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IDENTIFICATION OF NOVEL ONCOGENIC
PATHWAYS IN A PACLITAXEL-RESISTANT
MODEL OF TRIPLE NEGATIVE BREAST
CANCER
VERA KATHERINA KOHLBAUER
NATIONAL UNIVERSITY OF SINGAPORE
2016
IDENTIFICATION OF NOVEL ONCOGENIC PATHWAYS
IN A PACLITAXEL-RESISTANT MODEL OF TRIPLE
NEGATIVE BREAST CANCER
VERA KATHERINA KOHLBAUER
(M.Sc., University of Salzburg, AUSTRIA)
A THESIS SUBMITTED
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
YONG LOO LIN SCHOOL OF MEDICINE,
DEPARTMENT OF PHYSIOLOGY
NATIONAL UNIVERSITY OF SINGAPORE
2016
Declaration
I hereby declare that the thesis is my original work and it has been written by me in
its entirety. I have duly acknowledged all the sources of information which have been
used in the thesis.
The thesis has also not been submitted for any degree in any university previously.
Vera Katherina Kohlbauer
7 January 2016
i
Acknowledgements
First and foremost I would like to express my gratitude to my Ph.D. supervisor, Prof.
Yu Qiang, for his expert advice and encouragement throughout my Ph.D. studies.
Without his guidance and scientific knowledge, my Ph.D. would not have been
possible.
I am extremely grateful to Agency for Science, Technology and Research, A*STAR,
for sponsoring all of my Ph.D. studies as well as local and international conferences.
Furthermore, my gratitude goes to the Genome Institute of Singapore which has
provided me an inspiring and conductive environment to do my research and pursue
my project.
I would like to thank all members of the Cancer Therapeutics & Stratified Oncology
1 lab for their encouragement, constructive criticism and friendship. It was a pleasure
to work with them and I could not have asked for better colleagues. I especially have
to mention Michelle Pek and Puay Leng Lee who accompanied me throughout my
Ph.D. program. They have a special place in my heart for tolerating all my nonsense
and being a constant source of mental support. I cannot go without mentioning Gokce
Oguz, Maryam Jameelah, and Joanna Tan whose friendship and companionship mean
a lot to me and made my last 4 years so much better.
I would also like to thank Dr. Zhi Xiong who always had an open door for me to talk
about any problems or doubts I might have experienced during my studies.
Finally, I could not have completed this project without the constant love, support and
understanding of my parents and brothers.
I am but a child playing with pebbles by the sea, while vast oceans of truth lie undiscovered before me.
- Sir Isaac Newton –
ii
Table of Contents
Acknowledgements ........................................................................................................ i
Table of Contents .......................................................................................................... ii
List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii
List of Abbreviations ................................................................................................... xi
List of Publications .................................................................................................... xiv
CHAPTER 1: INTRODUCTION ................................................................................. 1
1.1 Breast Cancer ................................................................................................ 2
1.1.1 Molecular profiling – genetic classification of breast cancers .............. 3
1.1.2 Hormone receptor status – breast cancer subtypes ............................... 4
1.1.2.1 Therapeutic implications ................................................................... 5
1.1.2.2 Endocrine therapy for hormone receptor positive breast cancer ....... 6
1.1.2.3 Treatment for HER2/neu+ breast cancers ......................................... 7
1.1.3 Triple Negative Breast Cancer .............................................................. 8
1.1.3.1 Cellular origin of TNBC ................................................................. 10
1.1.3.2 Treatment strategies ........................................................................ 11
1.1.3.3 Recurrence ...................................................................................... 16
1.2 Mitotic inhibitors ........................................................................................ 18
1.2.1 Paclitaxel (Taxol®) .............................................................................. 19
1.2.2 Mode of action .................................................................................... 20
1.2.3 Mitotic arrest ....................................................................................... 20
1.2.4 Mitotic catastrophe .............................................................................. 22
1.3 Chemo-resistance ........................................................................................ 24
1.3.1 Cancer stem cells ....................................................................................... 25
1.3.2 Mechanisms of chemo-resistance .............................................................. 26
1.3.3 Resistance to microtubule disrupting drugs ............................................... 29
1.4 Aurora Kinase Signaling ............................................................................. 32
1.4.1 Aurora Kinases in cell cycle ............................................................... 32
1.4.2 The Spindle Assembly Checkpoint ..................................................... 35
1.4.3 Paclitaxel-induced activation of the SAC ........................................... 37
1.4.4 Aurora Kinases in Cancer ................................................................... 37
1.5 Protein Kinase C Signaling ......................................................................... 40
1.5.1 Structure and isoforms of PKCs................................................................. 40
iii
1.5.2 Activation of PKC isoforms and downstream signaling ............................ 41
1.5.3 PKCs in Cancer .......................................................................................... 43
1.5.4 PKC in Breast Cancer ................................................................................ 45
1.5.4. PKC-β ....................................................................................................... 45
1.5.5. The role of PKC-β in cell cycle regulation ............................................... 47
1.6 Aims and objectives of the study ................................................................ 49
CHAPTER 2: MATERIALS AND METHODS .................................................... 50
2.1. Cell culture and treatments .............................................................................. 51
2.2. Cryopreservation of cell lines .......................................................................... 51
2.3. Generation of Paclitaxel-resistant cell lines ..................................................... 52
2.4. Transfection of small interfering RNA ............................................................ 52
2.5. EC50 calculation ............................................................................................... 53
2.6. Cell viability assay ........................................................................................... 53
2.7. Flow Assisted Cytometry Analysis (FACS) .................................................... 54
2.8. Phospho-Histone H3 (Ser28) assay.................................................................. 54
2.9. Caspase 3 assay ................................................................................................ 55
2.10. Anchorage-independent Methylcellulose assay ............................................. 55
2.11. Tumorsphere assay......................................................................................... 56
2.12. 3D Matrigel anchorage-independent growth assay ........................................ 57
2.13. RNA extraction .............................................................................................. 57
2.14. cDNA conversion and quantitative RealTime-PCR (RT-PCR) ..................... 57
2.15. Microarray gene expression profiling ............................................................ 59
2.16. Protein extraction ........................................................................................... 59
2.17. Western blotting ............................................................................................. 60
2.18. H-score calculation ........................................................................................ 61
2.19. Statistical analysis .......................................................................................... 61
CHAPTER 3: RESULTS ......................................................................................... 62
3.1. Generation and characterization of Paclitaxel-resistant TNBC cell lines ........ 63
3.2. Gene expression profiling of SUM159PT Paclitaxel-resistant cell lines ......... 68
3.2.1. The NF-κB network .................................................................................. 69
3.3. Kinase inhibitor screen to identify Paclitaxel-sensitizing compounds ............ 74
3.4. Validation of top hits from the kinase inhibitor screen .................................... 78
iv
3.5. The PKC-β and PKA inhibitors sensitize resistant cells and induce apoptosis in
the presence of Paclitaxel ........................................................................................ 83
3.6. The PKC-β inhibitor Enzastaurin sensitizes chemo-resistant cells to Paclitaxel
in anchorage-dependant and –independent conditions ........................................... 87
3.7. Enzastaurin reduces tumorsphere growth in Paclitaxel-resistant TNBC cell
lines in the presence of Paclitaxel ........................................................................... 92
3.8. PKC-α inhibitors are not capable of re-sensitizing resistant cells to Paclitaxel
................................................................................................................................ 94
3.9. PKC-β expression and protein levels in breast cancer vs. normal breast
samples .................................................................................................................... 98
3.10. Mechanism of PKC-β signaling in resistance and upon sensitization ......... 101
3.11. GSK3β inhibitors recapitulate the effect of re-sensitization by Enzastaurin 104
3.12. Paclitaxel-resistant cell lines exhibit cross-resistance to other
chemotherapeutic compounds ............................................................................... 109
3.13. Enzastaurin can restore mitotic arrest in resistant cells upon treatment with
Paclitaxel ............................................................................................................... 113
3.13.1. Paclitaxel-induced mitotic arrest and cell death in sensitive cell lines . 113
3.13.2. Paclitaxel-resistant cells over come mitotic arrest by down-regulating
Aurora Kinases.................................................................................................. 117
3.14. Aurora B levels negatively correlate with Paclitaxel sensitivity in a panel of
basal-like breast cancer cell lines .......................................................................... 123
3.15. Targeted siRNA knock-down of Aurora B inhibits re-sensitization of
Paclitaxel-induced mitotic arrest ........................................................................... 125
CHAPTER 4: DISCUSSION ................................................................................. 129
4.1. Enzastaurin: mechanism of Paclitaxel-sensitization ...................................... 130
4.2. Aurora Kinase B expression levels as a biomarker for Paclitaxel-sensitivity in
basal-like breast cancer ......................................................................................... 133
4.3. Aurora Kinase B regulation ........................................................................... 134
4.4. Mcl-1 degradation by GSK3β in the presence of Paclitaxel .......................... 136
CHAPTER 5: CONCLUSION ............................................................................... 138
5.1 Conclusion ...................................................................................................... 139
5.2 Significance of findings and future prospects ................................................. 141
REFERENCES ........................................................................................................ 144
v
Summary
Triple Negative Breast Cancer (TNBC) is generally more aggressive that other
subtypes and shows early lymph node involvement. These cancers are characterized
by the lack of expression of the estrogen receptor, progesterone receptor, and the
epidermal growth factor receptor HER2/neu and, as such, don’t respond to endocrine
therapies targeting hormone receptors, or therapies with monoclonal antibodies
inhibiting downstream signaling of HER2/neu. Although they respond very well to
neoadjuvant chemotherapy, patients often suffer from relapse and distant metastasis,
coupled with resistance to chemotherapeutic drugs. In this study, we sought to
identify pathways critical for resistance and apply pharmacological methods to
sensitize cancer cells to chemotherapy.
We generated Paclitaxel-resistant TNBC cell lines, as Paclitaxel is a
chemotherapeutic agent often used in first line treatment of TNBC patients. The
TNBC cell lines, SUM159PT and MB231, were exposed to increasing concentrations
of Paclitaxel, subsequently, showed an approximately 1000-fold higher EC50 to
Paclitaxel, compared to their parental chemo-sensitive counterparts. We then used a
pharmacological approach to identify pathways which play an important role and
whose inhibition can sensitize resistant cells. Using a kinase inhibitor screen with 180
small molecule inhibitors targeting various kinases, we identified PKC-β inhibitors to
play an important role in re-sensitization. Upon validation, we found that PKC-β
inhibitors can successfully sensitize resistant TNBC cell lines to Paclitaxel, and
treatment with the PKC-β inhibitor Enzastaurin resulted in resistant cells undergoing
apoptosis in the presence of Paclitaxel. PKC-α inhibitors, on the other hand, were not
able to induce such a phenotype.
Paclitaxel induces cell death of cancer cells by stabilizing microtubules, which results
in mitotic arrest and subsequent cell death of parental, chemo-sensitive cells.
Paclitaxel-resistant cells, however, are able to surpass mitotic arrest in the presence of
vi
Paclitaxel. Interestingly, treatment with Enzastaurin was able to restore mitotic arrest
resulting apoptosis of the resistant cell lines. It has been shown that activation of the
Spindle Assembly Checkpoint (SAC) is a crucial event in Paclitaxel-induced cell
death, and one of the main players in this checkpoint is Aurora Kinase B.
Interestingly, Aurora Kinase B expression levels correlate with Paclitaxel-sensitivity
only in basal-like breast cancer cell lines but not in cell lines of the luminal subtype.
As expected, we found that the resistant cell lines had lower basal levels of Aurora
Kinase B as compared to the parental cell lines, even in the presence of Paclitaxel.
However, treatment with Enzastaurin could restore Aurora Kinase B expression
which resulted in mitotic arrest and subsequent cell death in the presence of
Paclitaxel. As expected, knock-down of Aurora B inhibited cell death in the resistant
cell lines upon combination treatment.
Taken together, this study shows that the small molecule inhibitor Enzastaurin can
successfully restore Paclitaxel-sensitivity in Paclitaxel-resistant TNBC cell lines.
Enzastaurin has been well studied in clinical trials and shows low toxicity in patients,
therefore using this inhibitor in combination with Paclitaxel is a potential therapeutic
strategy in treating patients with TNBC suffering from metastatic and recurrent
disease.
vii
List of Tables
Table 2.13 Primers used for quantitative RealTime-PCR of target genes
viii
List of Figures
Chapter 1
Figure 1.1 Distribution of breast cancer subtypes diagnosed in women in Atlanta
(USA) from 2003-2004
Figure 1.2 Intrinsic subtypes of Triple Negative Breast Cancer
Figure 1.3 Model of the human mammary epithelial hierarchy linked to cancer
subtype
Figure 1.4 Survival after a diagnosis of breast cancer
Figure 1.5 Cell fate in response to anti-mitotic drug treatment
Figure 1.6 Schematic representation of human Aurora A, B and C
Figure 1.7 Localization of Aurora kinase A and B together with PLK1 in the dividing
cell
Figure 1.8 Schematic sequence of Protein Kinase C (PKC) isozymes indicating the
domain structure of the PKC subfamilies and their activators
Chapter 3
Figure 3.1.1 Generation of Paclitaxel-resistant TNBC cell lines
Figure 3.1.2 Increase in the cancer stem cell population in Paclitaxel-resistant cell
lines
Figure 3.2.1 Changes in functions and pathways in Paclitaxel-resistant TNBC cell
lines
Figure 3.2.2 The NF-κB network in Paclitaxel-resistant cell lines
Figure 3.2.3 SUM159PT Paclitaxel-resistant cells are equally sensitive to NF-κB
inhibitors as compared to the parental cell line
Figure 3.3.1 Kinase inhibitor screen identifies PKC-β inhibitors to be able to re-
sensitize resistant cells
Figure 3.3.2 Top hit of the kinase inhibitor screen
Figure 3.4.1 Validation of the top 9 hits identified by the kinase screen
Figure 3.4.2 PKC-β and PKA inhibitors can inhibit colony formation of Paclitaxel-
resistant cell lines in the presence of Paclitaxel
Figure 3.5.1 PKC-β and PKA inhibitors induce Caspase 3 activation in the presence
of Paclitaxel
Figure 3.5.2 PKC-β and PKA inhibitors increase SubG1 fraction in Paclitaxel-
resistant cell lines in the presence of Paclitaxel
ix
Figure 3.6.1 The PKC-β inhibitor Enzastaurin sensitizes resistant cells to Paclitaxel in
a dose-dependent manner
Figure 3.6.2 Enzastaurin inhibits 3D colony growth in resistant cell lines in the
presence of Paclitaxel
Figure 3.6.3 Enzastaurin treatment drastically reduces Paclitaxel EC50 in SUM159PT
resistant cell lines
Figure 3.7 Enzastaurin in combination with Paclitaxel inhibits tumorsphere growth in
Paclitaxel-resistant cells
Figure 3.8.1 PKC-α inhibitors are not able to sensitize Paclitaxel-resistant cells in the
same manner as Enzastaurin
Figure 3.8.2 PKC-α inhibitors have no effect on colony formation of SUM159PT
Paclitaxel-resistant cells in the presence of Paclitaxel
Figure 3.9 PKC-β is highly expressed in breast cancer compared to normal breast
tissue in patient samples and cell lines
Figure 3.10 Western blot analysis of down-stream signaling of Enzastaurin treatment
on PKC-β, GSK3β and Mcl-1 in SUM159PT and MB231
Figure 3.11.1 Western blot analysis of the effect of GSK3β inhibitors in MB231
parental and resistant cells
Figure 3.11.2 A set of GSK3β inhibitors reduce colony formation in the resistant cells
in the presence of Paclitaxel
Figure 3.11.3 GSK3β activation leads to increased cell death in the resistant cells in
the presence of Paclitaxel
Figure 3.12.1 Paclitaxel-resistant cells exhibit cross-resistance to Vincristine which
can be reversed with Enzastaurin
Figure 3.12.2 Paclitaxel-resistant cells exhibit cross-resistant to Adriamycin
Figure 3.13.1 Paclitaxel induces mitotic arrest and subsequent cell death in MB231
parental cells
Figure 3.13.2 Up-regulation of Aurora Kinase B is crucial for Paclitaxel-induced cell
death
Figure 3.13.3 Enzastaurin restores levels of Aurora B in resistant cell lines upon
treatment with Paclitaxel
Figure 3.13.4 Various Aurora Kinase inhibitors can rescue cell death upon
combination treatment of Paclitaxel with Enzastaurin in Paclitaxel-resistant cell lines
Figure 3.13.5 Aurora Kinase B inhibition rescues cell death upon re-sensitization of
Paclitaxel-resistant cells by Enzastaurin
x
Figure 3.14 Aurora Kinase B expression negatively correlates with Paclitaxel
sensitivity in a panel of basal breast cancer cell lines
Figure 3.15.1 Aurora Kinase B is crucial for Paclitaxel-induced inhibition of cell
viability in parental and resistant SUM159PT and MB231 cell lines
Figure 3.15.2 Aurora Kinase B abolished Paclitaxel-induced mitotic arrest
Chapter 4
Figure 4.1 Increased expression of ABC transporters in Paclitaxel-resistant
SUM159PT cell lines
Figure 4.2 Major regulators of Aurora Kinase B
xi
List of Abbreviations
ABC ATP-binding cassette domain
ABCB1 ATP-Binding Cassette, Sub-family B, Member 1
ADCC Antibody-Dependent Cellular Cytotoxicity
ADR Adriamycin
AI Aromatase Inhibitor
Akt Protein Kinase B (PKB)
ALDH Aldehyde Dehydrogenase
APC Allophycocyanin
APC/C Anaphase-Promoting Complex/Cyclosome
ASCO American Society of Clinical Oncology
ATP Adenosin Triphosphate
AURKA/AURKB Aurora Kinase A/B gene
Bcl-2 B-Cell Lymphoma 2
Bcl-XL B-Cell Lymphoma-extra large
BRCA1/2 Breast Cancer 1/2, early onset
BTK Bruton’s Tyrosine Kinase
BUB1 Mitotic Checkpoint Serine/Threonine Kinase
CDK Cyclin-Dependent Kinase
CENP-E Centrosome-Associated Protein E
CHK2 Checkpoint Kinase 2
CK5/6, 14, 17 Cytokeratin 5/6, 14, 17
CPC Chromosomal Passenger Complex
CRISPR Clustered Regularly Interspaced Short Palindromic
Region
CSC Cancer Stem Cell
CTG CellTiterGlo®
CTGF Connective Tissue Growth Factor
DAG Diacylglycerol
DCIS Ductal Carcinoma In Situ
DFS Distant metastasis-Free Survival
DLBCL Diffuse Large B-Cell Lymphoma
DMSO Dimethyl Sulfoxide
DNA Deoxyribonucleic Acid
EGF Epidermal Growth Factor
EGFR Epidermal Growth Factor Receptor
EMT Epithelial-Mesenchymal Transition
EpCAM Epithelial Cellular Adhesion Molecule
ER Estrogen Receptor
ERK Extracellular signal-Regulated Kinase
ESA Epithelial Specific Antigen
FAK Focal Adhesion Kinase
xii
FBW7 F-Box and WD repeat domain-containing 7
FDA Food and Drug Administration
FITC Fluorescein Isothiocyanate
FLT3 Fms-related Tyrosine Kinase 3
GDP/GFP Guanosine Di-/Triphosphate
GOBO Gene expression-based Outcome for Breast cancer
Online
GPCR G-Protein Coupled Receptor
GSK3β Glycogen Synthase Kinase 3β
HER2, ERBB2 Receptor Tyrosine-Protein Kinase erbB-2
Hh Hedgehog
HR Homologous Recombination
IKK Inhibitor of IκB Kinase
INCENP Inner Centromere Protein
INT Tetrazolium
IPA Ingenuity® Pathway Analysis
JNK Jun-terminal Protein Kinase
LiCl Lithium Chloride
LOH Loss Of Heterozygosity
MAD1/2 MAX Dimerization Protein 1/2
MAP Microtubule-Associated Protein
MAPK, MEK Mitogen-Activated Protein Kinase
MBC Metastatic Breast Cancer
MCC Mitotic Checkpoint Complex
Mcl-1 Myeloid Cell Leukemia 1
MDM2 Mouse Double Minute 2 homolog
MDR1 Multidrug Resistance Protein 1
MSK1 Mitogen and Stress-activated Protein Kinase
MYC V-Myc Avian Myelocytomatosis Viral Oncogene
Homolog
mRNA Messenger RNA
NF-κB Nuclear Factor-κB
NCCN National Comprehensive Cancer Network
NCI National Cancer Institute
ORR Overall Response Rate
OS Overall Survival
P-gp P-glycoprotein
p-H3 Phospho-Histone H3
PARP Poly(ADP-ribose) polymerase
pCR Pathological Complete Response
PCR Polymerase Chain Reaction
PDGFR Platelet-Derived Growth Factor Receptor
PDK1 Pyruvate Dehydrogenase Kinase, isozyme 1
xiii
PFS Progression-Free Survival
PI3K Phosphoinositide 3-Kinase
PKA Protein Kinase A
PI Propidium Iodide
PKC Protein Kinase C
PLC Phspholipase C
PLK1 Polo-Like Kinase 1
PR Progesterone Receptor
PRKCB Protein Kinase C β gene
p53, p63, p38 Protein53/63/38
PTEN Phosphatase and Tensin Homolog
RACK Receptor for Activated Kinase
RICK Receptor for Inactivated Kinase
RKIP Raf Kinase Inhibitory Protein
RNA Ribonucleic Acid
RFS Relapse-Free Survival
SAC Spindle Assembly Checkpoint
SASP Senescence Associated Secretory Phenotype
SCID Severe Combined Immunodeficiency
SERM Selective Estrogen Receptor Modulatir
TAZ Transcriptional Co-activator with PDZ-binding motif
TGF-β Transforming Growth Factor β
TNBC Triple Negative Breast Cancer
TNKS1/2 Tankyrase 1/2
VCR Vincristine
VEGF Vascular Endothelial Growth Factor
xiv
List of Publications
1. Zhen Ning Wee, Siti Maryam J. M. Yatim, Vera K Kohlbauer, Min Feng,
Jian Yuan Goh, Yi Bao, Puay Leng Lee, Songjing Zhang, Pan Pan Wang,
Elgene Lim, Wai Leong Tam, Yu Cai,Henrik J Ditzel, Dave S. B. Hoon, Ern
Yu Tan & Qiang Yu, 2015, IRAK1 is a therapeutic target that drives breast
cancer metastasis and resistance to paclitaxel, Nat Commun. 6:8746
2. Conference paper: Vera K Kohlbauer & Qiang Yu, Identification of novel
oncogenic pathways in a Paclitaxel-resistant model of Triple Negative Breast
Cancer. Submitted for:
EACR Conference Series 2015, Cancer Genomics (July 2015),
Cambridge, UK
Cell Symposia: Hallmarks of Cancer: Asia (November 2014),
Beijing, China
Gordon Research Conferences: Cell Growth & Proliferation (June
2013), Vermont, USA
1
CHAPTER 1: INTRODUCTION
2
1.1 Breast Cancer
Breast cancer is the most common cancer among women (1 in 3 cancers diagnosed)
and the second most common cause of cancer deaths. According to the American
Cancer Society ACS (Cancer Facts & Figures 2015), it is estimated that in the United
States 231,840 women will be diagnosed with invasive breast cancer and 40,730
women will die from it in 2015. Breast cancer arises from ductal or lobular tissues
and is confined to these two regions at the initial stage of development and is
therefore termed in situ carcinoma. At this stage, the cancer is treatable (surgical
removal) with highly positive outcomes. However, if it is not detected it becomes
invasive and infiltrates surrounding tissue leading to cancers with aggressive
phenotypes with a high rate of metastasis and poor outcome.
In general, breast cancer is more common among women of older age, but most cases
diagnosed in younger women (age 35 and below) are of high grade, estrogen receptor
positive (Gonzalez-Angulo et al., 2005) and hereditary. About 40% of all familial
breast cancer cases are caused by genetic predispositions from mutations in high
penetrance genes such as BRCA1 and BRCA2, whereas as low prevalence genes like
TP53, CHK2 or PTEN (Kenemans et al., 2008) account for the rest. Mammographic
screening was introduced to women in order to detect breast cancers early on and
improve outcome, but there is much controversy around this topic, since many studies
achieved different results. Some studies reported a decrease in breast cancer mortality
(Gotzsche and Olsen, 2000) with regular screening in randomized trials, whereas
others claim that regular screening comes with such high cost (length bias, lead-time
bias and over-diagnosis), that they are not justifiable (Berry, 2013).
Breast cancers are very heterogenous with substantial morphological, pathological
and genetic variation. Therefore, classifying them into different subtypes provides
important implications for diagnosis and therapy. This can be done based on gene
3
expression profiles of different breast cancers, or based on their hormone receptor
status.
1.1.1 Molecular profiling – genetic classification of breast cancers
Microarrays enable gene expression profiling of thousands of genes in different
cancers and allow us to create molecular profiles of a tumor. Perou et al. examined
the expression levels of 8,102 genes in 65 breast cancer samples and classified then
into different intrinsic subtypes: Luminal, Basal-like, HER2+ and Normal breast
(Perou et al., 2000). Later it was found that the Luminal subtype can be further
divided into Luminal A and Luminal B. Luminal breast cancers are characterized by a
high expression of hormone receptors and hormone receptor-associated genes,
HER2+ cancers have a high expression of HER2 and a low expression of the estrogen
receptor (ER), and are more likely of high grade. Basal-like cancers manifest with a
high expression of basal epithelial genes, but low expression of ER and HER2, and
generally come with poor prognosis.
Notably, the most important clinical implication of breast cancer subtyping is for
prognosis and treatment. To date, there are various genomic assays available for
breast cancer, which give information on the type of cancer and the therapy which
will be most beneficial. Such platforms include Oncotype DX®, MammaPrint® and
Mammostrat®. Oncotype DX is a diagnostic test to estimate disease recurrence in
breast cancer patients and if they will benefit from chemotherapy after surgery. It is
for women with early stage, estrogen receptor (ER) positive breast cancer and takes
into account the gene expression levels of 21 genes (16 cancer genes and 5 reference
genes). It also calculates the recurrence risk for DCIS (Ductal Carcinoma In Situ).
Clinical studies are still ongoing with Oncotype DX. MammaPrint is based on the
expression levels of 70 genes in the Amsterdam 70-gene breast cancer signature to
calculate the risk if a patient will develop metastases if given chemotherapy. It has
4
been FDA-approved for early stage, lymph node negative, hormone receptor-positive
or -negative breast cancer and is carried out on paraffin-embedded or fresh tumor
samples. The Mammostrat test uses the score of 5 genes based on their expression
levels and assigns patients into different risk groups (high, moderate, low) to measure
their risk of recurrence. So far, only Oncotype DX has been included in the NCCN
and ASCO treatment guidelines.
