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Transcriptional control induced by bcr-abl and its role inleukemic stem cell heterogeneity.
Sarah Beatriz de Oliveira Pagliaro
To cite this version:Sarah Beatriz de Oliveira Pagliaro. Transcriptional control induced by bcr-abl and its role in leukemicstem cell heterogeneity.. Cancer. Université Paris-Saclay, 2020. English. �NNT : 2020UPASQ032�.�tel-03219351�
Transcriptional control induced by
Bcr-Abl and its role in leukemic
stem cell heterogeneity
Thèse de doctorat de l'université Paris-Saclay
École doctorale n° 569, Innovation thérapeutique : du fondamental à l'appliqué (ITFA)
Spécialité de doctorat : Physiologie, Physiopathologie
Unité de recherche : Université Paris-Saclay, Inserm, Modèles de cellules
souches malignes et thérapeutiques, 94805, Villejuif, France.
Référent : Faculté de pharmacie
Thèse présentée et soutenue à Villejuif,
le 14 Septembre 2020, par,
Sarah Beatriz DE O. PAGLIARO
Composition du Jury
Jean Henri BOURHIS
Directeur de recherche, Institute
Gustave Roussy
Président
Pierre SAVATIER
Directeur de recherche, INSERM U1208 Rapporteur & Examinateur
Paul COPPO
Professeur, Hôpital Saint Antoine, AP-
HP
Rapporteur & Examinateur
Jean Claude CHOMEL
Praticien hospitalier, Centre Hospitalier
Universitaire de Poitiers
Examinateur
Ali TURHAN
PU-PH, Université Paris-Saclay Directeur de thèse
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Page | 1
TRANSCRIPTIONAL CONTROL INDUCED BY BCR-ABL
AND ITS ROLE IN LEUKEMIC STEM CELL
HETEROGENEITY
Presented by Sarah B. de O. Pagliaro
Public Discussion (14 september 2020)
Inserm U935
Supervised by Prof A Turhan
Université Paris Saclay
ED 569
1. Chronic Myeloid Leukemia (CML) 9
1.1 Historical background 9
1.2 Philadelphia Chromosome 10
1.3 The Bcr-Abl protein 11
1.3.1 The Bcr-Abl isoforms 11
1.3.2 Consequences of BCR-ABL1 generation in hematopoietic cells 13
1.3.2.1 Signaling pathways 14
1.3.2.1.1 JAK/STAT 14
1.3.2.1.2 PI3K/AKT/CRK 14
1.3.2.1.3 RAS / MEK 15
1.3.2.1.4 SRC KINASES 16
1.3.2.1.5 DEREGULATION OF APOPTOSIS 16
2. Hematopoiesis 18
2.1 Hematopoietic stem cells (HSC) 19
2.1.1 In vitro detection tests 20
2.1.2 In vivo detection tests 21
2.1.3 Progenitors and precursors and mature cells 22
2.2 Regulation of the hematopoiesis 24
2.2.1 The microenvironment 24
2.2.1.1 Osteoblastic niche 25
2.2.1.2 Vascular niche 25
2.2.1.3 Molecular mechanisms within the microenvironment 26
2.2.1.4 Growth factors 27
2.2.1.4.1 Positive regulators of hematopoiesis 28
2.2.1.4.2 Negative regulators of hematopoiesis 28
2.2.1.5 Other stimulating factors for HSCs and progenitors 28
2.2.1.6 Intrinsic regulation 29
2.3 CML Stem Cells vs Hematopoietic Stem Cells 31
3 Modelling CML 32
3.1 Murine models 32
Page | 3
3.1.2 SCLtTA/BCR-ABL transgenic model 32
3.1.3 Xenograft models 33
3.2 Cellular models 35
3.2.1 Cell Lines 35
3.2.2 Primary leukemic cells 35
3.2.3 iPSC 36
4. Diagnosis. 37
4.1 Evolution of the disease and prognosis 37
5. CML Therapy 39
5.1 Treatment History 39
5.1.1 The introduce of TKIs, a turning point in CML history 39
5.1.2 Hematopoietic Stem Cell Transplantation (HSCT) 40
5.2. Conventional therapies 41
5.2.1 Chemotherapy 41
5.2.2 Interferon alpha (IFNα) 41
5.3 Targeted therapies – TKIs 42
5.3.1 1st generation TKI 42
5.3.1.1 Imatinib 42
5.3.2 2nd generation TKIs 43
5.3.2 1 Dasatinib 43
5.3.2.2 Nilotinib 44
5.3.2.3 Bosutinib 45
5.3.3 3rd generation TKI (mutation T315I) 45
5.3.3.1 Ponatinib 45
5.4 Mechanisms of resistance 46
5.4.1 Efflux mechanisms and carrier hOCT1 46
5.4.2 SRC overexpression 47
5.4.3 Increased expression of BCR-ABL1 47
5.4.4 Mutations of the ABL kinase domain 48
5.5 Resistance and mechanisms involved in the persistence of leukemic
stem cells 49
5.5.1 Activation of leukemia stem cell signaling pathways 51
5.5.2 Clonal evolution and gene instability 54
5.5.3 Quiescence phenomenon 55
6. Allogeneic HSCT in a post-TKI era 58
7. Tumor Heterogeneity : Overview 60
7.1 Implications of Heterogeneity 62
7.2 Heterogeneity of leukemic cells in CML 63
8. Transcriptional heterogeneity 64
8.1 Transcriptional heterogeneity in leukemia 64
9. Eukaryotic Elongation 2 Kinase 65
9.1 EEF2K in normal cells 65
9.21 EEF2K in cancer 66
10. Methods of single cell analyses 68
10.1 Single Cells and Computational analysis 72
10.2 Single cell isolation techniques 72
10.2.1 Data Analysis 74
10.3 C1 from Fluidigm 74
10.4 Monocle and Pseudotime 75
11. Single-cell applications in cancer 81
Objectives of the Thesis 82
Paper 1. Summary 83
Paper 1 Summary (in French) 86
Paper 1 – IN PRESS 89
Paper 2 Summary 104
Paper 2 Summary (in French) 107
Paper 2 IN PREPARATION 109
General Discussion 142
References 148
Page | 5
Abbreviations
5-FU 5-Fluorouracil
5-LO 5-lipoxygenase
ABL Abelson murine leukemia viral oncogene
AHI 1 Abelson Helper Intergration Site 1
AHR aryl hydrocarbon receptor
AKT RAC-alpha serine/threonine-protein
ALL Acute Lymphoid Leukemia
Alox5 Arachidonate 5-lipoxygenase
AML Acute Myeloid Leukemia
Ang-1 angiopoietin-1
AP Acute Phase
ATP adenosine triphosphate
B-ALL Acute B Lymphoid Leukemia
BCL2 B-cell lymphoma 2
BCL-X1 B-cell lymphoma-extra large
BCR Breakpoint Cluster Region
BFU burst-forming unit
BM Bone Marrow
BMDW Bone Marrow Donors Worldwide
BMT Bone Marrow Transplantation
BP Blastic Phase
CAD coronary artery disease
CAFC cobblestones Area Forming Cell
CCgR complete cytogenetic remission
CD(number) Cluster Differentiation (number)
CFC Colony Forming Cells
CFU Colony Forming Unit
CFU-M Colony Forming Unit - Macrophage
CLP common lymphoid progenitor
CML
CMP
Col1a1
CP
Chronic Myeloid Leukemia
common myeloid progenitor
collagen a1 type 1
Chronic Phase
CRKL CRK Like Proto-Oncogene
CSC cancer stem cells
CVA cerebrovascular disease
CXCL CXC chemokine ligand
CXCR CXC chemokine receptor
DNA deoxyribonucleic acid
ECM extracellular matrix
EPO erythropoietin
ERK Mitogen-Activated Protein Kinase
ET essential thrombocythemia
FACS Fluorescence Activated Cell Sorting
FLT3-L Fms-Like Tyrosine kinase 3-ligand
G Granulocyte
GAB2 Growth Factor Receptor Bound Protein 2
GATA Globin Transcription Factor
G-CSF Granulocyte-Colony Stimulating Factor
GEF Guanine nucleotide exchange factors
GEMM granulocyte, erythrocyte, monocyte, megakaryocyte
GM granulo-macrophagic
GM-CSF Granulocyte and Macrophage-Colony Stimulating Factor
GTP Guanosine-5'-triphosphate
HCK Hemopoietic Cell Kinase
HOXB4 Homeobox B4
HSC Hematopoietic Stem Cells
HSPC Hematopoietic Stem and Progenitor Cell
IFN-2b Interferon 2 beta
Page | 7
IFNα Interferon alpha
IL-2 Interleukin-2
IL-3 Interleukin-3
IL-4 Interleukin-4
IL-6 Interleukin-6
IM Imatinib
IRIS International Randomized Study of Interferon
JAK Janus tyrosine kinases
KLF4 Kruppel-like factor 4
LIN Lineage
Lmo-2 LIM domain only 2
LSC Leukemic Stem Cell
LTB4 Leukotriene B4
LTC-IC long-term culture initiating cells
MCL-1 Induced Myeloid Leukemia Cell Differentiation Protein
M-CSF Macrophage-Colony Stimulating Factor
MDR-1 Multi Drug Resistance 1
MEK Mitogen-Activated Protein Kinase
MF primary myelofibrosis
Mip-1α Macrophage inhibitory protein-1α
MK megakaryocyte
MMP matrix metalloprotease
MMR Major Molecular Response
MPP immature multipotent progenitor
