting the role of hypoxia tolerance
by Enol (http://string-db.org/).
221
D7Bw<)1348f
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Figure 4.18: Zoomed in region of a fixed modification search, iTRAQ fibrosarcoma 120 samples.
Annexin A5 (Anxa5) where functional properties are mentioned earlier in this chapter, has
several associations. Galectin-1 (Lgalsl - 2 peptides identified w ith a collective Mascot score
of 54) is one such protein and is thought to have functional influence on cell proliferation,
apoptosis and differentiation, therefore enhancing tumour progression (Banh 2011). Figure 4.5
displays a contradictory trend to this where all the other time points relative to the control are
notably decreased, again suggesting an inhibitory effect by CA-4-P.
The same response exhibited by Galectin-1 is true for S100 calcium binding protein A6 (see
Figure 4.5) (Calcyclin -2 peptides identified w ith a collective Mascot score of 56). Functions of
Calcyclin include an indirect role in cell motility and cytoskeleton restructuring (Ning et ol
2012).
222
The Tubulin proteins (Tubalc, Tubb5, Tuba 8) are shown with associations feeding into
Vimentin with branching from Tubb5 to Cofilin 1, a protein which too intervenes in the
regulation of morphological cell changes and cytoskeletal architecture.
Actin cytoplasmic 1 (Actb) is positioned prominently linking the proteins relevant in structural
integrity, necrosis and heat shock molecular chaperones expressed due to malfolded un
glycosylated proteins.
223
4.5 Results and discussion - Fibrosarcoma 188
4.5.1. HPLC UV profiles of fibrosarcoma 188 tumour fractionsUV| 280 . 0nmJ
UV) 215 . 0nm|V E G F 1 S S E l VEGF1SS E2
VE G F1SS E3 VEG F1SE E4
VEGF1ES E5 VE G F1E S E 6
VEGF1ES r
Figure 4. 19: UV profiles from the fibrosarcoma 188 fractions after SCX. (a) shows the UV profile from the first eluant (El) after 60mM KCI following the flow through (FT) fraction from strong cation exchange, (b) 120mM KCI (E2), (c) 180mM KCI (E3), (d) 300m M KCI (E4), (e) 500mM KCI (E5), (f) lOOOmM KCI (E6) and (g) (FT) after addition o f the combined iTRAQ labelled sample.
224
The UV profiles in Figure 4.19 appear to indicate a greater protein elution compared to those
in Figure 4.2 w ith a more even spread of UV signal, possibly indicating improved separation of
peptides.
4.5.2 Frequency of unique peptides identified
Num ber o f unique peptides identified and corresponding frequency
B no o f peptides
y _ t._1 2 3 4 5 6 7 8 9 10
F re q u e n c y
Figure 4. 20: Frequency of detection of unique peptides by LC-MALDI-MS/MS. The graph shows the range o f unique peptides detected. Here 49 unique peptides were only seen once whereonly 2 proteins had 10 unique peptides assigned to them.
4.5.3. Analysis using excelSpreadsheets were prepared employing the methods describedin section 4.43. Figure 4.21
displays a screen shot of an excel spreadsheet containing the fibrosarcoma 188 iTRAQ samples
used for protein analysis and ratio comparison.
225
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The proteins seen in Figures 4.22a and*4.22b are from the same excel spreadsheet separated
into two bar graphs to aid interpretation. These data has been generated via ProteinScape and
exported to excel for analysis employing the same parameters prior to examination as detailed
in the previous section entitled '4.3.4 Statistical analysis'.
The fixed filtered search performed on the fibrosarcoma 188 samples resulted in a greater
peptide yield in comparison to the fibrosarcoma 120 results. 45 proteins were identified,
opposed to 29 proteins in the fibrosarcoma 120 data set. The latter could be due to differing
factors during the tissue digestion, SCX or LC fractionation. Another factor to consider could be
the more CA-4-P resistant nature of the fibrosarcoma 188 tumour where different peptides
identified could be as a result of decreased suppression from high abundant proteins i.e.
Haemoglobin/ Histones.
The discussion that follows aims to highlight key responses from Figures 4.22a and 4.22b.
The haemoglobin peptides (Haemoglobin subunit alpha, Haemoglobin subunit beta-1,
Haemoglobin subunit beta-2 and Haemoglobin subunit epsilon-Y2) are consistently present in
Figure 4.22a to aid validation of the other protein responses however even with the fixed
filtered search parameters many different proteins are observable.
Heat shock protein HSP 90-beta is increased in the 6h-72h time points with what appears to be
a saturation point achieved in the 24h. This response by Heat shock protein HSP 90-beta was
discussed in Chapter 3 where evidence of the switch back to viable tissue was observed both in
the MALDI-MSI and immunohistochemistry.
A similar surge pattern of GAPDH levels (Figure 4.22a) seen previously in Figure 4.5 can be
observed. The sudden increase appears in the 0.5h fibrosarcoma 120 time point (Figure 4.5)
whereas in the fibrosarcoma 188 samples the surge is seen 6h post CA-4-P with decreased
levels apparent in the 24h and 72h samples. An explanation of the delayed response in the
Fibrosarcoma 188 data could be indicative of more resistant tumour model.
Tumour enhancing protein Galectin-1 (Figure 4.22a) is shown to be decreased relative to the
control but later on in the 72h sample a small increase is observable possibly indicative of
tumour regeneration.
Elongation factor 1-alpha 1 is a protein giving very different responses in the two tumour
models. In the fibrosarcoma 188 samples levels are elevated throughout the time points in
Figure 4.22b relative to the control. In contrast to this, levels in the fibrosarcoma 120 iTRAQ.
results are all decreased relative to the control. Elongation factor 1-alpha 1 is a known
229
regulator of the cytoskeleton (Shiina et al 1994,.Veremievaet al 2010). One explanation could
be that the elevated levels seen in the fibrosarcoma 188 samples maybe due to some form of
pharmacological conflict elicited by this CA-4-P resistant tumour model.
The Complement system exhibits regulatory influences on host immune, inflammatory
processes. Activation of this complex cascade of biological events is triggered via 3 main
activation routes; the alternative, classic, and lectin pathways. (Nunez-Cruz et al 2012)
Complement C3 is the key activator of the 3 main routes previously mentioned. Studies have
shown that increased Complement anaphylatoxin levels have been detected in the peritoneal
cavity fluid of ovarian cancer patients (Bjorge et al 2005). In vivo studies by Nunez-Cruz et al
(2012) used complement deficient mouse models crossed with epithelial ovarian cancer
susceptible mice. One of the key findings in this article was that tumour growth was either
absent or greatly retarded in the offspring, compared to the wild- type. Thus indicating the
importance of Complement C3 in tumour progression. Figure 4.22a shows that the iTRAQ ratio
responses from 0.5h, 6h and 24h are reduced after CA-4-P treatment with a slight increase
emerging from the 72h time point.
Plectin, not identified in the fibrosarcoma 120 data set is shown to have a greatly reduced
response relative to the control in Figure 4.22b. Plectin is has been shown to be present in high
levels in cancers such as head and neck squamous carcinoma and correlates with poor patient
survival (Katada et al 2012). The ITRAQ data presented here show good agreement with the
findings presented in Chapter 3 where MALDI-MSI and LC-ESI-MS/MS label free results
displayed increased Plectin levels in the Control fibrosarcoma 188 samples with a dramatic
decrease evident over the 0.5h-72h time course. Hence a decrease in Plectin expressions
protein associated with tumour aggressiveness, does therefore appear to be a response to CA-
4-P dosage.
The protein lists reported here were generated using ProteinScape applying fixed modification
Carbamidomethyl (C), and filteredusing a Mascot score >28 which yielded 45 protein
identifications. The protein lists generated using the alternative modification parameters; fixed,
variable filtered and variable gave the following proteomics outcomes:
fixed - 318 protein identifications, variable filtered - 51 protein identifications, variable -1 0 88
protein identifications.
230
4.5.4. Analysis with Scaffold Q+A screen shot of a protein list generated in Scaffold proteomic software from the LC-MALDI-
MS/MS results is shown in Figure 4.23. The corresponding list generated after applying iTRAQ
quantitative 3Q+ using the parameters detailed in section 4.4.4.
231
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The fold change ratios which can be seen in Figure 4.24 are displayed in Figure 4.25.
