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Development and Application of Serum Assay to Monitor Response
to Therapy and Predict for Relapse in
Acute Myeloid Leukemia
Mohsen Ghahremanlou
A thesis submitted in confirmatory with the requirements
for the degree of Master of Science
Department of Institute of Medical Science
University of Toronto
©Copyright by Mohsen Ghahremanlou, 2013
II
Development and Application of Serum Assay to Monitor
Response to Therapy and Predict for Relapse in
Acute Myeloid Leukemia
Mohsen Ghahremanlou
Master of Science
Department of Institute of Medical Science
University of Toronto
2013
Abstract
The diagnosis and monitoring of AML relies predominantly on the identification of blast cells in
the bone marrow and peripheral blood. While at the time of diagnosis the identification of
leukemic cells is relatively easy, during remission the identification of small numbers of blasts is
problematic. This is most evident by the fact that patients who achieve complete remission
frequently relapse, despite pathologic examination indicating a marked reduction in leukemic
cell burden. In this thesis I have explored the potential of using serum proteins secreted by
leukemic cells as a means of monitoring disease in patients. To identify proteins that might be
useful for monitoring, I took advantage of published gene expression arrays and looked into
online bioinformatics databases. Using specific characteristics, I was able to identify
approximately 107 candidate proteins secreted by AML cells. RT-PCR analysis and ELISA
assays were performed to evaluate the variability of expressions and serum level differences of
twelve different proteins in the list.
III
Acknowledgments
I would like to thank Dr. Mark Minden for his guidance throughout the years on this project, and
for giving me the ability to pursue such interesting research. I am also thankful to both of my
committee members, Dr. Peter Ray and Dr. Scott Tanner for their helpful advice, suggestions,
and experimental ideas.
I want to thank Dr. Rob Laister for all of his suggestions and ideas related to my project. Thank
you to all of Dr. Minden lab members for their ideas and for being great lab mates.
Also thank you to Erik Dzneladze and Ayesha Rashid for being great colleagues and for helpful
advice and ideas.
I would also like to thank my wife, Mojgan, my daughter, Ghazal, and my son, Bamdad, for all
their love support and encouragement throughout these research years.
IV
Table of Contents
Table of Contents………............................................................................................................... IV
List of Tables................................................................................................................................. VII
List of Figures................................................................................................................................ VIII
Abbreviations................................................................................................................................. XI
Chapter 1………………………….............................................................................................… 1
Introduction: Acute Myeloid Leukemia (AML)............................................................................. 1
1 Acute Myeloid Leukemia……………..…….............................................................................. 1
1.1 Hematopoiesis........................................................................................................................... 1
1.1.1 Normal Hematopoiesis………………….............................................................................. 1
1.1.2 Leukemic Hematopoiesis....................................................................................................... 3
1.2 History……………….............................................................................................................. 4
1.3 Epidemiology……………….................................................................................................... 5
1.4 Signs and Symptoms…………................................................................................................. 5
1.5 Ethiology and Risk Factors…………....................................................................................... 6
1.6 Classification Systems…………….......................................................................................... 6
1.6.1 FAB Classification................................................................................................................. 7
1.6.2 WHO Classification………………….…………………….................................................. 8
1.7 Pathogenesis…………………................................................................................................. 9
1.8 Treatment…………………….……………………………..................................................... 10
1.8.1 Past Therapeutic Approaches………...………………………............................................. 11
1.8.2 Recent Therapeutic Developments, Targeted Therapies…................................................... 11
1.9 Complete Remission…………………..………………........................................................... 12
V
1.10 Minimal Residual Disease (MRD)…………......................................................................... 13
2 MRD Detection Methods.............................................................................................................. 14
2.1 Cytogenetics………………….…………………..................................................................... 14
2.2 Florescent In Situ Hybridization methods…………………...……….............................……. 14
2.3 Flow cytometry……………………………..………………………………….……….…….. 15
2.4 Polymerase Chain Reaction methods…………….………………………………..…….…… 16
2.4.1 MRD Detection of Recurrent Fusion Genes……………….………………………...….….. 16
2.4.2 MRD Detection of Aberrant Gene Expression……………………….……………....…….. 16
2.4.3 MRD Detection of Point Mutations………………...……………………………….…...…. 17
2.5 Serum Marker Detection Methods………...………..…………………………………...……. 17
2.5.1 microRNA Markers…………………………...……………………………………........…. 18
2.5.2 Methylated-DNA Markers………………………...………………………………….…...... 18
2.5.3 Serum and Plasma Protein Markers……………………..…………………………….....…. 19
2.5.3.1 Western blot Techniques...................................................................................................... 20
2.5.3.2 Mass Spectrometry (Mass-Spec, MS).................................................................................. 20
2.5.3.3 Enzyme Linked Immunosorbent Assay (ELISA)................................................................ 21
2.5.3.4 Multiplexing methods.......................................................................................................... 24
3 Serum Proteins for Disease Monitoring........................................................................................ 25
Thesis Rationale…………….......................................................................................................... 29
Hypothesis........................................................................................................................................ 29
Objectives/Specific Aims................................................................................................................. 30
Chapter 2…………………………………………………………………………………….……. 31
Identification of serum markers for monitoring disease activity in patients with acute leukemia.. 31
Introduction……………………………………………………….................................................. 32
Material and Methods……………………….................................................................................. 35
VI
1 Study Design................................................................................................................................ 35
2 Samples........................................................................................................................................ 35
2.1 Serum and plasma processing protocol.................................................................................... 36
3 Microarray studies and data analysis……………........................................................................ 36
4 Real Time RT-PCR (Q-PCR)....................................................................................................... 37
5 Enzyme-Linked Immunosorbent Assay (ELISA)........................................................................ 38
Results.............................................................................................................................................. 40
1 Microarray Data Analysis Results................................................................................................ 40
2 Confirmation of array results by Q-PCR...................................................................................... 45
3 Assessment of Candidate Protein Levels in Serum of AML Patients.......................................... 49
3.1 Angiopoietin 1………………………………………………................................................... 50
3.2 LGALs3BP…………………………………………………………........................................ 51
3.3 GDF15……………………………………………................................................................... 55
3.4 CCL3…………………………………………………………………………………………. 58
3.5 IGFBP2…………………………………………………………………………….…….…… 61
3.6 MMP2………………………………………………………………………………….…..…. 64
3.7 HGF…………………………………………………………………………………….….…. 67
4 Evaluation of Protein Levels in Pre-treatment and Post-therapy AML Samples….…………… 71
Discussion........................................................................................................................................ 80
Discussion and Future Directions.................................................................................................... 84
References……………………........................................................................................................ 100
VII
List of Tables
Table 1. FAB Classification of Acute Myeloid Leukemia 7
Table 2. WHO Classification of Myeloid Neoplasms and Acute Leukemia 9
Table 3. Previously Investigated Serum Proteins Related to AML 28
Table 4. Selected Secreted Proteins in AML and ALL 35
Table 5. Sequences of Primers used in Q-PCR 37
Table 6. List of ELISA kits 37
Table 7. Disease Status, Pre-treatment and Post-therapy values of AML Samples 75
Table 8. Normal and Disease Values for LGALs3BP, GDF15, and HGF in ELISA 75
VIII
List of Figures
Figure 1. Hematopoietic and Stromal Stem Cell Differentiation 2
Figure 2. Hematopoietic stem cell and differentiation 3
Figure 3. Normal and Leukemic Hematopoiesis 4
Figure 4. Estimated Proportion of New Cases in 2011 for All Types of Leukemia 5
Figure 5. Indirect, Sandwitch, and Competitive ELISA Methodologies 22
Figure 6-15. Expression Levels of Some Cytokines in Valk et al. Dataset 41
Figure 16. Expression Levels of LGALs3BP and Areg in Valk et al. Dataset 43
Figure 17. Expression Levels of 7 Secreted Proteins in Valk et al. Dataset 43
Figure 18. Q-PCR Results for LGALs3BP and GDF15 in SK-BR-3 and AML Cell Lines 44
Figure 19. Q-PCR Results for Areg in AML Patient Samples 45
Figure 20. Q-PCR Results for Ereg in AML Patient Samples 45
Figure 21. Q-PCR Results for GDF15 in AML Patietn Samples 46
Figure 22. Q-PCR Results for LGALs3BP in AML Patient Samples 46
Figure 23. Q-PCR Results for SEPP1 in AML Patient Samples 47
Figure 24. Q-PCR results for 5 selected genes in leukemic patients 47
Figure 25. ELISA Results for ANGPT1 in Normal and AML Samples 49
Figure 26. ELISA Results for LGALs3BP in Normal and AML Samples 51
Figure 27. ELISA Results for LGALs3BP in AML Samples, Serum-Plasma Comparison 52
Figure 28-29. Scatterplot and Boxplot of Serum-Plasma Comparison for LGALs3BP 52
Figure 30. ELISA Results for LGALs3BP in AML Samples, Time-point Comparison 53
Figure 31. Scatterplot of Time-point Comparison for LGALs3BP 53
Figure 32. ELISA Results for GDF15 in Normal and AML Samples 54
IX
Figure 33. ELISA Results for GDF15 in AML Samples, Serum-Plasma Comparison 55
Figure 34-35. Scatterplot and Boxplot of Serum-Plasma Comparison for GDF15 55
Figure 36. ELISA Results for GDF15 in AML Samples, Time-point Comparison 56
Figure 37. Scatterplot of Time-point Comparison for GDF15 56
Figure 38. ELISA Results for CCL3 in Normal and AML Samples 57
Figure 39. ELISA Results for CCL3 in AML Samples, Serum-Plasma Comparison 58
Figure 40-41. Scatterplot and Boxplot of Serum-Plasma Comparison for CCL3 58
Figure 42. ELISA Results for CCL3 in AML Samples, Time-point Comparison 59
Figure 43. Scatterplot of Time-point Comparison for CCL3 59
Figure 44. ELISA Results for IGFBP2 in Normal and AML Samples 60
Figure 45. ELISA Results for IGFBP2 in AML Samples, Serum-Plasma Comparison 61
Figure 46-47. Scatterplot and Boxplot of Serum-Plasma Comparison for IGFBP2 61
Figure 48. ELISA Results for IGFBP2 in AML Samples, Time-point Comparison 62
Figure 49. Scatterplot of Time-point Comparison for IGFBP2 62
Figure 50. ELISA Results for MMP2 in Normal and AML Samples 63
Figure 51. ELISA Results for MMP2 in AML Samples, Serum-Plasma Comparison 64
Figure 52-53. Scatterplot and Boxplot of Serum-Plasma Comparison for MMP2 64
Figure 54. ELISA Results for MMP2 in AML Samples, Time-point Comparison 65
Figure 55. Scatterplot of Time-point Comparison for MMP2 65
Figure 56. ELISA Results for HGF in Normal and AML Samples 67
Figure 57. ELISA Results for HGF in AML Samples, Serum-Plasma Comparison 68
Figure 58-59. Scatterplot and Boxplot of Serum-Plasma Comparison for HGF 68
Figure 60. ELISA Results for HGF in AML Samples, Time-point Comparison 69
Figure 61. Scatterplot of Time-point Comparison for HGF 69
Figure 62. ELISA Results for LGALs3BP in Pre-treatment and Post-therapy AML Samples 72
X
Figure 63. Boxplot of Pre-treatment and Post-therapy Comparison for LGALs3BP in AML Samples 72
Figure 64. ELISA Results for GDF15 in Pre-treatment and Post-therapy AML Samples 73
Figure 65. Boxplot of Pre-treatment and Post-therapy Comparison for GDF15 in AML Samples 73
Figure 66. ELISA Results for HGF in Pre-treatment and Post-therapy AML Samples 74
Figure 67. Boxplot of Pre-treatment and Post-therapy Comparison for HGF in AML Samples 74
Figure 68. ELISA Results for LGALs3BP, Before and After Therapy, Based on Disease Status 76
Figure 69. ELISA Results for HGF, Before and After Therapy, Based on Disease Status 76
Figure 70. ELISA Results for GDF15, Before and After Therapy, Based on Disease Status 77
Figure 71. ELISA Results for CCL3 in Pre-treatment and Post-therapy AML Samples 78
Figure 72. Boxplot of Pre-treatment and Post-therapy Comparison for CCL3 in AML Samples 78
Figure 73,74. Q-PCR and ELISA Results for LGALs3BP in AML Samples 97
Figure 75,76. Q-PCR and ELISA Results for GDF15 in AML Samples 97
XI
Abbreviation
AFP Alpha-Feto Protein
ALL Acute Lymphoblastic Leukemia
AML Acute Myeloid Leukemia
ANGPT1 Angiopoietin 1
APL Acute Promyelocytic Leukemia
Areg Amphiregulin
B2M (β2M) Beta 2 Microglobulin
CA-125 Cancer Antigen 125
CBMD Chronic Bone Marrow Disfunction
CCL Chemokine C-C motif, Ligand 3
CD Cluster of Differentiation Molecule
cDNA Complementary DNA
CEA Carcinoembryonic Antigen
CEBPA CCAAT/enhancer-binding Protein Alpha
CFU Colony-Forming Unit
cHSP Circulating Heat Shock Protein
CLL Chronic Lymphocytic Leukemia
CLP Common Lymphoid Progenitor
CML Chronic Myelogenous (or myeloid) Leukemia
CMP Common Myeloid Progenitor
XII
CR Complete Remission
CXCL2 Chemokine C-X-C motif, Ligand 2
DNMT3a DNA Methyltransferase 3 Alpha
ELISA Enzyme-Linked Immunosorbent Assay
FAB French–American–British Classification Systems
FISH Florescent In Situ Hybridization
FLT3-ITD fms-like Tyrosine Kinase 3-Internal Tandem Duplication
GAL3 Galectin-3
GATA Transacting T-Cell Specific Transcription Factor
GDF15 Growth Differentiation Factor 15
GMP Granulocyte-Monocyte Progenitor
GPDH Glycerol-3-phosphate Dehydrogenase
HCV Hepatitis C Virus
HGF/SF Hepatocyte Growth Factor/Scatter Factor
HIV Human Immunodeficiency Virus
HSC Hematopoietic Stem Cell
IDH Isocitrate Dehydrogenase
IGFBP2 Insulin-like Growth Factor-binding Protein 2
LC-MS Liquid Chromatography-Coupled Mass Spectrometry
LDH Lactate Dehydrogenase
LGALs3BP Galectin-3-binding Protein
LSC (SL-IC) Leukemic Stem Cell (SCID Leukemic-Initiating Cells)
LT-HSC Long-Term Hematopoietic Stem Cell
XIII
LTS-IC Long-Term Culture-Initiating Cell
Mass-spec (MS) Mass Spectrometry
MDS Myelodysplastic Neoplasm
MEP Megakaryocyte-Erythroid Progenitor
MGMT DNA Methylguanine-Methyltransferase
miR microRNA
MM Multiple Myeloma
MMLV Moloney Murine Leukemia Virus
MMP Matrix Metalloproteinase
MPN Myeloproliferative Neoplasm
MPP Multipotent Progenitor
MRD Minimal Residual Disease
MRM Multiple Reaction Monitoring
ng/ml Nanogram Per Milliliter
NPM1 Nucleophosmin 1
PCR Polymerase Chain Reaction
pg/ml Picogram Per Milliliter
PI3K Phosphatidylinositide 3-kinases
PML Promyelocytic Leukemia
PMN Polymorph Nuclear Leukocyte
Pro-DC Pro-dendritic Cell
PSA Prostate-Specific Antigen
Q-PCR Quantitative Real Time Polymerase Chain Reaction
XIV
RARα Retinoic-acid Receptor-Alpha
REB Research Ethics Board
RT-PCR Real-Time PCR
RUNX1 Runt-related Transcription Factor 1
sIL-2R Soluble Interleukin-2 Receptor
SRC SCID-Repopulating Cell
SRM Selected Reaction Monitoring
STAT3 Signal Transducer and Activator of Transcription 3
ST-HSC Short-term Hematopoietic Stem Cell
TET2 Tet Methylcytosine Dioxygenase 2
WBC White Blood Cell
WHO World health Organization
WT1 Wilms Tumor 1 gene
1
Chapter 1
Introduction: Acute Myeloid Leukemia (AML)
1 Acute Myeloid Leukemia
Acute Myeloid Leukemia (AML), or acute myelogenous leukemia, is the most common type of
acute leukemia in adults [1-4]. AML is a cancer of the myeloid cell lineage. The disease is
characterized by the rapid growth of immature appearing white blood cells that accumulate in the
bone marrow and interfere with the production of normal blood cells. The resulting reduction in
the production of red blood cells, white blood cells, and platelets gives rise to the symptoms of
AML such as fatigue and shortness of breath due to anemia, easy bleeding and bruising due to
the lack of platelets, and frequent infections with bacteria and fungi due to the lack of normal
functioning white blood cells. Without any specific therapy or supportive measures AML
progresses rapidly and is typically fatal within weeks or months. While untreated AML is
uniformly fatal, it is possible to observe long term cures using intensive chemotherapy which
may include a stem cell transplant [1, 2, 4] .