MR Molecular Response
mRNA Message Ribonucleic Acid
mTOR Mechanistic Target Of Rapamycin Kinase
NANOG Nanog Homeoboc
NK Natural Killer
NOD-SCID Non-Obese Diabetic -SCID mutation
OCT Organic Cation Transporter
OPN osteopontin
PAOD peripheral arterial occlusive disease
PDFG Platelet-Derived Growth Factor
PDX Patient Derived Xenograft
PF4 Platelet Factor-4
Ph Philadelphia Chromosome
PI3K Phosphatidyl-inositol-3-kinase
PML Promyelocytic Leukemia
PTEN Phosphatase and Tensin Homolog
PTH parathormone
PV Polycythemia Vera
SCF Stem Cell Factor
SDF-1 Stromal Derived Factor-1
SH Src Homology
Smo Smoothened
SOX2 SRY-Box Transcription Factor 2
SR1 Stem Regenin
SRC Sarcoma
STAT Signal Transducer and Activators of Transcription
STI selective tyrosine kinase inhibitors
STIM Stop IMatinib
TCDD 2,3,7,8-tetrachlorodibenzo-p-dioxin
TGFβ Transforming growth factor beta
TK Tyrosine Kinase
TKI Tyrosine Kinase Inhibitor
TNF-α Tumor Necrosis Factor-α
TPO thrombopoietin
WHO World Health Organization
Wnt Wingless-Type MMTV Integration
Page | 9
1 Chronic Myeloid Leukemia (CML)
CML is a myeloproliferative disorder that is currently classified in the context of
myeloproliferative neoplasia along with polycythemia vera (PV), primary myelofibrosis
(MF) essential thrombocythemia (ET) chronic neutrophil leukemia, chronic eosinophilic
leukemia, and unclassifiable myeloproliferative neoplasias. 1
1.1 Historical background
The first descriptions of clinical cases corresponding to CML have been reported in 19 th
century. In 1840s, two cases of patients presenting with fever, major splenomegaly,
leukocytosis were published, almost simultaneously, by John Hughes Bennett2 in
Scotland; and Rudolph Virchow in Germany3. Both papers were preceded from a report
published in Paris, by Alfred Donne 4, that had described a possible case of leukemia,
although the lack of details did not allow conclusions to be drawn. Virchow described
terms as ‘’white blood’’, that became ‘’Leukamie’’ in German, while Bennett created the
term of ‘’leucocythaemia’’ or ‘’white cell blood’’5. About 30 years later, the origin of
leukemia was finally linked to the bone marrow by Ernest Neumann. CML have been
afterwards treated with approaches such as administration of low doses of arsenic and
starting from early 20th century by spleen irradiation.
The seminal discovery in the field of CML research occurred in 1960 where that the
Philadelphia Chromosome was described by Nowell and Hungerford.6 The presence
of the abnormality was later confirmed by other studies and the aberrant chromosome
was named as Philadelphia Chromosome (Ph). In 1973, due the evolution of the
cytogenetics technology (banding), Janet Rowley was able to demonstrate that the Ph
chromosome was formed by a translocation between Chromosome 22 and
Chromosome 9 [t(9;22)].7 In the early 1980s, the Abelson gene (ABL) was identified on
the Ph chromosome (Chromosome 22) of CML patients, while normally it should be
located on chromosome 9. 8In 1984, a group of scientists in Rotterdam unraveled the
BCR gene on Chromosome 22.,9 BCR-ABL fusion gene was clones identified by
scientists from the Weizmann Institute in Israel. 11,12
Figure 1. Schematic representation of clinical and biological developments in the field
of CML.
1.2 Philadelphia Chromosome
First described by Nowell and Hungerford, the Philadelphia Chromosome (Ph) is the
small 9q- chromosome formed from the reciprocal t( 9;22) translocation13. The
presence of a translocation between the long arms of chromosomes 9 and 22 - t(9; 22)
- was demonstrated in 1974 by Janet Rowley 14 and the BCR-ABL gene could be cloned
in 198415 This is the first neoplastic pathology characterized by the specific link between
a type of cancer and a biological marker.
The Philadelphia Chromosome is the pathognomic marker of CML, but can also,
although it is rare, be detected in AML patients, and its presence is linked to a bad
prognosis.16
The coexistence of normal HSC along with Ph1+ HSC has been demonstrated even at
advanced phases of the disease 17During the process of translocation, the breakpoint
cluster region (BCR) gene, normally located on chromosome 22, where the breaks
occurs in a small 5Kb region, fuses to the Abelson murine leukemia viral
Page | 11
oncogene homolog 1 (ABL1), normally located on chromosome 9, generating the BCR-
ABL fusion gene18. The encoded BCR-ABL protein exhibits an exacerbated tyrosine
kinase activity, that plays a central role in the leukemogenesis process19.
1.3 The Bcr-Abl protein
1.3.1 The Bcr-Abl isoforms
While the ABL gene breakpoints highly variate upstream exon a2, there are only three
known breakpoints regions in the M-BCR gene (between e13 and e14 or e14 and e15).
20 The BCR breakpoints are responsible for the three principal forms of BCR-ABL:
p190, p210 and p230. 21 Generally, the different BCR-ABL forms are linked to different
forms of leukemia. 18,22 The P190 form is found in 25% of cases of Ph-positive acute B
lymphoid leukemia (B-ALL) and less frequently in acute myeloid leukemia (AML) and
very rarely in CML 23,24. The P230 is associated to neutrophilic CML.25 The most
common form, P210, is present in hematopoietic cells from CML stable phase and,
also, in acute lymphoid (ALL) and myeloid leukemias (AML). 26–28
Figure 3 Bcr-Abl protein isoforms 29
P210 is present in the most cases of CML, where b2a2 or b3a2 mRNA are produced
as a result of a break in the major region of the BCR gene (M-BCR) generating the
fusion gene BCR-ABL which in turn generates Bcr-Abl mRNA and p210 Bcr-Abl
protein.
The breakpoint occurring the e1 of BCR gene (m-BCR), gives rise to the truncated Bcr-
Abl gene which in turn gives rise to p190 protein which is at the origin of Bcr-Abl-
positive acute lymphoblastic leukemia (ALL). Finally, the e19a2 mRNA whose
breakpoint is located in the micro region of the BCR gene (μ-BCR) gives the p230
protein found in the rare form of chronic neutrophilic leukemia characterized clinically
by the proliferation of white blood mature cells (neutrophils) without presence of
immature precursors fund in peripheral blood. 21
Figure 4 Philadelphia Chromosome (Ph), Bcr-Abl protein and its mRNA structure
Page | 13
The p210 Bcr-Abl protein contains more than 10 domains with a serine-threonine
kinase domain, a Rho-GEF domain and a coiled-coiled oligomerization domain from
Bcr. The coiled-coiled domain allows dimerization of the protein facilitating
transphosphorylation of adjacent Bcr-Abl molecules. This part of Bcr is fused with SH
domains, Abl DNA and actin binding domains. The SH1 domain has the Abl tyrosine
kinase activity with the ATP binding domain and the catalytic phospho-transferase
domain 18.
Although the fusion protein contains most of the coding sequences of Abl, the
myristoylation site is lacking, contributing to the constitutive kinase activity of Bcr - Abl
29. Indeed, the Abl protein is capable of self-inhibition through this site and via molecular
interactions involving its SH2 and SH3 domains.
1.3.2 Consequences of BCR-ABL1 generation in hematopoietic cells
As early as the 1990s, the mouse models demonstrated the oncogenic role of Bcr-Abl.