To analyse the data from fibrosarcoma 188 models the search parameters applied by Scaffold
proteomic software using the .dat file from fibrosarcoma fixed search gave information for 16
proteins (Figure 4.25). The subsequent data shows examples of protein sequences found and
spectra used for protein identification and relative quantitation by Scaffold 3 Q+ using LC-
MALDI-MS/MS. The following proteins detailed are; Vimentin, Hb subunit beta, Pyruvate
kinase isozymes M1/M2, Tenascin and Fibronectin precursor. The rationale for selection of
these proteins was to assess differences and similarities in the Scaffold 3 Q+ results in the
fibrosarcoma 120 model and observe proteins which did not appear in the VEGF 120 samples.
The Vimentin response observed by the resistant 188 model in Figure (4.26) bears some
similarity to the study mentioned by Handra-luca et al (2011) reporting elevated Vimentin in
pancreatic neoplasms. Although levels are still low in the 72h post CA-4-P sample a
considerable increase is evident from the 6h and 24h time points, not observed in the earlier
Vimentin VEGF 120 Log2 normalised intensity graph (Figure 4.9).
Hb subunit beta in Figure 4.26 does not exhibit a steady haemorrhagic response but levels still
remain increased toward the final 72h time point.
Results displayed in Figure 4.25 of Pyruvate kinase isozymes M1/M2 show greatly increased
fold change ratios when compared to the equivalent fibrosarcoma 120 graph in Figure 4.8. The
fibrosarcoma 188 6h value shows over a 3 fold increase along with a 2.5 fold increase in the
72h for Pyruvate kinase isozymes M1/M2, opposed to the 120 model which only shows a 1.8
fold in the 24h. Pyruvate Kinase isozyme M2 is found in tumour cell and proliferating tissues
alike and hypoxia-inducible factor 1 is said to play a role in its induction (Ye et al 2012).
Glycoprotein Tenascin, is seen in the fold change ratio graph (Figure 4.25). Tenascin appears to
exhibit a 'peak-trough-peak' response throughout the timecourse. Due to the non
identification of this protein in the fibrosarcoma 120 data set, it is difficult to validate or
comment on this reaction. Tenascin is reported to promote metastasis (Minn et al 2005,.
Oskarsson et al 2011). Studies by Oskarsson et al{2011) link Tenascin expression to the
aggressiveness of breast cancer cells and their ability to further produce lung metastases.
Considering the latter could the last 'peak' previously mentioned be a feature supportive of
the 'switch back to viability concept' overtime after treatment.
Fibronectin precursor is known to bind to Tenascin, interestingly when comparing the fold
change responses (Figure 4.25), both proteins appear to have an inverse fold change
235
relationship; Tenascin 'peak-trough-peak', Fibronectin 'trough-peak-trough'. Further study is
needed to confirm and comprehend this trend of protein-protein interaction.
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4.5.5 Protein relationship mapping of Fibrosarcoma 188 data using STRING 9.0The proteomic networks to follow will feature the same format as the protein relationship
analysis in section 4.4.5.
242
Figure 4. 31: iTRAQ Fixed modification search using Fibrosarcoma 188 tumours, visualised by STRING 9.0 (Confidence view).
244
Col4a2
A c tn l '
Anxal
Angp?S3Pled
Apoal
S100a6
Abca?"TV-
Anxa2Tubb5
Dnaja3
Hspa4
Faml35a
Prdx4
H istlh lc
Figure 4. 33: Zoomed in region of a fixed modification search, iTRAQ fibrosarcoma 188 samples, visualised through STRING 9.0.
Vimentin, Tenascin and Plectin are all grouped together in this zoomed region from Figure 4.33.
The roles of this trio all involve tumour progression through individual unique mechanisms as
discussed earlier in this chapter. A clear link can be seen with Tenascin and Fibronectin (fn l) to
support findings in the literature reported back in section 4.5.4.
Actin has associations either directly or indirectly with Vimentin, Tenascin and Plectin to
ascertain the ir roles in actin dynamics and general integrity and structural reorganisation.
Another strong association with Actin is the Zyxin Gene anadhesion plaque protein further
linking the latter to a Heat shock 90 containing group (not shown) via the Cingulin gene
(http://string-db.org/). Cingulin is thought to be involved w ith cell junction permeability.
245
p fTnfsfl2Pmpcb
Pkm2
Mdh2
AtpSal
AnxaB /
G m S lG B
Prdxl
Slc'tal
Prdx2
Kcna6
Figure 4. 34: iTRAQ fibrosarcoma 188 zoomed in region of a fixed modification search, visualised through STRING 9.0.
This section of the STRING fixed fibrosarcoma 188 search displays most of the proteins seen in
the 120 STRING results; Galectin, Annexin A5, Peroxiredoxin, Enolase 1. A protein in Figure
4.34 however does not feature in the fixed fibrosarcoma 120 STRING results, this being
Glucose-6-phosphate isomerase (Gpil). It has strong associations w ith hypoxia tolerance
regulator Enolase 1, interestingly Gpil is known to function as a 'tumour-secreted cytokine'
and features in angiogenesis encouraging endothelial cell motility (Ahmad et al 2011).
246
Csnkld H2afzLmna
Dctnl
ENSMUSCHsp90abl
HspOOaal
Cox4i2
MyhlOHsp90bl
Ubc2jl
Pmpcb
Figure 4. 35: iTRAQ fibrosarcoma 188 zoomed in region of a fixed modification search, visualised through STRING 9.0.
Many proteins that feature in Figure 4.35 are already familiar i.e. Heat shock proteins,
Calriticulin, Elongation factor 1 alpha. Perhaps disregarded is the important connection
between Histone 2A and Actin. Although widely acknowledged and easily detected in both
MALDI profiling and imaging, this connection could be regarded as a marker of the re-
organisational transition towards tissue necrosis during anti-cancer treatment. Numerous
associations can be clearly seen branching from these links downstream from Hsp 90 and
Elongation factor 1 alpha.
4.5.6 Protein dose response relationship analysis using String 9.0The follow ing data presented here analyses the protein dose response relationships and
associated pathways using the iTRAQ results from fixed medication searches, employing a filter
score of > 28. Fibrosarcoma 120 and 188 lists were prepared for import into proteomic
pathway software String 9.0 to observe proteins that were flagged either to exhibit a marked
increased expression or decreased expression. This proteomic information was taken from the
original iTRAQ spreadsheets featured in Figures 4.4 and 4.21. The normalised values were
highlighted in either green, yellow or red indicating response relative to the control e ither
increased, no change or decreased respectively.
247
4.5.6.1 iTRAQ F ib rosarcom a 120 dose re la tio n sh ip resu lts us ing S tr in g 9.0The protein pathways featured in Figure 4.36 show the proteins that were found to
demonstrate either increased or decreased responses in the particular treatment sample.
Aldaartl
Up - 24h post CA-4-P
Aldoartl
Gapdh
Up - 0.5h post CA-4-P
TubbS
Tuba8
Down - 24h post CA-4-P
U spell
H2afz
@ ~~ ^Down-0.5h post CA-4-P
0Pdia3
S100a6
H2afz
Figure 4. 36: iTRAQ proteomic response results using a Fibrosarcoma 120 treatment timecourse. Proteins are shown to either increase or decrease relative to the Control fibrosarcoma tissue, relationship links are shown between the proteins that have strong biological associations with each other.
In Figure 4.36 the increased proteins (Up - 24h post CA-4-P, Up - 0.5h post CA-4-P) displayed
do not show any associations w ith each other. However both 0.5h and 24h share increased
Keratin, type II cytoskeletal 1 (Krtl) and Fructose-bisphosphate aldolase A (Aldoartl).
Mentioned earlier in this Chapter, K rtl is thought key player in the mechanism of multidrug
resistance in some cancers (Tang et ol 2012). The unusual increase in GAPDH as previously
discussed is seen here in the 0.5h.
A ldoartl is a component of the glycolytic pathway and increased expression of this enzyme is
found in many cancers including cancer of the lung, kidney and cervix (Mhawech-Faucegliaet ol
2011,. Takashi et ol 1990,. Oijika et al 1991). Perhaps increased response of A ldoartl here is an
248
indicator of a tumour survival strategy against CA-4-P treatment. Elevated levels of this
enzyme could be indicative of a high glycolytic rate, a key factor in tumour survival and
metabolism (Saw 2006,. Beckner et al 1990).