1.1 Hematopoiesis
1.1.1 Normal Hematopoiesis
Bone marrow (BM) is the spongy core within the cavity of bones and between the plates of the
skull. Within this space hematopoietic stem cells (HSCs) reside in close proximity with stromal
cells that support the survival and growth of HSC. The HSC are multipotent and immature cells
that can develop into different components of blood such as red blood cells which carry oxygen
and maintain the oxygen supply of the cells, white blood cells which basically fight against
infections and platelets that help blood to clot [4, 5]. In addition to the ability of HSC to
2
proliferate and differentiate to give rise to all blood cell types, they can also undergo the process
of self renewal by which the HSC generates a cell identical to itself; this is crucial for the
lifelong requirement for production of blood cells.
Figure 1. Hematopoietic and Stromal Stem Cell Differentiation [6].
Researchers have been shown that the differentiation process is not random but is rather directed.
Also it has been shown that the interaction between hematopoietic organ stroma and
hematopoietic stem cells plays an important role in the differentiation process [7, 8].
Hematopoietic stem cells can differentiate into lymphoid and myeloid compartments, which can
then further differentiate into different mature functional end cells [5, 7-11].
3
Figure 2. Hematopoietic stem cell and differentiation [11].
1.1.2 Leukemic Hematopoiesis
AML is a cancer of the myeloid cell lineage and affects the production of normal neutophils,
monocytes, erythrocytes, and megakaryocytes. AML is characterized by increased numbers of
blast cells in the bone marrow. The term blast cell on its own describes a cell morphology and
does not necessarily indicate disease. Blast cells are immature appearing cells with prominent
nucleoli. They have a large nuclear to cytoplasmic ratio. In normal individuals there may be up
to 5% blasts in the bone marrow; these cells maintain the ability to differentiate and do not
produce disease. On the other hand in AML, the blast like cells appear at a frequency of 20% or
more (by definition) and undergo little if any differentiation.
4
Figure 3. Normal and Leukemic Hematopoiesis [12].
As the number of these abnormal cells increases in the blood and bone marrow, fewer
functioning blood cells and platelets are produced. While leukemia initiates in a single cell, the
progeny of this cell can enter the blood stream and spread to other parts of the bone marrow so
that over time the entire bone marrow is replaced by leukemic cells. This spread is likely
promoted by the natural tendency of normal HSC to enter the blood stream and then to become
re-established at a distant bone marrow site. This propensity to spread and invade is also
responsible for the growth of leukemic cells in lymph nodes, and other tissues such as liver,
spleen, skin, lung and gingiva [5, 11, 13].
1.2 History
One of the first recorded reports of leukemia was by John Hughes Bennett, an English physician
and pathologist in 1845 [14, 15].The term “leukemia” comes from the Greek words “leukos” and
“heima,” meaning “white blood” [1]. In 1913, four major different subtypes of leukemia were
recognized, these included chronic lymphocytic leukemia, chronic myelogenous leukemia, acute
lymphocytic leukemia, and erythroleukemia. In 1970, it was first confirmed that some patients
5
could be cured of leukemia, and by the 1980s and 1990s the cure rates for leukemia varied
tremendously based on the type of leukemia a patient had [14, 16].
1.3 Epidemiology
AML is the second most common cancer of blood origin in adults with a median age of 66 years
at the diagnosis with males having higher incidence rates than females; however, it can also
affect children between ages 0-19 years. The incidence of AML is approximately 1 in 100,000
during the first four decades of life, however with aging the frequency increases to on the order
of 20 in 100,000 by the age of 70. Interestingly, over the span of lifetime the forms of AML
change. In younger individuals normal cytogenetics and recurrent chromosomal abnormalities
involving two chromosomes predominate. In contrast, in older individuals there is an increased
frequency of chromosome deletions and loss and the occurrence of highly complex chromosome
abnormalities in the leukemic cells [1-3, 17].
Figure 4. Estimated Proportion of New Cases in 2011 for All Types of Leukemia [18].
1.4 Signs and Symptoms
General signs and symptoms of AML are fatigue, loss of appetite and weight loss, mild fever and
night sweats, bruising and bleeding, recurrent infections, headaches, enlarged spleen and
6
abdominal swelling, swollen gums, and discomfort and pain in bones and joints [1, 4]. These are
attributable to the reduction in normal output of blood cells, infiltration of tissues and the
production of cytokines and chemokines by AML cells.
1.5 Ethiology and Risk Factors
Since the first observation of the so called Philadelphia chromosome in chronic myelogenous
leukemia (CML) cells in 1961, a variety of chromosomal abnormalities and mutations have been
found in AML cells, indicating that it is a disease of acquired genetic changes. In most cases of
AML it is not possible to identify a reason for the acquisition of these changes, however in some
cases it is possible to identify either genetic traits that predispose to the development of AML or
environmental factors that contribute to the development of the disease. For example individuals
with congenital abnormalities such as Fanconi anemia, Down's syndrome, Bloom syndrome,
ataxia telangiectasia, and Blackfan-Diamond syndrome which are characterized by defects in
DNA repair, have a higher than normal chance of developing AML. Chronic bone marrow
dysfunction (CBMD) such as myelodysplastic syndrome and myeloproliferative disorders are
pre-leukemic conditions which can increase risk of AML. Environmental factors such as
chemicals and some drugs such as benzene, chloramphenicol and alkylating agents can also
increase the risk of leukemia. Finally, exposures to radiation either from the environment,
medical investigation or treatment and war are causally linked to the development of AML [1, 4,
19].
1.6 Classification Systems
AML is a cancer of the bone marrow; however it is not a single disease, but rather a group of
diseases that differ in how they present, how they appear down the microscope, with regards to
the presence of specific genetic abnormalities and how they respond to chemotherapy. In
7
recognition of this variability clinicians and hematopathologists have worked to develop
classification systems whose goals are to identify subtypes of disease and help to direct
treatment. The first formal classification system was the French-American-British (FAB) which
was based predominantly on morphology and defined AML as a disease with 30% or more blasts
in the bone marrow. This was replaced in 2001 by the World Health Organization (WHO)
classification that takes into account genetics, patient history and morphology [2, 4].
1.6.1 FAB Classification
The FAB classification, which was introduced in 1976, is a morphology based classification and
classified AML into different subtypes from M0 through M7, based on morphology and histochemical
staining of the cells. Based on this classification, M0 is an undifferentiated AML with the worst prognosis
compared to the average AML patients and includes 5% of all AML cases. In contrast M2 is a form of
AML with maturation, an incidence of 25-30% and with better prognosis [2, 4, 20-23]. It is important to
note that prognosis in AML depends on patients having received chemotherapy with a curative intent, as
without a support almost all patients will die in weeks to months.
Table 1. FAB Classification of Acute Myeloid Leukemia
FAB
subtype Description Comments
M0 Undifferentiated Myeloperoxidase negative;myeloid markers positive
M1 Myeloblastic without maturation Some evidence of granulocytic differentiation
M2 Myeloblastic with maturation
Maturation at or beyond the promyelocytic stage of
differentiation; can be divided into those with t(8;21)
AML1-ETO fusion and those without
M3 Promyelocytic APL; most cases have t(15;17) PML-RARα or another
translation involving RARα
M4
M4(Eo)
Myelomonocytic
Myelomonocytic with bone-
marrow eosinophilia
Characterized by inversion of chromosome 16 involving
CBFβ, which normally forms a heterodimer with AML1
M5 Monocytic
M6 Erythroleukemia
M7 Megakaryoblastic GATA1 mutation in those associated with Down's syndrome
AML1, acute myeloid leukemia 1; APL, acute promyelocytic leukemia; PML, promyelocytic leukemia;
RARα, retinoic-acid receptor-α
Adopted from: Nature Review Cancer 3, 89-101 (February 2003)
8
1.6.2 WHO Classification
The WHO classification was introduced more recently and incorporates clinical features and
biological characteristics such as cytogenetic, molecular genetics, immunologic markers, and
morphological features [1, 4]. In 2001, the World Health Organization (WHO), in collaboration
with the Society for Hematopathology and the European Association of Haematopathology,
published a classification for tumors of the hematopoietic and lymphoid tissues as part of the 3rd
edition of the series, (WHO Classification of Tumors). The 4th edition of this classification came
out in 2008 with even more clinical and molecular features to be more applicable, and
prognostically valid. Two key features of this classification system were the shift from defining
AML as 30% to 20% blasts in the bone marrow, and the introduction of molecular and
cytogenetic markers to define specific subtypes of AML [4, 24-26].
9
Table 2. WHO Classification of Myeloid Neoplasms and Acute Leukemia Myeloproliferative neoplasms (MPN)
Chronic myelogenous leukemia, BCR-ABL 1-positive
Chronic neutrophilic leukemia Polycythemia vera
Primary myelofibrosis
Essential thrombocythemia Chronic eosinophilic leukemia, not otherwise specified
Mastocytosis
Myeloproliferative neoplasms, unclassifiable
Myeloid and lymphoid meoplasms associated with eosinophilia
and abnormalities of PDGFRA,
PDGFRB, or FGFR1
Myeloid and lymphoid neoplasms associated with PDGFRA
rearrangement
Myeloid neoplasms associated with PDGFRB rearrangement Myeloid and lymphoid neoplasms associated with FGFR1
abnormalities
Myelodysplastic/myeoproliferative neoplasms (MDS/MPN)
Chronic myelomonocytic leukemia
Atypical chronic myeloid leukemia, BCR-ABL1-negative
Juvenile myelomonocytic leukemia Myelodysplastic/myeloproliferative neoplasm, unclassifiable
Provisional entity: refractory anemia with ring sideroblasts and
thrombocytosis
Myelodysplastic syndrome (MDS)
Refractory cytopenia with unilineage dysplasia
Refractory anemia Refractory neutropenia
Refractory thrombocytopenia
Refractory anemia with ring sideroblasts Refractory cytopenia with multilineage dysplasia
Refractory anemia with excess blasts
Myelodysplastic syndrome with isolated del (5q) Myelodysplastic syndrome, unclassifiable
Childhood myelodysplastic syndrome
Provisional entity: refractory cytopenia of childhood
Acute myeloid leukemia and related neoplasms
Acute myeloid leukemia with recurrent genetic abnormalities
AML with t(8;21)(q22;q22); RUNX1-RUNX1T1 AML with inv(16)(p13.1q22)or t(16;16)(p13.1;q22); CBFB-
MyH11
APL with t(15;17)(q22;q12); PML-RARA AML with t(9;11)(p22;q23); MLLT3-MLL
AML with t(6;9)(p23;q34); DEK-NUP214
AML with inv(3)(q21;q26.2)or t(3;3)(q21;q26.2); PRN1-EVI1 AML(megakaryoblastic) with t(1;22)(p13;q13); RBM15-MKL1
Provisional entity: AML with mutated NPM1
Provisional entity: AML with mutated CEBPA Acute myeloid leukemia with myedysplasia-related changes
Therapy-related myeloid neoplasms
Acute myeloid leukemia, not otherwise specified
AML with minimal differentiation
AML without maturation AML with maturation
Acute myelomonocytic leukemia
Acute monoblastic/monocytic leukemia Acute erythroid leukemia
Pure erythroid leukemia
Erythroleukemia, erythroid/myeloid Acute megakaryoblastic leukemia
Acute basophilic leukemia
Acute panmyelosis with myelofibrosis Myeloid sarcoma
Myeloid proliferations related to Down syndrome
Transient abnormal myelopoiesis Myeloid leukemia associated with Down syndrome
Blastic plasmacytoid dendritic cell neoplasm
Acute leukemias of ambiguous lineage
Acute undifferentiated leukemia
Mixed phenotype acute leukemia with t(9;22)(q34;q11.2);
BCR-ABL1 Mixed phenotype acute leukemia with T(v;11q23); MLL
rearranged
Mixed phenotype acute leukemia, B-myeloid, NOS Mixed phenotype acute leukemia, T-myeloid, NOS
Provisional entity: natural killer(NK) cell lymphoblastic
leukemia/lymphoma
B lymphoblastic leukemia/lymphoma
B lymphoblastic leukemia/lymphoma, NOS
B lymphoblastic leukemia/lymphoma with recurrent genetic abnormalities
B lymphoblastic leukemia/lymphoma with t(9;22)(q34;q11.2);
BCR-ABL 1 B lymphoblastic leukemia/lymphoma with t(v;11q23); MLL
rearranged
B lymphoblastic leukemia/lymphoma with t(12;12)(p13;q22) TEL-AML1
(ETV6-RUNX1)
B lymphoblastic leukemia/lymphoma with hyperdiploidy B lymphoblastic leukemia/lymphoma with with hypodiploidy
B lymphoblastic leukemia/lymphoma with t(5;14)(q31;q32)
IL3-IGH B lymphoblastic leukemia/lymphoma with t(1;19)(q23;p13.3);
TCF3-PBX1
T lymphoblastic leukemia/lymphoma
Adopted from: BLOOD, 30July2009. Volume 114, Number5
1.7 Pathogenesis
In most patients it is not clear as to what exactly caused their disease, however it is clear that
AML is a genetic disease based upon three observations. First, patients with inherited disorders
10
such as Down’s syndrome or syndromes that affect DNA repair such as Fanconi’s anemia have a
higher than normal occurrence of leukemia. Second, individuals exposed to DNA damaging
agents such as radiation, high electric fields or chemotherapy used to treat other cancers or
immunologic disorders, have an increased incidence of AML. Finally, recurrent chromosome
abnormalities and mutations are frequently found in the leukemic cells of patients with AML;
examples of these are shown in Table 2.
How these come about in the majority of patients is not known. However, given that t(15;17)
can be found following cancer treatment including chemo- and radiation therapy, it is likely that
these mutations arise in the majority of patients due to exposure to an environmental mutagen at
some point in time or due to the inherent error rate that exists in normal cells as they divide. In
most cases such changes are lost as they occur in cells undergoing terminal divisions towards an
end cell phenotype. However, if the change occurs in a stem cell or gives the cell stem cell
properties, the abnormality persists, and with the acquisition of other genetic and epigenetic
changes in the cell, culminates in the development of leukemia.