After transplantation of BCR-ABL-expressing stem cells into mice, a phenotype of
myeloproliferative type-CML syndrome was developed in these animals but all the
features of the disease could not be reproduced. 30,31 In fact, the transplantation of
retrovirally transduced 5-FU-enriched stem cells in irradiated mice generates a highly
aggressive leukemia leading to rapid death of mice in 2-4 weeks. In parallel, in vitro
experiments have demonstrated the oncogenic role of BCR-ABL which induces in
hematopoietic cells, growth-factor-independence and resistance to apoptosis 32,33.
These experiments suggested therefore strongly that BCR-ABL fusion gene is
responsible for the development of the disease.
The Bcr-Abl protein causes abnormal activation of several signaling pathways in
leukemic cells, giving them a proliferative and survival advantage. These activated
pathways are related to growth factor dependence, apoptosis, proliferation and cell
adhesion.
These pathways include Jak/STAT, PI3K/Akt and Ras/MEK, the major players in the
pathogenic role.
1.3.2.1 Signaling pathways
The Bcr-Abl is an oncoprotein located mainly in the cytoplasm that has a constitutive
tyrosine kinase activity34. The interaction of several cytoplasmic or membrane
components such as STATs and RAS respectively, will lead to activation of secondary
signaling pathways controlling mitosis and cell survival 35.
1.3.2.1.1 JAK/STAT
Janus tyrosine kinases (JAK) are activated by cytokine receptors. The binding of the
ligand on the receptors modifies their conformation allowing their dimerization and the
activation of JAK by phosphorylation. These proteins are ubiquitous and play an
important role in proliferation, differentiation, cell cycle and apoptosis of hematopoietic
cells.36
Cellular models of CML have demonstrated that Bcr-Abl kinase activity increases
activation of JAK2/STAT allowing proliferation and cell survival.36, activating JAK2 and
directly STAT5. JAK2 phosphorylates the tyrosine 177 residue of Bcr-Abl, allowing the
activation of STAT5-independent cell signaling pathway 37
STAT proteins are transcription factors of which only STAT1, STAT3 and STAT5a and
STAT5b are found in hematopoietic cells 38. STAT proteins participate in oncogenesis
by activating the transcription of target genes coding, for example, for the
overexpression of anti-apoptotic proteins (Mcl-1, Bcl-x1, Bcl2) or cell cycle proteins
(cyclin D1 or c-Myc). In cell lines or in primary CML cells, STAT5 is constitutively
activated. 36,39. However, the use of STAT5 K/O mice model using conditional BCR-
ABL expression has shown that STAT5 was essential for the maintenance and
development of leukemic cells. 40,41
It has been proposed that Bcr-abl can directly activate STAT5 and bypass the
endogenous regulation of JAK2 to promote oncogenesis 42.
1.3.2.1.2 PI3K/AKT/CRK
The PI3K pathway (Phosphatidyl-inositol-3-kinase) modulates the activation of
transcription factors promoting cell growth/survival and inhibition of cell death43.
Page | 15
AKT has multiple targets involved in resistance to apoptosis and proliferation. It is also
a proto-oncogene, which has many downstream targets including mTOR. The mTOR
pathway is responsible for controlling protein synthesis, cell size and autophagy. It has
been observed that Bcr-Abl inhibits autophagy via mTOR and that TKI treatment
restores this pathway 44
The Crk1 adapter protein is constitutively activated in the CML representing a marker
whose phosphorylation demonstrates the presence of Bcr-Abl TK activity 45. The other
proteins related to Crk1 and Bcr-Abl are: STAT, Cbl, PI3K and Ras.46 Via these
interactions, Crkl appears indispensable for oncogenesis mediated by Bcr-Abl. In
murine models, this interaction is necessary to obtain the phenotype of the
myeloproliferative syndrome 47.
1.3.2.1.3 RAS / MEK
The Ras protein has a GTPase activity and is an initiator of the MEK-1, MEK-2 and
ERK pathways.This pathway is activated in several cancers and is involved in
proliferation and cell survival 48.
Bcr-Abl1 activates Ras via Grb2/Gab2 which allows the activation of MEK/ERK
pathways and the transcription of target genes involved in cell proliferation and a
decrease in the expression of genes involved in the cell cycle and apoptosis 49. The
Ras pathway is therefore activated constitutively by Bcr-Abl. In mouse models,
inactivation of the Ras pathway attenuates the phenotype of CML 50.
Figure 5. : RAS Signaling pathway 50
1.3.2.1.4 SRC KINASES
The family of Src kinases is another group of Bcr-Abl target proteins, playing a major
role in cell proliferation and differentiation. They are also involved in the transmission
of extracellular signals 51.
In cellular models of CML, activation of some Src kinases is due to Bcr-Abl 52. Other
studies have shown that Src kinases are essential for the transformation of BCR-ABL
cell lines but also for the signal transduction of Bcr-Abl chimeric protein 51,53. In addition,
Src kinases contribute to the activation of STAT5 and AKT by Bcr-Abl 54,55.
However, the importance of these kinases in the pathogenesis of the disease remains
uncertain. In fact, mouse models show that Src kinases are not necessary for the
initiation of CML but are rather involved in the pathophysiology of acute lymphoblastic
leukemia 56.
1.3.2.1.5 Deregulation of apoptosis
Bcr-Abl has been shown to inhibit apoptosis in several ways, including stimulating
anti-apoptotic pathways or modulating the expression of pro-apoptotic proteins. Bcr-
Page | 17
Abl expression can inhibit apoptosis by increasing the expression of the antiapoptotic
proteins Bcl2 and Bcl-x11. The PI3K/Akt and STAT5 pathways are important
mediators of apoptotic function. Activation of STAT5 by Bcr-Abl increases the
expression of Bcl-X1 39 In addition, the PI3K pathway allows the retention of Bad
protein in the cytosol blocking mitochondrial depolarization preventing the activation
of caspases required for apoptosis 57.
Figure 6. Bcr-Abl signalling pathway58
2. Hematopoiesis
Mature blood cells have a limited lifespan and their production during the lifetime of an
organism is ensured by the regulated activity of hematopoietic stem cells. In adult
mammals, this process designed as hematopoiesis occurs primarily in the bone
marrow, where billions of blood cells are produced every day. The different cell types
are derived from very immature hematopoietic cells, or hematopoietic stem cells
(HSCs), followed by a maturation process before gaining the bloodstream 59. HSCs are
multipotent cells that are able to regenerate and sustain complete hematopoiesis when
transplanted into myeloablated recipients 60. This special ability is widely explored in
cancer treatment and other blood disorders via stem cell transplantation, the only
curative approach in several types of leukemias, including CML until the advent of TKI
therapies 61.
Hematopoiesis is a hierarchically organized process, departing from the most primitive
hematopoietic stem cells, which give rise to lineage-progenitor cells, such as common
myeloid and lymphoid progenitors (CMPs and CLPs, respectively) which in turn
generate differentiated and cytologically identified blood cells 62,63. However,
experimental data giving rise to this classic organization has been obtained by
transplantation of cell populations, rather that the analysis of steady state
hematopoiesis. Recent technological advances allowed the follow-up up single clonal
HSC without the stress induced by transplantation in mice 64 . These studies suggest
that in steady-state hematopoiesis, HSCs generate long-lived progenitors which can
give rise themselves to differentiated hematopoietic cells 64. In these conditions, the
kinetics of this phenomenon has also been suggested to be different from the classical
model, with early generation of MK progenitors from HSC then their rise to myeloid
cells and lymphocytes 65 66 67 69.The organization of the branches has also been
questioned, such as the bifurcation of erythroid/megakaryocytic/myeloid versus
lymphoid cell fates, as well as megakaryocytic and/or erythroid lineages 70–72.
Furthermore, some groups have being demonstrating that the lineage-commitment
may already be determined in primitive HSCs 73–75.
Page | 19
As research technology advances, it becomes increasingly apparent that the
hematopoiesis process may be more complex and heterogenous than one thought.
However, the elucidation of the clonal dynamics of HSC and their potential will require
experimental data like what has been obtained in Wiskott-Adrich patients transplanted
with genetically marked cells 68
2.1 Hematopoietic stem cells (HSC)
HSCs are characterized by 3 fundamental properties:
1 - self-renewal ability, ensuring their durability by giving both an identical daughter cell
that keeps all the properties of the mother cell, but also able to differentiate itself. Self-
renewal is possible due to the ability of asymmetric divisions probably under the
influence of the niche, allowing the stem cells to generate daughter cells engaged in
differentiation while remaining pluripotent.