The proteins that are decreased as a result in the 0.5h time point in Figure 4.36 include Tubulin
alpha (Tuba8) and Tubulin beta-5 chain (Tubb5), linked by a strong association. This decrease
could be as a result of disturbance due to the vascular disrupting agent. Although no direct link
is seen between Tubb5 and adjacent proteins Galectin-1 (Lgalsl) and Heat shock protein 60
(Hspdl), a close positioning is observed. The latter could echo the morphological transitions
underway by Tubulin hence induction of Hspdl and decrease in Lgalsl which mentioned
previously to be involved in cell proliferation, apoptosis and differentiation (Banh 2011).
The 24h decreased proteins in Figure 4.36 show a greater degree of protein-protein interaction
with triplet links between Lgalsl, Calcyclin (S100a6) and Vimentin (Vim) and a further linkage
between Elongation factor 1-alpha 1 (Eeflal) and Histone H2A.V (H2Afz).
S100a6, involved in restructuring of the cytoskeleton along with Vimentin and Lgalsl are
decreased in the 24h and appear to be retarded by the effects of CA-4-P. Low levels of Eeflal
in this CA-4-P susceptible tumour model reflect that at 24h the tumour tissue is almost totally
necrotic.
249
SlQ0a6
HspaS
U p - Oh postCA-4-P Down - Oh post CA-4-P
Figure 4. 37: Fibrosarcoma 120 proteomic response using String 9.0.iTRAQ increased and decrease protein response for tumour time point Oh post CA-4-P administration.
In the case of the fibrosarcoma 120 Oh time point there was only one marked early increase,
this being Keratin, type II cytoskeletal 1 (Krtl) (Figure 4.37). This remained elevated
throughout the later time point as shown in Figure 4.36. No associations were seen in the
corresponding decreased set of proteins in the Oh post CA-4-P. 78 kDa glucose-regulated
protein (Hspa5, Grp78) remains low in this early time point and would be expected to be
elevated here in the later time points. However the threshold levels determined in the
spreadsheet featured in Figure 4.4 did not highlight the values to be included in the increased
group of proteins. Upon close observation of the spreadsheet the values w ithin the 'no
change' (yellow) bracket for this protein did show a gradual increase throughout the time
points.
250
Aldoartl
Up com binationGapdh
H2afz
Pdia3
S100a6Eeflal
Down com b ination
Figure 4. 38: iTRAQ combinations of fibrosarcoma 120 increased and decreased proteins. The protein relationships in Figure 4.38 are a representation of combined responses throughout the time points previously discussed.
In summary of the combined responses seen in Figure 4.38, the increased protein combination
depicts the haemorrhagic pharmacological response, the potential multidrug strategy of Krtl,
an uncharacteristic influence on GAPDH and evidence of a need for tumour cell survival due to
the elevated levels of glycolytic enzyme Aldoartl. Amongst the proteins shown to decrease in
Figure 4.38 is a triangular triplicate linkage between Actin, Histone 2A and E efla l. This appears
to reflect the disrupting effects post CA-4-P treatment. The combination seen here
encompassing the transitional changes in cellular integrity and attempts by the drug to retard
growth and induce necrosis. The same effect is true for the other linear trio of proteins w ith
the decrease of Lgalsl, S100a6 and Vimentin. Links between Protein disulfide-isomerase A3
and Hspa5 imply evidence of the stress response and apoptotic processes respectively. Down
251
regulation of Protein disulfide-isomerase A3 was only seen in the 24h time point indicating
that extensive tissue necrosis was already present.
4.5.6.2 iTRAQ F ibrosarcom a 188 dose re la tio n sh ip resu lts using S trin g 9.0
Pfdn2
Rnfl60
Cct6a
Up - 72h post CA-4-P Down - 72h post CA-4-P
Figure 4. 39: iTRAQ proteomic response results using a Fibrosarcoma 188 treatment time course. Predicted functional partners are shown for increased Cct2 (indicated by the arrow) in the 72h time point and proteins that were decreased in the 72h post CA-4-P sample.
In Figure 4.39, the T-complex protein 1 subunit beta (Cct2) is shown with predicted functional
partners which include fellow chaperone proteins. Cct2 is known to have actin/ tubulin re-
organisational properties (http://string-db.org/). This increase could reflect a 'switch back to
viable tissue' strategy with an army of chaperones which favour cell proliferation and tumour
survival. RING finger protein 160 (Zinc finger protein 294, Rnfl60) and Triosephosphate
isomerase (Tp il) are shown to decrease in the 72h time point. No known links are shown
between the two proteins, Rnfl60 can be implicated in cell death and T p il involved in
glycolysis (Orosz et al 2006,.http://www.uniprot.org/).
252
Hist2h4
Hnmphl
HspyQabl
I I r f r l l r l c
Cct2
' ■
Colla2
Alb
Flna
Mem 5
Up - 24h post CA-4-P Down - 24h post CA-4-P
Figure 4. 40: iTRAQ proteomic response results using a 24h post CA-4-P Fibrosarcoma 188 treatment. The proteins shown here depict the responses seen that were either increased or decreased as a result of CA-4-P administration.
In Figure 4.40 there is a strong link evident connecting Actin and Hsp90 flanked by Hbb-y and
Heterogeneous nuclear ribonucleoprotein H (Hnrnphl).
The notable increase in 24h Hsp90 is seen in the immunohistochemical/ MALSI-MSI in Chapter
3, where abundant appears to reach saturation point. Elevated levels of Hsp90 could be
explained by the direct link here to Actin. The changes in Actin dynamics due to the drug could
indeed have a positive effect on Hsp90 expression, due to the need for protection against the
misfolding of Actin. Cct2 is also seen here to be increased as shown in the 72h (Figure 4.39)
response, again involved in Actin re-organisation. The Histone HI increased response could be
another indication of architectural remodelling of the tumour cells. The Haemoglobin subunit
epsilon-Y2 (Hbb-y) is again indicative of vascular disruption with Hnrnphl having mRNA
transcriptional influence on Hsp90.
P le d
Pdcd6ip
253
Rnfl60
Histlhl
VcpTpil
Cct2
Prdxl
_ McmSMem 10
TncHist2M
Up - 0.5h post CA-4-P Down - 0.5h post CA-4-P
Figure 4. 41: iTRAQ proteomic response results using a 0.5h post CA-4-P Fibrosarcoma 188 treatment. The proteins in the 'Up' 0.5h time point do not have any associations here, in the 'Down' group a strong associative link is present between McmlO and Mcm5 with a weaker relationship clear between Prdxl and Tpil.
The protein corresponding to Tenascin (Tnc) in Figure 4.41 is shown to be increased in the 0.5h
time point. This protein was discussed earlier in this Chapter, said to be involved in the
aggressiveness of breast cancer cells and the ir ability to further produce lung metastases
(Oskarsson et al2011). The other proteins in this group have no association here, but all seem
to be a sign of an active proliferating tissue, characteristic of this CA-4-P resistant tumour
model.
There are 2 linkages seen in Figure 4.41, a strong associative link is present between McmlO
and Mcm5 and a weaker relationship clear between Prdxl and Tpil.
The precise function of Protein MCM10 homolog (McmlO) is said to be unclear, it is thought to
play a role in DNA replication and the prevention of DNA damage, but is reported to perform
in different ways dependent on the particular organism (Ricke and Bielinsky 2004,.
http://string-db.org/). DNA replication licensing factor MCM5 (Mcm5) is known to have similar
functions to McmlO (Schultz et al 2009).
254
There is a link between Peroxiredoxin-1 (Prdxl) and glycolytic enzyme Triosephosphate
isomerase (Tpil). As mentioned previously in this Chapter, elevated expression of the
PRDXlgene is said to act as a protective mechanism against H20 2 induced apoptosis (Kalinina
et ol 2012). The level here is shown to be markedly decreased which could relate to the nature
of the resistant tumour type, where at this early treatment time point there is no requirement
as yet for protective mechanism against H20 2 induced apoptosis.
H istlhlc
Hsp90abl
Up combination
Hbb-yCct2
TncHnrnphl
Pdcd6ip
v *Prdxl
'
Rnfl60
Colla2
Tpil
t j
P led
Hist2Mf V I ■ ■
McmlO Mem 5
Hspaa
Down combination
Flna
Figure 4. 42: iTRAQ combinations of fibrosarcoma 188 increased and decreased proteins. The protein response relationships in Figure 4.42 are a representation of combined responses throughout the time points previously discussed in the iTRAQ fibrosarcoma 188 treatment time points.