1.1.2 Hormone receptor status – breast cancer subtypes
Although gene expression profiling of breast cancer can be used for predicitive and
prognostic purposes, it is still expensive and not widely available yet. Other than gene
expression profiling, the status of two hormone receptors can also be used to
distinguish different breast cancer subtypes. The different subtypes vary in the
expression status of the estrogen receptor (ER) and progesterone receptor (PR), and
the epidermal growth factor (EGF) receptor HER2/neu. Breast cancer can be
classified into four different subtypes based on their receptor expression status:
1. Luminal A: ER+ and/or PR+, HER2/neu-
2. Luminal B: ER+ and/or PR+, HER2/neu+
3. HER2+: ER- and PR-, HER2/neu+
4. Triple Negative Breast Cancer (TNBC): ER-, PR- and HER2/neu-
Out of these four, Luminal A is the most common subtype with 66.9%, followed by
TNBC (18.8%), Luminal B (8.3%), and HER2/neu+ (6.0%) (Lund et al., 2010).
Figure 1.1 shows the distribution of breast cancer subtypes in women diagnosed with
breast cancer from 2003-2004 where information on receptor status was available at
time of diagnosis.
5
Furthermore, incidence rates for TNBC were higher in black women compared to
white women, and TNBC is more common among younger women (age 35 and
below) (Carvalho et al., 2010).
1.1.2.1 Therapeutic implications
Subtyping breast cancer according to ER, PR and HER2/neu has therapeutic
implications since endocrine therapies can be used to treat hormone receptor positive
tumors and HER2/neu can be blocked with monoclonal antibodies. TNBC, on the
other hand, does not respond to any endocrine therapies or treatment with monoclonal
antibodies, and the predominant form of treatment to this day is chemotherapy. In
general, hormone receptor positive patients have a higher rate of survival compared to
patients lacking hormone receptor expression (Fisher et al., 1988, Crowe et al., 1991)
and adjuvant hormonal/ chemotherapeutic treatment can further increase survival rate
in these patients (Fisher et al., 2004).
Figure 1.1 Distribution of breast cancer subtypes diagnosed in women in Atlanta (USA)
from 2003-2004. Adapted from Lund et al. (Lund et al., 2010).
6
1.1.2.2 Endocrine therapy for hormone receptor positive breast cancer
Estrogen receptor signaling plays an important role in the normal development and
growth of the breast tissue. The crucial role of estrogen in breast cancer was
discovered over 100 years ago, when mortality rates for breast cancer patients
decreased after their ovaries had been removed.
ER+ breast cancers are dependent on positive signaling from activated ERs and when
this signaling is shut off, it can lead to reduction of hormone-dependent proliferation
and cell death. The ER status can be seen as a biomarker for responsiveness to
endocrine therapies. Therapies can either target ER signaling directly using pure
antagonists and selective estrogen receptor modulators (SERMs), or indirectly using
aromatase inhibitors (AIs) which block estrogen production.
There are two estrogen receptors, ERα and ERβ, that share 56% similarity in ligand
binding domains, and nearly identical DNA binding domains. However, they possess
different gene expression profiles and only a minority of genes regulated by ERβ is
also regulated by ERα. (Grober et al., 2011). ERβ expression is more dominant in
mammary tissues. It is expressed in epithelial and stromal cells, while ERα
expression is only found in a subset of epithelial cells. However, ERα is upegulated in
a large number of breast cancers and is linked to prognosis and response to hormone
therapy, in contrast to ERβ expression (Roger et al., 2001, Zhao et al., 2008). The
most commonly used antagonist targeting ERα is Tamoxifen, which binds to the
receptor without activating it. Tamoxifen is a selective estrogen receptor modulator as
it can act as an agonist in other tissues, such as the endometrium. It is standard
therapy for ER+ tumors in premenopausal women and on occasion used in
postmenopausal women. In addition to Tamoxifen, AIs are also effective in treating
ER+, early stage breast cancers (Harvey et al., 1999). The aromatase enzyme is
needed to convert androgen to estrogen and inhibition of its activity inhibits estrogen
7
production in the ovaries, thereby starving ERs of estrogen. AIs, such as anastrozole
and letrozole, have been approved for treatment of ER+ tumors in postmenopausal
women.
1.1.2.3 Treatment for HER2/neu+ breast cancers
Patients diagnosed with HER2/neu+ breast cancer show low overall survival (OS)
and relapse-free survival (RFS), and poor prognosis. Overexpression of HER2 leads
to increased activation of the MAPK and PI3K/Akt pathways, which leads to
increased cell proliferation and survival (Yarden and Sliwkowski, 2001). Many of
these patients respond well to treatment with Tratuzumab (Herceptin) which is a
humanized monoclonal antibody targeting the extracellular juxtamembrane domain
IV of HER2. Trastuzumab leads to a decrease in HER2 signaling followed by
inhibition of cell proliferation, while simultaneously inducing antibody-dependent
cellular cytotoxicity (ADCC) as it can activate immune effector cells (natural killer
cells) (Yamauchi et al., 2011, Spector and Blackwell, 2009). Furthermore, it can
inhibit the MAPK and PI3K pathways and inhibit angiogenesis (Nahta, 2012).
Despite it’s positive effects for patients with primary HER2+ breast cancer, many
tumors show resistance to single-agent treatment with Trastuzumab with a low
median survival of 9 months (Nahta, 2012). Trastuzumab in combination with
chemotherapy in an adjuvant setting can increase disease-free survival for patients
with early stage breast cancer (Romond et al., 2005). Metastatic breast cancer (MBC)
also shows a low response rate of 12-34% to Trastuzumab treatment as a single agent.
However, clinical trials have shown that combination treatment of Trastuzumab with
chemotherapy (Paclitaxel or Docetaxel) can increase response rates and OS (Esteva et
al., 2002). Similar effects were achieved when combined with other chemotherapeutic
agents such as cisplatin, carboplatin or ionizing radiation (Slamon et al., 2001).
8
This thesis focuses on TNBC, so we will describe this subtype in more detail in the
next part of the introduction.
1.1.3 Triple Negative Breast Cancer
TNBCs are more aggressive than other subtypes of breast cancer in terms of relapse
rates, tumor grade, and lymph node involvement (Haffty et al., 2006). Moreover, less
than 30% of patients diagnosed with metastatic TNBC have a 5 year survival rate due
to the lack of effective targeted therapies (Dent et al., 2007).
Approximately 70%-80% of all Basal-like breast cancers are also triple negative and
more than 80% of TNBC are basal-like, therefore these 2 terms are often used
interchangeably to describe the same breast cancer subtype (Weigelt et al., 2010). The
group of TNBCs can also encompass tumors that are of : (1) the claudin-low subtype,
which have a high percentage of cells that have stem-like properties and show
features of EMT; (2) the interferon-rich subgroup which has better prognosis than
TNBC tumors; and (3) the normal-like subgroup with a high content of normal and
stromal cells (Sotiriou and Pusztai, 2009). In addition, approximately 20% of basal-
like tumors express ER or HER2/neu (Turner et al., 2010).
Various immunohistochemical panels have been proposed to distinguish basal-like
subtypes, such as: (1) lack of the ER, PR and HER2/neu (TNBC); (2) expression of at
least one high molecular weight/basal cytokeratin (CK5/6, CK14, CK17); (3) lack of
ER and HER2/neu expression together with the expression of CK5/6 and/or EGFR
(Nielsen et al., 2004); (4) lack of ER, PR and HER2/neu expression together with
expression of CK5/6 and/or EGFR (Cheang et al., 2008). Despite the presence of
various different definitions of basal-like breast cancer, they have distinct clinical
9
presentation, histological features, response to chemotherapy, sites of distant relapse,
and outcome.
Even among TNBC cancers there is great heterogeneity. Lehmann et al. analyzed
gene expression profiles of 587 TNBC breast cancer cases from 21 databases and
performed clustering analysis (Lehmann et al., 2011). He found that, depending on
the gene expression profiles, the TNBC cases could be further classified into six
different subtypes, which are listed in the table below.
Subtype Gene expression profile
Basal-like 1 (BL-1) High in the expression of genes involved in cell cycle progression,
cell division, and DNA damage response pathway
Basal-like 2 (BL-2) High in the expression of genes involved in cell cycle progression,
cell division, and growth factor signaling
Immunomodulatory
(IM)
High in the expression of genes involved in immune processes and
cell signaling
Mesenchymal (M) High in the expression of genes involved in motility and
extracellular matrix
Mesenchymal stem-
like (MSL)
High in the expression of genes involved in motility, extracellular
matrix, and growth factor signaling
Luminal androgen
receptor (LAR)
High in the expression of genes involved in hormonally regulated
pathways
However, basal-like breast cancers and TNBC share many similarities in terms of
clinical prognosis and therapeutic responses (Badve et al., 2011). These cancers show
lymph node involvement, high rate of recurrence, and are biologically more
aggressive (Thike et al., 2010). Interestingly, basal-like breast cancers respond well to
neoadjuvant chemotherapy with higher pathological complete response (pCR)
compared to other subtypes (Lin et al., 2010). However, rates of distant metastasis
and recurrence are higher for these patients.
Figure 1.2 Intrinsic subtypes of Triple Negative Breast Cancer
Six subtypes of Triple Negative Breast Cancer based on gene expression profiling (Peddi et al.,
2012, Lehmann et al., 2011).
10
Although the overlap between basal-like breast cancer and TNBC is not complete,
many papers still use these terms interchangeably. Therefore, I will summarize the
findings for both in a collective manner.
1.1.3.1 Cellular origin of TNBC
The fact that basal-like breast cancer cells possess breast stem cell-like properties
gives rise to the question whether these tumors arise from breast stem cells. To date
there are many reports on the epithelial hierarchy in the human breast where an
undifferentiated ER-/PR-/HER2/neu- mammary stem cell (MaSC), capable of self
renewal and differentiation, gives rise two committed progenitors: the myoepithelial
and luminal progenitors (Figure 1.3). The myoepithelial progenitor then gives rise to
mature myoepithelial cells that surround the luminal epithelium and contact the
basement membrane. The luminal progenitor can be ER- or ER+ and will give rise to
mature ductal and alveolar cells which line the lumen of the mammary gland and are
also ER- or ER+ (Lim et al., 2009). Two different cell surface markers can be used to
identify different subpopulations of cells in the breast: CD49f and EpCAM. When
performing FACS analysis on highly purified subpopulations of MasSC/bipotent cells
(CD49f+/EpCAM-), committed luminal progenitors (CD49f+/EpCAM+) and mature
ER+/luminal cells (CD49f-/EpCAM+), it was found that the gene signature of the
luminal progenitors was very similar to the basal-like gene signature (Lim et al.,
2009). Together with the findings that BRCA1-mutated pre-neoplastic breast tissues,
which have a >80% chance to develop into basal-like breast cancer, show an
increased population of luminal progenitor cells, these findings suggest that the cell
of origin of sporadic and BRCA1-mutated basal-like breast cancer is the luminal
progenitor (Figure 1.3).
11
1.1.3.2 Treatment strategies
Patients with TNBC do not benefit from endocrine therapies targeting ER and PR, or
therapies using monoclonal antibodies to target HER2/neu, as they lack expression of
these specific hormone and EGF receptors. The mainstay of treatment until now is
surgery and chemotherapy, or a combination of both. Some newer studies have
identified certain receptors which play a role in tumorigenesis and progression of
TNBC, and which could be potential targets for targeted therapies. Such receptors are
EGFR and c-Kit, where it was found that c-Kit is highly expressed in 31% of basal-
like tumors and 11% of non-basal-like tumors (Nielsen et al., 2004). Moreover, 66%
of patients triple negative tumor cells and basal-like tumor cells have been shown to
Figure 1.3 Model of the human mammary epithelial hierarchy linked to cancer subtype
Subpopulations of normal breast tissue and potential cells of origin for the intrinsic subtypes;
various breast cancer subtypes molecularly compared to subpopulations from normal breast
tissue; defining expression patterns of luminal, mesenchymal or claudin-low, and basal-like
cells (Prat and Perou, 2009).
12
express EGFR, suggesting a therapeutic role for targeting EGFR (Reis-Filho et al.,
2005). Despite the promising preclinical data, clinical studies combining cetuximab,
an anti-EGFR antibody, with carboplatin in stage IV triple negative breast cancer
patients, showed that less than 20% of patients showed inhibition of the EGFR
pathway, suggesting alternative activation mechanisms (Carey et al., 2012).
We will look closer into the therapies available for TNBC patients.
Chemotherapy
Anthracyclines Anthracyclines are the most common chemotherapeutic drugs used
for the treatment of TNBC patients. These compounds intercalate between base pairs
and inhibit DNA and RNA synthesis preventing replication. They also inhibit
topoisomerase II, an enzyme involved in the relaxation of supercoiled DNA, leading
to DNA double strand breaks, which leads to activation of the DNA damage response
signaling cascade (Nitiss, 2009). If DNA repair fails, the cells will undergo apoptosis.
More recent studies have found that anthracyclines also induce histone eviction from
chromatin and subsequent deregulation of the DNA damage response and DNA repair
(Pang et al., 2013). Many studies show that TNBC patients are sensitive to
anthracycline-containing therapies. The clinical response to therapy containing
doxorubicin (an anthracycline) and cyclophosphamide, was higher in TNBC patients
compared with non-TNBC patients in terms of pCR, although overall outcome (OS
and DMFS) was still worse in comparison with non-TNBC patients (Carey et al.,
2007).
Taxanes TNBCs show a high rate of genomic instability, due to defective pathways,
such as mutations in BRCA1 (discussed above), therefore, using Taxanes may benefit
these patients. Taxanes are spindle poisons which stabilize microtubules, and it was
found that TNBC and HER2/neu+ patients are more sensitive to doxorubicin and
Paclitaxel when used in a neoadjuvant setting as compared to luminal- and normal-
13
like breast cancer patients (Rouzier et al., 2005). The addition of Paclitaxel to
cyclophosphamide and doxorubicin could also enhance DMFS and OS in node
positive, as well as TNBC and HER2/neu+ patients (Hayes et al., 2007). Several
studies show improved outcome for patients with the addition of a taxane to the
standard chemotherapy routine. One study showed great benefit with the addition of
docetaxel in TNBC patients receiving conventional 6 cycles of FEC (5-Fluorouracil,
epirubicine, cyclophosphamide) (Martin et al., 2010), while another study found that
weekly paclitaxel is much more effective than paclitaxel every 3 weeks (Sparano et
al., 2008).
Platinum agents The mechanism of action for platinum agents, such as carboplatin
and cisplatin, is intra- and inter-strand cross-linking of double-stranded DNA, thereby
preventing formation of the replication fork and production of double strand breaks.
Because TNBCs often harbor BRCA1 mutations or silencing, the DNA repair
cascade is not fully functional and the cells will undergo cell death (Hastak et al.,
2010). One study showed that neoadjuvant cisplatin as a single agent in TNBC
patient showed a pCR of 22% (50% of patients had a partial response) (Silver et al.,
2010). Another study found that 9 out of 10 patients with BRCA1 mutations achieved
pCR after neoadjuvant therapy with cisplatin (Byrski et al., 2009). Cisplatin
sensitivity is mediated by a p63-dependent tumor survival pathway in TNBC cells
(Leong et al., 2007) and, importantly, cisplatin yielded a higher pCR rate in patients
with p63+ tumors (Rocca et al., 2008). In a meta-analysis of randomized-controlled
trials of platinum agents for TNBC patients it was found that platinum-based
therapies yielded significantly better response rates and pCR rates compared to
therapies without platinum (Guan et al., 2015).
Combination of platin with a taxane-based primary chemotherapy has also shown to
be effective in patients with locally advanced breast cancer (Frasci et al., 2009).
Neoadjuvant cisplatin combined with paclitaxel and epirubicin in a weekly schedule
14
led to a 65% pCR. Moreover, when cisplatin was used in combination with epirubicin
and 5-Fluorouracil a complete clinical response of 88% was achieved (Sirohi et al.,
2008). Therefore, when patients show resistance or high toxicity to therapies with
anthracyclines, platinum-based therapies are a good alternative.
Targeted therapies
PARP inhibitors Poly(ADP-ribose) polymerase (PARP) is an enzyme involved in
DNA repair. Upon single strand breaks, PARP is recruited and its activation leads to
single strand repair. However, when PARP is inhibited, the repair system can no
longer be activated, leading to a stalling of the replication fork. The stalled replication
fork will collapse and induce a double strand break in the DNA, which can be
repaired by homologous recombination (HR). If the replication restarts, then the cell
will survive. In BRCA-deficient cells where homologous recombination is impaired,
the double strand breaks cannot be repaired, subsequently leading to cell death
(Livraghi and Garber, 2015).
The rationale for using PARP inhibitors in the treatment of TNBC comes from the
observations that breast cancers with mutated BRCA show impairments of the HR
pathway (Tutt et al., 2005) and some sporadic TNBCs harbor BRCA1 mutations
(Turner et al., 2004). Others lack BRCA1 mutations but may show genetic profiles
that are similar to tumors arising in BRCA1 carriers (van Beers et al., 2005), often
times because they have defective BRCA1 pathways. This is partly due to CpG island
methylation of the BRCA1 promoter and subsequent deactivation. Promoter
methylation of one allele is found in 11-14% of sporadic breast cancers (Jones and
Baylin, 2002) and often the second allele is lost due to loss of heterozygosity (LOH).
This leads to the absence of BRCA1 expression and an inhibition of the pathway.
Phase I and II clinical trials revealed that treatment with PARP inhibitors lead to
improved outcomes as monotherapy in breast cancers with BRCA1/2 mutation, as
15
well as in ovarian cancers, and pancreatic and prostate cancers with confirmed
BRCA1/2 mutations (Fong et al., 2009, Kaufman et al., 2015). Even in combination
therapy, PARP inhibitors showed promising results especially when given together
with platin-based agents in BRCA-related breast cancers (Balmana et al., 2014).
Moreover, there are randomized phase III studies ongoing to test PARP inhibitors vs.
single agent chemotherapy in breast cancer patients with metastatic disease and
BRCA1/2 germline mutations (https://clinicaltrials.gov/ct2/show/NCT01905592,
https://clinicaltrials.gov/ct2/
show/NCT01945775, https://clinicaltrials.gov/ct2/show/NCT02163694).
EGFR inhibitors EGFR is often over-expressed in TNBC, therefore it is one of the
targets in treatment for these patients. However, the results have not been positive. In
a more recent phase II randomized study, metastatic TNBC patients were treated with
a chimeric monoclonal antibody targeting EGFR alone (cetuximab), or in
combination with carboplatin (Carey et al., 2012), however, there were no significant
differences in PFS and both treatment groups showed rapid disease progression.
Despite these results, there are still phase I and II clinical trials ongoing with
cetuximab in combination with cytotoxic agents in metastatic TNBC.
VEGF inhibitors VEGF was found to be higher expressed in TNBC compared with
non-TNBC tumors, and that therapy with a recombinant humanized monoclonal
antibody targeting VEGF, bevacizumab, could cause tumor suppression (Yadav et al.,
2014). However, clinical trials were not conclusive. Although some trials showed
improved outcome in terms of ORR and PFS when adding bevacizumab to Paclitaxel
in first-line treatment for TNBC patients (Gray et al., 2009), or in addition with
docetaxel where it lead to an improvement in PFS (Miles et al., 2010), trials for the
use in second-line therapy for metastatic TNBC showed improved RR and PFS, but
not OS (http://meetinglibrary.asco.org/content/78329-102). Moreover, patients
receiving bevacizumab-based treatments in first-line treatment experienced high
16
toxicity, hence its approval has been withdrawn due to a modest risk-benefit ratio.
There is still one ongoing trial using bevacizumab in combination with an
anthracycline-taxane-based chemotherapy in TNBC patients (Gerber et al., 2013).
Although 40.1% patients showed pCR with improved DFS, only long-term studies
will show whether patients experience OS.
Tyrosine kinase inhibitors Other inhibitors targeting various kinases, such as c-Kit
and other tyrosine kinases are being validated. However, the effect of c-Kit inhibitors,
such as imatinib, in patients with TNBC and basal-like tumors is not clear as there are
problems with the specificity of the antibodies in immunohistochemical staining in
patient samples. Tyrosine kinases are also over-expressed in breast cancer metastatic
disease, however, the use of tyrosine kinase inhibitors proves to lead to more positive
outcomes in HER2/neu+ breast cancer patients (Ryan et al., 2008). Regardless,
tyrosine kinase inhibitors are still in clinical trials as monotherapy and combination
therapy.
1.1.3.3 Recurrence
Patients with basal-like and HER2/neu+ breast cancers respond well to neoadjuvant
chemotherapy composed of paclitaxel- and doxorubicin-containing chemotherapeutic
agents, as compared to patients with other subtypes of breast cancer, in terms of pCR
(Rouzier et al., 2005). Despite high response rates, many of these patients undergo
relapse. Indeed, a study found that when compared to non-TNBC patients, patients
harboring TNBC tumors treated with neoadjuvant chemotherapy showed higher pCR
rates, but decreased 3-year PFS and 3-year OS. Moreover, these patients suffered
increased visceral metastasis and shorter post recurrence survival (Liedtke et al.,
2008). Despite having decreased survival rates in the first 3 to 5 years after diagnosis
(Figure 1.4 (A)), patients with TNBC or basal-like tumors actually suffer less distant
relapse after this time, as compared to patients with other breast cancer subtypes
17
(Figure 1.4 (B)) (Foulkes et al., 2010, Tischkowitz et al., 2007). 10 years after
diagnosis, ER+ patients are more likely to suffer relapse than ER- patients.
Recurrence is often due to and accompanied by chemo-resistance to conventional
chemotherapeutic agents. Some cells survive chemotherapy and can survive long-
term and maintain cell viability and lead to recurrent tumors. We will discuss
mechanisms of chemo-resistance in later chapters in more detail.
Figure 1.4 Survival after a diagnosis
of breast cancer
A. Survival rate of 3744
patients according to
subtype. 639 women with
TNBC subtype were divided
into 2 groups: those with and
without basal markers
(cytokeratin 5, EGFR). (Data
from (Cheang et al., 2008)).
B. Hazard rates for distant
recurrence of TNBC and
non-TNBC patients. (Data
from (Dent et al., 2007).
18
1.2 Mitotic inhibitors
Mitotic inhibitors prevent cells from undergoing mitosis by disrupting microtubules,
which are the main components of the mitotic spindle. Microtubules are crucial for a
normal cell division as they bind to the kinetochores of sister chromatids and ensure
proper segregation and migration of chromosomes during anaphase.
Microtubules are made up of α- and β-tubulin dimers which bind to GTP on the (+)-
end of the microtubules. Once bound, the GTP hydrolyzes to GDP through contact
with the dimers. The stability of microtubules is dependent on whether the β-tubulin
dimer binds to a GDP or GTP on the microtubule. Dimers which are bound to GTP
lead to microtubule assembly, whereas dimers bound to GDP tend to fall apart. The
dynamic stability/instability is therefore dependent on the GTP/GDP bound to
microtubules. The binding of microtubules to kinetochores is closely monitored by
the Spindle Assembly Checkpoint (SAC), a cell cycle surveillance pathway, which
can forestall further cell cycle progression in case of any misalignment (Foley and
Kapoor, 2013). During the cell cycle, microtubules are assembled from the
centrosome and then bind to the chromosomes once they have aligned along the
metaphase plate. When the cell has passed through the SAC, the microtubules are
disassembled into single dimers again. Correct microtubule assembly and
disassembly is a requirement for proper cell division.
Mitotic inhibitors target microtubule dynamics, thereby disrupting cell division, and
can either be microtubule-stabilizing or –destabilizing, depending on their effects on
microtubule dynamics. Microtubule-destablizing agents inhibit microtubule
polymerization and inhibit the formation of the mitotic spindle at high concentrations.
Most of these agents bind to either of two domains on tubulin: the “vinca” domain
(vinca alkaloids such as vinblastine, vincristine, vinorelbine) or the “colchicines”
domain (colchicines and its analogs). Microtubule-stabilizing drugs, on the other
19
hand, stabilize microtubules at high concentrations and inhibit progression into the
anaphase. Such drugs include taxanes (Paclitaxel and Docetaxel), the epothilones, and
ixabepilone.
1.2.1 Paclitaxel (Taxol®)
Taxanes (complex diterpenes) are produced by plants of the genus Taxus (yews),
while some are now being synthesized artificially. The two main groups of taxanes
are Paclitaxel (Taxol®), which is derived from Taxus Brevifolia, and Docetaxel
(Taxotere), which is a semisynthetic analogue of Taxol (extracted from Taxus
Baccata (Fauzee, 2011). The empirical formula for Taxol is C47H51NO14, it has a
molecular weight of 853.9 and is insoluble in water (highly lipophilic).
The antitumor activity of Taxol was first confirmed in 1977 in a mouse model of
melanoma B16, and subsequently in mammary, lung and colon tumors. In 1984, the
National Cancer Institute (NCI) started a Phase I clinical trial of Taxol against various
cancer types. In 1992 it was FDA approved for ovarian cancer and shortly after for
use against breast cancer (Seidman, 1994). Since then, Taxol has been proven
effective against a number of solid tumors, including ovarian cancer, lung cancer,
breast cancer, colon cancer, sarcoma, multiple myeloma, AIDS-related Kaposi’s
sarcoma, and acute leukemias (Kumar et al., 2010, Sparano et al., 2008, Gill et al.,
1999, Rowinsky et al., 1989). Moreover, the antitumor effect of Taxol is increased
when it is used in combination with other chemotherapeutic drugs, such as
doxorubicin and various anthracyclines (Sledge et al., 2003, Kaufman, 1999), or
specific inhibitors, such as trastuzumab (Herceptin) in breast cancer (Klos et al.,
2003), or the Notch inhibitor in platinum-resistant ovarian cancer (Groeneweg et al.,
2014).
20
1.2.2 Mode of action
All drugs targeting microtubules bind to the β-tubulin subunit, and they all have
distinct binding sites, dependent on whether they stabilize or disrupt the microtubules.
Taxol binds to the β-subunit on three different peptides: amino acids 1-31 (Rao et al.,
1994), 217-231 (Rao et al., 1995), and 277-293 (Rao et al., 1999). It binds to the
microtubule (it cannot bind to single tubulin dimers such as colchicines and vinca
alkaloids) and enables microtubule polymerization in the absence of GTP. In
addition, it protects microtubules from destabilization (Orr et al., 2003). At low
concentrations, Taxol suppresses microtubule dynamics since it does not bind to all
available binding sites. At high concentrations, however, it reduces the number of
tubulin dimers available to assemble microtubules by stabilizing microtubule
polymers which leads to a mitotic arrest.