2 – Ability to differentiate into all hematopoietic lineages
3 - Long-term quiescence capacity under the influence of factors related to the niche
or intrinsic factors 60
Hematopoietic stem cells (HSCs) are present in very small quantities in adult bone
marrow (estimated at 1/100,000 in the bone marrow) and do not have specific
morphological characteristics recognizable in conventional cytology.59
The concept of stem cells within hematopoiesis was initially raised by Till and
McCulloch76. These authors have demonstrated that the hematopoietic reconstitution
of a lethally irradiated mouse receiving a bone marrow transplant passes through a
stage of cell colony development in the spleen, each of these colonies coming from a
cell called "CFU-spleen" (CFU- s). The presence of a stem cell at the origin of each of
these colonies was deduced due to the fact that cytological analysis of CFU-S showed
the presence of granulocytic, erythroid and megakaryocytic cells76. These dissected
and homogenized CFU-S could then reconstitute the hematopoiesis in other lethally
irradiated recipient mice showing their hematopoietic potential which was found to be
heterogeneous in the CFU-S 77 explaining the reduction in the reconstitution capacity
of the hematopoiesis after serial grafting78. The formal demonstration of the existence
of myelo-lymphoid murine hematopoietic stem cells was subsequently carried out
through defective retroviral-labeled stem cell transplantation experiments showing the
presence of my same retroviral insertion at the level of myeloid and lymphoid cells. in
the recipient mouse79. Since the development of characterization and isolation
techniques for stem cells with the discovery of the Sca1 antigen 80, it is currently
possible to purify murine stem cells by a combination of markers using Sca1 + cells,
Kit + lin- or SLAM markers 7 for the long-term reconstitution of hematopoiesis in mice
81.
The development of in vitro tests made it possible to evaluate the intermediate
compartment between the stem cells and the precursor and mature cells that can only
be identified by morphological tests.
2.1.1 In vitro detection tests
To identify rare HSCs in the bulk of cells, one of the most frequently used technique is
the flow cytometer. There are many surface markers that is currently used in both
research and clinical for phenotyping and isolation of HSCs. Along with all the other
criteria described above, these markers are also controversial among scientists. The
cell surface protein CD34 is, the most important primitive human hematopoietic cell
marker 1. It is estimated that only 1-5% of nucleated human bone marrow cells, around
1% of cord blood cells and less than 0.1% of normal peripheral blood cells express
CD34 62,63,64. However, CD34 is not exclusively expressed in hematopoietic stem cells,
but also in progenitor cells Later the differentiation stage of the progenitor, lower is the
expression of CD34. Therefore, despite CD34+ expression measured by flow
cytometry is widely uses to isolate hematopoietic stem and immature progenitor cells
for clinical engraftment, its expression alone does not guarantee an accurate measure
of these populations, demanding a combination of HSC markers 86.
Another widely used strategy for this purpose is the elimination of lineage differentiation
markers giving rose to a population designed as Lin-. Lineage antigens are not
expressed in HSPCs and combining Lin-/CD34high markers made it possible to
Page | 21
increase HSPCs enrichment up to 200-fold 87. Beyond CD34high/Lin-, it is very common
to add CD38 88,89 and CD45RA, which are absent in primitive cells, together with CD90
(Thy-1), which is higher expressed in primitive as compared to differentiated cells
90,90,91. Obviously, more specific the combination of markers, rarer are the cells,
consequently, more difficult is to isolate them.
Functional tests have been developed to highlight and to quantify HSCs by long-term
culture techniques.92–94. These cultures are based on the concept that the relatively
quiescent most primitive cells do not generate clonogenic cells requiring an initial
cycling period on a stroma and a suitable culture medium that will allow the
differentiation of these cells into progenitors. The long-term culture technique (LTC-IC)
remains the only one currently available for the in vitro identification of primitive cells in
humans. However, even if this technique makes it possible to quantify and highlight a
very immature population, the absence of differentiation markers continues to make
HSCs difficult to detect. Another less used technology is the cobblestones generation
cell detection technique (CAFC or Cobblestone area Forming cell). In this technique,
foci of hematopoiesis are morphologically identifiable in the adherent layer in the form
of "cobblestone areas" 95. In limiting dilution, cobblestone areas can be quantified
directly 96
2.1.2 In vivo detection tests
They make it possible to evaluate hematopoietic reconstitution in vivo after
hematopoietic cell transplantation in mice in order to demonstrate the presence of stem
cells. These are the only tests that formally identify the presence of genuine HSCs.
Evaluation of the hematopoietic reconstitution potential of human stem cells has
required the development of xenogeneic transplant systems in which immune
responses are reduced to extreme. The most commonly used immunodeficient mice
are NOD-SCID mice96, which show severe lymphopenia, macrophage deficiency and
complement deficiency. These mice have a deficiency of cellular and humoral immune
response. The experimental approach consists in injecting human hematopoietic cells
directly into the NOD-SCID mouse, irradiated to destroy the hematopoiesis of the
animal. Several months after transplantation, the presence of myeloid and lymphoid B
human cells is sought in bone marrow and primary lymphoid organs using FACS
analyses.97
2.1.3 Progenitors and precursors and mature cells
The commitment of HSCs in a differentiation pathway leads first to the formation of
highly clonogenic cells called hematopoietic progenitors. These progenitors are able to
ensure hematopoiesis in the short and medium term.75
There are several types of progenitors: immature multipotent progenitors (MPPs) with
increased proliferative capacity and more mature, lymphoid (LCP) or myeloid (MCP)
progenitors with limited proliferation potential. LCP will differentiate B, T and NK
progenitors. In the same way, the CMPs will give either erythroid progenitors (BFU-E,
then CFU-E), or granulo-macrophagic progenitors (CFU-GM) themselves at the origin
of the granular progenitors (CFU-G) and macrophage (CFU-M). These late progenitors
are committed to a single cell type.55
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Figure 7.Hematopoiesis and lineage differentiation98
Progenitors on a semi-solid medium were described in 1966 99. They are designed
under the term of colony forming cells (CFC). Several types of progenitors could
currently be characterized: CFU-GEMM capable of generating colonies comprising
granulocytic, macrophagic, megakaryocytic and erythroid cells, CFU-GM capable of
forming granular and macrophagic colonies and BFU-E capable of forming erythroid
colonies of different size depending on their proliferation potential.100
Figure 8. Examples of progenitors in a semi-solid medium
The differentiation of the progenitors results in the formation of precursors which are
the last stage of maturation of blood cells. Their maturation is done by a morphological
evolution (chromatin condensation, nucleoli disappearance and cytoplasm
reduction).59
2.2 Regulation of the hematopoiesis
2.2.1 The microenvironment
In 1978, Schofield proposed the "niche" hypothesis to describe the physiological
microenvironment that allows the survival of hematopoietic stem cells 101. The niche is
the microenvironment that protects stem cells from differentiation, apoptosis or other
stimuli It is an anatomical structure comprising both cellular and non-cellular
components integrating factors controlling the "fate" of stem cells 102. These niche
signals regulate the proliferation, survival and differentiation of stem cells, the adhesion
of stem cells to stromal cells and/or the extracellular matrix (ECM) anchors stem cells
in their niche near signals self-renewal. This specific environment linking stem cells
with supporting cells can polarize stem cells into the niche to promote asymmetric cell
division. Secreted factors may attract or, on the contrary, repel stem cells for their
migration.
The reference model remains to date based on the anatomical location and
distinguishes the osteoblastic niche and the vascular niche with the osteoblastic niche
specialized in the maintenance and quiescence of long-term HSCs and, on the
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contrary, the vascular niche allows the mobilization, the differentiation and proliferation
of these 103
2.2.1.1 Osteoblastic niche
The first direct evidence that osteoblast cells are involved in hematopoiesis are studies
in which human or murine osteoblast lines have been shown to secrete many cytokines
that promote the proliferation of HSCs in culture. The direct role of osteoblasts in the
regulation and maintenance of HSCs was shown in 2007 104. In this study, the
constitutive expression of PTH (parathormone) and its receptor, an important regulator
of calcium homeostasis and bone formation / resorption, was achieved using the
Col1a1 promoter (collagen a1 type 1). This activation resulted in a simultaneous
increase in the number of osteoblasts and the number of HSCs. In addition, the in vitro
maintenance of HSCs was more efficient with the stromal cells of these transgenic
mice, presumably due to the increased number of osteoblasts in the stromal cell
population compared to wild-type mice 105. The existence of this osteogenic niche has
been proven by a transgenic mouse model expressing the Herpes simplex virus
thymidine kinase gene under the control of the osteoblast-specific type I collagen
promoter, allowing depletion of these cells under the induction of ganciclovir 106.