1.8 Treatment
Historically, AML therapy started in the 18th century by using Arsenic compounds by Thomas
Fowler. He created a mixture of arsenic trioxide and potassium bicarbonate, known as "Fowler's
Solution", and used it for Hodgkin's disease, anemia, and leukemia therapy. Then in 1865, this
therapeutic method was used for the treatment of chronic myelocytic leukemia and in 1970s for
promyelocytic leukemia [14, 27, 28]. While modern day treatment of AML has moved beyond
the use of Fowler's solution so that cures can now be achieved in a significant proportion of
cases, more than half the patients with AML will still die of their disease. This is in contrast to
11
acute lymphoblastic leukemia where the cure rate in children is over 80% and cures in adults are
now approaching the same rate as in children.
1.8.1 Past Therapeutic Approaches
After the discovery of X-ray in about 1897, in the early 1920s when it was shown that daily
doses of radiation could reduce the size of tumors and that it has therapeutic benefits, X-ray
radiation became a therapeutic method to treat AML patients. Paradoxically, radiation exposure
has also been shown to be a predisposing factor for leukemia [29, 30].
Nitrogen mustard was the first chemotherapeutic agent which was discovered during World War
II. After that, in the 1940s, Sidney Farber found a compound related to folic acid named
Aminopterin (methotrexate) and used it to achieve remissions for acute childhood leukemia. It
was after this discovery that other researchers started inventing new drugs that could affect
different cell functions such as growth and replication [31, 32]. In 1950 George Hitching and
Gertrude Elion created 6-merptopurine (6-MP), a mixture of diaminopurine and thioguanine (6-
TG), to disrupt DNA synthesis. Both 6-TG and methotrexate are still in used in combination
with other drugs for leukemia therapy [33].
1.8.2 Recent Therapeutic Developments, Targeted Therapies
Today the approach to the treatment of patients with acute leukemia can be divided into three
broad areas. 1) no therapy, in which case the patient is likely to succumb to their disease in a
short period of time. 2) supportive care which includes i) transfusion support as the patients are
anemic and cannot survive without adequate numbers of red cells to carry oxygen; ii) antibiotics
in the event that they develop infection due to the lack of normal functioning neutrophils; and iii)
low dose chemotherapy for patients who have high count disease and would suffer it's
complications. With this approach patients can survive a few months to a year or more; the latter
12
is particularly the case for patients with low count disease. 3) induction chemotherapy. The goal
of this treatment is to reduce the levels of AML cells in the marrow and to allow the regrowth of
normal cells. To achieve this at the current time it is necessary to give high doses of
chemotherapeutic agents in combination. In general this consists of an anthracycline such as
daunorubicin or idarubicin and the nucleoside analogue cytarabine. These drugs are given over a
period of a week, and results in marked reduction of cells in the blood and bone marrow. The
drugs also damage other rapidly dividing tissues such as the lining of the gastro-intestinal tract.
With appropriate support with transfusions and antibiotics most patients survive the treatment
and by 28 days or so after starting therapy there often is evidence of recovery of normal blood
cell production. To confirm the efficacy of the treatment a bone marrow aspirate and biopsy are
done at the time of recovery. Successful treatment is referred to as complete remission which
requires a normal cellular bone marrow, blasts in the marrow of <5%, no red cell transfusions, a
platelet count of 100x109/L, a neutrophil count of 1x10
9/L and no circulating blast cells. While
complete remission is the goal of therapy, it is not equivalent to cure, as in almost all cases, if no
further therapy is given, the disease will recur. In order to reduce the risk of relapse, patients are
given several cycles of consolidation therapy. In some cases, where experience has indicated a
high relapse rate with chemotherapy alone, patients may receive a hematopoietic stem cell
transplant from a sibling or unrelated donor, with the view of reducing the chance of disease
recurrence.
1.9 Complete Remission
In 2005, the National Cancer Institute (US) and the international working group (IWG) for
diagnosis, standardization of response criteria, treatment outcomes, and reporting standards for
therapeutic trials in AML published a criteria for complete remission; these are outlined below:
13
Absolute polymorph nuclear leukocytes (PMN) count equal to or greater than 1x109/
L
Platelet count equal to or greater than 100x109/
L
No evidence of blast cells clusters or extramedulary leukemia, such as central nervous
system or soft tissue involvement in bone marrow biopsy
Normal cellular population in bone marrow aspiration
less than 5% blast cells in bone marrow aspirate without any morphologic abnormalities
such as Auer rods
Of note, the IWG definition of complete remission relies solely on microscopic examination and
therefore accepts that significant amounts of disease can still be present in the patient [34, 35].
1.10 Minimal Residual Disease (MRD)
While the goal of induction therapy is complete remission, and complete remission is necessary
in order to achieve long term remissions and cure, complete remission does not mean that the
disease has been completely eliminated. Proof of this is the observation that if only induction
therapy is given, almost all patients who had a complete remission will have a relapse of their
disease within several months. Moreover, even with continued consolidation therapy, depending
on the subtype of AML, 10-90% of cases will have disease recurrence within two years. This
recurrent disease in almost all cases has the same cytogenetic or molecular abnormality as was
present at the time of diagnosis. These observations indicate that morphologic remission, which
is remission identified by microscopic examination is incapable of identifying the presence of
cells that eventually cause disease relapse. This low level of disease, not detected by microscopy,
is referred to as minimal residual disease (MRD) and as stated above, is responsible for disease
recurrence.
14
2 MRD Detection methods
As MRD is the precursor of disease relapse, the development of methods to detect MRD in a
specific and highly sensitive manner continues to be pursued by many researchers. Since the
initial definition, complete remission methods and understanding of disease has evolved in a way
that makes it possible to identify lower and lower amounts of disease in the patient. The
motivator for this research is to help to direct the therapy of patients so as to obtain optimal
outcomes with the least cost and toxicity. In the following sections I will identify the different
methods that can be used to detect MRD and discuss the relative merits of each.
2.1 Cytogenetics
One of the limitations of microscopy is that it is not possible to clearly identify a cell as being
part of the leukemic clone, based on morphology only. For example cells with blast like
morphology may be part of the normal population of cells, while cells with the morphology of a
neutrophil may be derived from a leukemic cell. Based on this, about half the patients will have
in their leukemic cells distinctive cytogenetic abnormalities that identify the cells as being part of
the leukemic population. Cytogenetic methods have been used to assess the quality of remission.
However, because of the nature of the method, and the tendency to assess only 20 metaphase
spreads, the method is insensitive and is not routinely used as a method to identify MRD. For
example metaphases from normal repopulating erythroid and granulocytic precursors may be
over-represented in the recovery marrow, making it possible to miss the metaphases contributed
by the leukemic cells.
2.2 Florescent In Situ Hybridization methods
The technique of fluorescent in situ hybridization (FISH) makes it possible to assess the
frequency of a specific chromosomal abnormality in a population of cells. This overcomes the
15
proliferative problem identified above in classic cytogenetics analysis. However, for this method
to be used it is necessary to have probes that can identify cells with the specific abnormality in a
highly specific manner. For this reason, the use of FISH to measure residual disease is limited to
common recurrent gains and losses of chromosomes such as +8, -5/del(5)(q) and -7/del(7)(q). As
well, probes are available for the detection of recurrent translocations such as t(15;17), t(8;21)
and inv(16). One of the advantages of using this method is that it is possible to count many
metaphases, i.e. on the order of a few hundred to a thousand cells. However, for technical
reasons, the level of detection of MRD is on the order of 1/100 cells, and is therefore is not of
much use in general practice. In addition to the limitation of sensitivity, this method can only be
used for patients with a specific abnormality.
2.3 Flow cytometry
Through the flow cytometric assessment of AML cases at the time of presentation, it became
apparent that cells within the leukemic population could express on their surfaces combinations
of proteins not observed in normal populations of bone marrow cells. For example it is possible
to see on the same cell the co-expression of T-cell surface proteins with myeloid markers, or
proteins considered to represent early phases of differentiation co-expressed with markers
characteristic of late differentiation. With the advent of 10 color flow cytometry it is possible to
identify such aberrant expression in almost all patients. Depending on the number of cells
analyzed it has been estimated that flow cytometry can detect an aberrant cell at the level of 1 in
10,000 cells. A potential limitation of this method is if the cell with aberrant expression is a rare
cell in the leukemic cell hierarchy or if the aberrant expression changes between presentation and
relapse. Studies are ongoing to test the clinical utility of this approach in predicting relapse and
directing patient management.
16
2.4 Polymerase Chain Reaction methods
Amplification-based techniques including polymerase chain reaction (PCR), real time PCR (RT-
PCR), and real-time quantitative PCR (RQ-PCR), with detection limits on the order of 1 in 1,000
to 1 in 100,000 have the highest analytic sensitivity. These methods can be used both for
diagnosis of AML and for the detection of MRD following chemotherapy.
The PCR based methods can be broken down into three broad groups; 1) those that detect
recurrent fusion events; 2) those that measure the level of expression of genes aberrantly
expressed in AML cells; and 3) those that detect recurrent point mutations.
2.4.1 MRD Detection of Recurrent Fusion Genes
Recurrent chromosomal translocations such as t(9;22)(bcr-abl), t(15;17)(PML-RARa),
t(8;21)(AML1-ETO) and inv(16)(MYH11-RUNX2) generate novel fusion mRNA transcripts not
present in normal cells. Depending on the amount of input RNA, as few as 1 in 10,000 cells can
be detected. The methods for the above genes have been well established and are used in the
routine management of patients. For example, patients who have persistent PML-RARa at the
end of treatment are either given further treatment with arsenic or are referred for stem cell
transplant. Unfortunately this method is limited to those patients with recurrent translocations.
There is controversy in the literature as to which is the most appropriate tissue source for
monitoring, i.e. peripheral blood or bone marrow.
2.4.2 MRD Detection of Aberrant Gene Expression
In an attempt to make RNA qPCR monitoring useful for patients whose leukemic cells lack a
recurrent translocation, investigators have used real time PCR to measure the levels of genes that
are aberrantly expressed in AML cells such as SALL4, BAALC and WT1. WT1 is a
transcription factor that is expressed at very low to absent levels in normal bone marrow. Using
17
quantitative methods, it has been found that an inadequate degree of reduction at the end of
chemotherapy, or a rising level in RNA transcripts of WT1 can predict for relapse. While this is
a potentially useful method it suffers from problems of standardization, sample handling and a
change in expression of WT1 in the leukemic cells between presentation and relapse.
2.4.3 MRD Detection of Point Mutations
The above methods use RNA as the source of material for analysis. However, in some AML
cases there are recurrent point mutations that are present in cases of AML. These include
mutations of genes such as NPM1, DNMT3a, TET2, IDH1/2 and FLT3-ITD. Mutations that
involve the same bases in a recurrent manner can be used to monitor MRD eg NPM1 and
IDH1/2. Mutations that are scattered over the gene, e.g. DNMT3a and Tet2 are more difficult to
develop into an assay. Using specific primers it is possible to identify as few as 1 in 10,000 cells
carrying an abnormality. Monitoring of FLT3-ITD is also of potential value, however there are
reports of cases where this mutation is present at the time of diagnosis, but absent at relapse. It is
likely with improvements in sequencing technology that this technology can be applied to an
increasing number of patients. For these studies the best source of starting material is likely to be
bone marrow derived cells.
2.5 Serum Marker Detection Methods
The above assays use as their starting material bone marrow. While not terribly invasive it is a
somewhat uncomfortable procedure that patients do not look forward to. In general, bone
marrow aspirates are done at the time of diagnosis, to confirm remission a month later, and at the
end of therapy. In cases where there is a marker that can be monitored, bone marrows may be
repeated every three months or so for a period of two to three years. Based on criteria such as
cellularity, the presence of particles and so on, there is great variability in the quality of a bone
18
marrow sample obtained from a patient. In contrast to the problems of obtaining repeated bone
marrow samples from patients, it is a simple manner to obtain from the peripheral blood repeated
serum samples. For this reason investigators have been seeking markers of disease that may or
may not be derived from leukemic cells, and that are present in the blood. Potential target
molecules include microRNAs, methylated DNAs, and proteins that can be detected in patient's
serum samples by using different methodologies such as micro RNAs detection methods,
epigenetic analysis and SNP arrays, and proteomic methods [36, 37].
2.5.1 microRNA Markers
There are different miRNAs (miR) whose upregulation or downregulation can cause epigenetic
changes in AML and they could be used as diagnostic markers or for the detection of MRD.
Micro-RNA are particularly well suited as targets for monitoring disease. First, the number of
miR is relatively limited and so it is possible to identify a signature for a specific patient. Next
miR are relatively resistant to degradation which provides for reproducibility. Finally, miR can
be found in the circulation either free or within exosomes. Fayyad-Kazan et al. have identified
several miR that are increased or decreased at the time of presentation and return to normal
levels with remission. This suggests that such molecules may be useful for monitoring disease
over time [38-43].
2.5.2 Methylated-DNA Markers
Similar to miR, tumor cells release DNA into the circulation that carries the marks of the tumor
cell. These marks may either be recurrent point mutations of genes such as H-ras or may have a
methylation pattern that is characteristic of the tumor cell. This approach has been used to
identify evidence of ongoing disease in patients with colon cancer and melanoma. Horton et al.
have shown that it is possible, in acute leukemia, to detect methylated MGMT DNA in the serum
19
of some patients. At this time there is no data as to how fast this DNA disappears from the
circulation when a patient enters remission, nor the reliability of such markers as an indicator of
impending relapse.
2.5.3 Serum and Plasma Protein Markers
Cancer cells are continuously secreting and shedding proteins into the plasma. While many of
these proteins are common to many cell types, some proteins are either unique to the tumor cell
or are expressed at such high levels, that the finding of elevated levels of protein in the serum has
come to be synonymous with the presence of a specific malignancy. Examples of this are CEA in
colon and breast cancer, CA-125 in ovarian cancer, M-protein in myeloma and PSA in prostate
cancer. In these conditions changing levels of the marker have been found to be excellent
surrogates for identifying either disease progression or response of the disease to treatment. In
the past there have been reports of specific serum proteins of potential value in monitoring the
activity of AML, however these assays have not entered into the routine monitoring of AML
patients. While it is not clear why this has not happened, there are several possible explanations.
First, there is no one protein that is highly expressed by all AML cells, or even a large
proportion, that can be used in the same way as CEA for example. Second, a cost effective
highly sensitive and reproducible method is needed. Given the relative rarity of AML, and the
lack of a single marker for the disease, it is impractical to set up an ELISA type assay in the
clinical laboratory. In the following section I will discuss different and evolving methods for
detecting serum proteins, with a view to the development of an assay that can be of value for
most AML patients. Given the heterogeneityof AML, such an assay should be able to assess the
levels of multiple proteins at the same time ie is multi-plexed.
20
2.5.3.1 Western Blot Techniques
Western blot or immunoblot method is a routine technique used for detecting specific proteins. It
requires several steps to do the western blot including sample preparation such as cell and tissue
homogenates, extraction, and/or supernatants, transferring, blocking, detection process including
primary antibody and secondary antibody detection, and finally radioactive or fluorescent
detection. Western blotting as a means of monitoring levels of multiple proteins simultaneously
in serum is impractical as only a low level of multiplexing is possible, the method is relatively
insensitive and it does not provide data that is highly quantitative.