At low concentrations, where only a small part of the Taxol-binding sites are
occupied on the microtubules, it suppresses microtubule dynamics, whereas at high
concentrations, it increases polymer mass and induces microtubule bundle formation,
which has become a hallmark of Taxol binding (Schiff and Horwitz, 1980).
1.2.3 Mitotic arrest
Cancer cells are especially susceptible to treatment with Taxol as they undergo a
rapid cell cycle.
Microtubule stabilization leads to cell cycle arrest in the mitotic (M) phase. Cells
trapped in the M phase can undergo different fates, depending on the type and
concentration of drug used (Gascoigne and Taylor, 2009) and the strength of the SAC
signaling (Rieder and Maiato, 2004). After a mitotic arrest, cells can divide
unequally, undergo cell death during mitosis, or exit the mitotic arrest without
division. However, if they exit without division, they can arrest in the interphase or
21
undergo apoptosis, or they can pass through the cell cycle, which leads to tetraploidy.
The variation in response can also be seen among cells of the same origin. Gascoigne
et al. have summarized the different cell fates after treatment with anti-mitotic drugs
(Gascoigne and Taylor, 2009), as seen in Figure 1.5.
In a normal cell cycle, activation of the SAC will prevent Cyclin B1 degradation,
which is a requirement for M phase exit. Cyclin B1 is a protein involved in the
regulation of mitosis. Its level is highest during the mitotic phase and cells can only
progress through this phase after Cyclin B1 degradation. It was found, that in
mammalian cells treated with anti-mitotic drugs, despite SAC activation, Cyclin B1 is
slowly degraded which enables cells to exit mitosis, this is also called “mitotic
slippage” (Brito and Rieder, 2006). The different rates of Cyclin B1 degradation may
explain the variation in response to mitotic arrest. Mammalian cells that have an
activated SAC and do not exit mitosis will undergo a mitotic catastrophe. It has been
shown that targeting Cyclin B1 sensitizes breast cancer cells to Taxol (Androic et al.,
Figure 1.5 Cell fate in response to anti-mitotic drug treatment
Mitotic arrest after Paclitaxel treatment leads to chronic SAC activation and mitotic arrest, after
which they undergo one of several fates. (Derived from (Gascoigne and Taylor, 2009)).
22
2008), presumably because they will enter mitotic slippage which is dependent on
Cyclin B1 proteolysis (Brito et al., 2008).
1.2.4 Mitotic catastrophe
Until now, there is no clear cut definition of what a mitotic catastrophe really is. It
has been described by some as an aberrant form of mitosis (Ianzini and Mackey,
1997), or a delayed form of reproductive death (Waldman et al., 1996), but in most
cases it will eventually lead to cell death. Many events can lead to a mitotic
catastrophe such as DNA damage, ‘mitotic slippage”, or defects in mitosis, such as
mitotic arrest. Cell death following mitotic arrest can be through apoptosis or
necrosis.
Apoptosis following mitotic arrest requires activation of the SAC and mitotic
slippage (Tao et al., 2005). Although this relation is not entirely clear, it has been
found that BUB1 mediates caspase-independent cell death after mitotic arrest and
determines the cell’s fate (Niikura et al., 2007). BUB1 is bound to the kinetochores of
sister chromatids during mitosis (with a peak in the G2/M phase) to ensure correct
chromosome alignment and plays a major role in the formation and regulation of the
SAC. Together with BUB1, there are 13 other proteins that play a role in the SAC
(such as Aurora kinase B, MAD1, MAD2, BUBR1, BUB3 etc.). Aurora B is required
for the activation of the SAC and cells which have been depleted of Aurora B, fail to
undergo mitotic arrest, even in the presence of chromosome misalignment or
detachment from microtubules (Hauf et al., 2003). Taxol treatment causes a reduction
of inter-kinetochore tension due to microtubule stabilization and this leads to an
increase of BUBR1 and BUB1 at the kinetochores (Gorbsky and Ricketts, 1993) and,
moreover, Taxol-mediated mitotic arrest is dependent on Aurora B (Ditchfield et al.,
2003, Hauf et al., 2003) and the presence of MAD2 at kinetochores (Waters et al.,
23
1998). Survivin plays an important role in the maintenance of SAC and Aurora B
localization (Okada et al., 2004).
Several studies have shown that Taxol-induced apoptosis is caspase-dependent, as
shown in the human colon carcinoma cell line HCT116 (Llovera et al., 2012) and in
the Non-Small Cell Lung Cancer cell line NCI-H460 (Huisman et al., 2002). One
possible mechanism is the involvement of death receptors which activate Fas-
associated death domains and Caspase-8, leading to Bid cleavage and the disruption
of mitochondria. This is similar to many other DNA-damaging agents which induce a
caspase-dependent apoptotic response. It was found that wild-type p53, activated
after DNA damage, can inhibit mitotic catastrophe (Roninson et al., 2001) and many
tumor cells are deficient for wild-type p53, making them susceptible to mitotic
inhibitors (Gascoigne and Taylor, 2009).
24
1.3 Chemo-resistance
Chemo-resistance is one of the major challenges in the treatment of TNBC patients,
as chemotherapy is still the mainstay of treatment and recurrence rates with poor
outcome are high. Recurrence following chemotherapy can be attributed to the fact
that some cancer cells survive chemotherapy and are able to maintain viability and
survive long-term by undergoing cellular senescence and autophagy, as compared to
apoptosis. Cells undergoing senescence have exited the cell cycle and lost their
proliferative capacity, however, they maintain viability and metabolic activity (Saab,
2010). By exiting the cell cycle, these cells are protected from the cytotoxic effects of
chemotherapeutic agents. Moreover, it has been found that the senescence pathway
plays a role in breast carcinogenesis by promoting tumor progression through
stimulation of the neighboring cells with the senescence-associated secretory
phenotype (SASP) (Pare et al., 2013). It was found that senescent cells produce
lactate which is implicated in the tumor-stroma co-evolution. Breast cancer patients
with long-term survival were found to have lower levels of lactate in tumor biopsies
as compared with patients that died of cancer recurrence (Cao et al., 2012). On the
other hand, autophagy is a type II programmed cell death involved in cellular
homeostasis and protein and organelle recycling (Nishida et al., 2008). Its role in cell
survival/cell death is controversial: it can either be activated as part of the survival
pathway, or as part of the cell death process. Its role in cancer is even more
controversial where it has positive and negative effects on tumor cell survival (Notte
et al., 2011, Mathew et al., 2007). It is dependent on the type of tumor, tumor stage
and extent of the insult. Especially in TNBC cells compared to other breast cancer
cells, expression of the autophagy-related microtubule-associated protein, beclin-1,
light-chain (LC) 3A and LC3B, are high, with low expression in the stroma (Choi et
al., 2013). Moreover, LC3A is associated with tumor progression and poor outcome
in TNBC (Zhao et al., 2013). It was found that the effect of paclitaxel treatment can
25
be enhanced when inhibiting autophagy. Also, positive effects were seen after
treatment with an autophagy inhibitor combined with ER stress aggravators in TNBC
(Thomas et al., 2012).
1.3.1 Cancer stem cells
Chemo-resistance accounts for up to 90% of drug failures in patients with metastatic
cancer (Longley and Johnston, 2005). There are theories which suggest that a certain
population of cells survive chemotherapy, these are cells with properties similar to
stem cells and are responsible for tumor initiation and recurrence. These cells have
been called cancer stem cells (CSCs) (Dalerba et al., 2007a) as they have properties
similar to normal stem cells such as self-renewal and differentiation. The first
evidence of CSCs was reported in 1994 by Lapidot et al. in acute myelogenous
leukemia (AML) where they found cell surface markers by which they could select
for CSCs (CD34+ and CD38-) and, more importantly, these cells could give rise to
leukemic growth in severe combined immunodeficiency (SCID) mice (Lapidot et al.,
1994). CSCs of different origin express different cell surface markers and it was
discovered that breast cancer tumor-initiating CSCs are ESA+/CD44+/CD24-/low. Cells
selected based on these markers are capable of self renewal and differentiation (Al-
Hajj et al., 2003). Since then various CSCs populations in different solid tumors have
been identified based on cell surface markers such as CD133, EpCAM and CD90, as
well as aldehyde dehydrogenase (ALDH) activity (Yang et al., 2008, Dalerba et al.,
2007b, Singh et al., 2003).
CSCs have been linked to tumor expansion and relapse through their ability to escape
chemotherapy treatment. Many studies suggest that CSCs have adopted various
mechanisms of chemo-resistance and, although there is an overlap between chemo-
resistance and CSCs, not all cells which have acquired resistance are also classified as
CSCs.
26
1.3.2 Mechanisms of chemo-resistance
Two mechanisms are thought to contribute to chemo-resistance: instrinsic and
extrinsic resistance. Intrinsic resistance originates in the cancer cells itself which
gives them the capability to withstand chemotherapy (Wilson et al., 2006), whereas
extrinsic resistance is acquired by cells over a period of time via genetic and
epigenetic mutations in genes as a result of chemotherapy treatment (Gillet and
Gottesman, 2010). We will discuss some of the established mechanisms of chemo-
resistance in closer detail.
Multidrug resistance The main mechanism of resistance is a high expression of
membrane efflux pumps of the ATP binding cassette (ABC) family of transporters.
The most important family member involved in drug resistance is the ABC subfamily
B member 1 (ABCB1), also known as P-glycoprotein, or multidrug resistance protein
1 (MDR1), which is capable of actively pumping Hoechst dye. It is a glycosylated
membrane-associated enzyme which exports a number of different substrates. It has
been found to be expressed in many different tissues such as liver, kidney, intestines,
placenta and blood-brain-barrier, and hematologic and solid tumors. (Goldstein et al.,
1989). In the placenta and blood-brain-barrier its function is to protect from toxins,
whereas in other tissues it functions as an efflux pump for xenobiotics, toxins and
other drugs. In addition, ABCB1 transporters were found to be strongly expressed in
the neurons of the hippocampus formation, and expression was further up-regulated
upon induction of a status epilepticus in rat brains (Pardridge et al., 1997). In breast
cancer, it was found that EGF could increase phosphorylation of ABCB1 to enhance
multidrug resistance activity (Yang et al., 1997).
More importantly, ABCB1 is also expresses in certain stem cells. Chaudhary and
Robinson discovered that CD34+ hematopoietic stem cells show a high expression of
ABCB1(Chaudhary and Roninson, 1991). One study used ABCB1-deficient mouse
27
models to study the function of ABC transporters and suggested that efflux pumping
of xenobiotics which limits the success of chemotherapy may not be their only
function. One of their main functions is a protection from apoptosis of stem cells.
ABCB1 induces a stress-induced regeneration genetic program in normal tissues and
is activated in cancer cells due to cancer therapy (Israeli et al., 2005).
These results reveal an association between ABCB1 expression and stem cells, and,
in addition, between ABCB1 and cancer cells. This suggests that high ABCB1
expression could be linked to CSCs and also to chemo-resistance, as these
transporters can efflux vincas and taxances, effectively reducing their intracellular
drug concentration (Fojo and Menefee, 2005). In 1982, 6 years after the first link
between ABCB1 and chemo-resistance was established, many compounds were
developed which could inhibit ABCB1 and restore chemo-sensitivity in vitro. Despite
some promising results with cancer cells and tissue, they proved ineffective in clinical
trials due to many different reasons: low selectivity for ABCB1; poor potency; high
toxicity; and diverse interactions with other anticancer drugs (Crowley et al., 2010).
Another ABC transporter with importance in chemo-resistance is ABCG2. It was
found to be especially highly expressed in progenitor cells/reactive ductules in the
human liver, with a possible function of protection from cytotoxic agents (Borst and
Elferink, 2002). It is often associated with ABCB1 as it has very similar functions in
terms if efflux pumping of cytotoxic compounds, but in contrast to ABCB1 which
characterizes proliferating cells, ABCG2 is believed to be a marker for quiescent cells
(Ueda et al., 2005).
Aldehyde dehydrogenase Another functional marker of CSCs which confers
resistance to chemotherapeutics is Aldehyde dehydrogenase (ALDH). ALDH1 is a
cytosolic enzyme which oxidizes aldehydes and converts them into carboxylic acids
(Ikawa et al., 1983). There are 16 different ALDH isoforms and each isoforms has
28
specific functions in different types of cancer. ALDH1A3 has been found to be
significantly associated with distant metastasis, disease-free survival and overall
survival in breast cancer patients (Qiu et al., 2014). Since ALDH has been linked to
normal stem and progenitor cells, its role in CSCs has also been extensively studied.
Acute myeloid leukemic cells with elevated ALDH engraft better in NOD/SCID mice
compared to cells with low ALDH activity (Cheung et al., 2007). And as little as 20
breast cancer cells selected with an ALDH marker together with CD24-/CD44+
markers could form tumors (Ginestier et al., 2007).
In addition to its function in CSCs, ALDH plays a role in chemo-resistance.
Resistance to cyclophosphamide, a chemotherapeutic agent often used in first-line
treatment for TNBC patients, is accompanied by high levels of ALDH and could be
reversed by inhibition of ALDH in cyclophosphamide-resistant L1210 leukemic cell
lines (Hilton, 1984). It has since then been shown to be important in
cyclophosphamide-resistance in medulloblastoma (Friedman et al., 1992) and other
cancer systems, thereby confirming the importance of ALDH in chemo-resistance and
CSCs (Sreerama and Sladek, 1997). ALDH1A1 activity is also linked to gemcitabine-
resistance in human pancreatic adenocarcinoma. Ongoing studies with inhibitors
targeting ALDH activity or influencing ALDH expression can help to overcome
chemo-resistance in CSCs (Sullivan et al., 2010, Cortes-Dericks et al., 2014, Hellsten
et al., 2011). ALDH is an established marker for chemo-resistance, although its exact
function is not clear (Januchowski et al., 2013).
We will discuss mechanisms of resistance to microtubule-disrupting drugs more in
detail as this thesis is focusing on Paclitaxel-resistance.
29
1.3.3 Resistance to microtubule disrupting drugs
It is important to understand the specific mechanism of resistance to such agents in
order to overcome and develop new and more effective drugs. Resistance can be the
result of changes in the pharmacodynamics of these microtubule-disrupting agents
such as cellular efflux and metabolization, ineffective target interaction, and
resistance to apoptotic signals.
Alterations in microtubules Changes in the targets of microtubule-disrupting agents
contribute to resistance. Such changes can include changes in the conformation of
tubulin dimers/microtubules so that the drugs are unable to bind, and changes in the
GTPase activity needed for the synthesis of microtubules. Many proteins play a role
in microtubule dynamics and can contribute to resistance through alterations in their
expression levels, cellular localization, and post-translational modifications. These
include various microtubule-associated proteins (MAPs) (MAP1, MAP2, MAP4, tau,
STOP), and others like survivin which counteracts the effect of microtubule
destabilizing agents (Cheung et al., 2009), FHit which interacts with microtubules
and acts as a tumor suppressor (Chaudhuri et al., 1999) and stathmin which alters
drug binding and inhibits progression into the M-phase (Cheung et al., 2009). Low
expression of protein tau, which has the same tubulin binding site as Paclitaxel, is
associated with Paclitaxel-sensitivity, as seen in preclinical studies. Similarly, high
tau expression in ER+ breast cancer confers resistance to taxane-based chemotherapy
and sensitivity to hormone therapy (Smoter et al., 2011).
There are 13 different α- and β-tubulin isotypes involved in building of microtubules.
The composition of the microtubules and the types of tubulin involved play a role in
resistance. β-tubulin III has been shown to be associated with reduced taxane
sensitivity in several types of tumors including lung, breast and ovarian cancers (Seve
and Dumontet, 2008), and taxane-resistance has been attributed to increased levels of
30
βII and βV tubulins (Bhattacharya and Cabral, 2009, Haber et al., 1995).
Interestingly, resistance to taxanes is not the only role for β-tubulin III. It is also a
survival factor which can increase cancer progression independent of drug treatments
in lung cancer (McCarroll et al., 2010).
In addition to altered expression of tubulin, somatic mutations in β-tubulin also
contribute to taxane resistance. In paclitaxel-resistant ovarian cancer cell lines,
mutations at nucleotides 810 and 1092 of the HM40 isotype of β-tubulin lead to
impaired paclitaxel-driven polymerization (Giannakakou et al., 1997).
Resistance to apoptotic signaling Signaling downstream of microtubules also plays a
role in resistance. P53 is involved in a complex mechanism which was believed to
play a crucial role due to the fact that it can arrest the cell cycle and allow DNA
repair. It was observed that by inactivating p53, normal and human murine fibroblasts
could be sensitized to taxol (Wahl et al., 1996). However, it was later observed that
more often than not, mutant p53 lead to drug resistance to various agents due to a
disabled apoptotic machinery (Delia et al., 1996, Varna et al., 2009). In contrast, it
was later published that p53 is not involved in resistance to Paclitaxel or Docetaxel in
breast cancer (Noguchi, 2006). These conflicting results regarding the involvement of
p53 in resistance render it an ineffective marker for taxane resistance.
Apart from p53, other apoptotic regulators can influence taxane sensitivity. One such
effector is the pro-survival protein B-cell lymphoma-extra large (Bcl-XL). Using a
small molecule inhibitor targeting Bcl-XL, the cytotoxic effects of various
chemotherapeutic agents, including Paclitaxel, could be enhanced in a NSCLC cell
line (Shoemaker et al., 2006). In addition, certain microRNAs were also shown to
play a role. One such example is of miR-125b which could confer resistance to
Paclitaxel through suppression of the activity of the pro-apoptotic BAK1 in various
breast cancer cell lines (Zhou et al., 2010). Another microRNA with a role in
31
resistance is miR-148a, which sensitizes prostate cancer cell lines to Paclitaxel by
inhibiting the expression of mitogen and stress-activated protein kinase (MSK1)
(Fujita et al., 2010).
Resistance to other oncogenic signaling pathways Since chemo-resistance plays a
major role in the treatment of TNBC patients, many studies have been conducted to
unravel the importance of various pathways in conferring resistance. One such
pathway found to play a role in resistance to Taxol is the Hippo pathway. TAZ
(transcriptional co-activator with PDZ-binding motif) is a major component of the
Hippo-LATS pathway and has been found to be elevated in human breast cancer cells
and responsible for Taxol resistance (Lai et al., 2011). TAZ activates the
Cyr61/CTGF signaling pathway which modifies the Taxol response in breast cancer
cells. However, it is not clear how the activation of this pathway leads to increased
resistance.
32
1.4 Aurora Kinase Signaling
The Aurora Kinases are a family of evolutionary conserved serine/threonine kinases
that play an important role and regulate many processes during cell division. They
control the centrosome and nuclear cycles, they play crucial roles in chromosome
condensation, spindle dynamics, kinetochore-microtubule interactions, chromosome
orientation, and establishment of the metaphase plate. There are three Aurora
Kinases, Aurora A, B and C, that are very similar especially in the carboxy-terminal
catalytic domain, but they differ in the length and sequence of their amino-terminal
(Figure 1.6). All three kinases have an activating T-loop, a destruction box (D-box)
which leads to its destruction at the end of mitosis, and in addition Aurora A requires
phosphorylation of its D-box activating domain (A-box) for its degradation.
1.4.1 Aurora Kinases in cell cycle
Interestingly, the three Aurora kinases have distinct functions and subcellular
locations despite their similarities. While Aurora A associates with the spindle poles
during cell division regulating mitotic entry, centrosome maturation and separation,
and spindle pole polarity, Aurora B is an important member of the Chromosomal
Figure 1.6 Schematic representation of human Aurora A, B and C
The numbers indicate their size in terms of amino acids and their percentage of sequence
identities. Also shown are the kinase domain (green), the activating T-loops (yellow), the
destruction box (D-box, blue) and the D-box activating domain (DAD, red). (Figure adopted
from Keen N. et al (Keen and Taylor, 2004)).
33
Passenger Complex (CPC) together with INCENP, Survivin and Borealin/DasraB. It
localizes to the chromosomal centromeres during mitotic entry but will relocate to the
microtubules at the spindle equator during anaphase. When the cell undergoes
cytokineses, Aurora B accumulates at the spindle midzone and the cell cortex at the
cleavage furrow, and subsequently concentrates at the midbody. In addition, Aurora
B plays a crucial role for correct chromosome alignment and segregation, it regulates
the kinetochore function. Moreover, it is required for correct spindle checkpoint
function and cytokinesis.
Equally important for cell division, is Polo-like kinase 1 (PLK1), one of the five
members of the Polo-like kinase family. Inhibitors of PLK1 lead to mitotic arrest with
a monopolar or disorganized spindle, and the cells will eventually undergo mitotic
arrest (Steegmaier et al., 2007). All Polo-like kinases possess a highly conserved
polo-box domain (PBD) in the carboxy-terminal end, which can serve as a docking-
motif that brings the kinase into close proximity of its substrates (Park et al., 2010).
PLK1 can bind its substrates in a phospho-independent manner (Archambault et al.,
2008), or after it has undergone a priming phosphorylation by Cyclin-dependent
kinase 1 (CDK1) or PLK1 itself (Elia et al., 2003). Once bound, the substrates will
recruit PLK1 to the centrosome, kinetochore or the spindle midzone. It is not
completely clear how it regulates spindle formation, but it is well known that PLK1
plays a role in centrosome maturation and stable kinetochore-microtubule
attachments (Archambault and Glover, 2009). The effect of PLK1 inhibition is
similar to inhibition of the Aurora kinases, suggesting that they might co-regulate
certain mitotic processes. PLK1 and Aurora A play a crucial role in mitotic entry.
They co-localize at the centrosomes, but then PLK1 also localizes to kinetochores in
the prometa- and metaphase. Aurora B, however, is important in the prophase where
it localizes to the arms of the chromosome and the inner centromere, whereas in the
prometa- and metaphases it localizes solely to the inner centromeres. Aurora B co-
34
localizes with PLK1 on the central spindle, the cortex and midbody in the anaphase
and telophase where they regulate cytokinesis. Aurora A is degraded in the anaphase
and telophase, although a small portion remains at the central spindle and midbody,
but its function is unclear.
Figure 1.7 Localization of
Aurora kinase A and B
together with PLK1 in the
dividing cell
Aurora kinase B is localized
to the chromosome arms and
inner centromere in the
prophase, whereas in the
prometaphase and metaphase
it is only found at the inner
centromere. In anaphase and
telophase it co-localizes with
PLK1 on the central spindle,
cortex and midbody where it
regulates cytokinesis. (Figure
adopted from Lens S.M.A. et
al.. (Lens et al., 2010)).
35
1.4.2 The Spindle Assembly Checkpoint
Aurora B, along with the CPC, stabilizes the mitotic spindle (Gassmann et al., 2004)
by phosphorylating Stathmin/Op18 and the kinsein-13 microtubule-destabilizing
protein (MCAK), two proteins involved in the chromatin-driven spindle assembly
pathway (Ohi et al., 2004, Gadea and Ruderman, 2006).
The main components of the spindle assembly checkpoint (SAC) are MAD1, MAD2,
BUBR1, BUB1, BUB3, MPS1, and Aurora B, and it is activated during cell division
where it inhibits anaphase as long as the kinetochores are not attached to the
microtubules. Only when all kinetochores are stably bound, the SAC is satisfied and
inhibition of the anaphase is alleviated. Three of the SAC proteins, MAD2, BUBR1
and BUB3, as well as CDC20, make up the mitotic checkpoint complex (MCC),
which binds the ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C),
inhibiting its ubiquitin ligase ability on securin and cyclin B (Tang et al., 2001, Fang
et al., 1998). Securin is the inhibitor of a protease called separase which is required to
cleave the cohesion complex which binds the sister chromatids, while cyclin B
activates CDK1, inhibiting exit from mitotsis. CDC20 is a co-factor of APC/C and by
keeping it in check, the SAC can prolong the prometaphase until all the kinetochores
are attached to the microtubules and are bi-oriented along the metaphase plate.
Aurora B plays an important role in the SAC as it regulates kinetochore assembly.
Kinetochores are complex structures comprising of at least 80 different proteins at the
centromere of each sister chromatid (Cheeseman and Desai, 2008, Santaguida and
Musacchio, 2009), and they link the chromosomes to the microtubule polymers of the
mitotic spindle. The inner kinetochore is tightly associated with the centrosome DNA
throughout the cell cycle, whereas the outer kinetochore, which interacts with the
microtubules, is a dynamic structure only present during cell division. For correct
chromosome segregation, each sister chromatid has to attach to microtubules
36
generated from opposite spindle poles through its kinetochores. This is called a
amphitelic configuration, or bi-orientation. If the kinetochores are not correctly
attached, they can have a monotelic configuration (only one of the chromatids is
attached to microtubules), syntelic configuration (both chromatids are attached to the
same microtubule generated from one spindle pole), or a merotelic configuration (at
least one chromatid is attached to both microtubules generated by opposite spindle
poles). The role of Aurora B is to ensure correct kinetochore-microtubule attachments
by destabilizing incorrect attachments. Correct amphitelic attachments are stabilized
by tension through the mitotic spindles (Nicklas and Koch, 1969), whereby Aurora B
is an important tension sensor at centromeres and kinetochores (Biggins and Murray,
2001, Tanaka et al., 2002). Inhibition of Aurora B using small molecule inhibitors or
inhibitory antibodies has been shown to stabilize incorrect attachments to a single
spindle pole (Hauf et al., 2003). More recently, a “spatial separation” model has been
proposed which explains how Aurora B is able to sense tension. In this model,
tension sensing depends on the localization of Aurora B. When bi-oriented
kinetochores are separated, Aurora B at the inner centromere is separated from its
outer kinetochore substrates (Tanaka et al., 2002).
Several mechanisms contribute to the deactivation of the SAC. During the process of
“stripping”, MAD1 and MAD2 are removed from the kinetochores and this seems to
be a crucial step in deactivation (Maldonado and Kapoor, 2011). This process is
mediated by the minus-end microtubule motor dynein (Howell et al., 2001). Also
important is the disassembly of the inhibitory complexes in order to free Cdc20 so
that it can activate APC/C, thereby allowing it to ubiquinitylate securin and cyclin B,
allowing entry into anaphase and mitotic exit.