Treatment with ganciclovir resulted in a decrease in spinal cellularity, in the number of
HSCs and in progenitors of the different lineages. Discontinuation of the treatment
restored the presence of osteoblasts in OM and restored bone marrow hematopoiesis
(105)
2.2.1.2 Vascular niche
A close interaction between HSC and endothelial cells is not unexpected because they
result from the same common embryonic precursor, the hemangioblast 107. Studies
have shown that endothelial cells are able to support HSCs in vitro in the absence of
growth factors 108,109. The endothelial cells of the sinuses constitutively express
cytokines such as CXCL12 (CXC chemokine ligand 12) or SDF-1 (Stromal Derived
Factor-1), adhesion molecules such as E-selectin and VCAM1 (vascular cell adhesion
molecule 1) which are for the mobilization of HSCs and homing 110,111. This
"mobilization" towards the vascular niche involves the matrix metalloprotease (MMP) -
9 which induces the release of soluble SCF 112. An essential role of the vascular niche
is to assist HSC in migration phenomena during homing or peripheral mobilization.
Vascular and / or perivascular cells can also create hematopoietic niches, since many
HSCs are found around vessels and factors regulating the maintenance of HSCs 113.
The quiescent HSCs are even more numerous in the perivascular zones than in the
endosteal zones 114. The Nagasawa group confirmed the importance of vessels in the
regulation of hematopoiesis by reporting that primitive HSCs have a very close
interaction with reticular cells, strongly expressing SDF-1, found both near endothelial
cells and in the body. endosteum 115. It has been proposed that the vascular niche
promotes the proliferation and differentiation of hematopoietic cells and progenitors
while the osteogenic niche maintains the quiescence of HSCs 116.
2.2.1.3 Molecular mechanisms within the microenvironment
Factors secreted by osteoblasts also play a key role in maintaining HSCs in
quiescence. Among these factors are angiopoietin-1 (Ang-1), osteopontin (OPN) and
chemokine CXCL12. The binding of Ang-1 to its Tie2 receptor plays an important role
in HSCs adhesion and their maintenance in quiescence 116. Ang-1 also increases the
expression of N-cadherins on the surface of osteoblasts, and promotes the anchoring
of HSCs within the hematopoietic niche 117.
The OPN, recognized by the HSC via the CD44 receptor, acts as a negative regulator
of their proliferation and ensures their localization, in a quiescent state 118 CXCL12 or
SDF-1 (Stromal Derived Factor-1) is produced by the stromal cells of the hematopoietic
niche and interacts with the CXCR4 receptor, expressed on the surface of HSCs 103.
This chemokine is a chemo-attractant and its secretion regulates the migration and
localization of HSCs within the niche 119. CXCL12 is also involved in regulating the
number of HSCs present in the bloodstream 120
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2.2.1.4 Growth factors
Growth factors occur at all levels of hematopoiesis regulating the reservoir of HSCs,
but also cell expansion and cell differentiation. All these molecules, produced in vivo at
low concentrations, interact with each other to modulate their effects. They are
secreted either remotely, such as erythropoietin (EPO) or thrombopoietin (TPO)
secreted respectively by the liver and kidney, or by the cells constituting the
hematopoietic niche, such as G-CSF. The action of these factors necessarily requires
their binding to receptors, present on the surface of hematopoietic cells, as well as the
activation of signaling pathways. These factors may regulate hematopoiesis positively
or negatively 121,122.
Figure 9. Growth factors involved in hematopoietic differentiation 123
2.2.1.4.1 Positive regulators of hematopoiesis
Among these factors, three play an essential role in the self-renewal of HSCs: TPO,
SCF (Stem Cell Factor) and Flt3-L (Fms-Like Tyrosine kinase 3-ligand). TPO and its
Mpl receptor are involved in maintenance and expansion of adult HSCs. Mpl is
expressed on quiescent HSCs and this expression decreases with differentiation 124.
The SCF, coupled with its c-kit receptor, is also involved in the survival and / or
expansion of HSCs. Finally, the Flt3-L receptor is expressed on primitive hematopoietic
progenitors and plays an essential role in their survival, proliferation and differentiation
125. Other cytokines also occur during the early stages of hematopoiesis: IL-3
(Interleukin-3), IL-6 and GM-CSF (Granulocyte and Macrophage-Colony Stimulating
Factor) Other cytokines have more restricted action spectra and intervene in late
stages of hematopoiesis, or even during the terminal phases of differentiation: EPO,
G-CSF (Granulocyte-Colony Stimulating Factor), M-CSF (Macrophage-Colony
Stimulating Factor), IL-4, IL-7, IL-5 and IL-2.
2.2.1.4.2 Negative regulators of hematopoiesis
They play a determining role in hematopoietic homeostasis by inhibiting the self-
renewal and / or differentiation of HSCs and thereby regulating the number of cells.
They include TNF-α (Tumor Necrosis Factor-α), which induces the apoptosis of
hematopoietic cells, PF4 (Platelet Factor-4) which inhibits megakaryopoiesis, but
whose action also positively regulates viability, survival and adhesion of HSCs and
progenitors 126 and Macrophage inhibitory protein-1α (Mip-1α), which restricts the
differentiation of primitive hematopoietic cells 127.
2.2.1.5 Other stimulating factors for HSCs and progenitors
Other pathways can be used to simulate the expansion of HSCs and progenitors.
Rather than use cytokines, it is also possible to infect HSCs with recombinant
retroviruses encoding transcription factors involved in their development, and whose
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overexpression is not transformative. This was done in 1995 by G. Sauvageau's team
with the gene coding for HOXB4 homeoprotein. The constitutive overexpression of the
HoxB4 gene in hematopoietic cells caused a strong and persistent expansion of the
stem cell pool in vivo, without inducing their differentiation or malignant transformation,
even in the long term 128. In humans, it has also been shown that the infection of HSCs
with a retroviral vector containing the coding sequence of the HoxB4 gene made it
possible to obtain an expansion of these cells in vitro 129. More recently, Cooke’s team
has observed a massive expansion of HSCs and progenitors in all hematopoietic
lineages with an AHR antagonist StemRegenin (SR1) 130.
2.2.1.6 Intrinsic regulation
Many transcription factors occur during primitive or definitive hematopoiesis. Some
have an important role in the renewal of the hematopoietic stem cell and others allow
the differentiation of different lineages.
In a non-exhaustive way, the primitive hematopoiesis at the level of the hemangioblast
is regulated by the Stem Cell Leukemia hematopoietic transcription factor (SCL),
GATA-2, Lmo-2 and AML-1. The self-renewal of the stem cells is regulated by Ikaros,
HOX B4, GATA-2 and Notch1 while the differentiation is done via a variety of factors
such as the factors PU-1 and GATA1131,132
Figure 10. Role of transcription factors in hematopoiesis 133
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2.3 CML Stem Cells vs Hematopoietic Stem Cells
CML is known to affect the most immature hematopoietic cells, called leukemia stem
cells (LSCs). During the last 30 years, one of the major challenges in the field of CML
has been the possibility to identify LSC from normal HSC using specific markers.
Indeed, both share the same surface markers, which makes FACS isolation of LSCs
highly difficult and unreliable. In recent years, many studies have shown possible
markers that could ultimately be unique to LSC, such as CD33 (Herrmann et al., 2012),
CD36 135, CD44, CD47, CD52 136, IL1-RAP 136–139 and, last and most promising so far,
CD26 . Dipeptidyl peptidase-4 (DPP4; CD26) could contribute to the release of CML
LSCs from bone marrow into the bloodstream 140,141. Studies have also shown that
CD26 can be detected in TKI resistant subclones 142. However, at a glance, all these
data remain controversial and inconclusive.
Hematopoietic cells maintain their pluripotent characteristics due to a special
microenvironment, which is formed by a complex system that involves the presence of
different cell types, such as mesenchymal stromal cells, osteoblasts, osteoclasts,
endothelial cells, and neural cells combined with signalling pathways and specific
metabolic conditions 143–145. It is believed that modifications in this microenvironment
may be involved in the development of several hematological pathologies, such as
CML. There is much evidence that this same niche that preserves the pluripotency of
hematopoietic cells also contributes to the preservation of leukemic stem cells 146.
Many laboratories have been working to unravel the characteristics of the bone marrow
niche and understand how they may contribute to the emergence of pathologies.
Despite major advances in understanding this issue, there are still many challenges
ahead. An important feature of CML is the presence of immature pluripotent leukemic
cells in peripheral blood, whereas under normal conditions, HSC remain in the bone
marrow 147.