255
To summarise the combination of groups seen in Figure 4.42, the 'Up' combination of proteins
similarly to those in the Fibrosarcoma 120 proteins includes increased Hb, however this is
where the similarities cease. Tnc is now seen, a protein observed in aggressive cancers and
along with Hsp90, Cct2 gives the notion of a more active, drug resistant class of tumour tissue.
4.5.7 Relation of MALDI-MSI to iTRAQ proteomic responseThe relation of MALDI-MSI to other complementary proteomic results aims to authenticate the
protein induction responses observed. Figure 4.40 demonstrates some of the proteins that
have featured in the previous chapters in the format of an iTRAQ ratio response graph as seen
in Figure 4.22a/b.
iTRAQ ratios showing response relative to the control, normalised to the average of 1.
■ Control
PLECTIN Elongation factor 1- HB alpha HB beta-1 HB beta-2 Hbepsilon-Y2alpha 1
Protein
Figure 4. 43: Bar graph showing protein response relative to the control (113) normalised to an average of 1, using fibrosarcoma 188 tissue. The responses shown here depict a decreased expression if values are below 1 and increased expression if values are above 1 on the bar graph.
Figures 4.44 and 4.45 show MALDI-MSI images which are in agreement to the iTRAQ responses
of Plectin and Hb subunit alpha seen in in Figure 4.34.
256
The time course images shown here in Figure 4.44 and Figure 4.45 match the response
exhibited in the iTRAQ results graph (Figure 4.22a/ 4.22b) and label free quantitative LC- ESI-
MS/MS results for plectin in Chapter 3.
P lectin m / z 9 7 7 Control/saline
.850.5C
24 hours CA-4-P
72 hours CA-4-P
0.5 hours CA-4-P
L515.97
.181.44
250711 V I88 combined 678910 max (MEAN:977.320-977.370)
6 hours CA-4-PSL 1/1
Figure 4. 44: MALDI-MSI multiple Fibrosarcoma 188 sample on tissue digest of Plectin at m/z 977.
257
Corresponding to Hb rrt/z 1819
Control/saline
72 hours CA-4-P
0.5 hours CA-4-P
11_V1$ 3 t0_n-,3«_* « |M & *M 13*9 * ) )6 hours CA-4-PSC I/i
24 hours CA-4-P
Figure 4. 45: MALDI-MSI multiple Fibrosarcoma 188 sample on tissue digest o f Hb subunit alpha at m /z 1416.
258
4.5.8 Concluding RemarksOne of the basic challenges with iTRAQ is not only successful labelling but accurate
interpretation and consistent peptide-protein identifications. Decisions regarding search
thresholds greatly influence peptide/ protein yield and are important to reduce the false
positives scenario. The alternative selection of search parameters to mine the iTRAQ data
herein aimed to assess differences in peptide/ protein yield. The choice was made to use
mainly fixed filtered search data to reduce data volume and ease interpretation/ validation for
the purpose of this project. It would however be beneficial to perform further repeats of the
iTRAQ experiments using the two tumour models to aid validation of proteins found using
variable modification parameters. Over 1000 proteins were identified with the employment of
variable database search parameters, validation of treatment response and elucidation of key
effector molecules could be facilitated in conjunction with iTRAQ data processing packages.
The complimentary/confirmatory technique employed here in this chapter provided a
powerful means of quantitatively assessing a treatment time course. A diverse approach to
biomarker discovery could indeed aid validation of the temporal stability of a chosen
experimental model. The more conventional gel based approaches i.e. difference gel
electrophoresis could be employed in future work along with cleavable isotope-coded affinity
tags involving the biotinylated labelling of cysteine residues (Wu et al 2006).
The experimental work described in the following chapter describes a novel method for
MALDI-MSI image validation, employing an artificial construct as a standard which could be
engineered to include any protein target of interest.
259
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264
Chapter 5A novel multi-peptide recombinant
standard used in MALDI-IMS-MSI
265
5.1 IntroductionMany developments and innovations have been achieved in the field of mass spectrometry
over the last decade. Technological advances to enhance both mass resolution and mass
accuracy have been accomplished with various pioneering techniques.
Ion mobility has enabled observation of another dimension to mass spectrometry data with a
further separation method of isobaric ions. Nano scale chromatographic instrumentation now
provides a means to perform highly sensitive, high throughput proteomic analysis for both
targeted and undirected investigations (Angel e ta l 2012).
It can now be said that electrospray ionization (ESI) and matrix assisted laserdesorption
ionization (MALDI) are key techniques employable in all aspects of biomarker discovery and
proteomic investigative analysis.
Work by Atkinson et al (2007) displayed overlaid MALDI images using BioMAp which showed
the distribution of the bioreductive drug AQ4N and its active metabolite AQ4 in solid tumours.
Here in this paper, standards where included in the MALDI-MSI using AQ4N, AQ4 and ATP. This
enabled observation of the regional spatial distribution of the drug/ metabolite and whether
any co-localisation was visible.
An article by Djidja et al (2009) studying GRP-78 within pancreatic adenocarcinoma ffpe tissue
sections, reported the technique of using a digested recombinant standard for image
validation. MALDI-MSI with the employment of ion mobility produced BioMap images which
could be related to GRP-78 immunohistochemical staining.
Targeted proteomic MS methods aim to facilitate analyte identification and a novel method
using a multi-peptide recombinant standard for the MALDI-MSI of on tissue tryptic digests is
reported here.
266
5.2 Materials and Methods
5.2.1 Peptide sequences chosen for a recombinant protein standard based on MALDI-MSI and conventional proteomic analysis of fibrosarcoma 120 and 188 tissue.The pET vector detailed here was supplied by MWG-biotech (Germany). Unless otherwise,
stated the chemicals were purchased from Sigma Aldrich (UK).
The peptides listed in Table 9 were chosen based on the Mass spectrometric identifications
reported in Chapters 2-4 (with the exception of EGFR and Epiregulin).
The design of the 12 peptide construct was undertaken by Dr David Smith of Sheffield Hallam
University (UK). Organisation of the sequence was arranged to ensure thatthe charges were
evenly spaced across the synthetic protein in order to prevent intra cellular aggregation during
expression. The final protein had an expected mass of 16,667.26 Da.The DNA construct was
then sub cloned into the expression vector pET23a(+) and E-coli BL21 DE3 was chosen for
transformation.
Plasmid preparation, transformation and purification was performed by Dr David Smith of
Sheffield Hallam University (UK).
GRP-78 1887.9 VTH AVVTVPAYFN DAQREGFR 1564.7 MHLPSPTDSNFYRHSP- 90 beta 1513.7 GVVDSEDLELNISRPlectin 1385.7 AQAELEAQELQR
1350.5 DSQDAGGFGPEDRHaemoglobin beta chain 1302.6 VNSDEVGGEALGR
Tenascin 1219.6 ETFITGLD
Actin 1198.7 AVFPSIVGRPR
HSP-90 alpha 1168.5 LGIHEDSQNR
Vim entin 1093.5 FADLSEAANR
Epiregulin 983.4 CEVGYTGVR
Rho GTPase activating protein 2 844.5 RLTSLVR
Tag reporter A WLE HHH HHH
Table 9: Twelve peptides from target proteins chosen fo r positive identification.
267
MHl PSP TDS NFY RVN SDE VGG EAL GRA VFP SIV GRP RRL TSL VRE TFI TGL DAP RGV VDS EDL ELN ISR LGI HED SQN RFA DLS EAA NRA QAE LEA QEL QRD SQD AGG FGP EDR CEV GYT GVR VTH AW TVP AYF NDA QRAWLE HHH HHH
Figure 5 .1 : pETBIue-1 vector and sequence used fo r the synthesis o f the recombinant protein standard.
MHLPSPTDSNFYRVNSDEVGGEALGRAVFPSIVGRPRRLTSLVR ETFITGLDAPRGWDSEDLELNISRLGIHEDSQNRFADLSEAANR AQAELEAQELQRDSQDAGGFGPEDRCEVGYTGVRVTHAVVTVP AYFNDAQRA IWlLE iHHHHHHl
\ \Tryptophan His Tag
Figure 5. 2: The final sequence of the recombinant standard.