37
1.4.3 Paclitaxel-induced activation of the SAC
Paclitaxel stabilizes microtubules, thereby reducing the tension on the kinetochores
from mitotic spindles in bi-oriented sister chromatids. This process turns on the SAC
thereby stopping the progression of the cell cycle. Moreover, it was found that MAD2
is required to maintain taxol-mediated mitotic arrest (Shannon et al., 2002). CENP-E
is a microtubule plus-end-directed motor that is crucial for chromosome alignment
and when CENP-E is absent, unattached kinetochores are unable to sustain SAC
activity. However, the addition of taxol disrupts tension and can override CENP-E
absence and activate SAC activity (Wood et al., 2008). However, siRNA knock-down
experiments revealed that the absence of Aurora B compromised checkpoint arrest in
the Paclitaxel-treated colon cancer cell line DLD1 (Ditchfield et al., 2003). Moreover,
Aurora B knock-down also led to absence of microtubule-kinetochore interactions,
suggesting that Aurora B plays role in correct kinetochore attachments. In addition to
its role in destabilizing improper microtubule attachments, Aurora B also plays a
direct role in the SAC.
1.4.4 Aurora Kinases in Cancer
It was discovered that Aurora A and Aurora B are over-expressed in primary breast
cancer samples (Sen et al., 1997) and colon tumor samples (Warner et al., 2003),
suggesting that they are associated with tumorigenesis. Aurora A is most consistently
associated with cancer and its role is well studied. It has been found to be over-
expressed in many different kinds of cancer, such as colon, breast, bladder, liver,
gastric and pancreatic cancer (Sakakura et al., 2001, Goepfert et al., 2002) and its
over-expression is significantly associated with high grade tumors and poor prognosis
(Jeng et al., 2004). There is a correlation between Aurora A over-expression and
aneuploidy in gastric cancer. Aneuploidy is associated with poor outcome in a
number of malignancies, and in gastric and papillary thyroid cancer it is a metastatic
38
marker (Sasaki et al., 1999, Sturgis et al., 1999). Aurora A over-expression is also
associated with centrosome amplification which leads to bipolar mitotic spindle
defects, chromosomal segregation deficiency and aneuploidy. Centrosomal
abnormalities have been shown to arise in early stages of tumor development and
expand as the tumor progresses (Pihan et al., 2001), especially in brain, breast, lung,
colon and prostate cancer (Pihan et al., 1998, Lingle and Salisbury, 1999). However,
so far no direct link has been found between Aurora A over-expression and
centrosomal abnormalities, although they are often associated with each other.
Aurora A has also been reported to play an important role in transformation. It
phosphorylates and regulates the breast cancer-associated gene product BRCA1
(Ouchi et al., 2004), and it can regulate telomerase activity through c-Myc in ovarian
and breast epithelial carcinomas (Yang et al., 2004). Moreover, it interacts with p53
at multiple levels. It directly phosphorylates p53 at Ser315 mediating degradation of
p53 by MDM2 in cancer cells, and at Ser215 thereby inactivating its transcriptional
activity (Katayama et al., 2004, Liu et al., 2004). TP53 is the most common gene
mutated in a number of human cancers, whereby the most common mutations lead to
a complete loss or inactivity of the p53 protein (Kandoth et al., 2013). This shows the
importance of Aurora A in cancer, especially in transforming processes.
Aurora B has been found to be over-expressed in cancers such as breast, kidney,
colorectal, prostate and lung cancer, and high levels of Aurora B lead to multi-
nucleation and polyploidy in human cells (Tatsuka et al., 1998). Moreover, it was
found that high levels of Aurora B correlate with late stages of colorectal cancer
(Katayama et al., 1999). It may also play a role in carcinogenesis as high levels lead
to chromosome lagging in the metaphase, errors in chromosome segregations and
cytokinesis (Ota et al., 2002). Although Aurora B alone is not able to transform cells,
it potentiates H-Ras(G12V)-induced transformation (Kanda et al., 2005). During
mitosis, half of the Aurora B forms a complex with INCENP, Survivin and Borealin,
39
whereas the other half forms a complex with INCENP, but it is not clear which of
these complexes is involved in the potentiation of oncogenic Ras.
Aurora C, however, is a chromosome messenger protein and only expressed in the
testis and not somatic cells. It has been found to be up-regulated in cancer cell lines
such as HepG2, HuH7, MDA-MD-453 and HeLa cells, although its role is unclear
(Kimura et al., 1999).
40
1.5 Protein Kinase C Signaling
Protein Kinase C (PKC) has become an attractive target for drug discovery in the past
20 years as it functions as the receptor for the tumor promoter phorbol ester
(Castagna et al., 1982). It has gained even more interest since it was discovered to
play a role, not only in cancer, but also in other diseases such as diabetes, ischemic
heart disease and hear failure, autoimmune diseases, Parkinson’s disease,
Alzheimer’s and many other diseases.
1.5.1 Structure and isoforms of PKCs
PKC is a family of at least 12 highly related but distinct serine/threonine kinases
which play a role in signaling pathways involved in proliferation, differentiation,
angiogenesis and apoptosis, as well as transcription, translation, and cell-cell contact.
The different isoforms can be grouped into 3 classes depending on the number of
conserved regions (C1-C4) and the number of variable regions (V1-V5) (Khalil,
2010). The C1 region contains cystein-rich zinc finger-like motifs and the recognition
site for Diacylglycerol (DAG), phosphatidylserine and phorbol ester. The C2 region
of some isoforms contains the binding site for Ca2+, and the C3 and C4 regions
constitute the ATP- and substrate-binding lobes (Figure 1.8). Due to the PKC
isoforms containing different conserved and variable regions, they are activated
through different processes. The conventional PKC isoforms (α, βI, βII and γ) contain
all conserved regions (C1-C4) and all variable regions (V1-V5) and can be activated
by DAG, phoshatidylserine, phorbol ester and Ca2+. The novel PKCs (δ, ε, η and θ)
lack the C2 region and are therefore not affected by Ca2+, and the atypical PKCs (ζ
and λ/ι) have only one cysteine-rich zinc finger-like motif and can only be activated
by phosphatidylserine. Phospholipase C (PLC) is an enzyme which, when activated
by G-protein coupled receptors (GPCR), can generate DAG and 1,4,5-triphosphate
(IP3) by hydrolyzing phosphatidylinositol-4,5-biphosphate (PIP2). IP3 then binds to
41
Ca2+positive channels on the endoplasmic reticulum which release Ca2+ into the
cytoplasm. PKC can bind to Ca2+, translocate to the cell membrane and interact with
DAG. A fourth class of PKCs, the PKN subfamily, is distinctly different to the
previous 3 classes in that it has 3 tandem coiled motifs, known as HR1 domains,
which can bind to RhoA and play a role in Rho GTPase regulation (Flynn et al.,
1998).
Due to their highly homologous kinase domains and variable regulatory domains, the
PKC isoforms can activate a number of different cellular signaling pathways.
Therefore, targeting this pathway remains challenging and the question to ask should
not be “what” activity to target, but “where”.
1.5.2 Activation of PKC isoforms and downstream signaling
PKCs contain an autoinhibitory pseudosubstrate. When PKC is inactive, it is folded
in such a way that its kinase region is bound to its own pseudosubstrate. Upon
phosphorylation of serine/threonine residues by any of its activators, PKC can unfold
and bind its true substrate. Stimulation of PLC increases the DAG levels at the
Figure 1.8 Schematic sequence of Protein Kinase C (PKC) isozymes indicating the domain
structure of the PKC subfamilies and their activators
(Figure adopted from Cosentino-Gomes D. et al. (Cosentino-Gomes et al., 2012)).
42
plasma membrane, followed by the re-localization and activation of various PKC
isoforms. There are some requirements for the activation of PKC isoforms: regulation
of their intrinsic kinase domain activity through a priming phosphorylation of the
activation loop by PDK1; and phosphorylation (or charged residues) at the
hydrophobic sites within the C-terminal tail through mTORC2 (Facchinetti et al.,
2008, Ikenoue et al., 2008). Once activated, the C-terminal tail will fold back and
occupy the hydrophobic pocket to stabilize the active conformation. A recently
identified player in the activation of cPKCs and nPKCs is heat shock protein-90
(HSP90) (Gould et al., 2009). In order for the PKCs to mature so that they can be
phosphorylated by PDK1 and mTORC2, HSP90 and the co-chaperone Cdc37 need to
bind to a molecular clamp in the kinase domain formed by a conserved PXXP motif
in the carboxyl-terminal tail. If this binding is not possible, PKCs are unable to be
phosphorylated and will be degraded. Once HSP90 and Cdc37 have bound, PKCs can
be phosphorylated by PDK1, a step necessary for the processing of PKCs to their
fully phosphorylated forms.
When PKC binds to the membrane, it undergoes a conformational change to expose
its kinase binding region (Newton, 2003) and can activate downstream signaling
pathways, such as the MEK/ERK and PI3K/Akt (Cai et al., 1997, Balendran et al.,
2000). Different PKC isoforms show different subcellular localization depending on
the tissue type. The Receptors for Inactivated Kinases (RICKs) and the Receptors for
Activated Kinases (RACKs) bind PKC in an isozyme-specific manner and are
responsible for the translocation of PKC to the different cellular compartments
(Mochly-Rosen and Gordon, 1998). Phorbol esters can activate PKC by promoting its
translocation to the cell membrane, and this effect can be inhibited by
glycoglycerolipid analogues, which block its translocation and activation of the
downstream signaling pathways MAPK and FAK, which are involved in adhesion,
migration and proliferation (Colombo et al., 2011). Further downstream signaling of
43
PKC includes the modulation of membrane structure events, transcription regulation,
receptor desensitization, and immune response (Nishizuka, 1992).
It has been shown that the PKC isoforms α, βI and γ can phosphorylate GSK3β at
Serine 9 and that this renders GSK3β inactive (Goode et al., 1992). This in turn
inhibits the phosphorylation of c-Jun at its inhibitory phosphorylation site, thereby
allowing it to interact with its DNA binding site. GSK3β therefore acts as a cytosolic
bridge between PKC and c-Jun.
1.5.3 PKCs in Cancer
The discovery of the PKC activator, phorbol ester, has led to the belief that the
activation of PKC can promote carcinogen-induced tumorigenesis (Griner and
Kazanietz, 2007). It has functions related to cell survival, proliferation, invasion,
migration, angiogenesis, apoptosis and drug resistance. However, targeting PKC has
been unsuccessful so far as it is an unpredictable target. In some cancers it can act as
an oncogene, whereas it can have tumor suppressing functions in others. PKC-δ, for
example, can promote tumor progression in lung and pancreatic cancers under certain
conditions (Symonds et al., 2011), whereas it can act as a tumor suppressor in many
cell types (Reyland, 2007). In colon cancer cell lines, both loss and over-expression
of PKC-ζ could decrease tumorigenocity (Ma et al., 2013, Luna-Ulloa et al., 2011);
and PKC-α can induce (Wu et al., 2013) and suppress (Gwak et al., 2009)
proliferation of colon cancer cells. Moreover, PKCs can promote apoptosis through
different pathways. These include secretion of TNFα and TRAIL in prostate cancer
cells by PKC-δ which triggers an autocrine apoptotic loop; induction of cytochrome c
release and activation of Caspase 3 by PKC-δ in colon and prostate tumor-derived
cell lines. On the other hand, PKC-δ can also confer resistance to apoptotic signals by
activation of the Akt pathway and modulation of NF-κB-dependent gene expression
(Diaz Bessone et al., 2011). PKCs not only affect apoptosis in cancer but also
44
migration, invasion and tumor promotion. Over-expression of PKC-α can lead to
increased anchorage-independent growth, tumorigenicity and metastasis; over-
expression of PKC-βII enhances invasiveness through Ras and MEK signaling
(Zhang et al., 2004). It has been shown that PKC is able to phosphorylate Raf-1, but
this can only stimulate its autophosphorylation, not its activity towards MEK-1
(Macdonald et al., 1993). However, some studies on prostate cancer have found that
phosphorylation of PKC could inhibit the Raf Kinase Inhibitory Protein (RKIP)
thereby increasing Raf-1 activity. Activation of this pathway is linked with metastasis
in prostate cancer (Keller et al., 2004).
Recently, Gong et al. found opposing roles for conventional and novel PKC isoforms
in the activation and regulation of the Hippo-YAP pathway (Gong et al., 2015). This
pathway plays an important role in development by modulating cell number, cell
death and cell differentiation, and deregulation can be the cause for many types of
cancer (Yu and Guan, 2013). Important players of the Hippo pathway are the MST1/2
and LATS1/2 kinases, whereby LATS1/2 is phosphorylated by MST1/2. LATS1/2
can then phosphorylate and inhibit the transcriptional co-activators YAP and TAZ.
Moreover, it has been discovered that extracellular signals act through GPCRs to
regulate the Hippo-YAP pathway, and PKC is one of the main downstream actors of
GPCRs, linking the Hippo and PKC pathways. Interestingly, they found cell type-
specific roles for PKC in regulating the Hippo pathway. While the cPKCs can
dephosphorylate and activate YAP, the nPKCs phosphorylate YAP, thereby
inhibiting it. These effects occur through the activation/inhibition of LATS by the
different isotypes.
The diverse effects of PKC signaling on cancer cells could be the reason why PKC
inhibitors have been ineffective in clinical trials. Zhang et al. recently reported the
results of a meta-analysis of controlled trials using PKC inhibitors combined with
chemotherapy or chemotherapy alone in patients with non-small cell lung cancer
45
(Zhang et al., 2015). They found that PKC inhibitors even decreased the response
rates (RR 0.79) and disease control rates (RR 0.90) with no significant difference in
progression free survival and overall survival. This raises the question of whether loss
or over-expression of PKC isozymes can promote carcinogenesis and promote tumor
progression. Although phorbol esters are tumor promoters and considered PKC
activators, it is well known that chronic treatment of cells with Phorbol esters can
deplete PKC isozymes, rather than lead to hyperactivation of the PKC signaling
pathway. This might explain the unexpected results of clinical trials.
1.5.4 PKC in Breast Cancer
Various PKC isoforms are involved in normal mammary development, such as in the
control and activation of mitogenic/apoptotic signaling. Their expression and
localization are modulated during mammary gland development and differentiation.
Moreover, an over-expression of PKC isoforms has been reported in breast tumors
and breast cancer cell lines (Jarzabek et al., 2002). PKC-α, for example, has been
found by some to be over-expressed in human breast cancer cells and tumor samples
(Lahn et al., 2004), and others have found it to be down-regulated (Kerfoot et al.,
2004). Moreover, PKC-α was found to play a role in breast CSCs as its expression
positively correlates with aggressive TNBC, and inhibiting PKC-α could specifically
target CSCs with little effect on non-CSCs (Tam et al., 2013). Furthermore, PKC-α
was shown to suppress c-Jun phosphorylation in ER+ breast cancer cells, leading to a
down-regulation of ER-α expression. It is, therefore, a potential therapeutic target for
treating ER+ Tamoxifen-resistant breast cancer (Kim et al., 2014).
1.5.4. PKC-β
PKC-β has two major splice variants: PKC-βI and PKC-βII. Although it has been
found that both isoforms have slightly different functions, it is complicated to
46
interpret their roles since many early studies did not differentiate between the
different splice variants.
PKC-β has gained importance in breast cancer since the discovery of its role in
angiogenesis, and breast cancer tumorigenesis in rodent models and humans.
Especially in TNBC, the PKC-β inhibitor, LY379196, can inhibit growth of various
TNBC cell lines in vitro (Li and Weinstein, 2006). Although PKC-β drives tumor
growth and proliferation of cancer cells, over-expression can impair the tumorigenic
and metastatic potential of tumor-derived murine mammary cell lines, suggesting an
inhibitory role for PKC-β (Grossoni et al., 2009). In addition to growth inhibition,
over-expression could also revert a transformed phenotype by leading to re-
expression of fibronectin and extracellular matrix glycoproteins.
A recent study analyzed 8% of PKC mutations identified in human cancers and found
that the majority of these mutations are loss-of-function mutations, rather than gain-
of-function mutations (Antal et al., 2015). Moreover, they found that by correcting a
loss-of-function in PKC-β using Clustered Regularly-Interspaced Short Palindromic
Repeats (CRISPR) they could reduce tumor growth in a xenograft model of a patient-
derived colon cancer cell line. This study suggests that PKC isoforms act as tumor
suppressors rather than oncogenes, and that clinical studies are inconclusive as they
are focusing on diminishing PKC, rather than attempting to restore PKC function in
patients.
Despite these conflicting results, PKC-β inhibitors have also shown positive results in
breast cancer patients in preclinical and clinical studies. The potent PKC-β inhibitor,
Enzastaurin, could inhibit tumor growth and angiogenesis in human tumor
xenografts. Likewise, Phase II clinical trials involving Enzastaurin in patients with
high-grade gliomas and lymphomas have shown promising results, and prove the
47
safety for this inhibitor in terms of effectiveness and toxicity (Sledge and Gokmen-
Polar, 2006).
In this review we will focus mainly on the PKC-β isoform as this thesis explores the
effect of the PKC-β inhibitor Enzastaurin on Paclitaxl-sensitivity in breast cancer
cells.
1.5.5. The role of PKC-β in cell cycle regulation
The fact that PKCs are downstream of various growth factors led to the idea that they
have an effect on mitotic signals. Indeed, PKCs can influence cell cycle, but
depending on various cell internal and external factors, they can have positive or
negative effects on cell cycle progression (Black, 2010). The effect of the PKCs is
dependent on the timing and duration of PKC activation, the specific PKC isoform
involved, the cellular context, cell type, and the signaling environment. PKCs can
influence the activation of various cell cycle regulatory proteins, such as cyclins,
cyclin-dependent kinases (CDKs), and CDK inhibitors, whereby p21Waf/Cip1 and cyclin
D1 seem to be the key targets.
The role of PKC-β in cell cycle is largely stimulatory. It has been shown to have
effects on the G1 phase through phosophorylation and stimulation of CAK activity
(Acevedo-Duncan et al., 2002), promotion of Rb phosphorylation (Suzuma et al.,
2002), and enhancement of cyclin D1 expression in breast cancer cells (Li and
Weinstein, 2006). It is also critical for nuclear lamina disassembly during the G2/M
transition by phosphorylating lamin B in K562 erythroleukemia cells (Thompson and
Fields, 1996). Most importantly, PKC-β has been shown to regulate microtubule
function. It was found that PKC can phosphorylate the microtubule-associated
protein, tau, at Serine8 in peptide 369 (Correas et al., 1992). Tau proteins stabilize
microtubules and promote tubulin assembly, however, both of these functions are
reduced when it is phosphorylated by PKC at its tubulin binding domain. Further
48
phosphorylation by PKC on other Serine phosphorylation sites may even further
inhibit its tubulin binding capacity.
49
1.6 Aims and objectives of the study
One of the major complications in the treatment of TNBC patients is the high rate of
relapse after an initial positive response to a combination of different types of
chemotherapeutic agents. Many patients suffer from residual disease and the relapse
rate is high. A retrospective analysis of 269 TNBC patients found that the 5-year
disease-free survival was only 68.2% with most patients suffering recurrent disease in
the first 3 years following diagnosis (Ovcaricek et al., 2011). Once the patients
present with metastatic or relapsed disease, the survival time rarely exceeds 1 year.
The reason for this is that relapsed disease is often resistant to chemotherapy, and
combined with the lack of effective targeted therapies, the treatment options are
limited.
Our aim was therefore to generate a model of Paclitaxel-resistance in TNBC which
mimics the situation in a patient who develops chemo-resistant recurrent disease after
chemotherapy treatment. Applying a top-down approach to our model we then sought
to identify pathways which play a crucial role in resistance and are more highly
activated in resistant compared to parental cell lines. At the same time we aimed to
discover compounds that can revert resistance and sensitize resistant cell lines to
Paclitaxel which can then be translated into a clinical setting.
50
CHAPTER 2: MATERIALS AND METHODS
51
2.1. Cell culture and treatments
MDA-MB231 cell line was obtained from American Type Culture Collection ATCC
(Manassas, VA) and SUM159PT was obtained from Asterand Bioscience
(Hertfordshire, UK). MDA-MB231 and its resistant derivatives were cultured in
Dulbecco’s modified Eagle’s medium (DMEM) (Gibco®) supplemented with 10%
fetal bovine serum (FBS) and 5000U/mL penicillin/streptomycin (Invitrogen).
SUM159PT and its resistant derivatives were cultured in Ham’s F-12 Nutrient Mix
medium with L-Glutamine (Gibco®) and supplemented with 5% FBS, 10mM HEPES
(Gibco®), 5ng/ml Insulin, 1μg/ml hydrocortisone and 5000U/mL
penicillin/streptomycin. All cell lines were maintained at 37°C with 5% CO2 in a
humidified atmosphere. In addition, the resistant cell lines were maintained in
different concentrations of Paclitaxel: 500nM, 1uM, 50nM and 75nM for SUM159PT
500nM-TR and 1uM-TR, and MB231 50nM-TR and 75nM-TR, respectively. For all
cell-based assays, the cells were seeded without Paclitaxel and treatment was started
24 hours later.
2.2. Cryopreservation of cell lines
Cell lines were kept long-term in a liquid nitrogen tank (at -196°C) and short-term
(~6 months) at -80°C. To recover cell lines, they were thawed at 37°C and neutralized
with 8ml complete media. They were then spun down at 1000rpm for 3 mins and the
cell pellet was resuspended in 10ml complete medium and seeded in a 75cm2 flask.
Cells were grown and allowed to stabilize for 1 week before they were used for
experiments. Cells were used for approximately 25 passages, after which they were
discarded and new vials of frozen cells were recovered. To freeze down cells, they
were trypsinized, neutralized with complete medium and spun down at 1000rpm for 3
mins. Cells from one 150cm2 flask (80% confluency) were then resuspended in 5ml
freezing medium (90% FBS and 10% DMSO) and 1ml aliquots were transferred into
52
cryovials. For the freezing process, cryovials were placed into a Mr. Frosty cylinder
(NALGENE) which was filled with 99% isopropanol prior to transferring it into the -
80°C freezer. After one week, the cells were transferred to the liquid nitrogen tank.
2.3. Generation of Paclitaxel-resistant cell lines
Two TNBC cell lines were used to generate Paclitaxel-resistant derivatives,
SUM159PT and MDA-MB231. Prior to treatment, the EC50 to Paclitaxel was
measured for both cell lines. Cells were seeded in 6-well plates at a concentration of
0.5x105 cells/ml, 2ml per well and the treatment was started the next day at half the
concentration of the EC50. Once the surviving cells had recovered and started
proliferating again, the cells were passaged into a new plate and the Paclitaxel
concentration was doubled. This cycle continued until the final Paclitaxel
concentrations were achieved for all four resistant cell lines (Figure 1A). For each of
the two cell lines, 2 resistant derivatives were generated: SUM159PT 500nM-TR and
1uM-TR, and MDA-MB231 50nM-TR and 75nM-TR, growing in 500nM, 1μM,
50nM and 75nM Paclitaxel, respectively. The resistant cell lines were constantly
maintained in medium supplemented with Paclitaxel.
2.4. Transfection of small interfering RNA
siRNA transfection of cell lines was conducted using Lipofectamine RNAiMAX
(Invitrogen) according to the manufacturer’s protocol. Target-specific siRNA and
non-targeting control siRNA were purchased from Sigma Aldrich. Two target
sequences were used to knock-down AURKB: 1. siAurB (Pre) is pre-designed by
Sigma Aldrich (SASI_Hs01_00076963); 2. siAurB is custom designed according to
the sequence used by Ditchfield et al. (Ditchfield et al., 2003): 5’-
AACGCGGCACUUCACAAUUGA-3’. Non-targeting control used is MISSION®
siRNA Universal Negative Control #1 (SIC001) (Sigma Aldrich). For siRNA
transfection, 4μL siRNA (stock concentration 10μM) and 4μL RNAiMAX were
53
diluted in 100μL Opti-MEM medium (Invitrogen). After 5 minutes of incubation,
both solutions were combined and incubated for another 20 minutes at room
temperature. Before adding the transfection mixture onto the cells which were seeded
in 6-well plates 18 hours earlier (1x105 cells/well), medium was changed to 800μL
fresh complete medium. The end concentration of the siRNA used is 40nM. 24 hours
later, the cells were then trypsinized and re-seeded for further down-stream assays.
2.5. EC50 calculation
To measure the resistance of cell lines to various chemotherapeutic drugs, the half
maximal effective concentration (EC50) of the drugs was calculated in those cell lines.
This value gives the concentration of a drug, which induces a response halfway
between the baseline and the maximum. It is used to measure the potency of a drug
and thereby resembles the sensitivity/resistance of cells to the drug. To measure the
EC50 of various drugs in different cell lines, a cell viability assay was used (see Cell
viability assay). The cells were treated 24 hours after seeding with 9 different drug
concentrations and a control. As the cell lines had different sensitivity to the chemo-
drugs, different concentrations were used: 1) Paclitaxel and Vincristine: working
concentrations of 10μM, 5μM, 1μM, 500nM, 100nM, 10nM, 1nM, 0.1nM, 0.01nM;
2) Adriamycin: 50μM, 10μM, 5μM, 1μM, 100nM, 10nM, 1nM, 0.1nM, 0.01nM. Cell
viability was measured after 4 days treatment and GraphPad Prism was used to plot a
sigmoidal-dose response curve (three-parameter logistic equation) to measure the
EC50 concentration of the chemo-drugs.
2.6. Cell viability assay
To measure the cell viability of cells in response to treatment, 1250 cells were seeded
per well in a volume of 100μl in a white, opaque 96-well plate. After 24 hours, the
media was changed to fresh media containing the drugs in the working concentration.
Cell viability was measured every day for 5 days, or after 4 days of treatment, using
54
the CellTiter-Glo® Luminescent Cell Viability Assay (Promega) according to the
manufacturer’s instructions.
2.7. Flow Assisted Cytometry Analysis (FACS)
1x105 cells were seeded in 2ml complete medium per well in 6-well plates. 24 hours
later, the drugs were added into the wells, and the cells were harvested 4 days after
treatment. The cells were trypsinized, neutralized with complete medium and spun
down at 1000rpm for 3 mins. The cell pellet was washed once with 1.5ml ice-cold
phosphate-buffered saline (PBS) (Invitrogen) and then resuspended in 1ml ice-cold
70% ethanol. The samples were kept at 4°C for at least one hour and maximum 7
days. To stain the fixed cells, they were washed once with PBS and the cell pellet was
treated with RNase for 5 mins at room temperature (RT). Then 400μl of a 50μg/ml
Propidium Iodide (PI) solution was added to each sample, and they were incubated in
the dark at RT for 30 mins. The stained cells were analyzed for their DNA content by
FACS with FACSCalibur (Beckton Dickinson Instrument). The percentage of cell
death was defined as the SubG1 population quantified using the CellQuest software
(Beckton Dickinson).