3 Modelling CML
3.1 Murine models
3.1.1 Retroviral transduction/transplantation model
The “gold standard” of murine CML model has been established as early as in 1990 by
transplantation of 5-FU-enriched BCR-ABL-transduced bone marrow cells into an
irradiated syngeneic recipient. This method leads to the development of CML-like a
myeloproliferative disease, which however, is very aggressive as compared to CP CML
30. Subsequent studies have shown successful development of CML like syndrome in
100% of recipients in a very short time (2-4 weeks post transplantation) 148. In addition
to the rapid development of disease by recipients, another advantage is that influence
of other genes and mutations can also be explored by this model 31. The aggressivity
of the leukemia, leading to death of mice in 3-4 weeks, did not allow therefore, the
modelling of CP of human CML. This led to the development of inducible leukemia
strategies allowing to model more closely the chronic phase.
3.1.2 SCLtTA/BCR-ABL transgenic model
In this model, BCR-ABL expression is modulated by a tetracycline-controlled
transactivator (tTA) that specifically regulates BC ABL expression in the murine
hematopoietic stem/progenitor cells. This model can develop a CML-like disease
clinically similar do human CML (Li et al., 1999). Thus, the progress of the malignancy
is done is a slower pace compared to the retroviral models, being more likelihood with
human disease 149,150. This is also a better model for studying the endosteal and BM
microenvironment, as well as genomic instability 151.
Derivations of this model can be done to better adapt to each experimental profile, such
as SCLtTA/BCR-ABL double transgenic model with a transposon-based insertional
mutagenesis system, developed by Giotopoulos et al. (REF) In this version, it was
possible to mimic the additional chromosomal instability and mutagenesis of blast
crisis.
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3.1.3 Xenograft models
The xenotransplant model consist in engrafting human leukemic cells into
immunocompromised mice and follow up the frequency of LSCs and Ph+ cells
152,153.This methodology which allows quite reproducibly AML in immunodeficient
mouse (Bonnet and Dick) has been very difficult to apply to CML as the growth of CML
cells and progenitors from CP-CML patients are very difficult to obtain in vivo, requiring
the administration of human growth factors to mice . Transplantation of cells from more
advanced phases of the disease, such as blast crisis has been found to be more
successful.
Methods of generating scaffolds using human mesenchymal cells to mimic the BM
niche followed by injection of normal or leukemic cells have also been developed. 153–
155. but not considered to be standard experimental murine models in CML.
Figure 11. In vivo models of CML: A) Retroviral transduction/transplantation model B) SCLtTA/BCR-ABL transgenic model
and C) Xenograft model 156
Transcrip
tion
al con
trol in
du
ced b
y Bcr-A
bl an
d its ro
le in leu
kemic stem
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34
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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3.2 Cellular models
3.2.1 Cell Lines
In 1975, the K562 cell line carrying the Ph+ was established by Lozzio's team from a
pleural effusion of a patient with blast crisis CML 157. This cell line has been used
extensively in the world, but it is not representative of a chronic phase of the disease
and obviously cannot be compared to an equivalent without Ph1 chromosome. The
extensive experience using this cell line which has been very useful in preclinical
studies, showed also the spontaneous establishment of many different cell subclones,
with different cytogenetic and molecular characteristics according to different
laboratories. Over the years, several BC cells lines have been established from CML
patients with different characteristics. The ability to evaluate directly the effects of BCR-
ABL in a hematopoietic cell context has been subsequent established using BCR-ABL
gene transfer strategies. In both murine and human cell lines, this has been
accomplished by retrovirus-mediated BCR-ABL gene transfer. The murine lymphoid
BaF/3 cell line the growth of which depends on IL-3, become transformed to IL-3
independence upon BCR-ABL expression. A similar outcome can be generate in
humans using the UT7 line 158. Thus, starting from UT7 line, megakaryoblastic and
growth factor-dependent leukemic line, UT7-9 and UT7-11 clones were created by
BCR-ABL gene transfer. These 2 clones express BCR-ABL RNA and protein and they
are GM-CSF independent 158
3.2.2 Primary leukemic cells
The in vitro models of CML using primary leukemic cells include essentially the
techniques of CFC and LTC-IC, which allow the quantification of CML progenitors and
stem cells after long-term culture in the presence of permissive stroma such as MS-5
8,159,160
3.2.3 iPSC
IPSCs (induced pluripotent stem cells) result from the reprogramming of adult somatic
cells into pluripotent stem cells, in many ways resembling embryonic stem cells. The
first reprogrammed cells were derived from murine fibroblasts by the Yamanaka team
in Japan 161. Four transcription factors were retained as reprogramming media OCT3
/ 4, SOX2, C-MYC and KLF4. In 2007, two other teams reported the derivation of iPSCs
from human somatic cells 161,162. They were generated by reprogramming fibroblasts
either with retroviral vectors or with lentiviral vectors.
Because of their self-renewal capacity, iPS cell lines can be maintained in an
undifferentiated state and, thanks to their pluripotency properties, it is then possible to
differentiate them in all possible cell types.
Many iPSC patient-derived lines have been generated to model diseases. The first
modelizations were carried out by the team of Park 163. These lines make it possible to
dispose of cellular material carrying abnormalities responsible for a pathology, to study
them and to create an opportunity for the development of therapeutic strategies. The
first CML iPSC lines were generated in 2011 164 and in subsequent years, the use of
the iPSC-modelling of CML has been established in the field of CML. 165
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4. Diagnosis.
The diagnosis is made most frequently in the chronic phase. The main symptoms and
the usual signs of the disease are fatigue, anemia and splenomegaly. However, a
significant proportion of patients are asymptomatic, the diagnosis being made
fortuitously during a routine check-up. There are 600-800 new cases in France per year
(Incidence 1/100000). The sex ratio is towards men (H / F = 1.2) and the average age
is 53 years. 166
The BCR-ABL1 fusion gene is present in leukemic cells of all cases of CML but the
karyotype and research of the Philadelphia chromosome is still needed during the initial
assessment because no test can currently replace the prognostic value of cytogenetic
remission complete at 6 months and the karyotype can detect clonal abnormalities
associated undetectable by molecular biology. 167
4.1 Evolution of the disease and prognosis
The natural history of CML, which has been modified by the current TKI therapies,
included typically 3 phases:
The chronic phase, lasting from 3 to 5 years, often identified following a systematic
assessment showing hyperleukocytosis with significant myelemia. The cytological
marrow examination through a bone marrow aspirate is mandatory, showing
hyperplastic marrow involving myeloid cells. The diagnosis is confirmed by the
karyotype that finds the Philadelphia chromosome and the BCR-ABL transcript.
According to the WHO 2016 classification the accelerated phase is characterized by:
one or more of the following criteria:
• Persistence or increase of splenomegaly, not responding to treatment
• Persistent or increased number of leukocytes above 10 G / L, not responding to
treatment
• Persistence or increase in thrombocytosis (> 1000 G / L), not responding to treatment
• Persistence of thrombocytopenia <100 G / L, unrelated to treatment
• Presence of at least 20% of basophils in the blood
• Presence of 10-19% in the blood and / or bone marrow
• Presence of clonal cytogenetic abnormalities additional to Ph, including major
abnormalities (doubling of Ph, trisomy 8, isochromosome 17q, trisomy 19), or a
complex karyotype, or abnormalities in 3q26.
• Any new clonal abnormality in Ph + cells occurring during treatment
The blast phase or blast crisis marked by the presence of more than 20% medullary
blasts. In most cases, the blasts are myeloid type and in 30% of cases the crisis is
lymphoid type. This phase may be critical due the lack of a truly effective therapy.
This evolution has now been dramatically changed due the use of tyrosine kinase
inhibitors since 2001. 167–169170,171.
Figure 12. Phases of CML172
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5. CML Therapy
5.1 Treatment History
Initial therapy of CML included, arsenical preparations from 1865 until the beginning of
20th century where splenic radiation has been introduced to therapy (1902). The
efficiencies of these therapies were transient and despite many efforts suing
antileukasera (1932), benzene (1935), urethane (1950), leukapheresis (1960) and high
dose combined chemotherapy ( COAP protocols) the prognosis remained the same
with a median survival of 3-5 years, all patients succumbing to an acute leukemia 9. In
1953, David Galton introduced the alkylating agent, Busulfan, into clinics, with evidence
of successful cytoreduction but no effect on survival 173. It was only in the 80’s, the
introduction of Interferon-α changed the therapeutic strategy as the first remissions with
evidence of ph1-negative recovery were obtained with IFN albeit with modest increase
in survival 174. The addition of cytarabine IFN-2b was the first therapy with a clear
evidence of cytogenetic remissions and survival advantage and this combination
remained the standard therapy in patients not eligible for BMT until the introduction of
TKI therapies and 175.