5.2.2 Bacterial cultureBacterium - BL21(DE3) HsdS gal (llts 857 ind l Sam7 nin5 lacUV5-T7genel)
Growth Media - Lurnia-Bertani (LB) media, Bacto-tryptone (lOg/L), Yeast extract (5g/L), NaCI
(10g/L).
The above materials were dissolved in 900 ml deionised water and made up to 1 L. LB media
was sterilised by autoclaving for 20 min at 15 Ib/sq inch. After cooling filter-sterilised
carbenicillin (final concentration of 250 mg/ml) was added as appropriate.
1.5 g of bacto-agar was added to 100 ml of LB media before autoclaving. Antibiotics were
added (carbenicillin, final concentration of 250 mg/ml), to the cooling agar and mixed
268
thoroughly. 15-20 ml of molten agar mixture was added per 100 mm diameter Petri dish and
allowed to set, before storage at 4 °C.
Cultures were inoculated from freshly transformed bacteria (Section 2.2.7) grown on agar
plates (Section 2.2.3) or directly from existing glycerol stocks (15 % v/v glycerol). Liquid
cultures of less than 100 ml were inoculated directly, whereas for larger scale cultures (1 L),
overnight starter cultures of 5 ml were used for inoculation. Cultures were incubated at 37 °C
in an orbital incubator with shaking at 200 rpm.
5.2.3 Plasmid preparationPlasmid DNA was isolated from fresh overnight liquid cultures of E. coli BL21(DE3) with
carbenicillin selection using a Qiagen plasmid midi kit as described in the manufacturer's
instructions. Isolated DNA was deemed of suitable purity if the observed ratio of the
absorbance at 260 nm (A260) to that at 280 nm (A280) w as3 1.8. An A260 of 1.0 was assumed
equivalent to 35 mg/ml of single stranded (ss) and 50 mg/ml of double stranded (ds) DNA.
5.2.4 E. coli- transformationTubes containing 200 ml aliquots of competent cells were thawed at 4 °C and 20 ng or 200 ng
of plasmid DNA were added to separate 14 ml round bottom Falcon tubes.
A negative control lacking DNA was also included. Cells / DNA mixtures were incubated on ice
for 30 min and then heat shocked at 42 °C for 90 sec with mixing every 10 min. Cells where
then put on ice for 2 min and 800 ml of liquid LB media was added to each tube. These cultures
were incubated at 37 °C for 45 min to allow the cells to express antibiotic resistance.
Transformed cells were spread onto LB / agar plates containing carbenecillin (250mg/ml) and
incubated at 37 °C for 12 h.
A 20 ml starter culture was grown overnight at 37 °C in liquid LB media (Section 2.2.2) with
antibiotic selection (carbenecillin, 250 mg/ml). This culture was used to inoculate 10 x 2 L
flasks each containing 1 L of LB liquid media and carbenecillin (100 mg/ml). Cultures were
grown at 37 °C with shaking until the OD600 reached 0.6, protein expression was induced with
1 mM (IPTG). The cells were then incubated at 37 °C overnight and harvested by continuous-
flow centrifugation at 15,000 rpm at 4 °C (Heraus Contifuge 17RS ). The cell paste was frozen
and stored at -80 °C until required.
5.2.5 Protein PurificationThe required DNA sequence was synthesised by MWG-biotech (Germany), and sub cloned into
the bacterial expression vector pET23a(+) (Novagen). The plasmid was then transformed into
269
the E-coli strain BL21 DE3 (Promega Corporation). All bacterial cultures were grown in Luria
Broth(LB) media with 100 jag/ml ampicillin for selection. 10 ml of a 100 ml overnight starter
culture was used to inoculate a 1 L of LB media. Cultures were grown at 37 °C with shaking
until the OD600 reached 0.5, protein expression was induced with 1 mMisopropyl-(3-D-
thiogalactoside (IPTG). The culture was incubated for a further 2.5 h before the bacterial cells
where harvested by centrifugation at 10,000g for 20 min using a Sorvall RC G+.
Bacterial pellets where resuspended in binding buffer (20mM Sodium Phosphate + 20mM
imidazole pH 7.4)3ml per lg of cells before being lysed by sonication (lOx 20sec pulses ata 35%
duty cycle) using a Vibra Cell VCX 750 (750 Watts). Phenylmethylsulphonyl fluoride (PMSF) (50
|ng/ml), DNase (20 jug/ml), RNase (20 |ig/ml) to the lysate which was then cleared of insoluble
material by centrifugation at 8,000 g for lh using an Eppendorf5804R centrifuge. The clear
lysate was filtered through a 0.2 jam syringe filter and load onto a 1 ml Histrap FF column (GE)
at a flow rate of 1 ml/min. The protein was eluted over 20 column volumes using a 0 to 100%
gradient into elution buffer (20mM Sodium Phosphate + 500mM imidazole pH 7.4). Fractions
containing the recombinant protein where then desalted into 50mM ammonium bicarbonate
using PD10 column (GE). Trypsin digestion was performed on a 50 |al sample of desalted
protein using 1 j l x I of trypsin (lm g/m l) added for lh plus a further 1 j j . 1 of trypsin overnight.
Samples were stored at -80C until further use.
5.4.6 DigestionTrypsin was added (lul) (20ug/ml) for lh plus a further lu l of trypsin for overnight incubation
37°C and 5% C02.
270
5.4.7 MALDI-MSI - Chemicals and Materialsa-Cyano-4-hydroxycinnamic acid (CHCA), aniline (ANI), ethanol (EtOH), chloroform (CHCI3),
acetonitrile (ACN), octyl-a/b-glucoside (OcGIc), tri-fluoroacetic acid (TFA), ammonium
bicarbonate, were from Sigma- Aldrich (Dorset, UK). Modified sequence grade trypsin (20 pg
lyophilised) was obtained from Promega (Southampton, UK).
Tissue samples
Mice were injected sub-cutaneously in the flank with a 50 pi tumour cell suspension containing
1 x 106 cells in serum-free medium. The cells employed in this study were from the mouse
fibrosarcoma cell line, VEGF188. This has been engineered to express only the VEGF188
isoform. Tumours were allowed to grow to approximately 500 mm3, before CA-4-P treatment
(a single dose of 100 mg/kg i.p). The dosage for CA-4-P are consistent with animal studies
performed at clinically relevant doses (Galbraith 2003., Prise 2002). Mice were killed and
tumours excised at various times after treatment. All animal work carried out documented
herein was performed by Dr. J. E. Bluff, Tumour Microcirculation Group, University of Sheffield,
UK. Samples were provided as frozen excised tumours and stored at -80°C.
Experimental sections for on tissue trytic digestion and MALDI-MSI
6_2 Control (no treatment, saline i.p), 8_1 (6 h after treatment with CA-4-P) and 10_1 (72 h
after treatment with CA-4-P).
Tissue preparation
Frozen tissue sections were cut to give approximately 10pm sections, using a Leica CM3050
cryostat (Leica Microsystems, Milton Keynes, UK) (Djidja et al 2009). The sections were then
freeze thaw mounted on poly-lysine glass slides. Mounted slides were either used
immediatelyor stored in an airtight tube at -80 °C for subsequent use.
In situ tissue digestion and trypsin deposition
The tissue samples were washed initially with 70% and then 90% ethanol for 1 min then left to
dry, subsequently slides were immersed in chloroform for 10 s. Prior to matrix application,in
situ tissue digestion was performed with trypsin solution prepared (from lyophilised trypsin) at
20 pg/ml by addition of 50 mM ammonium bicarbonate (NH4HC03) pH 8, containing 0.5%octyl-
a/b-glucoside (OcGIc) based on a protocol by Djidja et al (2009).
271
The "Suncollect" (SunChrom, Friedrichsdorf, Germany) automatic pneumatic sprayer was used
to spray trypsin in a series of layers. The sections for MALDI-MS and MALDI- MSI were
incubated overnight in a humidity chamber containing H20 50%: methanol 50% overnight at
37°C and 5% C02.
5.4.8 Methods and instrumentation Matrix deposition
The matrix, a-cyano-4-hydroxycinnamic acid (CHCA) and aniline in acetonitrile:water:TFA
(1:1:0.1 by volume), was applied using either the Suncollect (at 5 mg/ml). Identical coordinate
settings were used as with the trypsin deposition, to ensure sample uniformity. Equimolar
amounts of aniline were added to the CHCA solution, i.e. 1 ml of 5 mg/ml CHCA solution
contained 2.4 pi of aniline. These matrix deposition parameters were based on methods from
Djidja et al (2009).