2.8. Phospho-Histone H3 (Ser28) assay
The cells were treated for 24 hours after which they were washed with PBS and fixed
in 1ml ice-cold PBS for at least one hour but not more than 7 days. They were then
washed twice with PBS, blocked in 1ml blocking buffer (PBS, 0.1% Tween-20) for
15 mins on ice, washed with 1ml staining buffer (PBS, 1% FBS) and transferred to a
1.5ml Eppendorf tube. The cells were spun down at 2000rpm for 2 mins and the
supernatant was vacuumed, leaving approximately 40μl with the cell pellet. The cells
were incubated with 10μl anti-Phospho-Histone H3 (Ser28) antibody (Cell Signaling
Technology, #9713) for 45 mins in the dark at room temperature. The cells were then
washed once with PBS, incubated with 50μl RNase for 5 mins, followed by 400μl
55
Propidium Iodide (50μg/ml) for 30 mins in the dark. The samples were analyzed
using FACS.
2.9. Caspase 3 assay
Caspase 3 is a marker for cells undergoing apoptosis and consists of a heterodimer of
17 and 12kDa subunits derived from a 32kDa proenzyme. The FITC-conjugated anti-
active Caspase 3 antibody was used to detect cells with activated Caspase 3 by flow
cytometry (BD Biosciences, #559341). The cells were seeded at a concentration of
1x105 cells/well in 2ml/well of 6-well plates and subjected to different treatment
groups 24 hours later. 24 hours after treatment, the cells were trypsinized and stained
with the FITC-conjugated anti-active Caspase 3 antibody according to the
manufacturer’s instructions. In short, the cells were washed with cold PBS once and
fixed in Cytofix/Cytoperm solution for 20 mins. They were then washed twice with
the Perm/Wash buffer and incubated with the FITC-conjugated anti-active Caspase 3
antibody for 1 hour. Then they were washed with the Perm/Wash buffer once and
analyzed by flow cytometry using the FACSCalibur. The amount of cells with active
Caspase 3 was determined using the CellQuest software.
2.10. Anchorage-independent Methylcellulose assay
The wells of an opaque 96-well plate with clear bottoms were coated with BactoTM
Agar before being used for this assay. To do so, the agar was dissolved in PBS to a
concentration of 3% and boiled until it was dissolved completely. It was then mixed
with complete medium to a final concentration of 0.6% and 80μl were used to coat
the bottom of each well. The plates were kept in the incubator (37°C, 5% CO2) for at
least 45 mins before use. The Methylcellulose powder (4,000 cP viscosity, 2% in
H2O; Sigma-Aldrich, St. Louis, MO) was autoclaved and dissolved in autoclaved
H2O to make a 2% stock solution. The cells were resuspended in fresh medium and
the cell concentration was measured. Consequently, the cells were diluted to a
56
concentration of 2x104cells/ml in 30ml complete media, and 10ml of 2%
Methylcellulose solution was added. The final cell concentration attained was
1.5x104cells/ml in complete media with 0.5% Methylcellulose. 1ml aliquots were
made to which the drugs were added and 100μl were seeded per well. Subsequently,
the cells were allowed to grow for 13 days in the incubator before they were stained
with 80μl of a 40μg/ml p-Iodonitrotetrazolium Violet (INT) solution and the colonies
were counted and analyzed using the GelCountTM automatic plate scanner (Oxford
OPTRONIX) and the GelCount Version 0.025.1 software (Oxford OPTRONIX).
Plates were scanned at 1200 dpi.
2.11. Tumorsphere assay
To measure the number of tumor-initiating cells, a tumorsphere assay was performed.
The tumorsphere medium used to grow breast cancer tumorspheres (mammospheres)
consists of 90ml MammoCultTM Basal Medium (Human) (STEMCELLTM
Technologies, Vancouver, CA), supplemented with 10ml MammoCultTM
Proliferation Supplements (Human) (STEMCELLTM Technologies), 200μl heparin
solution (stock 0.2%), and 200μl hydrocortisone (stock 250μg/ml). Cells were
trypsinized, neutralized with complete medium and spun down at 1000rpm for 3
mins. The cell pellet was washed once with 5ml PBS, resuspended in 3ml
tumorsphere media, and the cell count was determined. 5,000 cells were seeded in
one well of a low-attachment 96-well plate in a volume of 2ml and placed in the
incubator. After 24 hours, the drugs were added into the wells without changing the
medium. Three days later, the wells were topped-up with 1ml of fresh tumorsphere
medium, and after 7 days 300μl of a 40μg/ml INT solution were added per well and
incubated over night to stain the tumorspheres. The INT end concentration is 4μg/ml.
The number of tumorspheres was measured the next day using the GelCountTM
automatic plate scanner and the GelCount software. Plates were scanned at 1200 dpi
and the colony detection algorithm was optimized for each cell line individually.
57
2.12. 3D Matrigel anchorage-independent growth assay
An 8-well chamber slide (#384118, BD Biosciences) was pre-coated with 45μl of
7.6mg/ml growth factor-reduced matrigel (#356231, BD Biosciences) for 30 mins at
37°C. 5x103cells in a volume of 400μl in complete media with 150μg/ml matrigel
containing the different concentrations of the drugs were seeded into each well. The
cells were placed in the incubator and media was replaced with fresh media after 4
days. Phase contrast images were taken of the cells at different time points.
2.13. RNA extraction
Cell pellets were collected by trypsinizing monolayer cells, and the cell pellet was
washed once with ice-cold PBS. The cells were resuspended in 700μl Qiazol
(Invitrogen) to lyse the cells, after which 200μl chloroform was added and the
mixture was centrifuged at 13,000rpm for 15 mins at 4°C to separate RNA, DNA and
other cellular components. After centrifugation, the top clear layer was transferred
into a fresh 1.5ml eppendorf tube, followed by the addition of 540μl 100% ethanol.
The miRNeasy mini kit (Qiagen) was used to purify the RNA. In short, the samples
were transferred to the column and the RNA was allowed to bind to the membrane
via centrifugation. The columns were washed once with 700μl RW1 buffer, and twice
with 500μl RPE buffer. Subsequently, the RNA was eluted from the column with
50μl RNAse-free water. The concentration of the RNA was determined using a
NanoDrop ND-1000.
2.14. cDNA conversion and quantitative RealTime-PCR (RT-PCR)
To transcribe RNA into single-stranded complementary DNA (cDNA), a High
Capacity cDNA Reverse Transcription Kit (Applied Biosystems) was used. Briefly,
750ng of RNA were diluted in 25μl RNAse-free water. Then a master mix was
prepared with 5μl RT buffer, 5μl random primers, 2μl dNTP mix, 2.5μl
58
MultiScribeTM reverse transcriptase, and 10.5μl RNAse-free water, which was added
to the previously prepared RNA. A PCR was then performed in a thermo cycler at
25°C for 10 mins, followed by 37°C for 2 hours. To perform quantitative RealTime-
PCR, 0.44μl of the cDNA (15ng/μl) was added to 3.96μl RNAse-free water.
Concurrently, 0.4μl gene-specific primer mix (10μM) was added to 5μl of 2X master
mix from KAPA SYBR® FAST qPCR Kit (Kapa Biosystems). The two reactions
were then mixed together and subjected to quantitative RealTime-PCR. The reaction
mix was amplified and quantified with PRISM 7900 Sequence Detection System
(Applied Biosystems). 18S or Actin were used as internal controls. All the reactions
were analyzed using Applied Biosystems PRISM 7500 Fast Real-Time PCR system
in 96-well plate format.
Table 2.13 Primers used for quantitative RealTime-PCR of target genes
59
2.15. Microarray gene expression profiling
To control the quality of the mRNA used and to detect mRNA degradation, Agarose
gel electrophoresis was performed on a 1% Agarose gel. The microarray
hybridization was performed using the Illumina Gene Expression Sentrix BeadChip
HumanRef-8_V2 (San Diego, CA). In short, 500ng of RNA was used to perform
cDNA conversion and further processed into double-stranded cDNA. The purified
cDNA was used to generate biotinylated cRNA, which was likewise purified before
further processing. The biotinylated cRNA was hybridized onto the BeadChip,
washed, and stained with streptavidin-Cy3. The BeadChip was scanned using the
Illumina BeadArray Reader and the images were stored with indicated barcodes.
Illumina GenomeStudioTM was used to analyse the scanned images and the output
was used for further analysis with GeneSpringGXTM (Agilent Technology). The data
were analyzed by selecting Illumina single color as the experimental type and quartile
normalization was performed for the readings. Fold changes in gene expression of the
resistant cells lines were analyzed by pair-wise comparisons to the respective parental
cell lines.
2.16. Protein extraction
For total protein extraction, the cells were trypsinized, neutralized with complete
medium and spun down at 1200rpm for 3 mins and washed once with 1.5ml ice-cold
PBS. The volume of the cell pellet was estimated and resuspended in 5x the volume
of radioimmunoprecipitation assay (RIPA) lysis buffer (50nM Tris-HCl pH7.4, 1mM
EDTA, 150mM NaCl, 1% Igepal CA630, 0.5% sodium deoxycholate, 1mM
Na3VO4, 20mM NaF, 1mM PMSF, and complete protease inhibitor (Roche)). The
samples were kept on ice for 30 mins and vortexed every 5 mins for 10 seconds, and
then sonicated 3x 5 seconds at 20% maximum force, after which they were spun
down at 13,200rpm for 15 mins at 4°C. The supernatant containing the protein was
60
transferred into a fresh eppendorf tube and the concentration was estimated with the
DC Protein Assay (Bio-Rad) using BSA to generate a standard curve, and measured
using the Tecan XflourTM software.
2.17. Western blotting
A 10% or 12% SDS-PAGE gel was used to separate protein samples (20-30ug),
which were then transferred onto a PVDF membrane (Milipore) at 23V for 70 mins
using the Trans-Blot SD Semi-Dry transfer cell (Bio-Rad). The membranes
containing the protein were blocked with 5% non-fat milk (Bio-Rad) or 5% bovine
serum albumin (BSA) for 2 hours, or overnight. The primary antibodies were diluted
to their recommended working concentrations using the blocking buffer and
incubated on the membranes overnight, or for 2 hours RT, according to the
manufacturer’s instructions. The membranes were washed 3 times for 5 mins using
the washing buffer and incubated with the HRP-conjugated secondary antibody
diluted in blocking buffer for 1 hour at RT. The membranes were incubated with the
chemiluminescent ECL Substrate Kit (GE Healthcare) for 5 mins, and the signal was
subsequently detected using the ChemiDocTM Imaging System (BIO-RAD). The blots
were probed with primary antibodies against PKC-β (GTX113252) purchased from
GeneTex; phospho-PKC-βI (Thr641) (sc-101776), PKC (sc-80) and MCL-1 (sc-819)
purchased from Santa Cruz Biotechnology Inc.; Aurora B/AIM1 (#3094S), phospho-
Aurora A/B/C (T288/232/398) (#2914S), phospho-C-Raf (Ser289/296/301) (#9431P),
phospho-GSK3β (Ser9) (#5558S) and cleaved PARP (#9541S) purchased from Cell
Signaling Technology®; and β-Actin purchased from Sigma Aldrich.
61
2.18. H-score calculation
H-score was used as a semi-quantitative analysis to describe the extent of
immunohistological staining of patient tumor samples. The formula used is the
following:
Samples used to calculate H-score are derived from The Human Protein Atlas
(www.proteinatlas.org). Patient samples made available from this database were
stained with 2 different anti-PKC-β antibodies and grouped into low, medium and
high intensity staining. According to this grouping, the H-score was calculated for
normal breast tissue and breast cancer samples.
2.19. Statistical analysis
Statistical analyses of XY and bar graphs were performed using the GraphPad Prism
software. Experimental readings were performed in duplicates, triplicates or 6
replicates and a Student’s T-test was used to generate p-values for measuring
significance between measurements. All statistical tests and graphs are generated with
GraphPad Prism Version 6.
62
CHAPTER 3: RESULTS
63
3.1. Generation and characterization of Paclitaxel-resistant TNBC cell
lines
To study mechanisms of acquired chemo-resistance in Triple Negative Breast Cancer
(TNBC) cell lines and, subsequently, find ways to revert resistance we generated
chemo-resistant cell lines. We used Paclitaxel-resistance as a model as it is used in
first line treatment for patients harboring triple negative breast tumors. To generate
this model we used the two TNBC cell lines, SUM159PT and MDA-MB-231
(hereafter named MB231). The cell lines were seeded in a 2D culture and treated with
a Paclitaxel starting concentration of ½ of the EC50 concentration which leads to a
reduction of cell viability and apoptosis in approximately 30% of the cells. As soon as
the remaining cells had stabilized and started proliferating again, the Paclitaxel
concentration was doubled (Figure 3.1.1 (A)). After approximately 5 months, we had
generated two resistant derivatives for each of the two TNBC cell lines, stably
growing in different Paclitaxel concentrations. For SUM159PT, the resistant cell lines
grow in 500nM and 1μM Paclitaxel, and are termed 500nM-TR and 1μM-TR. The
MB231 resistant cell lines grow in 50nM and 75nM Paclitaxel, and are therefore
called 50nM-TR and 75nM-TR, respectively. The parental MB231 cell lines show a
lower intrinsic resistance to Paclitaxel, therefore the resistant cell lines cannot be
stably grown in higher concentrations. To measure resistance, we calculated the EC50
of Paclitaxel in these cell lines using a cell viability assay. The EC50 is the
concentration of a drug which gives a response halfway between the baseline and the
maximum. As seen in Figure 3.1.1 (B) and (C), the resistant cell lines show ~1000-
fold and 300-fold higher EC50 value compared to their chemo-sensitive counterparts
in SUM159PT and MB231, respectively.
64
Figure 3.1.1 Generation of Paclitaxel-resistant
TNBC cell lines
A. Paclitaxel-sensitive parental SUM159PT
and MB231 cell lines were treated with
½ of the Paclitaxel EC50. After initial
reduction in cell viability and cell death,
the remaining cells were further cultured
and the Paclitaxel concentration was
doubled. This cycle was repeated until 2
resistant derivatives were derived from
each cell line: SUM159PT 500nM-TR
and 1μM-TR; and MB231 50nM-TR and
75nM-TR
B. Paclitaxel EC50 for both parental and the
resistant cell lines was measured using
cell viability assay. The cells were
treated 24 hours after seeding and cell
viability was measured after 4 days
treatment (n=6). The EC50 was calculated
using Sigmoidal dose-response and
plotting the non-linear regression curve
fit (Error bars show ±SD, n=6)
C. Bar graph depicting the increase in
Paclitaxel EC50 in the resistant compared
to the parental cell lines, as measured
with cell viability assay.
B
A
C
65
As we know, chemo-resistance has been linked to cancer stem cells (CSCs), as these
are believed to confer resistance through mechanisms such as up-regulation of anti-
apoptotic proteins such as Bcl-2, and high expression of ABC transporters for
effective efflux pumping. Therefore, we examined the population of CSCs in the
resistant cell lines which can be identified by the expression of specific cell surface
markers. Breast CSCs are characterized by a high expression of CD44 and a low
expression of CD24. FACS analysis revealed a 3-fold increase in the CSC population
in the SUM159PT resistant cell lines, and a 3- and 5-fold higher amount in the
MB231 50nM-TR and 75nM-TR cell lines, respectively (Figure 3.1.2 (A) and (B)).
These results are in concordance with other studies showing an increase in the CSC
population in chemo-resistant ovarian and colorectal cancer cell lines (Cole et al.,
2014, Dallas et al., 2009). We performed a tumorsphere assay in order to validate the
increase in CSCs in SUM159PT parental and resistant cell lines (Figure 3.1.2 (C)).
Surprisingly, the tumorspheres generated by the SUM159PT resistant cell lines show
a different morphology compared to the parental cell lines. While the parental cell
lines build round distinct spheres, the spheres generated by the resistant cell lines
seem to be aggregating. It can be ruled out that these are merely simple cell
aggregates as they cannot be dissociated easily by pipetting and need to be
trypsinized to generate single cells, a characteristic of spheres with intact cell-cell
interactions (Manuel Iglesias et al., 2013). In addition, the high expression of the cell
surface marker CD44 can make the cells more “sticky”, so the spheres are able to
clump together.
66
A
B
C D
67
When we checked for the expression of the breast CSCs markers, CD44 and CD24,
we found that the expression of CD24 was reduced in the parental SUM159PT cell
line compared to the resistant cell lines, whereas there was an increased expression of
CD44 in the resistant cell lines. These results validate our FACS analysis, which
identified an increase in the CD44+/CD24- CSCs population in the resistant
compared to the parental cell lines.
Figure 3.1.2 Increase in the cancer stem cell population in Paclitaxel-resistant cell lines
A. FACS analysis of SUM159PT and MB231 parental and resistant cell lines. The cells
were stained with APC- and FITC-conjugated CD24 and CD44 antibodies,
respectively.
B. Bar graph depicting the percentage of CD24-/CD44+ population in parental and
resistant cell lines as determined by FACS analysis (A).
C. Images of tumorspheres of SUM159PT parental, 500nM-TR and 1μM-TR cell lines
after 7 days growth in anchorage-independent conditions.
D. RealTime-PCR was performed on SUM159PT parental and resistant cell lines to
determine mRNA expression of CD44 and CD24. mRNA expression values were
normalized against 18S and fold change values were plotted. (Error bars show ± SD,
n=3).
68
3.2. Gene expression profiling of SUM159PT Paclitaxel-resistant cell lines
To gain insights into the differential regulation of genes and pathways in the resistant
derivatives of SUM159PT compared to the parental cells, we performed microarray
analysis. After analyzing the microarray data with GeneSpringGXTM, we extracted
the list of genes significantly up-regulated in the 500nM-TR and 1uM-TR compared
to the parental cell line (fold change > 2; FDR<0.05) and analyzed them with
Ingenuity Pathway Analysis® (IPA). We examined the functions and pathways that
these genes are involved in and plotted the predicted Activation Z-score for each
pathway. This score represents the bias in gene regulation and predicts whether this
pathway is activated (Activation Z-score > 2) or inhibited (Activation Z-score < -2).
We found that genes up-regulated in both cell lines lead to an increased activation of
pathways and functions such as Cell Death and Survival, Cellular Movement,
Cellular Assembly and Organization, and Cellular Development, Growth and
Proliferation, among others (Figure 3.2.1). In contrast, increased activation of these
genes leads to an inhibition of apoptotic and cell death pathways. These findings are
not surprising as the resistant cell lines are maintained in Paclitaxel and are under
constant survival pressure, although they have acquired resistance. This means that
cell survival and proliferative pathways are up-regulated, while apoptotic pathways
are down-regulated. Moreover, resistant cell lines show an increase in microtubule
dynamics, a consequence of the mechanism of action of Paclitaxel, which stabilizes
microtubules, in order to maintain an intact cell cycle and chromosome segregation.
This comes hand in hand with an activation of cell cycle progression pathways, and
increased invasive and migratory properties of the resistant cell lines.
69
3.2.1. The NF-κB network
We then wanted to determine the gene networks which are more highly activated in
the resistant cell lines compared to the parental cell line. IPA analysis lets us
determine these networks and ranks them according to a score calculated by their
algorithm. This score is calculated based on the p-value which takes into account the
number of Focus Genes in a certain network. The p-value is calculated based on the
probability of finding f or more Focus Genes in a set of n genes randomly selected
from the Global Molecular Network and the size of the network to approximate its
relevancy to the list of Focus Genes. Fisher’s exact test is used to calculate the p-
Figure 3.2.1 Changes in functions and pathways in Paclitaxel-resistant TNBC cell lines
Microarray analysis was used to determine gene expression changes in the SUM159PT
resistant compared to the parental cell lines. After quantile normalization, significantly over-
expressed genes (FDR<0.05, fold change>2) common in both resistant compared to parental
cell lines (581 genes) were analyzed using Ingenuity Pathway Analysis®. The figure shows
significantly altered pathways and functions according to the Activation z-score, which is
dependent on the number of genes whose expression is significantly up-regulated in the gene
sets for each specific function/pathway.
70
value which in turn is used to determine the p-score. This score is defined as:
p-score= -log10(p-value). The top 5 networks with the highest scores when comparing
gene expression values in the SUM159PT resistant compared to the parental cell lines
are shown in Figure 3.2.2 (A). We zoomed into the highest ranked network and found
that most of the genes involved belong to the NF-κB complex (Figure 3.2.2 (B)). We
extracted the normalized gene expression values from the GeneSpring analysis of the
genes from Network 1 which are NF-κB-related genes and validated them using
RealTime-PCR (Figure 3.2.2 (C)). We found that, indeed, most of the genes are more
highly expressed in the resistant compared to the parental cell lines, but expression
values were not as drastic as in the microarray data.
71
NF-κB complex
Figure 3.2.2 The NF-κB network
in Paclitaxel-resistant cell lines
A. IPA was used to determine
enriched networks in
SUM159PT 500nM-TR
and 1μM-TR compared to
the parental cell lines.
Genes selected for the
analysis were commonly
up-regulated in both
resistant cell lines and had
a fold change cut-off=2 and
FDR<0.05.
B. The top network with the
highest score has the NF-
κB complex at the centre.
C. Gene expression values of
genes which play a role in
NF-κB signaling and are
found in the top network.
Gene expression values
from microarray analysis
shown are after quantile
normalization and relative
to the parental cell line
(fold change>2,
FDR<0.05). RealTime-
PCR analysis is normalized
to gene expression values
of SUM159PT parental
cells. 18S was used to
normalize mRNA
expression values (Error
bars show ±SD, n=3).
A
B
C
72
It has been shown that the NF-κB pathway plays an important role in chemo-
resistance and inhibition of this pathway (using disulfiram and copper) sensitizes
breast CSCs to Paclitaxel (Yip et al., 2011). To verify its role in our model of
Paclitaxel-resistance in TNBC cell lines, we used various NF-κB inhibitors to test
whether this pathway is more important for cell survival in the SUM159PT 500nM-
TR and 1μM-TR resistant cell lines compared to the parental cell line. We treated the
cells for 4 days with 3 IKK inhibitors: 1. BAY 11-7082, an inhibitor of the IκB kinase
(IKK) α subunit, IKKα, and of phosphorylation of cytokine-inducible IκBα
(IC50=10μM); 2. PHA-408, a potent inhibitor of IKKβ (IC50=40nM); 3. PS-1145, a
selective IKK inhibitor (IC50=100nM). The IKK kinase complex is made of the two
kinases IKKα and IKKβ, where IKKα plays a role in the non-canonical NF-κB
pathway and IKKβ in the canonical NF-κB pathway. As a negative control, to
determine whether the effect of treatment is specific to the NF-κB inhibitors, we
included a MEK inhibitor (PD0325901, IC50=0.33nM), and a PDK1 inhibitor
(BX795, IC50=6nM). We found that the resistant cell lines were only slightly more
sensitive to the NF-κB inhibitors compared to the parental cell line at high
concentrations, although the parental cell line also responded to the inhibitors in a
dose-dependent manner (Figure 3.2.3). The two inhibitors used as negative controls,
MEK and PDK1 Inhibitors, showed a dose-dependent effect in the resistant and
parental cell lines, although the resistant cell lines are more resistant compared to the
parental cell line.
These results don’t fit our hypothesis to identify activation of pathways specific to
Paclitaxel-resistant cell lines. This could possibly be because the NF-κB pathway is
important in TNBC, even without Paclitaxel-resistance, which is why various NF-κB
inhibitors are only slightly effective in the resistant cell lines. We therefore decided to
use a different approach to find pathways that play a role in Paclitaxel-resistance and
to find ways to revert resistance.
73
Figure 3.2.3 SUM159PT Paclitaxel-resistant cells are equally sensitive to NF-κB inhibitors
as compared to the parental cell line
SUM159PT parental and 500nM-TR and 1μM-TR resistant cell lines were treated for 4 days
with different concentrations for all inhibitors, after which cell viability was measured. All
measurements are normalized to the DMSO control and the percentage is plotted. (Error bars
show ±SD, n=3).
74
3.3. Kinase inhibitor screen to identify Paclitaxel-sensitizing compounds
To identify compounds that can re-sensitize resistant TNBC cells to Paclitaxel, we
performed a kinase inhibitor screen using 180 small molecule inhibitors targeting
different kinases. This screen was used to detect synthetic lethal drug combinations
with Paclitaxel, specific for the resistant cell lines.
The cells were seeded in 96 well plates, treated 24 hours later and after 4 days
treatment, cell viability was measured. The cells were treated either with the inhibitor
alone, or in combination with Paclitaxel (Figure 3.3.1 (A)). We then calculated the Z-
score for each treatment group for each resistant cell line separately. Z-score
calculations take into account the mean of a population (in this case the mean of all
the cell viability measurements for each treatment group (inhibitor vs. inhibitor +
Paclitaxel) for one cell line, and assigns each single measurement a score, depending
on whether it is above or below the population mean. Scores > 0 mean that the cell
viability is higher, whereas scores < 0 mean that cell viability is lower than the
population mean. When Z-scores of the inhibitor single treatment vs. inhibitor +
Paclitaxel treatment are plotted on a scatter plot (Figure 3.3.1 (B)), distinct
populations can be identified. The inhibitors most interesting to us are the ones
clustering in the II. Quadrant. These inhibitors show no effect on cell viability as
single treatment but significantly reduced cell viability in the combination treatment.
Inhibitors in the III. Quadrant also have an effect on the resistant cell lines as single
treatment and are, therefore, not interesting to us as they are not targeting pathways
specific to Paclitaxel-resistance. Inhibitors clustering in the I. Quadrant have no effect
as single treatment or in combination with Paclitaxel and we also eliminated these.
Many of the inhibitors in the II. Quadrant are PKC and PKC-β inhibitors, suggesting
a strong role for the PKC pathway in Paclitaxel-resistance.
75
Figure 3.3.1 Kinase inhibitor screen identifies PKC-β inhibitors to be able to re-sensitize
resistant cells
A. Schema for performing the kinase inhibitor screen. The cells were seeded in 96-well
plates and treated with 180 small molecule inhibitors targeting different kinases 24
hours later. All the inhibitors were used at 5μM unless otherwise stated (Figure 3.3.2).
One set was treated with the inhibitor alone, whereas another set was treated with the
inhibitor in combination with Paclitaxel. Cell viability was measured after 4 days of
treatment. Cell viability measurements were normalized to the DMSO control.
B. After normalization, the Z-score was calculated for each inhibitor by determining the
whole population mean for each cell line separately. The Z-score of single treatment
was then plotted against the Z-score for combination treatment on a scatter plot. The
inhibitors in the II. Quadrant are those with no effect as single treatment, but reduce
cell viability when combined with Paclitaxel.