2.5.3.2 Mass Spectrometry (Mass-Spec, MS)
Mass-Spec methods are analytical methods which can measure mass-to-charge ratios that can be
used for protein/peptides measurements. These methods are sensitive and consist of several steps
such as sample vaporization, ionization, mass analyzing, and detection. For this reason, sample-
matrix preparation procedures greatly influence the quality of mass spectra of peptides/proteins.
As mentioned, there are different mass-spec methods, such as affinity mass spectrometry which
can measure targeted cytokines under physiological conditions. It goes without saying that
sensitivity plays an important role in cytokine measurement and although some cytokines are in
ng/ml levels but most of the cytokines are in pg/ml concentrations in body fluids. Traditional
methods such as electrophoresis methods and mass-spec techniques, which can measure the
cytokines at ng/ml levels, are not suitable for cytokine measurements [44-46].
However, recent advances in mass-spec techniques have shown that they can be used for
detection of cytokines in lower pg/ml levels. One of the emerging techniques in this field is
Selected Reaction Monitoring (SRM) or Multiple Reaction Monitoring (MRM) which is very
useful in detecting a select set of proteins in biological samples. SRM is a targeted method which
21
can be useful in the detection and quantification of specific analytes in a liquid chromatography-
coupled mass spectrometry (LC-MS) system. In this system, a chromatography column and an
electrospray ionization source are connected in a way that the mass spectrometer acts as mass
filter and selectively detects specific molecular ions and fragmented ions. By using heavy ion
labeled peptides, it is possible to accurately measure the level of a protein in the pg range. One
challenge of MRM is to identify readily identifiable fragments of a protein. However once this
has been accomplished an advantage of MRM over other methods is that it can detect peptides
that have been degraded, which may not be the case with antibody methods that rely on specific
capture and detection antibodies. At this time MRM and SRM are relatively expensive [44-51].
Overall, although mass-spec methods are very sensitive, need small sample size, and can
differentiates isotopes, they have some disadvantages including the need for a large facility and
instrumentation and complicated computer software for analysing the results.
2.5.3.3 Enzyme Linked Immunosorbent Assay (ELISA)
ELISA methods were first introduced by Peter Perlmann and Eva Engvall in 1971 based on
previous work in the area of radioimmuno- and immunosorbent assays [52, 53].
ELISA is an analytical method that can detect proteins in a liquid phase, such as serum, plasma,
and cell lysates allowing for qualitative and quantitative detection of proteins. ELISAs can be
configured in different ways, allowing for detection and quantification; examples of the different
formats are outlined below:
1- Competitive ELISA, which is used for the detection of small molecules which do not
have multiple epitopes for binding to capture and detection antibodies. For example this
format is used to monitor levels of drugs and hormones.
22
2- Indirect ELISA, is used to detect the presence of a specific antibody against an antigen in
a sample such as serum. In this method, the antigen is absorbed to the microplate
followed by adding serum or plasma which might contain the specific antibody against
the antigen. If the sample contains antibody, then the antibody attaches to the antigen and
is subsequently detected by adding enzyme-conjugate anti-species specific antibody.
3- Sandwich or direct ELISA, can be used to detect large proteins which have multiple
epitopes to bind the two antibodies, such as the detection of antibodies against HIV and
HCV.
Figure 5. Indirect, Sandwitch, and Competitive ELISA Methodologies
In the sandwich ELISA, capture antibody, which is usually a polyclonal antibody against specific
proteins, is immobilized on the microplate. Then following adding samples to the wells, the
23
protein of interest binds to the capture antibody and becomes immobilized. This is followed by
incubation and washing steps. Then the enzyme-conjugated detection antibody, usually a
monoclonal antibody, is added and allowed to incubate. Following an appropriate incubation
time, the wells are washed and the substrate solution containing a chromophore, specific to the
enzyme is added to create a colored precipitate; the color intensity is directly proportional to the
protein concentration. Finally the acidic stop solution is added to the mixture and stops the
reaction. The spectrophotometer is used to read the color intensity, based on the measurement of
the light which passes through the liquid. The higher concentration of the protein gives rise to
more protein-capture-detection antibodies complex and results in a stronger signal.
The sandwich ELISA has some advantages over other ELISA methods. First, there is no need to
purify the samples before analysis as the method is designed so that it measures the protein
between capture and detection antibodies. Second, it is about 2 to 5 times more sensitive than
capture and indirect methods.
ELISA, which is considered as the gold standard method for cytokine measurement, has some
advantages over the other methods, but on the other hands it has its own weaknesses. ELISA
methods are reliable, highly specific and sensitive. The reagents and equipment are fairly
inexpensive compared to other methods, and they can be used for a variety of proteins, provided
that the desired antibodies and kits are available. Some of the disadvantages attributed to ELISA
techniques are as follow: 1) some plasma components may affect the enzyme activity; 2) some
altered proteins can create false positive or negative results; 3) need a larger sample volume than
other methods. Finally, and relevant to my work is the fact that there is only a low level of
multiplexing possible with ELISA, being on the order of 10-12 target proteins in a single assay
[54, 55].
24
2.5.3.4 Multiplexing Methods
Over the last decade there is an intense interest by researchers to comprehensively study the role
of the cytokines and their network in relation to the diseases. Due to the multiplicity of cytokines
and no a priori knowledge as to which cytokine or cytokines are of importance there is a large
demand for the ability to measure on the order of 10-50 cytokines at one time so as to establish a
comprehensive picture and to begin to uncover signalling networks. In order to reduce cost and
the use of samples there is a push to have methods that can measure many samples at once, using
the same volume as would be needed to assess one marker. Multiplexing techniques are the most
recent improvements in cytokine measurement. Multiplex methods and arrays provide an
effective way of evaluating a complex group of cytokines in such a way that is cost effective,
needs a small volume of sample, while maintaining the required high sensitivity and specificity.
These methods are designed based on the ELISA methodology in a way that they can measure
multiple cytokines in the same sample. Based on the desired application, at the present time,
there are three general formats for multiplex methods. The most common of these multiplexed
methods are bead-based. These methods utilize flow cytometry to detect the desired target bound
to beads. In these methods, each recognizable bead set is coated with a specific capture antibody
that binds to a specific epitope on the target molecule in suspension. Then a streptavidin-labeled
detection antibody or specific detection antibody plus streptavidin-phycoerythrin conjugate is
added to the mixture and the fluorogenic emission is detected using flow cytometer. Therefore,
by using different beads coated with different specific antibodies it is possible at the same time
and in the same sample to evaluate the level of multiple targets. Through multiplexing, in
addition to the obvious advantage of gathering data on many proteins at once, there are other
advantages including the use of less substrate, improved turn-around time, and reduced overall
25
cost. But on the other hand, care should be taken when choosing these methods and there are
some considerations for using these methods, including: These methods are rather new and there
are limited experiences with them and more side-by-side experiments with ELISA are needed. In
addition, there is the potential of cross-reaction between different antibodies/targets making it
important to determine the effect of combining reagents on the detected levels of the added new
target, as well as the targets already present in the assay. Important to consider in both ELISA
assays and bead based multiplexed assays is the effect on the starting source of material. Serum,
plasma collected with heparin as an anticoagulant or plasma collected with EDTA as the anti-
coagulant may give different results, even though the samples were drawn at the same time.
Another important factor is the stability of the protein. Some proteins are very stable, while
others can change in concentration by sitting in the tube prior to processing. In setting up ELISA
and bead based assays it is important to follow clearly defined standard operating procedures that
take into account the differences in sample collection, transport, separation and storage [54, 56-
60].
3 Serum Proteins for Disease Monitoring
The vascular system carries materials to and from all parts of the body. In doing so, the solutes
carried within the blood stream provide us with insight into what may be occurring at different
sites. This has been recognized for decades and has led to the development of legions of tests to
evaluate the functioning and diseases of different organs. For example lactate dehydrogenase
(LDH) has been used for decades as a non-specific way to assess damage to red cells, heart and
lungs or to monitor the activity of neoplasms such as lymphoma. With the development of
antibody based methods it has been possible to detect hundreds of different disease related
markers in the blood of patients. Among the most commonly used tests in the area of cancer are
26
CEA and PSA which are used in the diagnosis and monitoring of patients with colon and breast
cancer (CEA) or prostate cancer (PSA). The levels of these proteins in the blood rise and fall as
the disease progresses or regresses, in response to therapy. As such these markers are a useful
means of monitoring response to treatment at a relatively low cost, and without exposing patients
to radiation or other expensive imaging methods. The proteins that are used to monitor disease
activity are usually derived from the tumor cells themselves, but in some cases may be produced
by stroma cells in response to the presence of the tumor cells.
Serum proteins have not been as extensively studied in AML, however there are publications by
several groups that indicate the potential utility of measuring secreted proteins in the blood of
acute leukemia patients, examples of these are discussed below. Soluble IL-2R (sIL-2R) had
previously been noted to correlate with disease activity in ALL, to determine if sIL-2R might
also be of prognostic significance in AML Nakase et al. in assessed diagnostic levels in 32 AML
cases. They found that there were AML patients who had levels of sIL-2R much higher than in
normal individuals. The highest levels were found in cases with high levels of CD4 on the blast
cells. There was an association between the serum levels and expression of IL-2R on the surface
of blast cells. Finally, they demonstrated that AML patients with levels ≥ 2000U/ml had lower
response to therapy and shorter survival and so poorer prognosis. IL-2R consists of three
subunits of alpha, beta, and gamma, and it is the alpha chain that is expressed on the cell surface
and released as a soluble type in serum. In their study they diagnosed acute leukemia based on
FAB classification and cell surface markers [61]. In another study Loeffler-Ragg et al. looked for
serum CD44 levels and its prediction value for survival in low-risk myelodisplastic syndromes.
CD44 is a cell signalling molecule that shows variable expression on the surface of AML cells;
high level expression is associated with worse outcome. Through proteolysis CD44 can be
27
released into serum from the surface of cells. In their studies, this group found the highest levels
of sCD44 were seen in the serum of patients with CMML(chronic myelomonocytic leukemia),
RAEB (refractory anemia with excess of blasts), and in MDS transformed into AML (sAML).
Univariate analysis showed that elevated levels of sCD44 significantly correlate with shorter
overall survival in MDS patients [62]. In another study Hock et al. evaluated the circulating
levels and clinical significance of soluble CD40 in AML patients. They analysed serum and
plasma samples from AML, CLL, MCL, MDS, and MM patients and found that sCD40 was
significantly prognostic when age was included. Of note, they found that serum levels of normal
individuals had higher levels of sCD40 than plasma, and postulate that this is due to the release
of CD40 from platelets during the clotting process. There was no association between the level
of sCD40 and FAB subtype [63]. Yeh et al. in another study on circulating heat shock protein 70
(cHSP70) found that AML and ALL patients with higher levels of this circulatory protein had
significantly shorter survival, so it could act as a poor prognostic factor. HSP70 belongs to the
chaperone family which activates to protect cells upon exposure to various stresses. It acts as a
supporting molecule for folding newly synthesized polypeptides, protein transport across
membranes, and prevents protein aggregation. It can also help cells to survive during stress by
counteracting apoptosis pathways. Since this protein can be found only on tumor cells, it can
provide recognition site for natural killer cells. Although HSP70 is an intracellular protein and
does not have the secretory signal sequence, several mechanisms have been suggested for its
release such as cell turnover and exocytosis. In this study they found that higher levels of
cHSP70 in plasma are associated with poor prognosis in AML and ALL patients. They showed
that AML and ALL patients had significantly higher levels of cHSP70 than normal healthy
controls. Also they found a strong correlation between β2M, WBC count, and LDH in these
28
patients but no correlation between cHSP70 levels with response to therapy. They also observed
that AML patients with high levels of sHSP70 had shorter survival and higher LDH levels [64].
Table 3. Previously investigated Serum Proteins related to AML
29
Thesis Rationale
Acute myeloid Leukemia is the most common type of acute leukemia in adults. AML is a
complex group of diseases based on differences in cell surface proteins, gene expression and
response to therapy. A major problem in the management of patients with AML who have
achieved remission with induction style therapy, is to know how much more therapy the
individual patient requires. In some cases the presence of genetic markers such as fusion genes,
allows the clinician to determine whether the disease has been reduced to non-detectable levels
indicating a high chance of cure, vs the presence of persistent disease that indicates relapse
within a short period of time. Unfortunately for most AML patients, such markers are not readily
available. The use of secreted proteins has been studied in tumours such as breast, colon, lung
and prostate as a means of monitoring the amount of disease in a patient. In contrast, due to the
heterogeneity of AML, there are no established assays that can be used in a prognostic way in the
large majority of patients with leukemia. In this thesis I have begun the development of a serum
assay that may be of use broadly, in the management of AML patients.
Hypothesis
The detection of residual disease in AML patients is of use in directing therapy. However there
are no easy methods for helping to direct treatment. In the proposed work I will try to identify a
panel of serum secreted proteins that will be of value in monitoring AML patients for evidence
of disease recurrence. For clinical applications of biomarkers, there is a need for multiplex assays
using high throughput platforms. I hypothesize that the levels of serum secreted proteins or
cytokines will be different between AML patients and normal healthy individuals and also
patients with different forms of AML will have different patterns of cytokines. Based on this,
30
persistence or recurrence of these cytokines in the serum of patients in remission can be used as
predictable markers for disease relapse.
Objectives/Specific Aims
Finding and detecting selected secreted proteins in serum that have high levels in AML patients
compared to normal healthy individuals is the first outcome and goal of this study. My objective
is to evaluate the sensitivity and specificity of these proteins as potential tumour markers in
AML patients and establish a multiplexed serum assay that can accessible to all AML patients.
I will also try to determine if there is a meaningful difference between the levels of serum
secreted proteins in AML patients before and after therapy that can help in monitoring the AML
patients' status for complete remission and prediction of relapse.
The long term goal of this study is to determine the efficacy of multiplexing bead assay for
measurement of proteins secreted by AML cells and to evaluate whether multiplex assay is as
effective as enzyme-linked immunosorbent assay (ELISA) for monitoring MRD and relapse in
AML patients.
31
Chapter 2
Identification of serum markers for monitoring disease activity in patients
with acute leukemia
32
Introduction
Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) are diseases arising
due to mutation in a hematopoietic progenitor cell. Like normal bone marrow stem cells, the
leukemic stem cell resides in the bone marrow niche where it is provided growth and survival
signals from the supporting fibroblasts and osteoblasts. In some cases the progeny of the
leukemic stem cell can also enter the blood stream, sometimes at very high levels. Curative
treatment of AML is aimed at eliminating leukemic cells from the blood, and marrow, and
allowing the regeneration of normal cells; this state is referred to as remission. Despite the
achievement of remission and further chemotherapy, most patients eventually have a relapse and
die of their disease. This indicates that the use of light microscopy to identify blast cells is
inadequate as a reliable means of monitoring disease activity. To overcome this, investigators
have developed alternative ways of evaluating the presence of persistent disease in a remission
marrow.
One of the most sensitive methods is the use of RT-PCR to detect mutations that are
characteristic of the leukemic clone. This is especially useful for patients whose leukemic cells
contain a fusion gene such as PML-RARα, MYH11-RUNX2 or mutation of NPM1. These
abnormalities are causative of the disease and persist throughout the course of the disease. Other
mutations such as those of N and K-Ras and FLT3-ITD are also of use, but need to be used with
caution as in some cases the mutations are not present in relapsed cells. Depending on the target,
RT-PCR can detect on the order of 1 in 1000 to 1 in 10⁵ AML cells. Unfortunately this approach
is of use to only about half of AML patients. There is still controversy as to whether monitoring
can be done using peripheral blood as compared to bone marrow.