5.1.1 The introduce of TKIs, a turning point in CML history
The treatment of CML has undergone an important evolution, particularly in the 2000s
with the arrival of targeted therapies: the tyrosine kinase inhibitors (TKIs). CML is the
first cancer pathology caused by an acquired genetic abnormality that has benefited
from targeted drugs.
After preclinical results showing the efficiency of tyrosine kinase inhibitors called
“tyrphostin” in 1999176 CML treatment had its historical turnaround. A tyrosine kinase
inhibitor, initially developed to inhibit PDFG-receptor in solid tumors, has shown major
efficiency in BCR-ABL-expressing cells. This drug, initially called STI-571 and later
Imatinib, developed by Ciba-Geigy was developed into the clinical application by
Novartis, has shown to be able to induce major cytogenetic responses. Not only did
Imatinib significantly improved the lives of patients with CML, as it was also a pioneer
in target-specific treatment 177,178. Since then, second and third generation of TKIs have
been introducing into clinics.
5.1.2. Hematopoietic Stem Cell Transplantation (HSCT)
The first attempt to a human bone marrow transplant was in 1939 to a patient with
aplastic anemia 179. Just after the World War II, researchers managed to recovery
normal hematopoiesis by marrow transplantation in a mouse model with marrow
aplasia caused by radiation 180. In 1956, Barnes et al demonstrated an overcome of
acute leukemia in mouse models by bone marrow transplantation 181. In 1958, the first
BMT between unrelated donor was performed by Georges Mathé’s team in Paul
Brousse hospital in Paris, for Yugoslavian researchers who were accidentally
irradiated.182
Allogeneic transplants for leukemia between related donors and recipients was first
performed in Seattle at Fred Hutchsion Center by E. Donnall Thomas’ team 183. Six
patients were pre-treated with radio and chemotherapy and then received an infusion
of marrow from a normal donor. At the time, histocompatibility was not considered, and
all patient died by 3 months post transplantation. Methods to identify human leukocyte
antigens (HLA) in human were developed in the 60’s, allowing physicians to check
compatibility between donor and patient. In 1979, despite a previous almost failed
experiment reported with 100 transplantations, which only 13 were successful, Donnall
Thomas was encouraged to continue and successfully engrafted 50% of 126 leukemic
patients, being honoured with a Nobel Prize in 1990 for his discoveries 61,184,185. In
1988, the Bone Marrow Worldwide (BMDW) was funded, currently counting with more
than 23 million registered donors in more than 70 countries 186,187.
BMT allows reconstitution of normal hematopoiesis through conditioning and in the
allogeneic setting allows the elimination of leukemic cells via a graft versus leukemia
(GVL) effect. The use of bone marrow transplants made it possible to understand very
early that CML also represented a model of immunotherapy 188. Indeed, in all clinical
trials aimed at reducing the GVH-type response by allogeneic graft T-cell depletion,
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 41
there has been a consistent increase in relapse rates189. These observations have
formed the basis of immunologic cellular therapies performed by donor lymphocyte
(DLI) transfusion in patients who have relapsed after allogeneic transplant 190,191.
Currently the allogeneic transplant is no longer proposed in the first line, but only in
case of resistance to TKI.
Until a few years ago, HSC allografting could be considered currently the only proven
curative therapy for CML. However, recent TKI-discontinuation trials have shown that
in some selected patients, long-term remissions without recurrence of CML can be
observed. However, it is currently not known if these patients are cured, as late CML
relapses have been reported as long as 20-25 years after allogeneic bone marrow
transplantation. 192,193.
5.2 Conventional therapies
5.2.1 Chemotherapy
In the 1950s, chemotherapy was used to target rapidly dividing cells allowing the
normalization of white blood cells in the blood. Chemotherapy does not only target
cancer cells but also healthy cells and other dividing tissues with many side effects.
The two most commonly used chemotherapeutic agents were busulfan and
hydroxyurea. However, neither of these allowed to control the disease and the median
survival of the patients did not exceed 5 years 194. These treatments often made it
possible to obtain hematologic remission but never cytogenetic with persistence of the
Ph chromosome in 100% of the medullary mitoses.
Although, hydroxyurea, a cytoreductive agent, is still used to reduce tumor mass while
waiting for cytogenetic or genetic confirmation at the time of diagnosis of CML.
5.2.2 Interferon alpha (IFNα)
IFN α has been used since the 1980s in the treatment of CML. It is a cytokine with
antiproliferative properties. It was the first-line treatment until the arrival of TKIs and
this despite a very variable tolerance according to the patients.
IFN α has an inhibitory action on the growth of leukemic progenitors. Its action seems
to target progenitors already engaged in a certain differentiation but without sparing
the normal cells, explaining its hematological toxicity. Nevertheless, studies carried out
in the 1980s have shown that IFNα induces, alone or in combination with chemotherapy
(such as cytarabine), hematological remission in 60 to 80% of cases, complete
cytogenetic remission in 15 to 35% of cases. patients and a good cytogenetic response
(Ph + <35%) in 10 to 20% of additional patients 195,196 Modifications on INF- α formula
improved significantly its side effects – flu-like symptoms and severe fatigue
– and INF-α and its combinations are still used today, in special cases 197,198
5.3 Targeted therapies - TKIs
5.3.1 1st generation TKI
5.3.1.1 Imatinib
Imatinib, or Glivec®, the first tyrosine kinase inhibitor (TKI) introduced therapeutically
since 2001, drastically altered the natural course of the disease by revolutionizing
management and prolonging survival. Indeed, in the era of targeted therapies by TKIs,
responder patients (but only these) have a life expectancy identical to that of patients
of the same age in the general population 199
Imatinib is a 2-phenylamine-pyrimidine derivative that inhibits the kinase activity of Bcr-
Abl by competing for the ATP catalytic domain binding site of the chimeric protein 200.
In 1996, Druker reported the first in vitro effects of 2-phenylaminopyrimidine Abl1
tyrosine kinase inhibitor, then known as a 571 signal transduction inhibitor (STI) on cell
lines derived from CML 177.
The interaction between imatinib and Bcr-Abl is possible in the inactive and
dephosphorylated conformation of the oncogene 201. This makes it possible to stabilize
the oncogene in its inactive form and thus to block the autophosphorylation of the
enzyme and then the transmission of the signal 202. Thus Imatinib is able to reduce the
formation of CML CFC by 90% with no inhibitory effect on normal cells 200,202.
Two years after the Phase I study, an international randomized study of interferon /
cytarabine against STI (IRIS study) was conducted in 1100 newly diagnosed patients.
The results of this study changed the management of patients in view of more than
satisfactory results. The superiority of Imatinib was demonstrated in terms of
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 43
hematological, cytogenetic and molecular responses (97% versus 56% for the RHC,
74% versus 8% for the RCC and 85% versus 22% for the RMC) 203 . An 8 years follow-
up study report (IRIS) has shown that patients treated with Imatinib presented an
overall survival rate of 85% and only an average of 0.9% (annual rate) progressed from
AP to BC. Despite its satisfactory action, imatinib is less potent than the 2nd generation
TKI, which may not inhibit the kinase efficiently, allowing the development of secondary
resistance and loss of response 107,108.
Figure 13. Mechanism of action of Imatinib.
5.3.2 2nd Generation TKIs
The identification of resistance to Imatinib led to focus research and development
efforts on new TKIs effective against several types of acquired mutations.
5.3.2 1 Dasatinib
Similar to imatinib, dasatinib is also an ATP analogue derived from pyrido (2,3-d)
pyrimidine. It is a broad-spectrum inhibitor since it targets in addition to Bcr-Abl (in its
active and inactive conformation). ), several proteins of the Src kinase family (such as
Src, Lck, Yes, Fyn). 205,206 Dasatinib is active against the majority of kinase domain
mutations, except of T315I 207. It has proven 300-fold more potent than imatinib in
vitro208.
The phase 1 study of Dasatinib (140 mg / day) was conducted in 84 patients who did
not respond to Imatinib. In 35% of patients with chronic or accelerated phase, it has
been possible to obtain complete cytogenetic remission and in patients with blast
phase a temporary improvement in hematological and cytogenetic responses 209.
Phase 2 included 387 chronic phase patients and showed complete 40 and 75%
cytogenetic remission rates after 15 months of treatment in cases of imatinib
resistance and intolerance, respectively (134). The overall survival of Imatinib-
resistant and intolerant patients was 78% and 82% respectively, however only 30-
35% of patients remained for 5 years under Dasatinib 210.