A
B
76
To calculate whether the difference of single treatment vs. combination treatment was
significant for each compound, we used a Student’s t-test to calculate p-values. We
assumed that the cell viability measurements of the 4 resistant cell lines (SUM159PT
500nM-TR and 1uM-TR, MB231 50nM-TR and 75nM-TR) are replicates in the two
different treatment groups: inhibitor alone, and inhibitor + Paclitaxel. After
calculating the p-value between the two treatment groups, and thereby identifying
those inhibitors that have no effect when given as a single treatment but lead to
reduced cell viability in combination with Paclitaxel, we ranked the inhibitors
accordingly (Figure 3.3.2). As shown, some of the top hits included inhibitors
targeting proteins which have been known to play a role in chemo-resistance, such as
p38 in colorectal cancer (Grossi et al., 2014), the hedgehog pathway (Hh) in breast
cancer and prostate cancer (Chai et al., 2013, Singh et al., 2012), EGFR in ovarian
cancer cell lines (Servidei et al., 2008), the MEK5/ERK5 pathway, whose over-
expression was found to be associated with poor survival in breast cancer patients
treated with chemotherapy (Miranda et al., 2015), and BTK which, when inhibited,
can sensitize leukemic cells to chemotherapeutic drugs (Uckun et al., 2002).
77
Figure 3.3.2 Top hits of the kinase inhibitor screen
The inhibitors were ranked according to the p-value calculating the significant difference in cell
viability of inhibitor alone vs. combination treatment in the resistant cell lines. The cell
viability measurements in the 4 resistant cell lines (SUM159PT 500nM-TR and 1μM-TR, and
MB231 50nM-TR and 75nM-TR) for each treatment group were taken as replicates. Using this
method the inhibitors were ranked according to how significantly they could reduce cell
viability in combination with Paclitaxel, while having minimal effect as single agent. Cell
viability measurements are color-coded with red depicting low, and green depicting high
values. All inhibitors were used at 5μM unless stated otherwise.
78
3.4. Validation of top hits from the kinase inhibitor screen
For further validation, we decided to focus on novel targets in chemo-resistance such
as Kit/PDGFR, TNKS1/2, PKC-β, and FLT3, in addition to some of the previously
published targets (p38 MAPK, Hh, BTK, PKA), which serve to validate our own
screen, but also whose role specifically in breast cancer chemo-resistance is not clear.
We first validated 9 inhibitors (highlighted in yellow) which target the
aforementioned kinases with a cell viability assay at 2 different concentrations each
(1μM and 5μM). As shown in Figure 3.4.1 (A) and (B), all the inhibitors lead to a
reduction in cell viability in all the 4 resistant cell lines when used in combination
with Paclitaxel, but have no effect as a single agent. In addition, they also have no
effect in SUM159PT and MB231 parental cell lines. Cell viability is normalized to
Day 0, which means that if the bar drops below CTG Signal=0, it is suggestive of
apoptosis. In general, the SUM159PT resistant cells are more sensitive to the
combination treatment, with more inhibitors leading to apoptosis, compared to the
MB231 resistant cell lines. This can be linked to high Paclitaxel EC50 in these cell
lines.
79
The only inhibitors which were able to reduce cell viability below 0 in combination
with Paclitaxel in all the 4 resistant cell lines are the PKC-β inhibitors (LY333531
and Enzastaurin), as well as the PKA inhibitor (H-89) (except in MB231 50nM-TR).
Since the PKA inhibitor H-89 was also one of the top hits of the kinase screen with a
significant p-value calculation, and PKA has been previously reported to play a role
in chemo-resistance in breast cancer cells through regulation of the expression of the
Figure 3.4.1 Validation of the top 9 hits identified by the kinase screen
Validation of the 9 top hits in SUM159PT (A) and MB231 (B) parental and both resistant cell
lines. The cells were treated with inhibitor alone (at 1μM and 5μM each) or in combination
with Paclitaxel and cell viability was measured on Day 0 and Day 4. Day 4 measurements were
normalized to Day 0 and values were plotted on the bar graph. All the inhibitors had no or
minimal effect as single agent in the parental or resistant cell lines but could successfully
sensitize resistant cells to Paclitaxel when compared to the Paclitaxel treatment alone. (Error
bars indicate ±SD, n=6).
A B
A B
80
MDR1 protein, we also used this inhibitor for the following validation assay (Cvijic
and Chin, 1997).
We performed a Methylcellulose assay which measures colony formation in
anchorage-independent conditions. We found that both PKC-β inhibitors (LY333531
and Enzastaurin) and the PKA inhibitor (H-89) given at 1μM and 5μM each, led to a
significant decrease in the number of colonies in combination with Paclitaxel, but not
as single treatment (Figure 3.4.2 (A) and (B)). Additionally, the effect was slightly
stronger with the PKC-β inhibitors compared to the PKA inhibitor at low
concentrations, as seen for the SUM159PT resistant cells. Interestingly, the resistant
cell lines showed no change or a slight increase in colony formation in the Paclitaxel
compared to the DMSO treatment groups, suggesting that the resistant cells, to some
degree, rely on Paclitaxel for cell growth Paclitaxel treatment alone can even enhance
colony formation.
81
Figure 3.4.2 PKC-β and PKA inhibitors can inhibit colony formation of Paclitaxel-
resistant cell lines in the presence of Paclitaxel
Validation of the PKC-β (LY333531, Enzastaurin) and PKA (H-89) inhibitors in SUM159PT
(A) and MB231 (B) in an anchorage-independent Methylcellulose assay. The cells were seeded
in a Methylcellulose assay and treated for 13 days before the colonies were stained with INT
o/n. The plates were then scanned using a GelCount and colonies/well were counted. The
inhibitors could significantly reduce colony formation in the resistant cell lines in combination
with Paclitaxel but only had a slight or no effect when used a single agent. (Error bars show
±SD, n=6)
A B
82
For subsequent experiments, a Paclitaxel treatment control was added for all the
parental cell lines where they were treated with a very low dose of Paclitaxel. This
dose was derived from the cell survival of the resistant cell lines in their maintenance
doses of Paclitaxel. For SUM159PT, the 500nM-TR cell line is cultured in 500nM
Taxol, and according to the Sigmoidal curve formula when calculating the EC50,
98.9% of the cells survive. We then calculated how much Paclitaxel is needed for the
parental cells, so that 98.9% survive. For SUM159PT parental cells, 98.9% cell
survival is achieved with 0.73nM Paclitaxel. For MB231 parental cells the Paclitaxel
dose used is 0.46nM, derived from cell survival of the 50nM-TR cell line in 50nM
Paclitaxel.
83
3.5. The PKC-β and PKA inhibitors sensitize resistant cells and induce
apoptosis in the presence of Paclitaxel
To test whether the reduction in cell viability after combination treatment is really
due to an induction of cell death, we performed 2 different apoptotic assays: Caspase
3 and Propidium Iodide (PI) staining. Both assays are flow cytometry-based, where
the former detects cells with activated Caspase 3, and the latter detects the apoptotic
cell population based on DNA fragmentation. For these assays we used the PKC-β
inhibitor Enzastaurin and the PKA inhibitor H-89. We first performed a Caspase 3
assay and found that single treatment with either of the inhibitors had little to no
effect on the activation of Caspase 3 in any of the resistant cell lines (Figure 3.5.1).
However, in combination with Paclitaxel we observed a significant increase in
Caspase 3+ cells, whereby the combination with Enzastaurin yielded the greatest
amount of apoptotic cells in all 4 resistant cell lines. The combination treatment with
the PKA inhibitor and Paclitaxel only resulted in a mild increase in Caspase 3+ cells.
As expected, Paclitaxel treatment alone could not increase the amount of Caspase 3+
cells in the resistant cell lines. Moreover, the parental cell lines showed a slight
increase in Caspase 3+ cells after single treatment with Enzastaurin alone, but there
was no further increase when Paclitaxel was added to the treatment.
84
We then performed PI staining to validate these results in the resistant cell lines. We
treated the cells for 4 days with either the inhibitors alone or in combination with
Paclitaxel. The cells were then fixed and stained with PI which intercalates into the
Figure 3.5.1 PKC-β and PKA inhibitors induce Caspase 3 activation in the presence of
Paclitaxel
Measurement of Caspase 3+ cells after treatment with PKC-β and PKA inhibitors at different
concentrations with and without Paclitaxel in SUM159PT (A) and MB231 (B) parental and
resistant cell lines. The Paclitaxel concentration used for SUM159PT and MB23 parental cells
is 0.73nM and 0.46nM, respectively, whereas for the resistant cells, the maintenance
concentration was used. The cells were treated for 4 days, after which they were fixed with
70% ethanol for 1 day and then stained with a FITC-conjugated anti-Caspase 3 antibody. FACS
was performed to detect FITC-positive cells (n=1).
A B
85
DNA, thereby allowing us to measure the DNA content of the cells. A normal 2N
DNA content means that the cells are in the G1 phase, a DNA content of 4N means
that the cells are in the G2/M phase, and a DNA content less than 2N suggests that
the cells have undergone DNA fragmentation due to apoptosis. This is called the
SubG1 population which can be measured using this assay and represents the
population of apoptotic cells. As can be seen in Figure 3.5.2, single inhibitor
treatment did not lead to apoptosis, whereas the combination with Paclitaxel lead to a
significant increase in the amount of apoptotic cells in the resistant cell lines in a
dose-dependent manner. Interestingly, the percentage of cells undergoing apoptosis
was greater when they were treated with the PKC-β inhibitor Enzastaurin as
compared to the PKA inhibitor in all 4 cell lines.
A B
86
We therefore decided to focus on PKC-β as a target for chemo-resistance in triple
negative breast cancer cells. For one, in the top 40 hits of the kinase inhibitor screen,
3 target hits were PKC-β inhibitors (LY333531, Enzastaurin and LY317615), and one
was a pan-PKC inhibitor (Sotrastaurin) (Figure 3.4). This is strongly indicative of the
importance for the role of PKC-β in chemo-resistance. Secondly, it has a significant
effect in reducing cell viability and anchorage-independent growth in resistant cells,
and induces high levels of apoptosis in combination with Paclitaxel. And thirdly,
PKC-β inhibitors are the most advanced in clinical trials out of all the validated hits,
and have undergone successful Phase III clinical trials for complications of diabetes
(Danis and Sheetz, 2009), as well as various pre-clinical and clinical trials for
treatment of patients with different hematological and non-hematological cancers,
such diffuse large B-cell lymphoma (DLBCL) (Robertson et al., 2007), non-small cell
lung cancer (Oh et al., 2008), metastatic colorectal cancer (Glimelius et al., 2010),
and recurrent high-grade gliomas (Kreisl et al., 2010). Although the responses have
been mixed and negative so far, Phase II studies were conducted which used
Enzastaurin in combination with Paclitaxel and Carboplatin as first-line treatment for
ovarian cancer to increase progression-free survival (Vergote et al., 2013).
Enzastaurin is an oral drug with high safety in patients, also in combination with
chemotherapeutic drugs. This PKC-β-specific inhibitor was advanced for clinical
development due to its anti-angiogenic activity in human tumor xenografts , where it
leads to decreased VEGF expression and microvessel density (Keyes et al., 2004).
Figure 3.5.2 PKC-β and PKA inhibitors increase SubG1 fraction in Paclitaxel-resistant cell
lines in the presence of Paclitaxel
Measurement of SubG1 fraction upon treatment with PKC-β and PKA inhibitors at different
concentrations with and without Paclitaxel in SUM159PT (A) and MB231 (B) resistant cell
lines. The cells were treated for 4 days with Enzastaurin and H-89 alone or in combination with
Paclitaxel, and subsequently fixed with 70% ethanol for 1 day. They were then stained with
Propidium Iodide for 30 mins before they were analyzed by FACS. Both inhibitors were able to
increase SubG1 fraction in the resistant cell lines upon Paclitaxel treatment, whereas Paclitaxel
alone is not sufficient to induce cell death. (n=1).
87
3.6. The PKC-β inhibitor Enzastaurin sensitizes chemo-resistant cells to
Paclitaxel in anchorage-dependant and –independent conditions
To look closer into the effect of PKC-β inhibition on chemo-resistance, we focused
on Enzastaurin for further experiments.
We used the SUM159PT parental and 1uM-TR, and MB231 parental and 75nM-TR
cell lines and treated them with different doses of Enzastaurin and measured cell
viability over a course of 4 or 5 days. The Paclitaxel concentration used for
SUM159PT and MB231 parental cell lines was 0.73nM and 0.46nM, respectively,
and the resistant cells were treated with the maintenance dose of Paclitaxel.
Expectedly, both the parental cells showed no effect, even in combination with
Paclitaxel, but for both resistant cell lines, a dose-dependent and time-dependent
effect is seen when Enzastaurin is given in combination with Paclitaxel (Figure
3.6.1). Enzastaurin at 2.5μM is sufficient to significantly decrease cell viability in the
resistant cell lines in the presence of Paclitaxel.
88
Figure 3.6.1 The PKC-β inhibitor Enzastaurin sensitizes resistant cells to Paclitaxel in a
dose-dependent manner
Measurement of cell viability in SUM159PT (A) and MB231 (B) parental and resistant cell
lines after treatment with Enzastaurin with and without Paclitaxel. The Paclitaxel concentration
used for SUM159PT and MB23 parental cells is 0.73nM and 0.46nM, respectively, whereas for
the resistant cells, the maintenance concentration was used. SUM159PT and MB231 cells were
treated over a period of 4 or 5 days, respectively, with different concentrations of Enzastaurin
with and without Paclitaxel and cell viability was measured each day. Measurements were
normalized to Day 0 and log2 values were plotted. Paclitaxel treatment alone has no effect in
the resistant cell lines but Enzastaurin can sensitize cells to Paclitaxel. (Error bars show ±SD,
n=3)
A
B
89
To examine colony growth and morphology of the cells upon treatment, we
performed a 3D colony growth assay on Matrigel and found that all the parental and
resistant cell lines grew slightly smaller colonies when treated with Paclitaxel alone
or Enzastaurin alone, despite only using the maintenance concentrations of Paclitaxel
(Figure 3.6.2). This can be explained by the fact that although the cells are resistant to
Paclitaxel, they are still undergoing selective pressure which selects for highly
resistant cells. The combination treatment, on the other hand, has a minimal effect on
the parental cell lines, but can dramatically reduce colony growth in the resistant cell
lines. Moreover, for both SUM159PT and MB231, the size of the colonies grown by
the resistant cells is smaller compared to the parental cells, which can be explained by
their slightly slower growth rate.
Figure 3.6.2 Enzastaurin inhibits 3D colony growth in resistant cell lines in the presence
of Paclitaxel
Measurement of 3D growth in SUM159PT (A) and MB231 (B) parental and resistant cell lines
after treatment with Enzastaurin and Paclitaxel. The cells were seeded on Matrigel, treated the
same day and imaged after 9 days of treatment.
A
B
90
We then went on to calculate the degree of re-sensitization through combination
treatment by measuring the Paclitaxel EC50 as determined by cell viability in
SUM159PT parental and resistant cell lines. The cells were treated for 4 days with
increasing concentrations of Paclitaxel together with Enzastaurin at 2 different
concentrations (1μM and 2.5μM) and cell viability was measured 4 days later. As
expected, there was no change in the EC50 of Paclitaxel in the parental cell lines when
Enzastaurin was combined with Paclitaxel, whereas Enzastaurin treatment drastically
reduced EC50 in the resistant cell lines (Figure 3.6.3). Enzastaurin at both
concentrations was able to restore EC50 similar to that in the parental cell line,
although a slight dose-dependent effect can be seen in both resistant cell lines. At a
concentration of 2.5μM, Enzastaurin is potent enough to reduce the EC50 by
approximately 500-fold in both the resistant cell lines (4.7μM to 13.5nM in 500nM-
TR, and 4.9μM to 13.3nM in 1μM-TR), which is similar to the EC50 of Paclitaxel in
the parental cell line (3.8nM).
91
Figure 3.6.3 Enzastaurin treatment drastically reduces Paclitaxel EC50 in SUM159PT
resistant cell lines
A. Paclitaxel EC50 measurements in SUM159PT parental, 500nM-TR and 1μM-TR cell
lines upon Enzastaurin treatment. The cells were treated for 24 hours with increasing
concentrations of Paclitaxel in combination with Enzastaurin at 1μM and 2.5μM, after
which cell viability was measured. The EC50 was calculated using Sigmoidal dose-
response and plotting the non-linear regression curve fit (Error bars show ±SD, n=6).
B. Bar graph depicting the EC50 values of Paclitaxel in SUM159PT 500nM-TR and
1μM-TR as determined by previous experiment (n=1)
A
B
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3.7. Enzastaurin reduces tumorsphere growth in Paclitaxel-resistant
TNBC cell lines in the presence of Paclitaxel
To test whether the combination treatment has any effect on the cancer stem cell side-
population, we performed a tumorsphere assay. To do so, we first seeded the
SUM159PT and MB231 resistant cells in a 2D culture and treated them with
Paclitaxel and Enzastaurin as single or combination treatment. Four days later, the
dead cells were washed away and the remaining surviving cells were seeded for a
tumorsphere assay and grown for 7 days. As seen in Figure 3.7, single treatment in
both the resistant lines had no significant effect on the number of tumorspheres, in
contrary, Paclitaxel treatment alone even enhanced the amount of tumorspheres in
SUM159PT 1μM-TR. This is due to the selection the cells undergo in the presence of
Paclitaxel. The combination treatment, however, successfully eliminated tumorsphere
growth as seen by a significant reduction in tumorsphere number compared to the
Paclitaxel treatment group. These results are especially interesting since the cancer
cells are believed to be the ones surviving chemotherapy and leading to metastasis
and disease recurrence. Finding a way to target these cells is a major challenge in
overcoming cancer. We show here that the combination treatment of Paclitaxel with
the PKC-β inhibitor Enzastaurin can successfully eliminate this side population.
93
Figure 3.7 Enzastaurin in combination with Paclitaxel inhibits tumorsphere growth in
Paclitaxel-resistant cells
Measurement of tumorsphere growth in SUM159PT 1μM-TR and MB231 75nM-TR cell lines.
The cells were pre-treated with either single or combination treatment of Paclitaxel and
Enzastaurin for 4 days in 2D culture. The surviving cells were then seeded in ultra-low
attachment conditions for 7 days. They were then stained with INT o/n and the number of
tumorspheres was counted using a GelCount. (Error bars show ±SD, n=2).
94
3.8. PKC-α inhibitors are not capable of re-sensitizing resistant cells to
Paclitaxel
It has been shown before that PKC-α plays an important role in breast cancer stem
cells and targeting PKC-α can specifically target breast CSCs (Tam et al., 2013). This
is due to a switch from EGFR to PDGFR signaling in CSCs which leads to the PKC-
α-dependent activation of FRA1. To test wether re-sensitization of Paclitaxel-resistant
cells is isoform-specific, we tested different PKC-α inhibitors in an attempt to achieve
the same re-sensitization effect as with the PKC-β inhibitors. We treated SUM159PT
and MB231 parental and resistant cells with 2 different PKC-α inhibitors and
measured cell viability over the course of 5 days. The two inhibitors we used, Gö-
6976 and Ro-32-0432, inhibit PKC-α with an approximately 3-fold lower IC50
compared to PKC-β (Gö-6976: 2.3nM vs 6.2nM, Ro-32-0432: 9nM vs 28nM). Both
inhibitors had no significant effect as single treatment in both parental and resistant
cells in SUM159PT and MB231 (Figure 3.8.1). In addition, the parental cells were
not effected by the combination treatment, whereas the resistant cells showed only a
minor effect. MB231 75nM-TR showed no significant reduction in cell viability
when PKC-α inhibitors were combined with Paclitaxel, but SUM159PT 1μM-TR
exhibited reduced cell viability with only one of the inhibitors (Ro-32-0432).
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Figure 3.8.1 PKC-α inhibitors are not able to sensitize Paclitaxel-resistant cells in the
same manner as Enzastaurin
Measurement of cell viability in SUM159PT (A) and MB231 (B) parental and resistant cell
lines after treatment with the PKC-α inhibitors, Gö-6976 and Ro-32-0432, and Paclitaxel. The
Paclitaxel concentrations used were the maintenance concentrations for the resistant cell lines
and corresponding concentrations for the parental cell lines. The cells were treated with 2
different PKC-α inhibitors at different concentrations and cell viability was measured each day.
The measurements were plotted relative to measurements on Day 0. (Error bars show ±SD,
n=6).
A
B
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We then tested these inhibitors together with another PKC-α inhibitor, Ro-31-8220,
in anchorage-independent conditions. SUM159PT parental and both resistant cell
lines were seeded for a Methylcellulose assay, treated for 13 days after which they
were stained in order to count the number of colonies. In both resistant cell lines,
500nM-TR and 1μM-TR, there was no significant difference between inhibitor single
treatment and in combination with Paclitaxel (Figure 3.8.2). Ro-31-8220 inhibited
colony formation only in the parental cell lines at a high concentration, as single
treatment and in combination with Paclitaxel. The parental cells grew slightly less
colonies when Paclitaxel was added.
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These results show that the effect of re-sensitization in Paclitxel-resistant TNBC cell
lines is isoform-specific. Inhibitors specifically targeting PKC-β can significantly
reduce cell viability in 2D and 3D assays, and induce apoptosis in the presence of
Paclitaxel, whereas PKC-α-specific inhibitors show no significant effects. Although
PKC signaling is very broad and many PKC isoforms share similar functions, there
are differences in the downstream targets of the various isoforms which can account
for their different functions in Paclitaxel-resistance and re-sensitization. PKC-α
promotes invasion of breast cancer cells and its expression correlates with tumor
grade and poor prognosis (Lonne et al., 2010), but it has not been linked with chemo-
resistance, which is consistent with our results.
Figure 3.8.2 PKC-α inhibitors have no effect on colony formation of SUM159PT
Paclitaxel-resistant cells in the presence of Paclitaxel
Measurement of colony formation in anchorage-independent conditions in a Methylcellulose
assay of SUM159PT parental and resistant cell lines. The Paclitaxel concentrations used are the
maintenance concentrations for the resistant cell lines and 0.73nM for the parental cell line. The
cells were seeded for the assay and simultaneously treated with the inhibitors, Ro-31-8220, Gö-
6976 and Ro-32-0432, at 1μM, 2.5μM and 5μM. The cells were stained with INT after 13 days
and the colonies were counted. (Error bars show ±SD, n=6).
98
3.9. PKC-β expression and protein levels in breast cancer vs. normal
breast samples
To expand the results we derived from experiments on cell lines to patient samples,
we explored 2 different publicly available online databases. The Human Protein Atlas
portal combines knowledge about the human proteome derived mainly from
antibody-based methods combined with transcriptomics analysis. Higher protein
levels of PKC-β were detected in ductal carcinoma and lobular carcinoma tissue
samples compared to normal breast tissue (Figure 3.9 (A)). To quantify the intensity
of the staining we then calculated the H-score for two groups: normal breast tissue vs.
breast cancer. The H-score is calculated from the intensity of staining from each
sample (low, medium and high) and the amount of samples of each staining intensity
in the combined group (see MATERIALS AND METHODS 2.17). We found that the
H-score was higher for the breast cancer samples compared to the normal breast
tissue, strongly suggesting that PKC-β plays a role in carcinogenesis in breast cancer.
Activation of PKC-β depends on PDK1, PLCγ, as well as DAG and Ca2+ and takes
place at the cell membrane, after which it translocates to the cytoplasm where it can
phosphorylate downstream targets such as GSK3β. Recent studies have shown that
PKC isoforms in TNBC cancers can relocate to the nucleus upon activation by TGFβI
and IL-1β, however, it is not clear what the exact mechanism behind this
translocation and the function of PKC in the nucleus is (Paul et al., 2014).
To get a clearer picture about the importance of PRKCB (PKC-β gene) in the
development of breast cancer, we examined mRNA expression in Breast Ductal
Carcinoma In Situ (DCIS) and normal breast tissue using Oncomine, a database of
cancer transcriptome profiles. DCIS is a type of non-invasive breast cancer where
ductal cells have undergone transformation but have not spread beyond the milk ducts
and have not invaded into the surrounding breast tissue. Often, DCIS progresses to
99
invasive breast cancer later on. As seen in Figure 3.9 (B), PRKCB mRNA levels were
significantly higher in Breast DCIS compared to normal breast in the Ma Breast 4
dataset. We also examined the expression levels of GSK3B, a direct downstream
target of PRKCB, which follows the same trend as PRKCB and also has significantly
higher mRNA levels in Breast DCIS. We then wanted to explore PRKCB expression
in the stroma of invasive ductal breast tumors, as stroma is an important player in
tumor formation, invasion and metastasis. The bulk of stroma surrounding tumors are
cancer-associated fibroblasts (CAFs) that are activated by cancer cells and have been
shown to induce EMT in breast cancer cells through paracrine TGF-β signaling (Yu
et al., 2014). In addition, one study found that PKC-β is up-regulated in the tumor
microenvironment in mouse mammary tumor virus-polyoma middle T-antigen
(MMTV-PyMY) induced mammary tumorigenesis (Wallace et al., 2014). When PKC-
β was diminished in the stromal compartment, the mice formed smaller tumors with
diminished collagen deposition. In concordance with these results, we found that
PRKCB was also increased in the stroma of invasive ductal breast carcinoma patient
samples compared to normal breast samples in the Karnoub dataset (Karnoub et al.,
2007).
These results suggest that PKC-β plays an important role in the development of breast
cancer, as well as in established ductal and lobular carcinoma.
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Figure 3.9 PKC-β is highly expressed in breast cancer compared to normal breast tissue
in patient samples and cell lines
A. The Human Protein Atlas. Patient samples (n=6) were stained with anti-PKC-β
antibodies in normal breast tissue, and ductal and lobular carcinoma of the breast
(n=28). Sample staining is shown for each group. The H-score was calculated
according to the staining intensity given by the database. To calculate the H-score the
staining intensity of each sample (1,2 or 3 for increasing intensity) was multiplied
with the percentage of samples in each intensity group and all 3 values were added
together. This calculation was performed for normal breast tissue and breast cancer
samples separately.
B. Oncomine. PRKCB and GSK3B expression are shown in Breast DCIS (GSK3B and
PRKCB: n=11) compared to normal breast tissue (GSK3B and PRKCB: n=14) (Ma
Breast dataset), and in the stroma of invasive ductal breast carcinoma (n=7) compared
to normal breast tissue (n=15) (Karnoub Breast dataset).