Instrumentation
MALDI- IMS/MS and MALDI- IMS/MSI were performed using a HDMS SYNAPT™ G2 system
(Waters Corporation, Manchester, UK) and Mass Lynx software (Waters Corporation, UK).
Image acquisition was performed using raster imaging mode at 100 pm spatial resolution,
Biomap 3.7.5.5 software (http://www.maldi-msi.org/) and HDI 1.1 software was used for
image generation. Instrument calibration was performed using standards consisting of a
mixture of 217 polyethylene glycol (Sigma-Aldrich, Gillingham, UK) ranging between m/z 100
to 218 3000 Da prior to MALDI-IMS-MSI analysis. HDI 1.1 parameters were set to the following;
specificity type - IMS MS, MS, Number of most intense peaks -1 0 0 0 , resolution -1 0 ,0 0 0 , Low
energy intensity threshold - 50. The low intensity threshold was to allow low abundant species
to be potentially included. HDI images were chosen for analysis based on the highlighted
recombinant standard and m/z ratio listed in the software displaying the peak intensity and
corresponding drift time. HDI correlation filter tool was set to R1 minimum 0.5 and R
maximum 1.0 for peaks analysed at m/z 1350 and m/z 1032.
To enable simple visual comparison between images, BioMap data were normalised to m/z
877/ m/z 1066 (peaks arising from the aCHCA matrix).
272
5.3 Results and Discussion
5.3.1 MALDI-IMS-MS Peptide mass fingerprint of the digested recombinant DNA constructThe following MALDI-IMS peptide mass fingerprint is from a tryptic digest of the recombinant
synthetic DNA construct, used as a protein standard for MALDI-IMS image peptide validation.
The PMF in Figure 5.3 displayed a clean tryptic digest spectra with chosen peptides easily
observable showing excellent spectrum intensity.
273
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5.3.2 MALDI-IMS-MSI of fibrosarcoma 188 tumour tissue, Control and post CA-4-P treatm entThe follow ing MALDI-MSI was acquired at 100pm resolution using various fibrosarcoma 188
treatment time points to investigation this novel multi-peptide recombinant standard.
The images featured in Figures 5.4- 5.7, display an assortment of peptides from the digested
construct visualised by BioMap imaging software, therefore there is no ion mobility
component to ion selection here. Such images feature further on within this Chapter.
CHCA m /z 8 7 7 -
post 72h post CA- .
4-P ■
*(P_291D.C
170113 10 1 recomb max (MEAN :S77.015-877.055 ) SL 1/1
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T e n a s c in C m /z H H is tone 2A m /z
1 2 1 9 - p o s t 72h
post CA -4-P9 4 4 - post 72h
post CA -4-P
170113_ 10_ 1 _recomb_ma_div (ME*N: 1213.57-1213.70) SL 1/1 170113_10_1 _recomb_ma* (MEAN:944.535-944.575) SL 1/1
Figure 5. 4: MALDI-MSI of on tissue tryptic digests and digested recombinant standard in 72h treated tissue. The spatial distribution o f peptides; Actin (m/z 1198) with arrow indicating position of the spotted recombinant standard, Tenascin (m /z 1219), Histon 2A (m /z 944) with bracket showing the absence o f the standard and CHCA peak at 877 to show inverse image correlation.
275
276
Figure 5.5 - The spatial distribution of peptides; Rho GTPase activating protein 2 (N- chimaerin m /z 844), GRP-78 (m /z 1887), HSP-90 a (m /z 1168), Histone H4 (m /z 1325) displaying absence o f the standard to validate recombinant signal and Plectin (m /z 1385).
CHCA m /z 1066 - post 6h post CA -4-P
190113_8_1_recomb_ma>: (JulEAN: 1066.21-1066.33) SL 1/1
A c tin m /z 1198 - post 6h post CA -4-P
190113_8_1 _recomb_maK (MEAN: 1198.78-1198.90) SL 1/1
H istone H3 m /z 1032 - post 6h post CA -4-P
Figure 5. 6: MALDI-MS images displaying peptides distribution in fibrosarcoma 188 6h treated tissue, employing a DNA artificial construct for image validation. The spatial distribution o f peptides; Actin (m/z 1198) with arrow indicating position o f the spotted recombinant standard, Histone H3 (m/z 1032)showing the absence of the standard and CHCA peak at 1066 to show inverse image correlation.
277
CHCA m/z 877- I Actin m/z 1198 - I Grp78 m/z 1887 -
Control . J Con trol/Saline _ C o n tro l/S a lin e
278
Figure 5.7 - The spatial distribution of peptides; Actin (m/z 1198) with arrow indicating position of the spotted recombinant standard, GRP-78 (m/z 1887) displaying apparent low intensity as seen in Chapter 3 Figure 3.31 also usingFibrosarcoma 188 time course results post CA-4-P treatment except via LC-ESI-MS/MS., Vimentin (m/z 1093), Histone 2A (m/z 944) showing the absence of the standard and CHCA peak at 877 to show inverse image correlation.
The MALDI-MSI in Figures 5.4 - 5.7 all display good validation of the multi-peptide standard
due to the absence in signal intensity of peptides; Histone 2A, Histone H3 and Histone H4
which are typically high abundant within on tissue tryptic digests. These peptides were not
chosen for the construct and therefore co-validate the MALDI images in conjunction with the
recombinant standard.
Interestingly, there is a signal for Rho GTPase activating protein 2 in the fibrosarcoma 188
tissue here (Figure 5.5), albeit low intensity. Some of the proteins identified by ESI-LC-MS/MS
and iTRAQ LC-MALDI are highlighted in the tumour tissue images which again are of low
intensity. This reiterates the apparent ion suppression discussed in previous Chapters, a factor
typical of high abundant species in MALDI digest spectra. It has to be considered what ions of
interest are present within the so called 'spectral grass' and in future experiments how this
data can be amplified for analysis.
279
5.3.3 Recombinant MALDI-IMS-MSI visualised through HDI 1.1 softwareHDI 1.1 imaging software is a useful tool for the processing and visualisation of MALDI-IMS-MSI.
Interactive spectra and drift time plots enable further confirmation of species of interest.
The HDI analysis that follows has the capacity for the user to preset threshold and peak picking
parameters to facilitate the analysis of low abundant species.
The peptides Actin, HSP-90 and Histone H3 are featured in the following HDI analysis using the
identical recombinant standard MALDI-MSI results in Figures 5.4 - 5.7.
The HDI images here are shown as superimposed images on top of the original histological
section used for on tissue tryptic digestion and CHCA deposition.
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The MALDI-IMS-MSI visualised by HDI 1.1 software in Figures 5.7 and 5.9 gives a good
conceptual illustration of separation by drift time. Furthermore how this unique technology
can benefit MALDI image analysis and validation due to the vast dissimilarity in the
corresponding images for m/z 1198.7 and m/z 1198.6.
Another useful tool of the HDI software is the image correlation filter as shown in Figures 5.15
and 5.16. After selection of a particular ion of interest, running the image correlation filter
option displays the peaks within the spectrum that have the same spatial distribution pattern
as the peak picked. Therefore peaks that co-exist are observable. The following HDI screen
images are by using this option and show examples of Plectin m/z 1350 and Histone H3 m/z
1032. The correlation peak lists generated by selecting m/z 1032 mostly contained other
histones i.e. Histone 2A m/z 944.5, which confirms the correlation between the spatial
distribution of other Histones. However this was unsuccessful for Plectin m/z 1350 as fellow
Plectin peaks were not selected. This was thought to be due to the strong peptide signals from
the spotted recombinant standard which where all listed in the peak list correlation results. In
order to discriminate between each peak when using a recombinant standard in future work,
the standard could be sprayed directly to the tissue when using the HDI correlation filter tool.
Teamed with the recombinant digested construct, the MALDI-IMS images reported here
therefore propose a novel for methodology for inclusion into the MALDI-MSI workflow.
The potential for this dynamic recombinant standard is very promising and could be
engineered to include any prospective biomarkers for both research and diagnostic screening
applications in the clinical workplace. This concept alone is in accordance with the forward
momentum of MS applications.
289
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5.3.4 Cross validation of Plectin and HSP-90 response in fibrosarcoma 188 tissue employing a multimodal proteomic strategy.