33
Another method is the use of multi-color flow cytometry to identify minimal residual disease
(MRD) in the bone marrow of patients. This requires the identification of an aberrant pattern of
expression of cell surface proteins on the individual patient's blast cells. Once a patient specific
signature is identified, serial analysis of bone marrow samples can be used to show loss of the
leukemic clone over time, or persistence and re-emergence of the clone. The advantage of this
approach is that it can be applied to almost all patients. Disadvantages include the need for bone
marrow samples, the possibility that the relapsed sample will have a different cell surface profile
compared to the initial disease and the likelihood that during remission, residual leukemic cells
are sporadic in the bone marrow.
In other types of malignancy in which repeat sampling of the tumor site is not so facile, and the
tumor may have spread to distant sites, clinicians for decades have used serum markers to
evaluate the presence and activity of disease. Examples of this are carcinoembryonic antigen
(CEA) for colon and breast cancer and alpha-fetoprotein (AFP) for testicular cancer. In these
cases the tumor cells make the protein of interest, and therefore in general the level of antigen in
the serum is proportional to the tumor load in the patient. In the literature there are reports of the
identification of serum proteins in the serum and cerebral spinal fluid of AML and acute
lymphoblastic leukemia patients.
One of the problems with prior attempts to use serum proteins to monitor disease levels in
patients with acute leukemia is that, due to the marked heterogeneity of the disease, no one
marker could be found that might be useful for a large subset of patients. Recently cost effective
multiplexed flow based assays to detect serum proteins have been developed that overcomes the
problem of using large numbers of ELISA assays in order to identify a serum protein marker or
markers that would be of use in monitoring the individual patient's disease. To begin to build
34
such an assay, it was first necessary to identify candidate proteins secreted by AML cells. To do
this, I took advantage of gene expression arrays to identify proteins potentially secreted by the
AML cells in a subset of patients. This exercise allowed me to identify 107 proteins of potential
use. To demonstrate the value of this approach, I then tested the expression of 12 of these
proteins using ELISA assays. Of those 12 proteins, I found that LGALs3BP, IGFBP2, HGF, and
GDF15 were of potential value for incorporation into a multiplex assay.
35
Material and Methods
1 Study Design
Serum and plasma samples were collected in a prospective manner from patients presenting at
the Princess Margaret Hospital for evaluation and treatment of bone marrow disease. All samples
were collected following written informed consent and given a unique identifier number to
protect patient confidentiality. This collection and analysis of samples was approved by the
research ethics board of the Princess Margaret Hospital/University Health Network.
2 Samples
All serum and plasma samples of AML patients and also normal healthy age matched subjects
were collected at the Princess Margaret Hospital based on University Health Network Research
Ethics Board (REB) approved informed consent. In addition to presentation samples, for some
cases we obtained samples at a time after diagnosis. Based on the Research Ethics Principles and
Tri-Council Policy Statement (TCPS), we designed free informed consent in a way to respect for
human dignity, vulnerable persons, privacy and confidentiality, and to minimize harm and
maximize benefits. All information about the samples including: lab reference number, lab part
number, bioarchive number, receiving, processing, and storage dates and times, the number of
vials, and the location and map of each sample have been reserved in a password protected
bioarchive file as a reference.
As mentioned earlier, all of the samples chosen had already been diagnosed as one of the AML
subgroups, using cytogenetics, pathological findings and based on FAB and/or WHO
classification.
36
2.1 Sample and Plasma Processing Protocol
1- All samples were collected, in the case of the serum sample, allowed to clot, and processed
within 1-2 hours of being recieved by the laboratory.
3- Samples were centrifuged at 3000 rpm for 10 minutes to separate serum or plasma from cells.
4- 600 µl of serum or plasma were aliquoted into 4 Eppendorf tubes of 0.6 ml volume.
5- All tubes were set on dry ice for 5-10 minutes to allow snap freezing.
6- All samples were stored in a locked -70ºC freezer.
7- When samples were used, the date of freezing and thawing was recorded.
3 Microarray studies and Data Analysis
A gene expression microarray study was previously performed by Valk et al. on peripheral blood
and bone marrow samples from 285 patients with AML using Affymetrix U133A GeneChips
containing around 13000 genes. For data analysis they used Omniviz, significance of analysis of
microarray, and prediction analysis of microarrays software. Using unsupervised clustering
which involved Pearson's correlation coefficient, they indentified 16 groups of AML patients
based upon gene expression signatures. They identified specific genes within each group or
cluster to permit the successful identification each group [65].
Table 4. Selected Secreted Proteins in AML and ALL
37
The panel of approximately 3500 potentially secreted proteins described by Gonzalez et al. was
used as my major resource for identifying secreted proteins in the Valk data set. Gonzalez et al.
used secreted protein discovery initiative (SPDI), web-based secreted protein database (SPD),
and sequence-based supervised signal peptide-prediction algorithms (SignalP), and Phobius [66].
In addition to the Gonzalez list of secreted proteins other references and databases were also
used including: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), The
Human Protein Atlas, Human Protein Reference Database, HUGE Protein Database, Oncomine,
BioCarta, Information Hyperlinked Over Proteins (iHOP), CoreMine, PubGene, GeneCards, and
UCSC Genome Bioinformatics.
4 Real Time RT-PCR (Q-PCR)
Real-Time reverse transcriptase chain reaction (RT-PCR), also known as quantitative real time
polymerase chain reaction (Q-PCR) was performed on RNA samples of 51 AML patients which
were isolated using RNeasy Plus Mini Kit (Cat. No. 74106, Qiagen, Canada), according to the
manufacturer's instructions. The expression levels of 5 target genes, amphiregulin (Areg),
epiregulin (Ereg), growth differentiation factor 15 (GDF15), galactoside-binding, soluble, 3
binding protein (LGALs3BP), and selenoprotein P, plasma 1 (SEPP1), and the control
housekeeping gene GPDH were quantified using Mx3000p™ Systems (STRATAGENE®) with
MxPro QPCR Software (Agilent Technologies). 1 µg of total RNA was used to generate cDNA
using Moloney murine leukemia virus (MMLV) reverse transcriptase kit (Cat. No. 28025-013)
from Invitrogen, according to the manufacturer's instructions. The real-time RT-PCR was
performed using the SYBR green method. Primer preparations were done using "Harvard
Medical School Primer Bank" and UCSC In-Silico PCR was used to test the primers. The primer
sets used, were:
38
Table 5. Sequences of Primers used in Q-PCR
Oligo Name Sequence (5'->3')
EREG F CTGGTGTCCGATGTGAACACT
EREG R CCGACGACTGTGATAAGAAACA
GDF15 F GGGCAAGAACTCAGGACGG
GDF15 R TCTGGAGTCTTCGGAGTGCAA
AREG F CCCAAAACAAGACGGAAAGTGA
AREG R GCTGACATTTGCATGTTACTGCT
LGALs3BP F CCATCAGCGTGAATGTGCAG
LGALs3BP R TCAGCATCCACACTCATGGTG
SEPP1 F GCAGCAGTGAGCTTTCAGAGA
SEPP1 R TGACCCTTGTGCTTATGGTGG
After acquiring the Q-PCR data, all results are normalized to GAPDH. Results were then
compared to normal bone marrow, with normal bone marrow being set as 1.
5 Enzyme-Linked Immunosorbent Assay (ELISA)
Specific Elisa kits were chosen and purchased from different companies based on the sensitivity
and previous references. The reason was that not all companies offer the Elisa kits needed with
the desirable sensitivity and the supporting references. Based on this, Elisa kits were purchased
as follow:
Table 6. List of ELISA kits
Elisa kit Manufacture Cat.Number
LGALs3BP eBioscience BMS234
Clusterin R&D Systems DCLU00
GDF15 R&D Systems DGD150
CXCL2 USCN E91603Hu
JAG1 USCN E91807Hu
GAL3 eBioscience BMS279/2
ANGPT1 R&D Systems DANG10
CCL3 eBioscience BMS2029/NST
IGFBP2 R&D Systems DY674
MMP2 R&D Systems DMP2F0
HGF R&D Systems DHG00
Areg R&D Systems DY262
39
Elisa experiments were carried out for 12 proteins including: LGALs3BP, Clusterin, GDF15,
CXCL2, JAG1, GAL3, ANGPT1, CCL3, IGFBP2, MMP2, HGF, and Areg.
40
Results
1 Microarray Data Analysis Results
There are a number of potential ways to identify proteins that may be useful in monitoring
disease in cancer patients. Given the heterogeneity in gene expression of AML demonstrated by
Valk et al., and others, I decided to identify a list of candidate proteins using in silico analysis.
To do this I created a number of criteria.
First, the gene had to be expressed at a significant level in the Valk data set. Based on experience
of others in the laboratory I set this at a level of 200 Normalized Signal Intensity.
Second, the expression of the gene had to be higher in the AML samples than in the normal
CD34 and the normal bulk samples in the Valk data set.
Third, increased expression of the gene should be present in >10% of all cases or identified a
particular disease subgroup in the Valk dataset.
From the Gonzales et al. dataset and using other reference databases including Gene Ontology,
Kyoto Encyclopedia of Genes and Genomes (KEGG), The Human Protein Atlas, Human Protein
Reference Database, HUGE Protein Database, Oncomine, BioCarta, Information Hyperlinked
Over Proteins (iHOP), CoreMine, PubGene, GeneCards, and UCSC Genome Bioinformatics I
compiled a list of approximately 3500 secreted proteins. I then interrogated the Valk data set,
one by one, with each of these genes using the rules outlined above. Based on this I identified
107 candidate genes (Table 4). Examples of the graphs generated for ten of the 107 candidate
genes are shown below (Figures 6-15).
GDF-15 is most highly expressed in patients of group 7, but sporadically in patients of other
groups. The expression by normal cells is very low.
41
HGF is very highly expressed in the patients of group 12 (t(15;17)). However, high level
expression of HGF is also seen in other subsets of patients but not at the same level as in group
12. The expression by normal cells is very low.
Amphiregulin is expressed at very high levels sporadically across the data set and does not
identify any particular subgroup of patients. The expression by normal cells is very low.
IGFBP2 like HGF is expressed at very high levels in group 12 patients. However, high level
expression of IGFBP2 is also seen in other groups of patients. It is of note that for the non-group
12 patients, high IGFBP2 expression identifies a different subset of patients than HGF.
LGALSs3BP like amphiregulin is expressed at high levels sporadically across the patient
population. In general these two genes are highly expressed together in patient samples.
However, there are occasional cases where one is high, and the other is low. Angiopoietin 1 is
highly expressed by a large proportion of cases of AML. However, it should be noted that for
this gene, there is high level expression by normal CD34 expressing cells.
42
Figures 6-15. Expression Levels of Some Cytokines in Valk et al. Dataset
43
A goal of my research is to develop a panel of secreted cytokines that would be useful for
assessing all AML patients, I combined gene expression patterns either as pairs or as multiples.
The utility of this is shown below. At a first glance LGALs3BP seem to identify the same
patients as with amphiregulin, however as seen in Figure 16 there are cases that show increased
expression of both genes or only one of the genes.
44
Figure 16. Expression Levels of LGALs3BP and Areg in Valk et al. Dataset
By overlaying the gene expression of seven secreted proteins it is possible to see that a useful
marker can be found for almost every single case. It is also apparent that for some cases more
than one marker can be found (Figure 17).
Figure 17. Expression Levels of 7 Secreted Proteins in Valk et al. Dataset
45
2 Confirmation of array results by Q-PCR
From the array analysis I identified approximately 107 potentially useful genes. A downside of
the array analysis is that it was done using samples not available to me for confirmation. To
determine if I could identify patient samples with variable expression of potentially useful
secreted proteins among patients seen at the Princess Margaret Hospital I undertook Q-PCR
analysis for five genes of potential interest; these were Areg, Ereg, GDF15, LGALs3BP, and
SEPP1. All studies were done in duplicate (technical replicates) and the level of expression
compared to the housekeeping gene GAPDH. I first assessed the expression of the GDF15 and
LGALs3BP genes in leukemic cell lines available in the laboratory and a breast cancer cell line
known to have high expression of LGALs3BP and GDF15. As can be seen below there are very
high levels of LGALs3BP in the breast cancer line. High, but variable levels were evident in the
leukemic cell lines (Figure 18).
Figure 18. Q-PCR Results for LGALs3BP and GDF15 in SK-BR-3 and AML Cell Lines
46
Figure 19 illustrates the relative fold expression levels of Areg in 51 AML samples normalized
to normal bone marrow. As can be seen here, some AML patients showed higher expression and
some had lower expression for Areg, relative to a normal bone marrow ( normal total marrow).
Figure 19. Q-PCR Results for Areg in AML Patient Samples
Figure 20 demonstrates Ereg expression levels in 51 randomly selected AML samples. Variable
expression is evident across the panel of samples. It is interesting to note that while several
patients showed increased expression of both genes, there is also a set of patients showing
increased expression of EREG but not AREG.
Figure 20. Q-PCR Results for Ereg in AML Patient Samples
47
Figure 21 demonstrates the expression levels of GDF15 in the same 51 AML samples.
Figure 21. Q-PCR Results for GDF15 in AML Patient Samples
Figure 22 shows the expression levels of LGALs3BP in the 51 AML patients. In comparison to
AREG, EREG and GDF15, the majority of the patients had increased levels of expression of
LGALs3BP RNA.
Figure 22. Q-PCR Results for LGALs3BP in AML Patient Samples
Figure 23 demonstrates the expression levels of SEPP1 in the same 51 AML patients. As for
LGALs3BP, the majority of patients had increased expression of SEPP1.
48
Figure 23. Q-PCR Results for SEPP1 in AML Patient Samples
As mentioned in above, different cytokine genes have different expression levels in different
groups of AML patients. Putting together all the above Q-PCR gene expression results give us a
better understanding of the patterns of expression for different cytokine genes (Figure 24).
Figure 24. Q-PCR results for 5 selected genes in leukemic patients. As it shows in this figure each
patient had different expression levels for the above genes. It can be seen that patient number
100006 showed a very high expressions for GDF15, however it had lower expressions than NBM
for Areg and Ereg.
42
49
3 Assessment of Candidate Protein Levels in Serum of AML Patients
Having determined that there was high but variable level of gene expression of potentially
secreted proteins, in patient samples in the PMH Leukemia Tissue Bank, I wanted to determine if
there was also variation in protein expression in the serum of de novo AML patients. As part of
the routine leukemia bank sample collection efforts, plasma and serum was obtained from all
new patients seen by the Acute Leukemia service of the Princess Margaret Hospital (PMH)
beginning in August 2010; samples were collected following informed written consent according
to a Research Ethics Board approved protocol.
Through data mining I had identified some 107 genes that showed variable levels of expression
at the RNA level in AML samples. As my goal is to develop a serum assay that will allow for the
timely assessment of patients with AML at the time of diagnosis and following therapy I went on
to measure the levels of a selected set of proteins from normals and AML patients at diagnosis
and following initial diagnosis. While my ultimate goal is to have a multiplexed assay, I decided
to carry out the initial survey using available ELISA kits. In selecting the ELISAs for my initial
assays I focussed on proteins that showed high level of expression in greater than 10% of patient
samples in the Valk dataset. In addition I chose proteins that had been found to be highly
expressed in the serum of patients with other forms of cancer or had been implicated in affecting
the growth of malignant cells. Based on this I identified 12 proteins for initial evaluation (Table
6); kits were purchased from commercial sources. In deciding to move forward with a protein we
decided to restrict our studies to those in which the serum and plasma levels for normal and
leukemic cases were essentially the same, as we did not want interference from proteins that
could be stored in platelets or activated coagulation factors to alter the results. Consequently we
did not further pursue clusterin, CXCL2, Jag1, MMP2 and Areg as candidate proteins. I also did
50
not further pursue IGFBP7 as in one patient increased levels of IGFBP7 was observed following
a filgrastim injection that resulted in a white blood count of 69x109/L and a neutrophil count of
55x109/L. The results for seven potentially useful proteins biomarkers are presented below.