A B
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3.10. Mechanism of PKC-β signaling in resistance and upon sensitization
To understand how the inhibition of PKC-β can re-sensitize cells to Paclitaxel once
they have acquired resistance, we examined the modulation of signaling pathways
upon PKC-β inhibition. We first treated MB231 parental cells with increasing
concentrations of Enzastaurin for 24 and 48 hours and found no change in the total
protein levels of PKC-β, but phosphorylation of GSK3β on Serine 9 (GSK3βSer9) was
diminished (Figure 3.10 (A)). GSK3β is directly phosphorylated by PKC-β (Goode et
al., 1992), and is a reliable pharmacodynamic marker for Enzastaurin activity
(Miranda et al., 2015). Serine 9 phosphorylation leads to inactivation of its kinase
activity, therefore PKC-β inhibits GSK3β activity. Phosphorylation of PKCs is not a
reliable marker for its activation and activity as they don’t require phosphorylation of
the enzyme. Rather translocation of the protein to different cellular compartments
induced by upstream signaling is a reliable marker for monitoring its activity (Rosse
et al., 2010).
When we subjected SUM159PT and MB231 parental and resistant cell lines to single
treatment with 2.5μM Enzastaurin or combination treatment with Paclitaxel, we
found similar results. We examined phosphorylation of PKC-β at Thr641 which
mediates its binding to Hsp70 thereby regulating its stability and phosphorylation.
Thr641 is also needed for the catalytic function and autophosphorylation of PKC-β.
Protein levels of Thr641-phosphorylated PKC-β remained unchanged in all treatment
groups, but GSK3βSer9 was diminished upon Enzastaurin treatment in all cell lines
(Figure 3.10 (B)). Moreover, GSK3βSer9 basal levels were slightly lower in the
resistant cell lines compared to the parental cell lines in the DMSO treatment groups,
suggesting that GSK3β is over-activated and could be directly involved in chemo-
resistance as part of the PKC-β signaling cascade. We examined Mcl-1 protein levels
in order to deterimine its role in apoptosis of resistant cell lines upon combination
102
treatment. It has been published earlier that the pro-survival protein Mcl-1 plays a
crucial role in regulating apoptosis upon treatment with spindle poisons (Wertz et al.,
2011). Mcl-1 is directly phosphorylated by GSK3β on Serine 159 (Maurer et al.,
2006) and when phosphorylated, it can interact with the tumor-suppressor FBW7, the
substrate-binding component of a ubiquitin-ligase complex, which leads to its
ubiquitinylation and degradation. As seen below, Mcl-1 is degraded only in the
resistant cell lines when treated with Paclitaxel and Enzastaurin. Interestingly,
treatment with Enzastaurin alone has no effect on Mcl-1 levels in either the parental
or resistant cell lines.
These results show that Enzastaurin acts by inhibiting the inhibitory Serine 9
phosphorylation on GSK3β which activates GSK3β, and in turn leads to Mcl-1
degradation only when Paclitaxel is present in the treatment group.
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Figure 3.10 Western blot analysis of down-stream signaling of Enzastaurin treatment on
PKC-β, GSK3β and Mcl-1 in SUM159PT and MB231
Cells were treated for 24 hours and then harvested for western blot analysis.
A. MB231 parental cells were subjected to treatment with increasing concentrations of
Enzastaurin. At high concentrations, total PKC-β levels were slightly reduced, but
GSK3βSer9, a pharmacodynamic marker for Enzastaurin activity, was completely
diminished in all treatment groups.
B. SUM159PT and MB231 parental and both resistant cells were subjected to 24 hour
treatment with Enzastaurin and Paclitaxel as single treatment or in combination.
A
B
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3.11. GSK3β inhibitors recapitulate the effect of re-sensitization by
Enzastaurin
We used a GSK3β inhibitor and an activator to investigate whether the inhibition of
GSK3βSer9 downstream of PKC-β can also re-sensitize resistant cells to Paclitaxel, or
if it is merely a side-effect of Enzastaurin treatment. SB216763 has the same effect on
GSK3β Serine 9 phosphorylation as Enzastaurin. It acts as a GSK3β “activator” and
diminishes GSK3βSer9 phosphorylation. Lithium Chloride (LiCl), on the other hand,
has the opposite effect and inhibits GSK3β by increasing Serine 9 phosphorylation
levels. The mechanism by which LiCl inhibits GSK3β is unclear but there are 2
proposed mechanisms: 1) it is a competitive inhibitor with regards to Mg2+; 2) it
inhibits potassium deprivation, which would otherwise lead to dephosphorylation of
GSK3β (Kramer et al., 2012). We treated MB231 parental and 75nM-TR resistant
cells with these inhbititors alone or together with Paclitaxel for 24 hours to examine
GSK3βSer9 levels. As seen in the western blot below, SB216763 successfully inhibits
GSK3β phosphorylation in both cell lines, whereas LiCl leads to increased levels of
phosphorylated GSK3β (Figure 3.11.1). These results are consistent with previously
published data (Tan et al., 2005). Other activating phosphorylation sites of GSK3β
show no change after treatment with the inhibitors alone or in combination with
Paclitaxel. Mcl-1 levels remain unchanged in all the treatment groups in the parental
cell lines, whereas they are slightly reduced in the combination treatment groups in
the 75nM-TR cell lines, more so in the SB216763 + Paclitaxel treatment group.
105
We then performed an anchorage-independent Methylcellulose assay to examine the
effect of the GSK3β inhibitors in 3D growth conditions in SUM159PT parental and
1μM-TR, and MB231 parental and 75nM-TR cell lines. The cells were treated for 13
days with 4 different inhibitors: 3 GSK3β “activators” (BIO, A1070722 and
SB216763) at 2.5μM and 5μM each, and one GSK3β inhibitor (LiCl) at 1mM and
2mM. The colonies were then stained with INT and counted.
In SUM159PT and MB231 parental cell lines, none of the inhibitors showed a
decrease in colony formation in the combination treatment groups compared to single
treatment with the inhibitors alone (Figure 3.11.2). However, SUM159PT 1μM-TR
cell line showed significant dose-dependent reduction in colony formation when
treated with the 3 GSK3β “activators” in the presence of Paclitaxel, whereas LiCl, the
true GSK3β inhibitor, had no effect on colony formation, even when Paclitaxel was
added. The results for MB231 75nM-TR cell line are not so conclusive because the
Figure 3.11.1 Western blot analysis of the effect of GSK3β inhibitors in MB231 parental
and resistant cells
MB231 parental and 75nM-TR cell lines were treated with SB216763 (5μM) and LiCl (2mM)
alone or in combination with Paclitaxel for 24 hours and then harvested for western blot
analysis. Paclitaxel was used at 0.46nM for the parental and 75nM for the resistant cell line.
SB216763 diminished GSK3βSer9, similar to Enzastaurin, whereas LiCl slightly increased
protein levels.
106
Paclitaxel only treated group showed reduced colony formation compared to the
DMSO treatment group, which is unexpected. This could be because in general, the
MB231 cells are not able to grow and form distinct colonies when seeded together
with Methylcellulose on the Bacto-Agar. The colonies formed by these cells are
“loose” and the cells seem to disperse, so the software used to count the colonies may
not give reliable results.
These results shows that GSK3β “activators”, which have the same effect on
GSK3βSer9 as Enzastaurin, are also able to sensitize resistant cells to Paclitaxel.
To determine wether the treatment with GSK3β compounds can induce apoptosis in
the presence of Paclitaxel, we treated SUM159PT 1μM-TR and MB231 75nM-TR
Figure 3.11.2 A set of GSK3β inhibitors reduce colony formation in the resistant cells in the
presence of Paclitaxel
Anchorage-independent Methylcellulose assay was used to determine colony formation in
SUM159PT parental and 1μM-TR, and MB231 parental and 75nM-TR cell lines after treatment
with various GSK3β inhibitors. The cells were treated for 13 days with different inhibitors alone
or in combination with Paclitaxel and, subsequently, stained with INT o/n before the colonies
were counted. (Error bars show ±SD, n=6).
107
cell lines with either SB216763 or LiCl alone or in combination with Paclitaxel for 4
days and performed cell cycle analysis using PI staining. Since we know that
Enzastaurin in combination with Paclitaxel can induce cell death in both cell lines, as
determined by an increase in the SubG1 population, we used this as our positive
control. The combination of SB216763 and Paclitaxel has a very similatr effect in the
SUM159PT resistant cell lines where the amount of cell death is almost identical to
the positive control (Figure 3.11.3 (A)). Surprisingly, the MB231 resistant cell line
showed a much more modest increase in the SubG1 population compared to
Paclitaxel treatment alone. One reason could be that SB216763 was not able to
completely deplete GSK3βSer9 protein levels in this cell line, as determined by
western blot analysis (see Figure 3.11.1). As expected, LiCl does not increase cell
death in combination with Paclitaxel in either of the cell lines when compared to the
Paclitaxel treatment group alone.
Although the amount of SubG1 in the GSK3β “activator” + Paclitaxel treatment
group in MB23 75nM-TR is much lower than the Enzastaurin + Paclitaxel treatmen
group, the number of floating cells in the wells, which represent cells that are most
likely undergoing apoptosis, are similar (Figure 3.11.3 (B)).
108
This data suggests that GSK3β plays an important role in chemo-resistance and
activation of GSK3β can re-sensitize cells to Paclitaxel. This could be the reason for
the effectiveness of Enzastaurin in re-sensitization, as GSK3β is directly downstream
of PKC-β and is phosphorylated by PKC-β.
Figure 3.11.3 GSK3β activation leads to increased cell death in the resistant cells in the
presence of Paclitaxel
A. Measurement of SubG1 fraction in the SUM159PT and MB231 resistant cell lines
after 4 days of treatment with SB216763 (5μM) and LiCl (2mM) as single treatment
or in combination with Paclitaxel. The Paclitaxel concentration used is 1μM and
75nM for the SUM159PT and MB231 resistant cell lines, respectively. (Error bars
show ±SD, n=3).
B. Microscope images of different treatment groups in SUM159PT and MB231 cell lines
before harvesting for cell cycle analysis analysis.
A B
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3.12. Paclitaxel-resistant cell lines exhibit cross-resistance to other
chemotherapeutic compounds
Vincristine (VCR) is a spindle poison that belongs to the same class of
chemotherapeutic drugs as Paclitaxel, although its effect on microtubule stability is
opposite. While Paclitaxel binds to tubulin and promotes microtubule stabilization,
VCR binds to tubulin dimers, thereby inhibiting microtubule assembly. Both drugs
lead to cell cycle arrest in the mitotic phase, but cells treated with VCR arrest in the
prometaphase. Due to the missing spindle tubules in these cells, the kinetochores
remain unattached and the SAC remains inactive, leading to mitotic arrest (Rudner
and Murray, 1996).
When tested whether Paclitaxel-resistant cells showed cross-resistance to VCR, we
found that, indeed, the resistant cells lines are also more resistant to VCR, compared
to the parental cell lines, as measured by their EC50 (Figure 3.12.1 (A)). SUM159PT
Paclitaxel-resistant cell lines exhibit a 1000-fold increase in the EC50 compared to the
parental cell lines (3.62μM vs. 3.17nM), whereas the shift in EC50 of VCR is less
drastic in the MB231 resistant cells compared to parental (23.21nM vs. 3.01nM). In
both resistant cell lines, however, Enzastaurin is able to restore VCR-sensitivity and
reduce the EC50 similar to those of the parental cells.
To examine whether Enzastaurin can induce apoptosis in the resistant cell lines upon
VCR treatment, we treated cells with increasing concentrations of VCR and measured
the amount of SubG1 population as a measure of apoptosis (Figure 3.12.1 (B)).
Although VCR alone could induce small amounts of apoptosis, there is a significant
increase in apoptotic cells in both resistant cell lines when Enzastaurin is added to
VCR treatment. Therefore, Enzastaurin is able to restore VCR-sensitivity in the
resistant cell lines by inducing apoptosis.
110
We also included another chemotherapeutic drug, Adriamycin, which is not a spindle
poison and targets cells through a different mechanism, in order to determine whether
re-sensitization with Enzastaurin is dependent on mitotic arrest. It is routinely used
for the treatment of a number of different cancers such as breast, gastric and ovarian
cancers, sarcoma and mutiple myeloma (Cortes-Funes and Coronado, 2007, Weiss,
Figure 3.12.1 Paclitaxel-resistant cells exhibit cross-resistance to Vicristine which can be
reversed with Enzastaurin
A. Paclitaxel EC50 measured by cell viability assay in SUM159PT parental and 1μM-TR,
and MB231 parental and 75nM-TR cell lines. The cells were treated for 4 days before
cell viability was measured. (Error bars show ±SD, n=6)
B. Percentage of SubG1 fractions after 4 days of treatment in SUM159PT 1μM-TR and
MB231 75nM-TR cell lines with different concentrations of Vincristine (0.1μM, 1μM
and 5μM for SUM159PT 1μM-TR, and 1nM, 10nM and 100nM for MB231 75nM-TR)
with and without Enzastaurin (2.5μM). (Error bars show ±SD, n=3, **: p-value <0.01,
***: p-value<0.001, ****: p-value <0.0001).
A
B
111
1992). Adriamycin (ADR) is an anthracycline, it intercalates into the DNA and
inhibits topoisomerase II, an enzyme that detangles and relaxes DNA supercoils
(Tewey et al., 1984). Another mechanism of action has been proposed which includes
the generation of free radicals that cause damage to the cellular membranes and DNA
(Gewirtz, 1999). By inhibiting topoisomerase II, the DNA chain which has been
broken for replication, will not be released and the cells are unable to undergo
mitosis.
Paclitaxel-resistant SUM159PT and MB231 cells also show higher resistance to ADR
compared to the parental cell lines, as determined by an approximate 1000-fold
increase in the EC50 to ADR (Figure 3.12.2 (A)). However, Enzastaurin is only partly
able to restore ADR-sensitivity in SUM159PT, but not in the MB231 resistant cell
line. When we determined the SubG1 fraction in single and combination treatment
groups, we found that althoug ADR can reduce ADR EC50 in SUM159PT resistant
cell line, it is unable to induce significantly higher levels of apoptosis in combination
with Paclitaxel (Figure 3.12.2 (B)). In MB231 75nM-TR, the increase in apoptotic
cells is also not sighnificant in the combination vs. single treatment group. This is
likely due to the difference in their chemo-toxic effects. This data suggests that
Enzastaurin is only able to significantl restore sensitivity for drugs that induce mitotic
arrest as part of their cytotoxic mechanism.
112
Figure 3.12.2 Paclitaxel-resistant cells exhibit cross-resistance to Adriamycin
A. Paclitaxel EC50 measured by cell viability assay in SUM159PT parental and 1μM-TR,
and MB231 parental and 75nM-TR cell lines. The cells were treated for 4 days before
cell viability was measured. (Error bars show ±SD, n=6).
B. Percentage of SubG1 fractions after 4 days of treatment in SUM159PT 1μM-TR and
MB231 75nM-TR cell lines with different concentrations of Adriamycin (0.1μM,
1μM and 5μM) with and without Enzastaurin (2.5μM). (Error bars show ±SD, n=3, *:
p-value<0.05).
A
B
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3.13. Enzastaurin can restore mitotic arrest in resistant cells upon
treatment with Paclitaxel
In order for Paclitaxel to be effective, the cells must undergo cell division and reach
mitosis. Paclitaxel then leads to stabilization of mitotic microtubules and,
subsequently, mitotic arrest. It has been found that Paclitaxel-induced cell death is
dependent on the activation of the spindle assembly checkpoint (SAC), and the
suppression of Mad2 and BubR1, two important checkpoint genes, results in higher
resistance to Paclitaxel (Sudo et al., 2004).
3.13.1. Paclitaxel-induced mitotic arrest and cell death in sensitive cell lines
To validate these results in our cell lines, we treated MB231 parental cell lines with
Paclitaxel and used an anti-phospho-Histone H3 (p-H3) antibody to detect cells in the
mitotic phase. We found that upon treatment with Paclitaxel for 24 hours, 16.87% of
the cells were arrested in the mitotic phase compared to only 1.5% in untreated
conditions, and at 48 and 72 hours, this population decreased dramatically,
accompanied by an increase in the number of multinucleated cells (Figure 3.13.1
(A)). This can be explained by the fact that cells arrested in the mitotic phase can
undergo different fates. One consequence of mitotic arrest is mitotic cell death, but
cells that can overcome mitotic arrest can slip into the next interphase (mitotic
slippage), or undergo one round of aberrant mitosis. As correct mitosis is not
possible, the cells become tetraploid and will eventually undergo senescence or cell
death (Vitale et al., 2011). Additionally, we observed an increase in the number of
apoptotic cells from 24 to 72 hours determined by the SubG1 population, indicating
that most of the tetraploid cells and those arrested in the mitotic phase, do in fact
undergo cell death later on (Figure 3.13.1 (B)).
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To look closer into the mechanism of cell death upon treatment with Paclitaxel, we
treated SUM159PT and MB231 parental cells with increasing concentrations of
Figure 3.13.1 Paclitaxel induces mitotic arrest and subsequent cell death in MB231 parental
cells
A. MB231 parental cells were treated for 24, 48 and 72 hours with 50nM Paclitaxel before
they were harvested and stained with Propidium Iodide and anti-pH3 antibody. FACS
analysis was performed measuring DNA content and intensity of p-H3 staining.
B. MB231 parental cells were treated for 24, 48 and 72 hours with 50nM Paclitaxel, then
harvested, fixed and stained with Propidium Iodide. FACS analysis was performed to
measure SubG1 population in the different treatment groups. (n=1).
C. Bar graph depicting percentages of SubG1 after Paclitaxel treatment as determined by
the previous experiment.
B
A
DMSO
Paclitaxel
50nM
DMSO
Paclitaxel
50nM
C
115
Paclitaxel and performed western blot to detect changes in signaling pathways. The
cells were harvested at 24 hours when most of the affected cells are still arrested in
the mitotic phase. We found that Aurora B levels are increased, especially in
SUM159PT cells, which is due to the activation of the SAC in the mitotic phase
(Figure 3.13.2 (A)). It is believed that unattached kinetochores lead to an
accumulation of Aurora B at the centromeres of chromosomes in the metaphase plate
and Aurora B is stabilized when there is a lack of tension from the microtubules
between the sister chromatids (Musacchio and Salmon, 2007). This is consistent with
the fact that Paclitaxel arrests cells in the mitotic phase inhibiting tension on the
chromatids. Furthermore, levels of phosphorylated C-Raf are higher in the Paclitaxel
treated samples, and it has been shown that C-Raf phosphorylation is a requirement
for Paclitaxel-induced apoptosis (Blagosklonny et al., 1996). C-Raf, when activated,
can post-translationally phsophorylate Mcl-1, which leads to its ubiquitination and
subsequent proteasome-mediated degradation. Mcl-1 downregulation correlates with
increased levels of Poly(ADP-ribose) polymerase (PARP) cleavage, one of the main
cleavage targets of Caspase 3 and a marker for cells undergoing apoptosis.
We then went on to determine whether the inhibition of Aurora B could abolish
Paclitaxel-induced cell death. To do so, we treated SUM159PT and MB231 parental
cells with the Aurora B inhibitor VX-680, a potent and highly selective Aurora
Kinase inhibitor. As expected, we found that treatment with VX-680 could abolish
cell death in Paclitaxel-treated cells up to appromximately 70% in the MB231
parental cells (Figure 3.13.2 (B)). The effect in SUM159PT cells is milder but
remains notable with approximately 40% reduction. As seen in the microscope
images, Paclitaxel treated cells evidently undergo cell death with an increase in the
number of floating cells, whereas the addition of VX-680 can rescue apoptosis
(Figure 3.13.2 (C)).
116
These results show that Aurora Kinases B is upregulated upon Paclitaxel treatment of
parental Paclitaxel-sensitive cells, and that inhibition of Aurora Kinases partly
inhibits Paclitaxel-induced cell death.
Figure 3.13.2 Up-regulation of Aurora Kinase B is crucial for Paclitaxel-induced cell
death
A. SUM159PT and MB231 parental cells were treated with increasing concentrations of
Paclitaxel (Taxol) for 24 hours and downstream signaling was determined using
western blot analysis.
B. SUM159PT and MB231 parental cells were treated with 50nM Paclitaxel alone or in
combination with the Aurora Kinase inhibitor (VX-680) at 1μM for 4 days and then
harvested for FACS analysis. The SubG1 fraction was determined to detect the
amount of cell death. (n=1).
C. Microscope images of cells before they were harvested for FACS analysis.
A
B C
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3.13.2. Paclitaxel-resistant cells over come mitotic arrest by down-regulating
Aurora Kinases
We then investigated the effect of Paclitaxel and Enzastaurin treatment on signaling
in the resistant cells, especially in terms of Aurora Kinase activation. As seen in
Figure 3.13.3, we found that C-Raf is increasingly phosphorylated upon combination
treatment, more so in SUM159PT resistant cells. As expected, this treatment group
also shows increased levels of cleaved PARP, indicative of apoptosis. Aurora B
levels are lower in both resistant cell lines compared to their parental counterpart.
However, combination treatment of Enzastaurin and Paclitaxel is able to restore
Aurora B levels, whereas single treatment alone has no effect. These findings are
similar to Aurora A, however the increase in protein levels upon combination
treatment are not as striking in MB231 resistant cell lines. Moreover, Aurora A levels
in the MB231 resistant cell line are similar to the parental cell line. These results
suggest that Enzastaurin treatment can restore SAC activation and mitotic arrest in
the resistant cell lines in the presence of Paclitaxel, by restoring accumulation and
stabilization of Aurora Kinases, especially Aurora Kinase B.
118
Figure 3.13.3 Enzastaurin restores levels of Aurora Kinase B in resistant cell lines upon
treatment with Paclitaxel
SUM159PT parental and 1μM-TR, and MB231 parental and 75nM-TR cell lines were treated
with Paclitaxel or Enzastaurin (2.5μM) alone, and in combination. Paclitaxel was used at
0.73nM and 1μM for SUM159PT parental and resistant cell lines, and 0.46nM and 75nM for
MB231 parental and resistant cell lines. Cells were harvested after 24 hours and levels of
protein were detected using western blot analysis.
A. Protein levels detected by western blotting. The blots were incubated with antibodies
targeting Serine 388-phosphorylated C-Raf, cleaved PARP, Aurora kinase A and B.
B. Quantification of Aurora kinase B bands detected by western blotting. Image Lab 4.1
software was used to quantify bands relative to the DMSO control of the parental cell
line for both SUM159PT and MB231.
A
B
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We then dived further into elucidating the function of Aurora Kinases in re-
sensitizing resistant cells to Paclitaxel. To do so, we screened various Aurora Kinase
inhibitors in the resistant cell lines in order to determine whether these can rescue
cells when treated with Paclitaxel and Enzastaurin. If the addition of an Aurora
Kinase inhibitor to the combination treatment can inhibit cell death in resistant cell
lines, the we can conclude that Aurora Kinases are required for the re-sensitization
effect. This, in turn, suggests that cell death upon combination treatment is a result of
mitotic arrest induced by Paclitaxel due to Enzastaurin.
We found that out of all the Aurora Kinase inhibitors we used (VX-680, TAK901,
AZD1152-HQPA, MLN8237, and ZM447439), VX-680 could rescue combination
treatment effect in all the 4 resistant cell lines while having minimal effect on cell
viability when used as single treatment (Figure 3.13.4 (A)). In general, all the
inhibitors showed a more significant rescue effect in the MB231 resistant cell lines,
whereas VX-680 had the strongest effect in SUM159PT resistant cells. VX-680
(Tozasertib) is a potent and selective small-molecule inibitor of the Aurora Kinases
with apparent inhibition constants (Ki(app)) of 0.6nM, 18nM and 4.6nm for Aurora
Kinases A, B and C, respectively.
120
Figure 3.13.4 Various Aurora Kinase inhibitors can rescue cell death upon combination
treatment of Paclitaxel with Enzastaurin in Paclitaxel-resistant cell lines
A. SUM159PT and MB231 resistant cell lines were treated with various Aurora Kinase
inhibitors alone or in combination with Paclitaxel and Enzastaurin. Aurora Kinase
inhibitors were all used at 1μM and 5μM. Cell viability was measured at Day 0 and
Day 4. Dat 4 measurements were normalized to Day 0 measurements and plotted
(Error bars show ±SD, n=6).
B. Table listing Aurora Kinase inhibitors with the respective Ki (inhibitory constant)
concentrations for the different Aurora Kinases (in nM).
B
A
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To show that, indeed, the Aurora Kinase Inhibitor VX-680 can inhibit cell cycle
arrest in the resistant cell lines upon combination treatment, we performed p-H3
staining of the different treatment groups at 24, 48 and 72 hours in MB231 75nM-TR.
We found that the resistant cell line does not undergo mitotic arrest in the presence of
Paclitaxel, as do the parental chemo-sensitive cell lines, as shown earlier. However,
when Enzastaurin is added to the Paclitaxel treatment, mitotic arrest is restored in the
resistant cell lines, as seen by an increase in the number of p-H3+ cells from 2.65%
(Paclitaxel treatment only) to 34.51% (combination treatment) (Figure 3.13.5).
Enzastaurin treatment alone has no effect on the mitotic phase. At later time points,
48 and 72 hours, the cells that have arrested in the mitotic phase become
multinucleated, as suggested by a 4N DNA content. They remain pH3-positive,
therefore they are in the mitotic phase, due to the effect of Paclitaxel, which stabilizes
microtubules. We showed earlier that VX-680 can rescue decreased cell viability
upon combination treatment, and here we show that this is due to the inhibition of cell
cycle and mitotic arrest. Addition of VX-680 to the combination group depletes
mitotic arrest upon combination treatment. When the resistant cells fail to activate the
SAC, then they cannot undergo mitotic arrest and, subsequent cell death.
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Figure 3.13.5 Aurora Kinase B inhibition rescues cell death upon re-sensitization of
Paclitaxel-resistant cells by Enzastaurin
Cells were treated for 24, 48 or 72 hours with 75nM Paclitaxel, 2.5μM Enzastaurin, and 1μM
VX-680 in different combination groups. They were then stained with anti-phospho-H3
antibody and FACS analysis was performed.