The confirmation and validation of any scientific hypothesis is regarded a challenge, a range of
complementary techniques could indeed affirm marked responses seen in data generated.
The changes in the levels of Plectin throughout the treatment time course displayed a high
percentage of co-variance in the LC-ESI-MS/MS, with the same trend visible in the iTRAQ and
immunohistochemical results of the fibrosarcoma 188 data set.
The MALDI-IMS-MSI of plectin that follow include images from the Control fibrosarcoma 188
tissue and the 72h post CA-4-P time point with recombinant digested standard.
Plectin m /z 1385 -
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Figure 5 .17: MALDI-MSI o f fibrosarcoma 188 on tissue tryptic digests fo r the spatial distribution o f Plectin at m /z 1385.
The spatial distribution of Plectin in Figure 5.17 is in good agreement with the anti-Plectin
immunohistochemical staining featured previously in Chapter 3.
Figures 5.18 and 5.19 use Plectin and HSP-90 to show some of the cross validation that the
techniques in this project aimed to achieve. The samples used for ESI-LC-MS/MS and iTRAQ
were performed on different biological sample time course sets but are still in good agreement.
292
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Figure 5.18: MALDI-MSI and conventional proteomic techniques to asses a dose relationship in Plectin.293
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Figure 5.19: MALDI-MSI and conventional proteomic techniques to asses a dose relationship in HSP-90.
5.4 Concluding RemarksPrediction of the future requirements to progress MS experimental analysis could focus on
further technological advancement of ion mobility for proteins. In scientific publications, much
of the subject matter has focused on pharmaceutical and metabolomic analysis (Thelen and
Miernyk 2012).
Future progression in the separating capacity of ion mobility could improve peptide resolution
thus peptide identification in MALDI-MS, during the acquisition of complex tryptic digest
samples. This could potentially result in a peptide yield comparable to high throughput
shotgun LC chromatographic systems.
If such advancements in ion mobility separation come into fruition, this technology along with
many other additional components could reveal numerous concealed biological molecules.
Optimisation in sample preparation, solution chemistry and enzymology could also result in
prevention of lost proteins to minimise the suppression and spectral dominance by
haemoglobin, structural proteins and such like (Lapthorn et al 2013).
The chart in Figure 5.20 contains points to consider when performing on tissue enzymatic
digestion for MALDI-MSI. The issues and factors included are mainly based on personal
experience throughout the project reported herein. Insights were also gained from an oral
presentation by Hamzah Neil Freeman during a worshop entitled 'On-Tissue Enzymatic
Digestion for MALDi MS Imaging' (H Freeman 2012).
The recombinant protein reported here could hold great potential for quantification
techniques; N15 labelling is one such method for relative quantification. Labelled and
unlabelled samples can be combined and analysed using LC-MS/MS. The only difference
between the two samples being their mass, this then will be used to quantify the peptides
within the sample. The latter is a novel method that could also be applied to MALDI-MSI where
the N15 labelled recombinant could be applied as an internal standard directly to tissue
section and used for peptide quantification analysis.
295
!s a washing step required
for buffer salts
Figure 5. 20: Issues and factors to consider when performing on tissue digestion for MALDI imaging experiments.
ReferencesAngel TE, Aryal UK, Hengel SM, Baker ES, Kelly RT, Robinson EW, Smith RD (2012) Mass
spectrometry-based proteomics: existing capabilities and future directions. The Royal Society of
Chemistry.41. 3912-3928
Atkinson SJ, Loadman PM, Sutton C, Patterson LH, Clench MR (2007) Examination of the
distribution of the bioreductive drug AQ4N and its active metabolite AQ4 in solid tumours by
imaging matrix-assisted laser desorption/ionisation mass spectrometry. Rapid Communications
in Mass Spectrometry. 21.1271-1276
Djidja M-C, Claude E, Snel MF, Scriven P, Francese S, Carolan V, Clench MR (2009) Maldi-ion
mobility separation-mass spectrometry imaging of glucose-regulated protein 78 kDa (Grp78) in
human formalin fixed paraffin-embedded pancreatic adenocarcinoma tissue sections.Journal of
Proteome Research. 8. (10) 4876-4884
Freeman HN (2012) Optimisation of a low-volume on-slide protocol for imaging collage-specific
peptide fragments. Workshop - On-tissue enzymatic digestion for MALDI MS Imaging.
Stevenage BioScience Catalyst, GlaxoSmithKline.
Lapthorn C, Pullen F, Chowdhrylon BZ (2013)Mobility Spectrometry-Mass Spectrometry (IMS-
MS) of Small Molecules: separating and assigning structures to ions. Mass Spectrometry
Reviews.32.4 3 -7 1
Thelen JJ and Miernyk JA (2012) The proteomic future: where mass spectrometry should be
taking us. Biochemical Journal.444.169-18
297
Chapter 6Conclusion and Future Work
298
The aims of the study reported in this thesis were to develop and utilise mass spectrometry
imaging techniques (MALDI-MSI), in combination with conventional proteomic methodologies,
to investigate protein induction in vascular-targeted strategies.
Proteins thought to be involved in tumourigenesis and drug treatment resistance and were
observed along with the responses from proteins identified via the techniques used, in this
global analysis study.
The collaboration between the techniques used; MALDI-MSI, ESI-LC-MS/MS, LC-MALDI-MS/MS
with iTRAQ labelling was intended to provide cross validation of the effects post
administration of vascular disrupting agent CA-4-P.
During the initial experimental work reported in Chapter 2 using MALDI-MSI, one of the major
challenges was the management and analysis of the large amounts of data generated. The
latter especially applicable to ion mobility MALDI-MSI.
The gross haemorrhagic pharmacological response elicited by CA-4-P was visible by MALDI-MSI
throughout the fibrosarcoma 120 time course. The latter encouraged the prospect that other
proteins could potentially be observed induced via a dose response relationship.
It was hoped that the use of PCA-DA, PCA and PLSDA would reveal more subtle changes taking
place in the tumour samples however at present these are masked by the dominance of the
changes in the haemoglobin signals and other proteins of high abundance. MALDI-MS of the
fibrosarcoma 120 tissue in Chapter 2 was one such example, most likely to be due to the CA-4-
P susceptible nature of this tumour model.
The manually performed MALDI-IMS-MS/MS in Chapter 2 contained numerous signals which
may had been post translationally modified in ways other than what was pre-selected for
searching, thus hampering peptide identifications. Many of the MS/MS spectra studied clearly
contained overlapping MS/MS fragmentation patterns.
The Fibrosarcoma 188, a CA-4-P resistant experimental was introduced in Chapter 3. The use
of immunohistochemistry and label free LC-ESI-MS/MS shotgun proteomics aimed to
compliment and aid interpretation of the MALDI-MSI data.
The experimental work carried out using LC-ESI-MS/MS and label free quantitation revealed
many proteins connected with necrosis, cell structural reorganisation, polymerisation, tumour
survival and stress induced molecular chaperones. The inverse correlation of structural
proteins, haemoglobin and heat shock molecular chaperones gave the required validation and
identification to relate these responses to those seen in MALDI-MSI.
299
In addition to this the striking differences between HSp-90 and Plectin showed the distinct
regional differences in the MALDI-MSI, immunohistochemistry and LC-ESI-MS/MS results.
The haemoglobin time course using the resistant 188 tumour model gave quite different
results to those previously seen in the MALDI-MSI of the fibrosarcoma 120 data set. The first
indication of the 'switch back to viability' concept was revealed. This regenerative attempt by
the tumour could also be correlated to the HSp-90 and Plectin responses recently discussed.
The relationship pathways generated by using STRING 9.0 proteomic network software gave
an invaluable insight into the activity of the active tumour milieu and provided a means of
linking the identified proteins to their functional partners. Protein-protein interactions could
be observed to help interpretation of the MALDI-MSI and LC-ESI-MS/MS response graphs.
One such pathway revealed a link incorporating Ras GTPase-activating-like protein (IQGAP1), a
protein thought to be involved with tumour progression and metastasis. The decreased
response from IQGAP1 over the time course suggests an inhibitory effect by CA-4P. Similar
responses to treatment were exhibited by Plectin and Transforming growth factor-beta-
induced protein ig-h3 (Tgfbi). Future anti-IQGAPl/ anti-Tgfbi immunohistochemistry of the
fibrosarcoma 188 tissue could determine whether the staining of these two proteins reveals
co-localisation with Plectin, potential implicated in the 'viable tissue switch'.