3.1 Angiopoietin 1
For angiopoietin 1 I assessed serum levels from 3 normal individuals and 59 patients with AML.
As can be seen in the figure the normals had significant levels of protein (23.04-37.58 ng/ml). Of
note are two patients whom had levels 2-3 times higher than normal. Patient 090624 had
myelofibrosis that transformed to AML; he died during induction therapy. Patient 090589 had
chronic phase CML that has responded to imatinib. No post treatment sample is available for this
patient. AML Patients had levels ranged from 0.10 to 122.50 ng/ml (mean 14.29, median 5.93).
Figure 25. ELISA Results for ANGPT1 in Normal and AML Samples
51
3.2 LGALs3BP
In figure 26 the serum levels of LGALs3BP are shown for 20 normal samples and 104
presentation leukemia samples. For the normals the level ranged between 1886.4 and 9004.2
ng/ml with the mean and median being 4443.7 ng/ml and 4020.15 ng/ml respectively. For the
AML patients the levels ranged between 1885 and 17804.9 ng/ml (mean 9131.94, median
8525.1 ng/ml). Thirty seven of the 104 patients had levels of 10,000 to almost 18,000 ng/ml. . As
there is the potential for marked differences in the level obtained using serum or plasma, I
compared the levels of LGALs3BP in serum and plasma samples collected at the same time. As
can be seen, for this protein there are no marked differences between seruma and plasma
(Figures 27-29). In anticipation of the potential clinical use of monitoring LGALs3BP in patient
samples, the same set of samples were evaluated by ELISA in January 2012 and March 2012.
While the samples tested in March were consistently lower, the value of t critical was 2.14
compared to 5.36 for t statistics (p <0.001), and the Pearson correlation coefficient measured
0.949 (r = 0.949) which confirmed significant linear dependance between two variables in the
experiments (Figures 30,31).
52
Figure 26. ELISA Results for LGALs3BP in Normal and AML Samples
53
Figure 27. ELISA Results for LGALs3BP in AML Samples, Serum-Plasma Comparison
Figure 28-29. Scatterplot and Boxplot of Serum-Plasma Comparison for LGALs3BP
54
Figure 30. ELISA Results for LGALs3BP in AML Samples, Time-point Comparison
Figure 31. Scatterplot of Time-point Comparison for LGALs3BP
55
3.3 GDF15
Serum and plasma levels for GDF15 were assessed in the same manner as for LGALs3BP. For
the 20 normal serum samples, the levels ranged between 351.7 and 1157.03 pg/ml (mean 667.2,
median 665.12 pg/ml). In the AML patients the levels ranged between 460.36 and 8611.44 pg/ml
(mean 2894.30, median 2232.07 pg/ml). By using 1500 as the upper limit of normal 69 patients
had elevated levels of GDF15 at presentation (Figure 32). When I compared the serum and
plasma concentrations at the time of presentation, there was no marked difference between the
two sample types ( r = 0.992 and p < 1.0001)(Figures 33-35). As for LGALs3BP the detected
level of GDF15 was consistently lower in a sample that had been thawed, refrozen and re-tested
two months later (Figures 36,37).
Figure 32. ELISA Results for GDF15 in Normal and AML Samples
56
Figure 33. ELISA Results for GDF15 in AML Samples, Serum-Plasma Comparison
Figure 34-35. Scatterplot and Boxplot of Serum-Plasma Comparison for GDF15
57
Figure 36. ELISA Results for GDF15 in AML Samples, Time-point Comparison
Figure 37. Scatterplot of Time-point Comparison for GDF15
58
3.4 CCL3
CCL3 was measured in 20 normals and 103 AML samples. The normals ranged between 0.052-
55.44 pg/ml (mean 12.16, median 5.22 pg/ml). For the AML samples variabiltiy was observed
with levels ranging from 0.04 to 614.08 pg/ml (mean 47.78, median 8.39 pg/ml)(Figure 38). For
CCL3 no significant differences were observed between the levels of CCL3 in serum vs plasma
samples (Figures 39-41). For samples tested at two different time points, I found that for CCL3
the second measurement was often higher than the first measurement (Figures 42,43).
Figure 38. ELISA Results for CCL3 in Normal and AML Samples
59
Figure 39. ELISA Results for CCL3 in AML Samples, Serum-Plasma Comparison
Figure 40-41. Scatterplot and Boxplot of Serum-Plasma Comparison for CCL3
60
Figure 42. ELISA Results for CCL3 in AML Samples, Time-point Comparison
Figure 43. Scatterplot of Time-point Comparison for CCL3
61
3.5 IGFBP2
The level of IGFBP2 was determined for 5 normals, and 49 AML samples. The normals ranged
between 31.39-463.41 ng/ml (mean 146.42, median 80.0 ng/ml) while the AML samples varied
between 32.90-1003.37ng/ml (mean 196.42, median 114.60 ng/ml). It is of note that among the
normals four samples ranged between 31.39 and 100 ng/ml, with one sample having a level of
463.41 ng/ml. No other information is available for that individual regarding their state of health
at the time of providing the sample. For the AML samples many cases were low, with levels
under 200 ng/ml. However, several cases had very high levels (Figure 44). There was variation
between serum and plasma samples; interestingly the variation was not always in the same
direction between samples (Figures 45-47). There was also inconsistent variability in the serum
samples tested at two different time points (Figures 48,49).
Figure 44. ELISA Results for IGFBP2 in Normal and AML Samples
62
Figure 45. ELISA Results for IGFBP2 in AML Samples, Serum-Plasma Comparison
Figure 46-47. Scatterplot and Boxplot of Serum-Plasma Comparison for IGFBP2
63
Figure 48. ELISA Results for IGFBP2 in AML Samples, Time-point Comparison
Figure 49. Scatterplot of Time-point Comparison for IGFBP2
64
3.6 MMP2
The serum levels of MMP2 were measured in 20 normal, and 105 AML samples. The normals
levels ranged between 210.96 and 321.8 ng/ml (mean 256.49, median 252.78 ng/ml). For the
AML samples the levels ranged between 144.41 and 382.88 ng/ml (mean 240.43, median 237.04
ng/ml)(Figure 50). Variation was noted between serum and plasma samples from some but not
all patients (Figures 51-53). There was little difference in the measured levels of MMP2 between
samples tested in January, and then 2 months later in March, following once cycle of thawing
and re-freezing (Figure 54,55).
Figure 50. ELISA Results for MMP2 in Normal and AML Samples
65
Figure 51. ELISA Results for MMP2 in AML Samples, Serum-Plasma Comparison
Figure 52-53. Scatterplot and Boxplot of Serum-Plasma Comparison for MMP2
66
Figure 54. ELISA Results for MMP2 in AML Samples, Time-point Comparison
Figure 55. Scatterplot of Time-point Comparison for MMP2
67
3.7 HGF
HGF levels were measured in 20 normals and 103 AML samples. Normal levels ranged between
394.55-1660.6 pg/ml (mean= 866.57 and median= 784.27 pg/ml). For the AML samples levels
ranged between 423.7-13916.34 pg/ml (mean= 3689.67, and median= 1815.65 pg/ml). While the
level of HGF was found to be quite low in normal individuals, very high levels could be seen in
subsets of AML patients. Previously it had been reported that there are high levels of HGF
mRNA in cases with the acute promyelocytic form of AML (APL). Among the 103 samples
there were 8 with this form of leukemia. For these patients HGF was markedly elevated, ranging
from 3100 to 13665 pg/ml. These patients are indicated on the Figure 56 by *. However, it
should be noted that there were other cases that had high levels of serum HGF at presentation
that did not have APL. In general there was good correlation between serum and plasma levels of
HGF (Figures 57-59). As noted before, testing the same sample at two different time points was
associated with altered readings in some but not all samples (Figures 60,61).
68
Figure 56. ELISA Results for HGF in Normal and AML Samples
* APL Samples *****
69
Figure 57. ELISA Results for HGF in AML Samples, Serum-Plasma Comparison
Figure 58-59. Scatterplot and Boxplot of Serum-Plasma Comparison for HGF
70
Figure 60. ELISA Results for HGF in AML Samples, Time-point Comparison
Figure 61. Scatterplot of Time-point Comparison for HGF
71
4 Evaluation of Protein Levels in Pre-treatment and Post-therapy AML
Samples
A major goal of this project was to identify serum markers that are elevated in the serum of patients at
presentation, and vary over the course of the patient’s disease in a way that can predict either for
continued complete remission or the development of relapse. To begin to determine if any of the
proteins I was studying might be of value in this context, I obtained plasma samples for a subset of
patients at two times. These times were random with regards to where the patients were in regards to
their treatment course. The results of this analysis for several of the proteins is shown in the graphs and
tables below. I restricted this analysis to LGALs3BP, HGF and GDF15 as these markers showed good
consistency between serum and plasma, and also were higher than normal in a large proportion of the
patient samples. For all three proteins variation was seen between the presentation and later sample. In
the graphs of Figures 62, 64 and 66 the samples are arranged in numeric order. To try to visualize a
relationship between either persistent remission or the occurrence of relapse in these patients, Table 7
was organized with the continuing remission samples at the top and patients with persistent disease or
disease that evenually relapsed below; this is also shown graphically in Figures 68-70 where the patients
with continuing remission are on the left and those with persistent or disease that relapsed some time
after the sample are on the right. For LGALs3BP, GDF15 and HGF the level of the serum protein for
patients in continuing remission either decreased between presentation and the later time point or if low
at presentation stayed low at the second time point. While this is encouraging for the development of a
test, it is important that post treatment levels also reflect disease activity. For LGALs3BP and HGF one
can see that this is not the case. For example, for LGALs3BP a patient who had persistent disease the
post treatment level had fallen into the normal range (pt 110131). In addition for patients who were
found to relapse two to three months after the post sample was obtained, even though the pre-treatment
72
samples were elevated, the immediate pre-relapse samples were lower than at diagnosis and within the
normal range eg LGALs3BP pt 090575, HGF pts 090596 and 100020. While the predictive nature of
LGALs3BP and HGF appears to be poor, GDF15 demonstrated potential. For all patients in continuing
remission the post samples were in or near the normal range. In contrast patients who had persistent
disease or disease that would eventually relapse the post samples were often well above normal and in
several cases were higher than the presentation sample eg pts 090624, 090596, 100020 and 090476.
73
Figure 62. ELISA Results for LGALs3BP in Pre-treatment and Post-therapy AML Samples
Figure 63. Boxplot of Pre-treatment and Post-therapy Comparison for LGALs3BP in AML Samples
Upper
Normal
Level
74
Figure 64. ELISA Results for GDF15 in Pre-treatment and Post-therapy AML Samples
Figure 65. Boxplot of Pre-treatment and Post-therapy Comparison for GDF15 in AML Samples
Upper
Normal
Level
75
Figure 66. ELISA Results for HGF in Pre-treatment and Post-therapy AML Samples
Figure 67. Boxplot of Pre-treatment and Post-therapy Comparison for HGF in AML Samples
Upper
Normal
Level
*APL Samples
76
Table 7. Disease Status and Pre-treatment and Post-therapy values of AML Samples
Pt. Number
Status at 2nd sample
LGALs3BP (ng/ml) GDF15 (ng/ml) HGF (ng/ml)
Pre Post Pre Post Pre Post
090564 Cont. CR Dec 15235 9563.4 Inc 0.91 1.2 Inc 1.172 2.404
090583 Cont. CR NC 7297.3 6249.4 NC 1.3 1.66 NC 0.424 0.828
090658 Cont. CR Dec 12725 3487.8 Dec 3.33 1.09 Dec 9.052 0.511
090707 Cont. CR NC 7523.3 5835.4 Dec 4.89 1.69 Dec 2.249 0.005
100099 Cont. CR Dec 8666.7 1978.1 NC 1.61 1.52 Dec 1.204 0.668
100318 Cont. CR NC 7147.3 7650.6 NC 3.8 2.8 Inc 0.719 1.268
100685 Cont. CR Dec 7195.2 1676.2 NC 1.44 1.13 Dec 5.963 1.252
110067 Cont. CR Dec 14081.3 3661 NC 1.48 1.14 Dec 3.951 1.172
110144 Cont. CR Dec 13963.2 5391 NC 1.46 1.65 Dec 3.184 0.444
110162 Cont. CR Dec 16153.4 4603.4 NC 0.74 0.8 Dec 10.148 1.302
110093 Persistent Disease NC 4634.7 4847.3 NC 5.54 5.16 Inc 1.29 3.854
110131 Persistent Disease Dec 8810.9 5432.8 NC 3.28 2.44 Dec 1.427 0.204
110346 Persistent Disease Inc 2307 3350.9 NC 1.64 1.49 Dec 1.753 0.284
090624 Relapse NC 16030.7 15121.1 Inc 2.69 4.15 Dec 3.972 1.404
100507 Relapse NC 8704.3 7837.9 Inc 0.85 2.49 NC 3.919 2.946
100155 Early relapse Dec 9270.1 5391.6 Dec 4.31 1.99 NC 1.756 1.247
090548 Rel. +2 months Inc 1885 2475.6 NC 0.46 0.53 Dec 0.978 0.005
100357 Rel. +2 months NC 3466.1 3492 Inc 0.76 0.84 Dec 0.738 0.028
090575 Rel. +3 months Inc 3674.3 5974.4 Inc 0.63 1.13 NC 0.599 0.541
090596 Rel. +3 months Dec 14607.5 9121.3 Inc 1.57 2.3 Dec 7.037 0.005
100020 Rel. +3 months Dec 15131.7 9333.6 Inc 4.16 5.4 Dec 8.667 0.05
090698 Rel. +6 months Dec 2933.5 1121.6 Inc 1.89 4.08 Inc 1.106 2.424
090476 Rel. +7 months Dec 12507.6 5339.5 Inc 1.3 2.34 Dec 5.861 1.063
110101 Rel. +12 months Dec 6086.2 3348 NC 2.98 3.01 Dec 3.029 1.297
090538 Rel. +26 months Dec 7547.7 1730.5 Inc 3.02 4.55 Dec 1.495 0.613
110227 Not on tx Dec 14374.9 7522.3 NC 1.1 1.15 NC 1.87 2.258
Table 8. Normal and Disease Values for LGALs3BP, GDF15, and HGF in ELISA
77
Figure 68. ELISA Results for LGALs3BP in Pre-treatment and Post-therapy AML Samples, Based
on Disease Status
Figure 69. ELISA Results for HGF in Pre-treatment and Post-therapy AML Samples, Based on
Disease Status
Upper
Normal
Level
Upper
Normal
Level
*APL Samples
78
Figure 70. ELISA Results for GDF15 in Pre-treatment and Post-therapy AML Samples, Based on
Disease Status
Upper
Normal
Level
79
CCL3 pre and post treatment samples
For CCL3 the post serum samples were always lower than the pre-samples regardless of clinical
status. For example 110346 which was markedly elevated at presentation was very low in the
second sample, despite the persistence of disease in this patient (Figures 71, 72).