123
3.14. Aurora B levels negatively correlate with Paclitaxel sensitivity in a
panel of basal-like breast cancer cell lines
As shown earlier, Aurora B and partially Aurora A levels are downregulated in the
resistant compared to the parental cell lines, therefore we went on to explore whether
there is a correlation between Aurora Kinase expression and Paclitaxel sensitivity in a
panel of basal and luminal breast cancer cell lines and the normal breast epithelial cell
line MCF10A. Aurora Kinase B (AURKB) and A (AURKA) expression levels were
obtained from the Neve et al. dataset on Gene Expression-Based Outcome for Breast
Cancer Online (GOBO) (Neve et al., 2006), where they performed transcription
profiling of a panel of 51 breast cancer cell lines. We measured Paclitaxel EC50 in all
cell lines using cell viability assay. We then plotted both sets of data on a scatter plot
with AURKB/A expression on the y-axis and Paclitaxel EC50 on the x-axis. As seen
in Figure 3.14, there is no significant correlation between AURKB expression and
EC50 when all the cell lines are combined. However, when we selected only for the
basal breast cancer cell lines, the correlation is highly significant, with an R2
coefficient of determination of 0.8411 (p-value=0.0013). This suggests that Aurora B
plays an important role in Paclitaxel-sensitivity, which can be attributed to its
function in mitotic spindle attachment and mitotic arrest.
We went on to determine whether Aurora A levels also determine sensitivity to
Paclitaxel, as Aurora A plays an important role in early mitosis (prophase) in
regulating correct function of the centrosomes. Interestingly, we found no correltation
between AURKA expression and Paclitaxel EC50 in all of the breast cancer cell lines,
suggesting that Paclitaxel sensitivity is independent of Aurora Kinase A.
124
Figure 3.14 Aurora Kinase B expression negatively correlates with Paclitaxel sensitivity in
a panel of basal breast cancer cell lines
AURKB/A expression values were obtained from Neve et al. dataset (Neve et al., 2006) from
Gene Expression-Based Outcome for Breast Cancer Online (GOBO). Transcription profiling
was performed using A-AFFY-33-Affymetrix GeneChip Human Genome HG-U133A [HG-
U133A]. Expression values were obtained by performing Robust Micro-Array Average (RMA)
normalization and the values were Log2 transformed. AffyID for AURKB: 209464_at, AffyIDs
for AURKA: 208079_s_at and 204092_s_at. Paclitaxel EC50 was measured using cell viability
assay. Cells were treated for 4 days with increasing concentrations of Paclitaxel (n=6).
125
3.15. Targeted siRNA knock-down of Aurora B inhibits re-sensitization of
Paclitaxel-induced mitotic arrest
To further examine whether the rescue effect seen with VX-680 is specific to the
Aurora B isoform, we performed siRNA-based knock-down of AURKB. We
knocked-down Aurora B using 2 different siRNA sequences (siAurB (Pre) and
siAurB) in SUM159PT parental and 1μM-TR, and MB231 parental and 75nM-TR
cell lines. 24 hours after knock-down we seeded the cells to measure cell viability
over a course of 5 days with different treatment groups. As seen in the Figure 3.28
(A), both siRNA sequences are able to significantly rescue cell viability in the
MB231 parental cell lines upon Paclitaxel treatment at 5nM and 50nM. However, the
effect is milder in SUM159PT parental cells lines, which is not surprising as the basal
levels of Aurora B mRNA expression are lower in SUM159PT compared to MB231
cell lines (Figure 3.15.1 (C)). This trend can further be explained by Paclitaxel
sensitivity of both cell lines, as SUM159PT is intrinsically more resistant to
Paclitaxel compared to MB231.
In case of the resistant cell lines, siRNA knock-down of Aurora B significantly
rescued cell viability upon combination treatment in SUM159PT 1μM-TR (only
siAurB (Pre)) and MB231 75nM-TR (Figure 3.15.1 (B)). Again, the rescue effect is
stronger in MB231 resistant cell lines compared to SUM159PT resistant cell lines due
to the higher levels of AURKB mRNA (Figure 3.15.1 (C)) . When the mRNA levels
are already very low, as in SUM159PT cells, it is no surprise that the knock-down
effect is weak. In general, resistant cell lines have lower levels of AURKB mRNA
and this is in agreement with the database results showing a negative correltation
between Paclitaxel EC50 and AURKB expression levels (Figure 3.14).
126
A
B
C
127
To examine whether this rescue effect is due to the inability of mitotic arrest and SAC
activation as a result of Aurora Kinase B knock-down, we performed cell cycle
analysis. We treated MB231 parental and 75nM-TR cells with combination treatment
for 24 hours after knock-down and performed pH3-staining. Our results replicate our
findings using the Aurora Kinase inhibitor VX-680. SiRNA-based knock-down of
Aurora Kinase B abolished mitotic arrest after Paclitaxel treatment in the parental cell
lines, and after combination treatment in the resistant cell lines (Figure 3.15.2).
Figure 3.15.1 Aurora Kinase B is crucial for Paclitaxel-induced inhibition of cell viability
in parental and resistant SUM159PT and MB231 cell lines
A. siRNA knock-down of Aurora B in SUM159PT and MB231 parental cell lines. Two
siRNA sequences were used to knock-down Aurora B in the cell lines (siAurB (Pre)
and siAurB) at a concentration of 40nM. 24 hours after siRNA treatment, cells were
re-seeded treated with 5nM or 50nM Paclitaxel and cell viability was measured over
a course of 5 days and normalized to the DMSO control. (Error bars show ±SD, n=6,
****:p-values<0.0001).
B. siRNA knock-down of Aurora B in SUM159PT 1μM-TR and MB231 75nM-TR.
Two siRNA sequences were used to knock-down Aurora B at a concentration of
40nM. After 24 hours, the cells were re-seeded and treated with DMSO, or 2.5μM
Enzastaurin + Paclitaxel 1μM or 75nM in SUM159PT 1μM-TR and MB231 75nM-
TR, respectively. Cell viability was measured over a course of 5 days and
measurements were normalized to the DMSO control. (Error bars show ± SD, n=6,
****:p-values<0.0001).
C. Validation of Aurora B knock-down. 48 hours after siRNA treatment, cells were
harvested and Aurora B (AURKB) mRNA levels were determined using Realtime-
PCR.mRNA expression values were normalized to Actin (ACTB) (Error bars show
± SD, n=3).
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These results suggest that Enzastaurin can restore Paclitaxel-induced mitotic arrest in
the resistant cell lines dependent on Aurora Kinase B.
Figure 3.15.2 Aurora Kinase B abolished Paclitaxel-induced mitotic arrest
p-H3 staining of MB231 parental and 75nM-TR cell lines after Aurora B siRNA knockdown.
Cells were treated with two different siRNA sequences targeting Aurora Kinase B (siAurB
(Pre) and siAurB) at a concentration of 40nM. 24 hours later, the cells were re-seeded and
treated with Paclitaxel alone (parental) or Paclitaxel + Enzastaurin (resistant) and p-H3 staining
was performed after 24 hours of treatment.
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CHAPTER 4: DISCUSSION
130
4.1. Enzastaurin: mechanism of Paclitaxel-sensitization
The exact mechanism by which Enzastaurin inhibits cell viability is still unclear.
Enzastaurin was originally synthesized as a Protein Kinase C (PKC) β-specific
inhibitor based on the structure of Staurosporine, a natural compound which is a
potent inhibitor of PKC but has limited selectivity for ATP-dependent kinases and
individual PKC isozymes in vitro (Faul et al., 2003). Moreover, PKCs regulate ATP-
binding cassette sub-family B member 1 (ABCB1). Interestingly, it was found that
Enzastaurin could sensitize ABCB1-expressing Vincristine-resistant neuroblastoma
and rhabdomyosarcoma cells, while having minimal effect on the cell viability of
non-resistant cells (Michaelis et al., 2015). It was suggested that Enzastaurin directly
interacts with ABCB1 and inhibits ABCB1 ATPase activity, rather than through
PKC, as determined by docking studies. Moreover, siRNA-targeted knock-down of
PKC-β, but not PKC-α, could inhibit the efflux pumping mechanism of ABCB1.
Other studies show that PKC-α and -β can directly phosphorylate and regulate
ABCB1, although this seems to be cell type specific. It was found that in ovarian
cancer cells PKC-α and PKC-β knock-down could reverse ABCB1-mediated multi
drug resistance and Taxol-resistant cells (Masanek et al., 2002).
In the SUM159PT Paclitaxel-resistant cells lines we have generated, we found a
notable increase in the ABCB1 expression when compared to the parental cells as
determined by microarray analysis (Figure 4.1). Although many of the ABC
transporters show increased expression in the resistant cell lines, ABCB1 expression
is the most distinguished. Therefore, the effect of re-sensitization to Paclitaxel
through Enzastaurin treatment could be due to its direct or indirect inhibition of
ABCB1 activity.
131
ABCB1, also known a P-glycoprotein (P-gp) or multidrug resistance protein 1
(MDR1), is phosphorylated by PKCs and PKA at the serine residues in its linker
region (Chambers et al., 1994). Some have even suggested that these
phosphorylations are required for the translocation and activation of ABCB1
(Germann et al., 1996). There are 3 PKC-mediated phosphorylation residues at
Ser661, Ser667 and Ser671, and 3 PKA-mediated phosphorylation residues at Ser667,
Ser671 and Ser683, and all of them are in the intracellular linker region anchoring the
ABCB1 protein in the cellular membrane. Interestingly, the 2 classes of inhibitors
which showed the greatest effect in the kinase inhibitor screen on our Paclitaxel-
resistant cell lines were PKC and PKA inhibitors. We decided to focus on PKC-β for
this project as these inhibitors induced slightly higher levels of apoptosis in the cell
lines in combination with Paclitaxel, and PKC-β inhibitors are already being used in
clinical trials. However, the kinase screen revealed an important role for PKA in
Figure 4.1 Increased expression of ABC transporters in Paclitaxel-resistant SUM159PT
cell lines
Microarray analysis was performed to determine gene expression changes in Paclitaxel-
resistant SUM159PT 500nM-TR and 1μM-TR cell lines compared to the parental cell line.
Quantile normalization was performed and after fold change cut-off =2 and FDR<0.05, the list
of significantly over-expressed genes was obtained. The graph shows the log2 gene expression
of various ABC transporters in the 2 resistant cell lines when normalized to the parental cell
line.
132
Paclitaxel-resistance, similar to PKC-β, which could be due to its role on ABCB1
phosphorylation and activation.
Moreover, the Enzastaurin concentration of 2.5μM, which we have used for all the
experiments in this study, did not lead to a reduction in the phosphorylation levels of
PKC-β (Figure 3.10).
These results suggest that one possible mechanism of Enzastaurin activity is through
inhibition of ABCB1 activity, either directly or indirectly through PKC-β, or a
combination of both. In order to investigate whether Enzastaurin regulates PKC-β,
thereby sensitizing cells, knock-down experiments should be performed. If this is
true, knocking down PKC-β should sensitize resistant cells to Paclitaxel in a similar
manner as Enzastaurin treatment. Similarly, over-expression of PKC-β in parental
chemo-sensitive cells should render the cells more resistant to Paclitaxel.
133
4.2. Aurora Kinase B expression levels as a biomarker for Paclitaxel-
sensitivity in basal-like breast cancer
We have shown that there is a correlation between Paclitaxel-sensitivity, as
determined by the EC50, and AURKB expression in basal-like, but not luminal-like,
breast cancer. The correlation is significant in 8 different cell lines (p-value=0.0013,
R2=0.8411) (Figure 3.27). Moreover, the Paclitaxel-resistant cells show down-
regulation of Aurora Kinase B in the presence of Paclitaxel, whereas when treated
with Enzastaurin and Paclitaxel, Aurora B levels and Paclitaxel-sensitivity are
restored (Figure 3.24). Also, siRNA-targeted knock-down of Aurora B confers
resistance to Paclitaxel (parental cells) and combination treatment (resistant cells)
(Figure 3.28). These results suggest that levels of Aurora Kinase B determine
Paclitaxel-sensitivity.
Paclitaxel is often used in first line treatment for TNBC in patients with primary
breast cancer due to high positive response to neoadjuvant chemotherapy, but the
relapse rate is high and the patients often suffer from metastatic and recurrent tumors.
In these cases it is crucial to find the right treatment option as these tumors are high
grade and progress quickly.
We propose the use of Aurora B expression levels as a biomarker for Paclitaxel-
sensitivity. The expression levels of Aurora B in the tumor tissue vs. normal breast
tissue can potentially determine the patient response rate to therapy containing
Enzastaurin in combination with a Paclitaxel-containing chemotherapy regimen. This
is especially beneficial for patients, as it allows us to eliminate Paclitaxel-based
therapies in patients with low levels of Aurora B, or to use Enzastaurin in
combination with Paclitaxel to restore Aurora B levels. In addition, we found that
Paclitaxel-resistant cells exhibit cross-resistance to other spindle poisons such as
Vincristine, therefore the use of Aurora B as a biomarker and its implications in
treatment can be extended to other mitotic spindle poisons.
134
4.3. Aurora Kinase B regulation
We have shown that Aurora Kinase B plays an important role in Paclitaxel-resistance
in our model of TNBC cell lines. Moreover, we showed that Aurora Kinase B levels
negatively correlate with Paclitaxel-sensitivity in a number of basal, but not luminal,
breast cancer cell lines. Our Paclitaxel-resistant cell lines have reduced expression
levels of Aurora Kinase B as compared to the parental cell line and Aurora Kinase B
knock-down inhibits Paclitaxel-induced cell death. This data suggests that modulation
of Aurora Kinase B expression could influence sensitivity to Paclitaxel. There are a
number of proteins which have an impact on Aurora Kinase B function through
phosphorylation and dephosphorylation (Figure 4.3). The major players are INCENP,
survivin and Borealin which are member of the Chromosomal Passenger Complex,
together with Aurora Kinase B.
The two major activators of Aurora Kinase B are TLK1 and Chk1, whose direct
mechanism of Aurora activation are not clear. More interestingly, the two most
important inhibitors of Aurora Kinase B are the protein phosphatases PP1 and PP2A.
Figure 4.2 Major regulator of Aurora Kinase B
Red arrows indicate protein phosphorylation events, whereas protein phosphatases are indicated
in blue (Carmena et al., 2009).
135
They form complexes with Aurora Kinase B and negatively regulate its kinase
function (Sugiyama et al., 2002).
Although there are no reports of PKC directly regulating Aurora Kinase B function, it
has been found that PKCs phosphorylate α-tubulin in the α/β-tubulin dimer which
results in elongation of microtubules in human breast cells (De et al., 2014). In
MB231 cells with high intrinsic PKC activity, this effect could be suppressed when
the cells were treated with a PKC inhibitor. These results link PKC to microtubules
and suggest that it could interact and influence Aurora Kinase B, however, they found
that PKC-α is most likely responsible for α-tubulin phosphorylation, rather than PKC-
β. Moreover, one study found that PKC-δ plays a role in meiotic spindle organization
in mouse oocytes and is associated with the meiotic spindle, thereby further
strengthening the possibility of PKC-β regulating Aurora Kinase B function through
its association with the mitotic spindle.
It is not clear whether the increase in Aurora B levels observed in the resistant cell
lines after sensitization with Enzastaurin is the reason for the cells being sensitized to
Paclitaxel, or merely a side effect of the mitotic arrest.
136
4.4. Mcl-1 degradation by GSK3β in the presence of Paclitaxel
Mcl-1 stability is primarily regulated by GSK3β, as it can phosphorylate Mcl-1 at
Ser155, Ser159, and Thr163 (Ding et al., 2007, Maurer et al., 2006). Once Mcl-1 is
phosphorylated, it can be ubiquitinated and will subsequently undergo proteasome-
mediated degradation. Three E3 ubiquitin ligases have been identified which
ubiquitinate Mcl-1: 1) Mule was the first ligase identified to ubiquitinate Mcl-1
(Zhong et al., 2005); 2) β-TrCP ubiquitinates and leads to Mcl-1 degradation which is
dependent on Mcl-1 phosphorylation by GSK3β (Ding et al., 2007); 3) FBW7 is a E3
ubiquitin ligase which ubiquitinates Mcl-1 in a GSK3β phosphorylation-dependent
manner (Wertz et al., 2011). Mcl-1 down-regulation is crucial for apoptosis induced
by mitotic arrest after treatment with spindle poisons. It was shown that inactivation
of FBW7 could increase Mcl-1 levels and resistance to Taxol and Vincristine in
ovarian cancer cell lines. Moreover, FBW7-depleted cells showed an increase in cells
undergoing mitotic slippage and a decrease in cells undergoing apoptosis, when
treated with Taxol or Vincristine.
In agreement with the reports demonstrating the importance of GSK3β in inducing
apoptosis through Mcl-1 upon Paclitaxel treatment, our Paclitaxel-resistant cell lines
show increased levels of Serine 9 phosphorylation of GSK3β, the inhibitory
phosphorylation site. Upon treatment with Enzastaurin, GSK3βSer9 levels are
completely depleted, however, this only leads to Mcl-1 degradation in the presence of
Paclitaxel. One possible reason is that Mcl-1 requires a priming phosphorylation in
order to be recognized by GSK3β as a substrate (Morel et al., 2009). It was shown
that upon cellular stress, the c-Jun-terminal protein kinase (JNK) phosphorylates Mcl-
1 on Thr144, which acts as a priming phosphorylation so that GSK3β can then
phosphorylate Mcl-1 at its own phosphorylation site. It has been shown that
Paclitaxel treatment activates JNK (Wang et al., 1999) and that inhibition of JNK
decreases Paclitaxel-induced apoptosis without changing the cell cycle profile (Wang
137
et al., 2006). This may explain why only in the combination treatment of Enzastaurin
+ Paclitaxel of our Paclitaxel-resistant cell lines, but not in the single treatment with
Enzastaurin alone, Mcl-1 undergoes degradation which subsequently leads to
apoptosis.
On the other hand, prolonged mitotic arrest leads to Mcl-1 destruction (Harley et al.,
2010), which leads to activation of BAX and BAK and subsequently mitochondrial
outer membrane permeabilization and activation of the caspase cascade and induction
of apoptosis (Youle and Strasser, 2008). Mcl-1 degradation upon mitotic arrest is
crucial for the induction of apoptosis. Degradation is regulated by the APC/CCdc20
complex, a multi-component ubiquitin ligase which is active during mitosis. It has
been shown that phosphorylation of Mcl-1 at Thr92 is mediated by Cdk1/cyclin-B
upon mitotic arrest which then leads to its proteolytic destruction by APC/CCdc20.
During mitosis, Mcl-1 levels gradually decrease and when mitotic arrest is too long
and Mcl-1 levels are too low, the cells will undergo apoptosis. Therefore, Mcl-1
degradation upon combination treatment in our resistant cells is possibly a result of
prolonged mitotic arrest.
These are two possible mechanisms of Mcl-1 degradation during re-sensitization of
Paclitaxel-resistant cells with Enzastaurin. At this point it is unclear if either of the
two possible mechanisms, a combination of both, or even a different mechanism is
responsible for Mcl-1 degradation in the resistant cell lines upon re-sensitization.
Either way, to examine whether Mcl-1 degradation is required for sensitization of
resistant cell lines, Mcl-1 over-expression should be able to block the effect of
Enzastaurin and rescue the sensitization effect of Enzastaurin with Paclitaxel in the
resistant cell lines.
138
CHAPTER 5: CONCLUSION
139
5.1 Conclusion
In this study we developed an in vitro model of Paclitaxel resistance in Triple
Negative Breast Cancer by treating the TNBC cell lines, SUM159PT and MB231,
over a long period of time with increasing concentrations of Paclitaxel and
maintaining them in medium containing Paclitaxel. The stable cell lines exhibited
EC50 values for Paclitaxel which were approximately 1000-fold and 300-fold higher
than their parental counterparts in SUM159PT and MB231, respectively. Gene
expression profiling showed that pathways for cell viability, cell survival,
proliferation, invasion, migration and microtubule dynamics were more highly
activated, whereas cell death and apoptotic pathways were less activated in the
resistant compared to the parental cell lines. Although, according to IPA analysis, the
network with the highest score has the NF-κB complex at its core, NF-κB inhibitors
were not selective for the resistant over the parental cell lines. This shows that the
NF-κB pathway is important in both resistant and sensitive cells.
In order to determine pathways which play a crucial role in resistance to Paclitaxel,
we performed a pharmacological screen with 180 small molecule inhibitors targeting
various kinases. The screen revealed PKC-β inhibitors to be effective in sensitizing
resistant cells to Paclitaxel. In the presence of Paclitaxel, the PKC-β inhibitor
Enzastaurin reduces cell viability, colony formation and induces cell death. We found
that the resistant cell lines show reduced levels of Aurora Kinase B, a member of the
spindle assembly checkpoint which plays an important role in Paclitaxel-induced cell
death (Sudo et al., 2004). Although the mechanism of sensitization is not entirely
clear, Enzastaurin restores Aurora Kinase B levels in the resistant cell lines, thereby
reinstating mitotic arrest and subsequent apoptosis when treated with Paclitaxel.
Interestingly, we found an inverse correlation between Aurora Kinase B and
Paclitaxel-sensitivity in basal-like, but not luminal, breast cancer cell lines, which
reinforces the importance of Aurora Kinase B in Paclitaxel-induced cell death.
140
Knocking-down Aurora Kinase B in the parental Paclitaxel-sensitive cell lines using
siRNA renders them more resistant to Paclitaxel treatment, whereas Aurora Kinase B
knock-down in the resistant cell lines inhibits reduction of cell viability upon
combination treatment of Paclitaxel with Enzastaurin. This shows that Aurora Kinase,
indeed, plays an important role in Paclitaxel-sensitivity. Moreover, knock-down
inhibited mitotic arrest in the presence of Paclitaxel in the parental and Enzastaurin-
treated resistant cell lines.
In summary, a kinase inhibitor screen identified PKC-β inhibitors which can re-
sensitize Paclitaxel-resistant TNBC cell lines by restoring Aurora Kinase B
expression.
141
5.2 Significance of findings and future prospects
TNBC is the most aggressive subtype of breast cancer. The mainstay of treatment is
chemotherapy which shows positive results in a neoadjuvant setting, however,
approximately 70% of patients will relapse and present with recurrent disease, often
accompanied by chemo-resistance and poor survival outcome. Based on the results
from this study, the PKC-β inhibitor Enzastaurin can potentially be used in the
treatment of TNBC patients suffering from recurrent and metastatic disease.
Especially since recurrent disease is a major issue in the treatment of these patients.
Despite the fact that clinical trials with Enzastaurin and Paclitaxel have proved that it
is safe when administered in combination with chemotherapeutics, the results
regarding improvement of patient survival have been inconclusive. A phase II
double-blind study which was conducted with and without Enzastaurin in
combination with Paclitaxel and Carboplatin as first-line treatment in advanced
ovarian cancer showed a slight increase in PFS for patients which received
Enzastaurin, however it was not significant (Vergote et al., 2013).
To provide proof of concept, in vivo studies using xenograft models should be
conducted. We propose to generate an in vivo mouse model of Paclitaxel resistance.
For this, chemo-sensitive cells are injected orthotopically into the mammary fat pad
of mice, once the tumors have grown they will be treated with Paclitaxel until they
regress. The mice are then monitored for recurrence (approximately 3-4 months) and
subsequently randomized into 4 different treatment groups: Control, Paclitaxel,
Enzastaurin, and combination of Paclitaxel with Enzastaurin. If it is true that
Enzastaurin can sensitize resistant cells to Paclitaxel, then mice in the combination
treatment group should have significant reduction of tumor size and increased
survival advantage compared to mice in other treatment groups. The tumors of mice
in the control, Paclitaxel, and Enzastaurin treatment groups, on the other hand, should
continue to grow.
142
As we have shown earlier, the Paclitaxel-resistant cell lines we generated show
resistance to other chemotherapeutic compounds: the spindle poison Vincristine; and
Adriamycin. Interestingly, Enzastaurin can greatly sensitize resistant cells to
Vincristine but to a much lesser extent to Adriamycin. This suggests that the
mechanism of sensitization by Enzastaurin is specific to microtubule-disrupting
drugs. Similar to the Paclitaxel resistance mouse model, in vivo mouse models of
Vincristine and Adriamycin resistance should be established to give proof-of-concept
that Enzastaurin can sensitize to Vincristine and Adriamycin. This would greatly
enhance the significance of using Enzastaurin in treating recurrent and chemo-
resistant TNBC patients, as Vincrstine and Adriamycin are 2 drugs which are also
often used in first line treatment of TNBC tumors. More importantly, since the
sensitization effect is stronger for Vincristine, Enzastaurin can potentially be used to
treat cancer recurrence in patients suffering from acute leukemia, Hodgkin’s and
Non-Hodgkin’s lymphoma, neuroblastoma, rhabdomyosarcoma, Ewing’s sarcoma,
multiple myeloma, thyroid cancer, brain tumors since Vincristine is one of the main
drugs used in first-line treatment of these patients.
To measure the true effect of Enzastaurin in increasing Paclitaxel-sensitivity, a
clinical trial should be conducted with patients suffering from residual and recurrent
disease and that have previously been treated with a combination of chemotherapeutic
agents containing Paclitaxel. These cancers are more likely to have developed
mechanisms of Paclitaxel resistance and will respond to treatments comprising of
Paclitaxel and Enzastaurin.
Furthermore, the mechanism of re-sensitization by Enzastaurin needs to be studied in
more detail. Although Enzastaurin was originally developed as a PKC-β inhibitor, it
has been shown to have other targets as well, such as ABCB1 (Michaelis et al., 2015).
Moreover, since the various PKC isoforms are structurally similar, we need to
identify the specific isoforms inhibited by Enzastaurin. Therefore, we need to
143
specifically knock-out the PKC-β isoform and determine whether this can sensitize
our Paclitaxel-resistant cell lines, similar to Enzastaurin treatment. CRISPR can be
used to knock-out PRKCB, rather than just performing a gene knock-down, which
does not ensure complete elimination of gene function. This is important since
residual gene expression can often be enough to induce down-stream signaling and
will not give us correct information of the function PKC-β in Paclitaxel-resistance.
In addition, as this study shows that there is a negative correlation between Aurora
Kinase B expression and Paclitaxel-sensitivity, Aurora Kinase B levels should be
monitored in patient tumor cells during the course of diagnosis and treatment. The
expression of Aurora Kinase B in the tumor tissue and the tissue surrounding the
tumor should be measured at the time of diagnosis and taken as a reference should the
patient present himself with residual or recurrent disease. If the levels of Aurora
Kinase B have decreased, it can be considered to treat the patient with a combination
of Paclitaxel and Enzastaurin.
Moreover, the strategy of restoring Aurora Kinase B expression to sensitize resistant
cancer cells can be applied to other cancers which show a reduction in Aurora Kinase
B levels as the disease progresses. Therefore, this study not only benefits TNBC
breast cancer patients but those presenting with different types of chemo-resistant,
recurrent cancers.
144
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