Another protein that would be expected to show an inverse correlation to Plectin/
IQGAPl/Tgfbi, is the protein Alpha-enolase. Increased expression of Alpha-enolase is said to be
indicative of aggressive tumour progression as claimed of Plectin. However in these data
Alpha-enolase showed an increase in abundance overtime whereas Plectin quite markedly
decreases.
Over a thousand proteins were present in the LC-ESI-MS/MS results but only a fraction were
selected by Scaffold 3 proteomic software for analysis. Conclusions were made that further
optimisation not only during sample preparation but throughout data-dependent acquisition
mode could prove invaluable to advance proteome analysis and coverage. This would result in
a greater number of unique peptides per protein.
An opportunity to analyse the fibrosarcoma 120 samples with LC-ESI-MS/MS could serve to
complete the comparison between the susceptible and resistant tumour models
iTRAQ. quantitation of both fibrosarcoma 120 and 188 samples was performed in Chapter 5.
Similarly to the LC-ESI-MS/MS results, numerous proteins were identified and various PTM
parameters were employed in order to reduce and affirm the relative responses observed.
300
Different biological tumour samples were used compared to those of the ESI study with an aim
to validate overall responses previously seen.
The main differences in the responses observed between the two tumour models employed
(120 and 188) here were that of Elongation factor 1-alpha 1 and Vimentin.
In the fibrosarcoma 188 samples Elongation factor 1-alpha 1 levels are elevated throughout
the time points relative to the control. In contrast to this, levels in the fibrosarcoma 120 iTRAQ
results are all decreased relative to the control. It was thought that elevated levels seen in the
fibrosarcoma 188 samples maybe due to some form of pharmacological conflict elicited by this
CA-4-P resistant tumour model.
Vimentin levels decrease overtime in the fibrosarcoma 120 iTRAQ response results with a
more erratic response leading to an increase seen by the 188 results.
Overall the dose relationships observed in the iTRAQ data by the proteins involved in
haemorrhaging, structural remodelling, tumour survival and the stress response were in good
agreement with the other techniques employed here. Further optimisation and/or more
experimental repeats could both ascertain the relative responses seen and achieve optimum
percentage of iTRAQ labelling.
Although a widely practised technique, it was concluded that MALDI on tissue digestion
imaging still has many potential courses for optimisation. The chart in Chapter 5 aimed to
summarise points and issues for consideration. The requirement for the best buffer, enzyme,
solvent and matrix could indeed reveal numerous concealed biological molecules, essential in
the understanding of cancer. Additionally, another consideration in the assesment of
proteomic dose response relationships using MALDI-MSI is the effect of post translational
modifications. The apparent disappearance of a protein within an image time course could be
due to such alteration i.e. phosphorylation of peptides due to having a serine/
threonine/tyrosine present in the sequence or having undergone methylation/acetylation.
MALDI-MSI has the potential to forge a place in the workflow of clinical diagnostics. Targeted
approaches for the observation of disease biomarkers could be visualised using MALDI-MSI
and serve as a complimentary technique to standard clinical imaging.
The novel method reported here using a multi-peptide recombinant standard could prove an
important diagnostic tool for the analysis of patient biopsies and tissue micro-arrays. The
exciting prospect is the diversity of a multi-peptide recombinant standard. As mentioned
301
earlier in Chapter 5, the artificial construct can be engineered to include any prospective
biomarkers for both research and diagnostic screening applications.
This initiative could indeed further the impetus of MS applications in cancer research.
302
Appendices
304
Appendix 1: PublicationsCole LM, Mahmoud K, El Enazi M, Mohamed S, , Tozer GM, Smith DP, Clench MR (2013)
Recombinant "IMS TAG" Proteins - A new method fo r validating bottom up matrix assisted
laser desorption - ion mobility separation - mass spectrometry imaging 9MALDI-IMS-MSI).
Rapid Communications in Mass Spectrometry. In Press
Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M , Tozer GM, Clench MR
{2011)lnvestigation of protein induction in tumour vascular targeted strategies by MALDI MSI.
Methods.54. 442-453
Trim PJ, Djidja M-C, Muharib T, Cole LM, Bryn Flinders, Carolan VA, Francese S. Clench MR
[2012)lnstrumentation and software fo r mass spectrometry imaging—Making the most of
what you've got. Journal of Proteomics.75. (16) 4931^4940
Trim PJ, Djidja MC, Atkinson SJ, Oakes K, Cole LM, Anderson DM, Hart PJ, Francese S, Clench
MR[2010)lntroduction of a 20 kHz Nd:YV04 laser into a hybrid quadrupole time-of-flight mass
spectrometer for MALDI-MS imaging.397. (8) 3409-19
305
Appendix 2: Oral presentations• Cole LM, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR. MALDI-MSI
and Conventional Proteomic Techniques to Compare Protein Induction in
Combretastatin Resistant and Susceptible Tumours. Ourense Imaging Conference,
Spain, September 2012
• Cole LM, Bluff JE, Claude E, Wulfert F, Carolan VA, Paley M, Tozer GM, Clench MR,
Investigating Treatment Response of Combretastatin Resistant and Susceptible
Tumours using MALDI-MSI and Conventional Proteomic Techniques, ASMS Conference,
Vancouver, May 2012
• Cole LM, Bluff JE, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR. Proteomics
of Combretastatin Treated Fibrosarcomas Using lon-Mobility MALDI Mass
Spectrometry Imaging. East Midlands Proteomics Workshop, Loughborough University,
November 2011.
306
Appendix 3: Poster presentations• Cole LM, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR. Potential
Markers of Tumour Viability and Necrosis in CA-4-P Treated Fibrosarcomas Employing
a Multimodal Proteomic Approach. ASMS Minneapolis, June 2013.
• Cole LM, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR. MALDI-MSI
and Conventional Proteomic Techniques to Compare Protein Induction in
Combretastatin Resistant and Susceptible Tumours. NCRI Liverpool, November 2012
• Cole LM, Bluff JE, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
Investigations into Treatment Response in Tumour Vascular Targeted Strategies Using
MALDI-MS. British Mass Spectrometry Society, Cardiff, Wales, September 2011.
• Cole LM, Djidja M-C, Bluff JE, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
MALDI-MSI to Investigate Treatment Response in Tumour Vascular Targeted Therapy.
American Society for Mass Spectrometry, Denver, USA, June 2011.
• Cole LM, Djidja M-C, Bluff JE, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
MALDI-Mass Spectrometry Imaging to Investigate Treatment Response in Tumour
Vascular Targeted Therapy. Cancer Imaging Conference, City University, London, April
2011.
• Cole LM, Djidja M-C, Bluff JE, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
Peptide Analysis with In-situ Tissue Tryptic Digestion using MALDI-MS Techniques to
Investigate Protein Induction in Tumour Vascular Targeted Strategies. East Midlands
Proteomics Workshop, Nottingham November 2010.
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
In-situ Tissue Tryptic Digestion to Facilitate Peptide Analysis using MALDI-MS
Techniques to Investigate Protein induction in Tumour Vascular Targeted Strategies.
BMSS Cardiff 2010
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
MALDI-MS Techniques to Investigate Protein induction in Tumour Vascular-Targeted
Strategies. British Association for Cancer Research, Hallmarks of Cancer Conference,
Edinburgh, June 2010.
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
In-situ Tissue Tryptic Digestion to Facilitate Peptide Analysis using MALDI-MS
307
Techniques to Investigate Protein induction in Tumour Vascular Targeted Strategies.
American Society for Mass Spectrometry, Salt Lake City, USA, May 2010.
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
MALDI-MS Techniques to Investigate Protein induction in Tumour Vascular-Targeted
Strategies. Cancer Imaging Conference, Oxford University, April 2010.
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
Comparative Study of Detergent Addition for In-situ Tissue Tryptic Digestion to
Facilitate Peptide Analysis Using MALDI-MS Techniques. East Midlands Proteomic
Workshop 2009
• Cole LM, Djidja M-C, Bluff J, Claude E, Carolan VA, Paley M, Tozer GM and Clench MR.
A Comparative Study of Detergent Addition for In-situ Tissue Tryptic Digestion to
Facilitate Peptide Analysis Using MALDI-MS Techniques. Bremen IMSC 2009
308