Figure 71. ELISA Results for CCL3 in Pre-treatment and Post-therapy AML Samples
Figure 72. Boxplot of Pre-treatment and Post-therapy Comparison for CCL3 in AML Samples
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Discussion
In this thesis I have begun the development of a serum based assay that could be used to monitor
the activity of disease in patients with AML. That is a protein marker that is high at presentation,
falls during remission, and becomes elevated at some time point before full blown disease is
again recognized clinically. Such markers are frequently used in the management of patients with
diseases such as ovarian, breast, colon and prostate cancer, but have not found utility in AML.
One of the reasons for this is that AML is a highly heterogeneous disease for which it is unlikely
that a single marker will be of utility for all or even a high proportion of AML patients.
There are a number of ways to identify secreted proteins in the serum of patients. As I was
interested in identifying proteins produced by the leukemic cells across the whole spectrum of
AML I decided on a two step approach. First, was to use bio-informatics and published gene
expression data of AML cases to identify secreted proteins with high level expression. As I
wanted to find proteins not produced at high levels by normal hematopoietic stem cells or mature
blood cells, I required that the candidate protein have low levels of RNA expression in the
arrays. In Table 4 of the thesis I identified 107 proteins that had the desired characteristics, and
in aggregate would provide information for almost all AML patients.
The second step was to verify that the candidate proteins were expressed at high levels in the
serum of some AML patients. Recognizing that it would be impossible to study all of the
proteins I decided to focus on proteins with known biologic function, had been studied in other
cancers, and for which there were commercially available ELISA assays. Subsequently I
assessed the presence of 12 different proteins in the sera of up to 100 AML patients. The proteins
assessed, included LGALs3BP, clusterin, GDF15, CXCL2, JAG1, Gal3, Angpt1, CCL3,
70
72
73
76
54
60
69
45
46
47
51
52
53
61
62
67
55
66
63
56
57
58
48
49
50
68
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IGFBP2, MMP2, HGF, and Areg. For several of the proteins the ELISA did not appear to work
properly eg clusterin, Gal3 and JAG1.
A major purpose of my study was to identify proteins that might be of potential in assessing
disease activity at a time point after diagnosis. For the purpose of this discussion I will focus on
those proteins for which I had paired presentation and post samples. However, it is worth noting
that there was variable expression of Angpt1 across AML patients, and in future studies this
protein should be assessed in post treatment samples.
The proteins for which I had pre and post treatment data included LGALs3BP, HGF, GDF15 and
CCL3. For these four proteins there was variation in the level of serum protein, ranging from
normal to several times normal. Such variability suggested that the proteins may be of value in
monitoring disease activity. However, this was true for only one of the proteins, GDF15. For
LGALs3BP, HGF, and CCL3 the post treatment levels were generally lower in the patients who
had a long term complete remission. However, the levels were also lower in patients who had
persistent disease or were destined to relapse within two months of the post sample being taken.
This was most dramatic for CCL3 where in all cases the post sample was very low. The reason
for this is not clear, but may indicate that CCL3 levels at presentation are not due to the leukemic
cells, but may reflect something else happening within the patient. For example, many patients
present with a diverse array of infections.
In contrast to the above three proteins, GDF15 presents potential promise as a means of
following disease activity in patients. For patients who achieved complete remission the GDF15
levels fell, although not always to the normal range. For patients with persistent disease the
levels of GDF15 stayed the same or increased over time. Finally for patients in remission, but
eventually had a relapse of their disease, an increase in the level of GDF15 compared to the
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presentation sample, was evident in some of the cases anywhere from 2 to 26 months before
relapse. These results suggest the potential value of using GDF15 monitoring of AML patients,
but require formal testing, in which samples are collected on a monthly or every other month
basis in post therapy patients. It is also important to determine the effect of perturbing the
hematopoietic system with chemotherapy to ensure that such treatment does not induce GDF15
expression.
In addition to measuring protein levels at two time points, I also explored aspects of sample type
and sample storage on results. While it is ideal to always use the same substrate, i.e. plasma or
serum, this is not always possible. For this reason I compared levels in serum and plasma
collected at the same time. For LGALs3BP, HGF and GDF15 there were no major differences
between the two different sample types. However for other proteins such as IGFBP2 the plasma
levels tended to be higher than those found in serum. This may be due to degradation of the
protein during the clotting process.
The other assay characteristic I explored was the effect of freeze thawing on the level of a
protein. For my experiments, samples were collected, aliquoted and frozen at -70ºC in 600 µl
volume in 0.6 ml Eppendorf tubes. For testing the samples were thawed and then tested. The
remaining material was refrozen and tested 2 months later. In general the levels were higher in
the first sample as compared to the second sample. This indicates that freeze/thawing has an
effect on the measured protein levels and therefore should not be done. As one moves the test
into the diagnostic laboratory it will be necessary to determine if there is a difference between
samples tested fresh vs storage at 4ºC vs storage at -70ºC.
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In conclusion, I have identified some 100 secreted proteins whose levels show variation across
the spectrum of AML and ALL patients. In my analysis of 12 of these proteins I have identified
one, GDF15, that is of potential value in serial monitoring of patients with AML. Further serial
testing of other proteins including Angpt1 is warranted so as to develop a panel of proteins that
can be used to conveniently monitor disease activity in AML patients.
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Discussion and Future Directions
Acute myeloid leukemia (AML) is the most common acute leukemia occurring in adults. It is
characterized by an increased production of myeloid cells blocked in their ability to differentiate
into functional end cells, and a marked reduction of normal blood cells, including red blood cells,
platelets, and neutrophils. AML is not a single disease but a complex group of diseases based on
different clinical, morphological, and gene mutations signature characteristics. Bennett et al. was
the first group that identified morphological differences between AML patients [15]. However,
more in depth and precise categorizations continue to evolve by understanding the recurrent
chromosomal abnormalities and recurrent point mutations, and also recognizing characteristic
changes in gene expression and epigenetic background of AML cells. This complexity of AML
is illustrated in the gene expression analysis performed by Valk et al. in which they recognized
16 different groups/clusters of AML with each group having a distinct prognostic significance.
Their gene expression analysis of 285 AML patients had indicated that the expression levels of
some of the genes in acute myeloid leukemia patients were higher, compared to normal controls.
Using different characteristics such as age, sex, blood cell count, bone marrow blasts and
platelets count, cytogenetic abnormalities such as Inv16, and molecular features such as FLT3-
ITD mutation they could define and verify 16 distinct groups or clusters of AML patients with
the minimum number of genes to identify each group. In addition, this classification strategy
helps in determining disease prognosis and also therapeutic decisions. Significance analysis of
microarrays (SAM) and Prediction analysis of microarrays (PAM) statistical methods were used
in this study to identify the genes that have significantly different expressions between different
groups of AML and also to categorize the subgroup genes that defines each predefined class.
Based on this clustering methodology each group can be determined by a minimum number of
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identifiable genes, which in some of these groups there are some discriminative genes allowing
for overlapping signatures. Examples of such genes are CEBPA in clusters 4 and 15 and FLT3 in
cluster 2 and 6. SAM analysis for cluster 12, which includes all acute promyelocytic leukemia
(APL) cases, demonstrated a group of genes such as fibroblast growth factor 13 (FGF13),
macrophage-stimulating 1 growth factor (MST1), and hepatocyte growth factor (HGF) that were
specific for this cluster. PAM analysis showed the minimum number of genes specific for each
cluster analyzed by SAM and validated the genes defined for subclasses in each cluster. The best
predictors were HGF for t(15;17), ETO for t(8;21), and MYH11 for inv(16).
AML patients who do not receive any form of therapy will die within days to months. Although
supportive care with transfusions, antibiotics and low dose chemotherapy can extend survival to
months or a year or so, only induction therapy followed by consolidation type therapy can
achieve long term cure. The main goal of this type of treatment is to reduce the AML cells in the
bone marrow and to allow regrowth of normal hematopoietic cells and finally to achieve
complete remission. But complete remission does not mean cure and depending on different
subtypes of AML many of these patients will experience disease recurrence. This is due to the
persistence of a small number of leukemic cells, called minimal residual disease, which are not
detectable with current methods, which remain in the patients and cause disease relapse. For this
reason, physicians need a reliable method to detect minimal residual disease to be able to answer
the three important questions related to chance of relapse in AML patients, a) how much post-
remission therapy in enough? b) what is excessive? c) when is the best time to start therapy
against MRD and d) when is the time to stop the treatment? With the increasing knowledge of
leukemias and the development of the more sensitive methodologies in detecting molecular
markers, the chance of detecting MRD related markers to direct therapy has increased. Based on
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this, microscopy and cytogenetic, FISH, and flow cytometry methods hold a capability to detect
only a group/groups of AML patients with different morphological abnormalities, specific
chromosomal abnormalities, and some aberrant expressions. PCR methods have the highest
analytical sensitivity to detect recurrent gene fusions such as Bcr-abl and PML-RAR, aberrantly
expressed genes such as WT1, and recurrent point mutations such as NPM1c, DNMT3a and
FLT3-ITD. In childhood acute lymphoblastic leukemia (ALL), the presence of detectable
malignant clone by PCR of the unique Ig or TcR rearrangement at about a month after starting
therapy, predicts for relapse with standard therapy. In chronic myeloid leukemia (CML) and
Ph+ALL the level of Bcr-abl is used to adjust the dose and type of kinase inhibitor, or to
recommend for a bone marrow transplant. Quantitative RT-PCR (qRT-PCR) also can measure
the level of PML-RAR in bone marrow and can identify the onset of early relapse and also can
determine the adequacy of the therapy. In AML, recurrent chromosomal translocations such as
RUNX1-ETO and MYH11-RUNX2 also appear to be useful for monitoring minimal residual
disease (MRD) and directing therapy. Unfortunately such markers are available for only about
30% of cases and the difficulty in techniques and the reproducibility of the results should also be
considered. Furthermore, utilizing bone marrow aspiration as a sample needed for doing these
tests is considered as undesirable by the patients. For this reason the use of secreted proteins has
been studied in tumours such as breast, colon, lung and prostate as a means of monitoring the
amount of disease in a patient. On the contrary, due to the heterogeneity of AML, there are no
established serum protein assays that can be used in a prognostic way in the large majority of
patients with leukemia. A few attempts have been made to evaluate serum or plasma proteins for
their role in predicting prognosis and relapse of AML. While some of the studies showed
promise in predicting response to therapy, they were unable to show a relationship between the
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levels of such proteins in serum/plasma and the remission status of the patients and the
effectiveness of those proteins and MRD levels in AML patients. For example Nakase et al.
demonstrated that sIL-2R, which is expressed on the cell surface of activated lymphocytes and
released as a soluble protein in serum, had higher levels in AML patients vs normal healthy
controls. They could also show that there was a positive correlation between sIL-2R gene
expressions and the levels of the protein in serum. However, they found that this serum protein
was not predictive for therapy and hence not prognostically valuable [61]. In another attempt
made by Loeffler-Ragg et al. on sCD44, a protein which is considered as a regulator in the early
stages of normal hematopoiesis which can be released as a soluble protein in serum following
proteolytic processing, found a significant difference between the low levels of sCD44 in normal
controls and multiple myeloma (MM) and AML patients which is in accordance with other
studies showing the same phenomenon in regard to the patients with renal failure, arthritis, and
lung cancer. Although their results showed significantly shorter survival in those patients with
higher blood levels of sCD44, they could not identify a clear correlation between plasma levels
of this secreted protein and response to therapy. The facts that the source(s) of circulating levels
of sCD44 is unknown and that its serum levels ranged significantly higher than plasma levels in
normal samples suggests for more in-depth studies to find out the kinetics of its release and to
investigate the significance of this secreted protein in the evaluation of the disease status in AML
patients undergoing therapy [62]. Hock et al. and Yeh et al. also tried to find a prognostic
significance for the circulating levels of sCD40 and cHSP70; in both studies they could show
that normal controls had lower levels of these proteins in their plasma than AML patients. Also
based on their results, the survival rate was shorter in the patients with higher levels of serum
HSP70. For sCD40 there was an unclear correlation between higher levels of sCD40 and
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survival rate. Yet again they were unable to demonstrate a clear association between the
circulating levels of this protein and response to therapy for AML patients [63, 64]. All of the
above mentioned studies are of high value but failure to achieve decisive results may be due to
the lack of fundamental research in choosing and evaluating the right secreted protein(s) for
AML patients and their prognostic values. For example, by looking into the Valk et al. dataset
for sIL-2R, sCD40, and cHSP70 I have found that the expression levels for these proteins are
very low, especially for sCD40 and also there is a high expression levels for sIL-2R, cHSP70,
and sCD44 in normal controls and CD34 samples. The cHSP70 even shows a flat level for all
AML groups and also in normal samples and for sIL-2R there is an even higher level of
expression in normals and CD34 samples than in most of the AML patient groups. sCD44 which
has a substantial higher levels across all AML groups yet has a high expression levels in normals
and CD34 samples [65]. All of these observations indicate that there should be more in depth
research to choose the protein of benefit for the patients. Furthermore based on the Valk et al.
study there are 16 groups and for some groups there are sub-groups of AML patients with
different characteristics, cytogenetic, and genetic abnormalities which noticeably can be
observed in gene expression differences in microarray data. These gene expression disparities
may cause the differences in protein expressions across the entire AML groups and based on this,
no single cytokine can represent the whole groups of AML. It should also be noted that there are
other factors which contribute to the levels of such protein to be expressed such as post
translational modifications or the fact that there are other cells, other than leukemic cells, that
can also express and secrete theses proteins.
In another study, in search for the regulators of embryonic human stem cell pluripotency,
Gonzalez et al. screened the mammalian extracellular proteome and have detected approximately
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3500 mammalian genes related to secreted proteins or single-pass transmembrane proteins for
which their function is not fully understood. Using genome wide disease association studies they
have identified 806 purified secreted proteins. In this study they utilized secreted protein
discovery initiative (SPDI) and the web-based secreted protein database (SPD) for previously
predicted genes related to secreted proteins. Looking into gene ontology and other sources they
could detect and eliminate the false positive and duplicate predictions. The false positive
predictions were the proteins which localized in mitochondria and those multipass
transmembrane proteins [66]. In this thesis I have analyzed Valk et al. microarray data to find the
genes related to secreted proteins and finally to find AML related cytokines with some of these
cytokines already having been studied by other researchers to have known physiological
activities, or have specified role in cytokine signaling pathways. To achieve this goal I took
benefit of Gonzales et al. database and other references such as Gene Ontology, Kyoto
Encyclopedia of Genes and Genomes (KEGG), The Human Protein Atlas, Human Protein
Reference Database, HUGE Protein Database, Oncomine, BioCarta, Information Hyperlinked
Over Proteins (iHOP), CoreMine, PubGene, GeneCards V3, and UCSC Genome Bioinformatics.
The panel of 107 secreted proteins that came out as a result was carefully selected, not to have
high levels of expression for the normal samples or for CD34 samples. All of the selected
secreted proteins have high level of expressions and most of them have well recognised
characteristics in biology of cancer cells; candidate proteins include HGF, GDF15, LGALs3BP,
CCL3, IGFBP2, and MMP2.
HGF, hepatocyte growth factor/scatter factor (HGF/SF), was found to be one of the cytokines of
choice which had low levels of expression for CD34 and normal bone marrow samples and had
significant high levels of expression in groups 11 and 12 of 16 AML groups in Valk
90
classification. HGF is a multifunctional secreted protein with different biological activities
including tumor suppression, mitogenetic, proliferative and invasion effects. It is produced by
mesenchymal cells such as vascular smooth muscle cells and fibroblasts and leukemic cells [67-
69]. Through binding to its receptor c-Met, a proto-oncogene, with tyrosine kinase activity, HGF
can activate different cellular signaling pathways, including PI3K/Akt, STAT-3, and RAS. HGF-
cMet pathway can lead to tumor growth and metastatic progression in cancer cells and it can be
used as a therapeutic target in different cancers such as papillary renal cell carcinoma, breast,
lung, gastric, multiple myeloma, and leukemia [67, 68, 70-72]. By using immunohistochemistry,
Kentsis and his group found that HGF and cMet were expressed together in nearly 42% of AML
patients, associated with PML-RARA and RUNX1 (AML1-ETO) genetic abnormalities [72].
This observation is consistent with the results of Mendler and his research group. In the research
performed on 175 AML patients under age of 60 and 225 patients over age 60, Mendler et al.,
showed that RUNX1 mutations, although are common in older patients, are associated with poor
outcome in both younger and older AML patients with normal cytogenetic features [72, 73].
Other researchers also had similar findings on prognostic significance of HGF in AML patients
but not in MDS patients [74].
GDF15 with low levels of expression for CD34 and normal bone marrow samples, had high
expression levels for groups 6 and 7, in Valk dataset. GDF15, macrophage inhibitory cytokine-1
(MIC-1), placental bone morphogenetic protein, nonsteroidal anti-inflammatory drug-regulated
gene-1 (NAG-1), prostate-derived factor (PDF), or placental TGF-β is a member of the tumor
growth factor beta (TGF-β) superfamily and can be found in normal individuals with serum
levels between 200-1150 pg/ml [75]. There are various reports regarding variable functions of
this protein; it has been reported that it can induce cartilage formation during early stages of
91
endochondrial bone formation. Other reports showed that GDF15 is a neurotrophic factor, and
can inhibit proliferation of primitive hematopoietic progenitors, and also can inhibit TNFα
production by activated macrophages [76-84]. GDF15 is produced mainly by macrophages and
its secretion by other blood cells and circulating platelets is not significant and this makes it
reliable to measure it without interference [75, 85]. Also it has been reported that p53 can
increase GDF15 expression by targeting its promoter region [86-88]. Based on the findings, the
expression level of MIC-1 is increased in different cancers including prostate, metastatic colo-
rectal, breast cancers, and in multiple myeloma [75, 89-93]. The fact that GDF15 is secreted by
activated macrophages suggests that it may be involved in chronic inflammation. This was
shown with its increased levels in atherosclerosis, cardiovascular disease, and in rheumatoid
arthritis (RA). Based on this, there are several studies showing that GDF15 may involve in
activating Akt, extracellular signal-regulated kinases (ERK), and p53 signaling pathways [85, 86,
94-97]. Brown and his group have proved that GDF15 plays an important role in inflammatory
mechanism which will affect cancer development and progression by reducing tumor
lymphocyte infiltration. The mechanism underlying this, involves inhibition of anti-tumor
immune response, through leukocyte recruitment inhibition [75, 88]. Polymorphism in the
coding region of mature GDF15 protein is one main contributor in predisposing to cancer and
also in patient survival [98]. Having allele D is associated with better survival, but on the other
hand, carriage allele H involves with the increased risk of prostate cancer [88, 98, 99].
LGALs3BP, Mac2-BP, or 90K, another selected protein in my list, showed different expression
levels across the entire groups of AML in Valk microarray dataset, with high expression levels
for groups 2,3,5, and 10, and low levels of expression for groups 8, 12, and also in CD34 and
normal bone marrow samples. LGALs3BP or Mac-2BP is a 90-kDa oligomeric glycoprotein
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which was first identified as a tumor-secreted protein in breast cancer and as a ligand of galectin-
3 or lactose-specific S-type lectin. It is secreted by different cell types, including hematopoietic,
peripheral blood mononuclear, and mucosal epithelial cells [100]. It can bind to multiple proteins
such as collagenase, fibronectin, and nidogen which are mediators of cell-matrix and cell-cell
adhesions and are important during cell invasion and migration. LGALs3bp through induction of
IL2 has a stimulatory activaty on natural killer cells and lymphokine-activated killer cells and is
known as an immune system regulator. In noninflammatory cells, it can also stimulate IL6 in
bone marrow stem cells and mediate bone invasion in metastatic neuroblastoma [101, 102].
Elevated levels of Mac-2BP correlate with prognosis in different cancers including breast and
lung cancer. Piccolo and his group showed that in addition to its prometastatic role, LGALs3BP
secreted by breast cancer cells functions significantly as a pro-angiogenic factor by induction of
tumor VEGF by activation of the PI3k/Akt pathway and stimulation of endothelial cell
tubulogenesis. Immunohistochemical and gene expression analysis showed significantly higher
levels of LGALs3BP in different types of human malignancies. Furthermore, clinical studies
have revealed that elevated serum or tumor tissue levels of LGALs3BP are associated with a
shorter survival in patients with breast carcinoma, lymphoma, pleural mesothelioma, and non-
small cell lung carcinoma, and development of metastasis in a variety of human cancers [103]. In
another study Lee and his group found that LGALs3BP has dual effects in colorectal cancer
cells. Originally it suppresses the progression of cancer cells by interaction with CD9/CD82
complex at the membrane, but then it can be neutralized through binding to galectins. But on the
other hand, LGALs3BP in blood increases the aggregation of tumor cells to promote tumour
metastasis. For this reason, they showed that the interaction between secreted LGALs3BP and
galectins in colorectal cancer cells plays an essential role in cancer progression and distant
93
metastasis [100]. Fogeron et al., have shown that the morphology of the centrosome are
deregulated in cancer cells. They have looked into around 23 centrosomal and cell-cycle
regulatory proteins and found that LGALs3BP, which is a centriole associated protein, is one of
the proteins that is deregulated in cancer cells. This protein has a dual role in the centrosome of
cancer cells; when overexpressed it can trigger centrosome hypertrophy and when
downregulated it cause centriolar substructures accumulation [104]. Whitman and her group on a
study of 243 patients found the adverse effects of FLT3-ITD on prognosis and survival in
patients aged > 60 with normal cytogenetics. They found that the outcome for this age group
with FLT3-ITD mutation is shorter survival rate and poor prognosis than FLT3-WT. Their
microarray results showed overexpressions of many significant genes which encodes some
biologically key proteins such as IGFBP2 which encodes proteins related in AKT pathway or
WT1, an immunotherapeutic target. Among upregulated genes, LGALs3BP had the highest
expression level in their data and showed the direct relation between this gene and occurrence of
FLT3-ITD in these patients [105].
IGFBP2, is another candidate protein. This protein belongs to insulin-like growth factors binding
proteins (IGFBPs) that carries insulin-like growth factor (IGF) and modulate its transportation in
blood and helps in localization and its accessibility in each cell types [106]. IGFBP2 can increase
cell proliferation through mediating IGF2 and high levels of expression have been seen in AML
patients compared to healthy individuals in Dawczynski et al. study. They demonstrated that
patients with risk of developing relapse had even higher levels of expression (p= 0.06) [107]. It
has been shown that higher expression levels of IGFBP2 have correlated with poor prognosis in
children's with acute lymphocytic leukemia and drug resistance in AML patients [107-109].
Dawczynski and his group found that, in contrast to peripheral blood and bone marrow mono-
94
nuclear cells, leukemic cells had higher levels of expression of IGFBP2. Furthermore, it has been
shown that there is a correlation between the elevated levels of IGFBP2 and increased risk of
relapse after hematopoietic stem cell transplantation in childhood leukemia [107, 108, 110].
Vorwerk et al. reported that high levels of IGFBP2 and low levels of IGFBP3 at the time of
diagnosis correlates with relapse prediction in ALL patients [110]. Additionally, Zakhary and his
group demonstrated that serum levels of IGFBP2 were significantly higher in children with ALL,
than in control group [109].
MMP2, type IV collagenase, gelatinase A (CLG4A), belongs to a zinc-dependent endopeptidase
family. These proteins can degrade extracellular proteins which results in endothelial cells
migration and for this reason they play an important role in angiogenesis, especially MMP2 and
MMP9. During tumor formation, endothelial and inflammatory cells and also stromal cells can
express MMPs. In cancer matrix these proteins can cleave several angiogenic factors such as
TGF-beta and FGF and result in cancer progression [111-113]. Increased angiogenesis and
vessel density which correlates with VEGF expression has been demonstrated in AML patients.
Furthermore, researchers have been shown abnormal expressions of MMP2 and MMP9 in ALL
patients. Increased levels of these proteins have also been investigated in various cancers
including lung, ovarian, and gastric carcinoma [114-118]. Although MMP2 function in AML
patients has not yet been investigated, researchers have been found an increased level of
expression for this protein in leukemic bone marrow vs normal bone marrow samples and have
suggested a possible role for this protein in AML [111, 119, 120]. Marquez-Curtis and his group
also found an increased level of expression for MMP2 and MMP9 in MDS. They proposed that
although normal erythroblasts do not express MMP2, due to the dyserythropoiesis process
involved in MDS, all leukemic erythroblasts express this enzyme [121]. Some investigators such
95
as Arimura and Brew has been shown that in addition to its enzyme activity in degradation of
matrix, MMP2 can also acts such as TNF-alpha and Fas ligand and increase apoptosis [122,
123]. There are some inconsistencies between reports of different researchers on prognostic
significance of MMP2 and MMP9 in MDS. Lin et al. has shown longer survival rate correlated
with lower expression levels of MMP2 but Klein and Kuittinen proposed lower survival in
leukemic patients who showed higher levels of MMP2 expressions [119, 124]. In a study by
Reikvam and his group, on a group of AML patients, they found a significant correlation
between complete remission after induction therapy and low levels of MMP2. Based on this,
high MMP2 levels correlates with poor prognosis and low survival [125].
CCL3 also named macrophage inflammatory protein-1-alpha (MIP-1A) is a chemokine which is
released by natural killer cells (NK) with other inflammatory cytokines including IFN-gamma
and CCL4 (MIP-1B) as a result of immunoregulatory function of these cells [126]. It is also an
inducible cytokine whose expression is upregulated due to inflammations [127]. It can also be
released by CD34+ stem cell-derived monocytes in response to cancer cells. Different cytokines
and chemokines including C-C chemokine and CXC chemokine families including CCL3,
CCL4, and CXCL8 are produced from cancer cells and tumor-infiltrating macrophages (TIMs)
and can be found in the tumor microenvironment [128]. It has been shown that CCL3 can be
expressed constitutively and appears in a low level, but different cell types can express
detectable amount of CCL3 under influence of different inducers. These cells including
monocytes, macrophages, normal blood cells, platelets and bone marrow CD34+ cells [129].
Davids and his group showed that leukemic cells, by releasing different chemokines such as
CCL3 and CCL4 and stromal cells, by secreting CXCL22 and CXCL9-12, help in organizing
CLL homing and directing these cells within tissue microenvironment. Leukemic cells, in CLL,
96
by secreting CCL3, CCL4, and IL-8 are responsible for recruiting T cells and variety of other
cells resulting in pro-survival signaling pathways. On the other hand, stromal cells provide anti-
apoptotic signals and contribute in cell trafficking and drug resistance [130]. It also has been
shown that activated CLL cells highly express and secretes CCL3 and CCL4 and elevated levels
of these chemokines in plasma of CLL patients were strongly associated with poor prognosis
[131, 132]. It has been shown that hypoxia and low oxygen tension in AML bone marrow, due to
the accumulation of immature cells, can induce hypoxia inducible factor-1 (HIF-1 alpha)
expression, a heterodimeric transcription factor, that can regulate angiogenesis-regulated genes
and can modulate and increase the expressions of several cytokines including CCL3, CCL4, and
VEGF [133]. Park and Kim looked into the extracellular acidification (low pH
microenvironment) related to inflammation and have found, among 353 macrophages related
genes, 193 genes upregulated including some members of CXCL family chemokines and 160
down-regulated genes including CCL3 and CCL4, not only in RNA levels but also in protein
levels in serum samples. In a process of acute or chronic inflammation, acidification is a
common feature in the inflammation region (pH 5.5-7.0) which is a result of short-chain fatty
acid production due to bacterial metabolism, hypoxia that occurs in the inflammation area, and
lactate formation by infiltrating neutrophils and macrophages [134]. It has been shown that
serum TNF-alpha levels decreases in response to lipopolysaccharide (LPS) and results in the
reduction of inflammatory responses and phosphatidylserine-dependent phagocytosis in
macrophages increases in response to microenvironment acidification [135, 136]. Park and Kim
proposed that the down-regulation of some of the inflammation related cytokines may be due to
different inflammation-regulatory pathways and also depends on the stage of the inflammation
involved [134]. Bristow and Shore demonstrated that transcription factor RUNX1 which can
97
regulate transcription of different genes such as CSF-1, IL-3, and CCL3 and histone acetyl-
transferase MOZ are both essential in hematopoiesis and chromosomal translocations and
rearrangements involved in these gens have been found in acute myeloid leukemia. RUNX1
(AML-1/ETO) has a dual role as a transcription factor. It can recruit gene co-repressors such as
mSIN3A and act as an activator, but on the other hand, it can be a repressor by interacting with
some histone acetyltransferases such as CBP and MOZ. They also found that CCL3 (MIP-1) has
two binding sites on its promoter region for RUNX1 and that MOZ also synergistically acts as an
activator in the binding sites of the promoter. There are several chromosomal rearrangements
that can transform RUNX1 into a transcriptional repressor such as AML-1/ETO which disrupts
normal differentiation of hematopoietic stem cells. CCL3 acts as a lymphocyte chemo-attractant
and a pro-inflammatory cytokine and also can inhibit proliferation of hematopoietic stem cells
and immature progenitors. Based on this, any deregulation in CCL3 expression, such as
translocation t(8;21) which give rise to transformation of RUNX1 into AML/ETO which acts as
a repressor on promoter region, may result in progression of leukemic cells by disrupting HSC
proliferation [137-139].
In the work presented here, I have identified over 100 proteins of potential utility in
characterizing AML and ALL at diagnosis and as a potential means of predicting disease
outcome, when used in a serial manner. Future work will include:
1. Development of a bank of samples collected as serum and plasma at defined points in
time over a two year period for patients being treated for acute leukemia.
2. Comparing gene expression levels at diagnosis to the protein level in serum/plasma. In
preliminary work I have found that in some cases there is a high level of RNA and serum
protein for a particular protein, while in other cases there may be a high serum protein
98
level, but low level expression of the RNA for the protein in the leukemic cells. While the
former is compatible with the serum protein being derived from the leukemic cells, and
hence a marker of disease mass, the latter situation suggests that in some cases high
levels of protein are derived from normal cells, possibly in response to the leukemic cells,
or as consequence of some other process occurring in the patient such as infection in the
lungs, liver or skin. For the purpose of disease monitoring it will be interesting to
determine if the marker is more predictive if it is present in leukemic cells (RNA) and
serum (protein) as compared to not in the leukemic cells, but present in serum (Figures
73-76).
Figure 73,74. Q-PCR and ELISA Results for LGALs3BP in AML Samples
Figure 75,76. Q-PCR and ELISA Results for GDF15 in AML Samples
99
3. The other proteins of potential diagnostic/prognostic/monitoring value should be assessed
in a comprehensive manner using presentation, remission and relapse samples from a test
cohort of patients. RNA data should be obtained for the presentation samples as well.
4. The characteristics of how best to store samples and how often they may be thawed and
frozen need to be determined.
5. A multiplex assay would be desirable, either on a Luminex or SRM type platform.
6. Given the cumbersome nature of ELISA assays, it will be necessary to identify a cost
effective platform that can be used either in real time or on a once a week basis to provide
information to the treating team.
100
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