Clinical trials comparing dasatinib versus imatinib have shown that CCgR rates were
similar to imatinib (86% and 82%, respectively), while MMR and MR4.5 rates were
better with dasatinib than imatinib (64% versus 46% and 17% versus 8%, respectively)
211–213.
5.3.2.2 Nilotinib
Nilotinib is also an aminopyrimidine and it was developed from Imatinib by
crystallographic analysis. Like imatinib, it is a competitive inhibitor of ATP, with a
higher affinity than imatinib for Bcr-Abl 214. It is able to bind to the inactive
conformation of the oncogene. In vitro and in vivo, this TKI prolongs the survival of
mice injected with imatinib-resistant Bcr-Abl cells
The phase 1 study was conducted in patients resistant to Imatinib. Complete
cytogenetic remission was observed in 35% of these patients 215. The phase 2 study
included 321 chronic phase resistant or intolerant patients with Imatinib or both and
the results. Complete cytogenetic remission rates were 45% and 78% overall survival
at 4 years. As with Dasatinib, only 31% of patients remained on Nilotinib 216.
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Nilotinib was approved in 2007 by the U.S FDA as an analogue of imatinib with much
higher affinity for the ATP binding site. A clinical trial (ClinicalTrials.gov number,
NCT00471497.) comparing imatinib and nilotinib shows that patients treated with
nilotinib presented higher CCgR rates by 12 months (80% against 65% with imatinib)
and also better MMR rates (44% compared to 22% with imatinib) 204,211,217. However,
nilotinib treatment is related to occurrence of vascular events, such as peripheral
arterial occlusive disease (PAOD), coronary artery disease (CAD), cerebrovascular
disease (CVA), and the risk to develop diabetes. It is also not indicated in case of F359V,
E255K/V and Y253H mutations 218,219.
5.3.2.3 Bosutinib
Bosutinib is an oral tyrosine kinase inhibitor that targets Src/Abl activity. In Phases I
and II studies including patients who previously had 2 or 3 other TKIs, 24% of
patients achieved complete cytogenetic remission. After 2 years of treatment, the
progression-free rate was 73% and 83% overall survival. With a median follow-up of
28 months, only 29% of patients remained on Bosutinib 220. Bosutinib was
subsequently tested in 286 patients resistant or intolerant to Imatinib, 47% of whom
had complete cytogenetic remission. The phase III Bosutinib Efficacy and Safety in
Newly Diagnosed CML (BELA) trial compared bosutinib to imatinib in CP-CML.
Patients treated with bosutinib reached CCgR and MMR faster than with imatinib, but
12 months CCgR average rates were not different between the two drugs (70%
versus 68%), while MMR rates were higher with bosutinib (41% versus 27%).221,222
5.3.3 3rd generation TKI (mutation T315I)
5.3.3.1 Ponatinib
Ponatinib is the first and, currently, the unique of 3rd generation TKI class. It is highly
potent due its action against a vast spectrum of kinase domains and the only drug able
to overcome the T35I mutation problem. Its clinical use was approved by the U.S FDA
in 2012 for patients with resistant CML or intolerant of prior TKI therapy. In 2013, due
its serious adverse vascular events, the U.S FDA withdraw ponatinib from the market.
In 2014, after additional follow-up studies have showed satisfactory safety results,
ponatinib marketing has been resumed, with initial lower doses. 223–226
5.4 Mechanisms of resistance
As early as 2003, it has been shown that resistance to TKI could occur during
therapy (or in vitro models, using cell lines or patient cells (Review CML)
These studies revealed different mechanisms of resistance including:
1- BCR-ABL independent mechanisms: efflux mechanisms and membrane
transporters, the different mechanisms of persistence of leukemic cells (quiescent
cells, gene instability and signaling pathways) (described in the next chapter
2- BCR-ABL-dependent mechanisms: BCR-ABL amplification and oncogene
mutations
5.4.1 Efflux mechanisms and carrier hOCT1
Efflux transporters are frequently active transporters, belonging to the superfamily of
ATP-binding cassette proteins (ABC). This family includes the P-glycoprotein (P-gp or
ABCB1), responsible for the expulsion of Imatinib from the cell is encoded by the gene
MDR-1 (Multi Drug Resistance 1). The Pgp pump allows the exit of endogenous or
exogenous hydrophobic substrates such as therapeutic agents. This transporter is
overexpressed in the cells of patients in the transformation phase and involved in
resistance to blast phase chemotherapy 227, as well as resistance to Imatinib 228.
The hOCT transporter (human Organic Cation Transporter 1) has been shown to be a
significant factor affecting the intracellular bioavailability of the active ingredient
229,230.Overexpression of OCT1 in patients promotes the efficacy of imatinib 231. Indeed,
a high rate of OCT1 seems to be predictive of a good response to Imatinib while
patients with a low level of OCT1 require higher doses of Imatinib for an optimal
response. Neither dasatinib 232 nor nilotinib 233 is affected by OCT1 activity.
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5.4.2 SRC overexpression
The Src kinase family plays a central role in hematopoietic cell membrane receptor
signaling and nine of their members are present on myeloid cells (including SRC,
LYN and HCK) 234 and can be activated by BCR-ABL1 52. Imatinib resistance and
progression of the disease, particularly the lymphoid blast phase, may be mediated
by increased expression of LYN and HCK 235,236. The family of Src kinases is also
involved in Imatinib resistance because they maintain BCR-ABL1 in its active
conformation to which Imatinib cannot bind 237. Similarly, in patients who have failed
treatment with Nilotinib, increased expression of LYN has been found 238.
5.4.3 Increased expression of BCR-ABL1 as a mechanism of resistance
Amplification of BCR-ABL1 occurs more generally in advanced stages of the disease
239. This event, which has long been known from the cytogenetic point of view by the
duplication of the Ph1 chromosome, was first reported in 3 out of 11 patients in the
blast phase of CML who developed acquired resistance to Imatinib 240. It is not
established, however, whether this resistance to Imatinib is a direct result of the
increased expression of BCR-ABL1 protein 199, or the consequence of other factors
involved in the transformation, such as mutations in the Abl kinase domain, or the
consequence of increased genomic instability. In a subsequent study of 66 Imatinib-
resistant patients, BCR-ABL amplification was only found in 2 patients 241 and
overexpression of BCR-ABL1 is thought to be responsible for resistance to Imatinib in
a minority of cases. It seems that cells expressing strongly BCR-ABL1 are more rapidly
less sensitive to Imatinib because of the earlier appearance of resistant Imatinib mutant
subclones 242. In the same way, resistance to Nilotinib in vitro has also been found in
connection with overexpression of BCR-ABL1 238
5.4.4 Mutations of the ABL kinase domain
The appearance of mutations in the kinase domain of BCR-ABL can be associated with
resistance to TKI, especially during advanced stages of the disease. The mechanism
most frequently described in terms of acquired resistance to Imatinib is the presence of
mutations resulting in amino acid substitution in the kinase domain preventing the
attachment of the drug and this by affecting residues essential for the attachment of
TKI or by preventing BCR-ABL from becoming inactive conformation avoiding fixation
of Imatinib. The incidence of mutations is of the order of 40 to 90% depending on the
method of detection, the nature of the resistance and the phase of the disease 243.
In 2001, the first mutation described involving BCR-ABL signal transduction was the
T315I 240 mutation. Threonine 315 forms a hydrogen bond with Imatinib, this
fundamental amino acid is replaced by a larger isoleucine preventing Imatinib fixation
in the BCR-ABL ATP pocket. The T315I mutation is found in 4 to 19% of Imatinib-
resistant patients 244,245 resulting in resistance to all 2nd generation TKIs. Although the
T315I mutation confers resistance to all TKIs and median survival is low (approximately
1 year 246,247, long-lasting cytogenetic responses have been obtained with 2nd
generation TKI (162). However, ponatinib is the only effective drug on these mutations,
particularly in terms of deep 4.5 grade molecular responses.
Four categories of mutations were recognized in relation to the clinical resistance to
Imatinib affecting: the imatinib binding site, the ATP binding site (P loop), the catalytic
domain and the activation domain.
P-loop mutations account for approximately 50% of mutations in Imatinib-resistant
patients 248. These mutations destabilize the conformation required for Imatinib uptake
and appear to be associated with greater blast transformation 249 and a poorer
prognosis regardless of their sensitivity to Imatinib 244,248,250P-loop mutations have been
reported to be associated with poorer prognosis compared to other categories of
mutations (165) (167). However, other studies have not confirmed this prognosis (169),
this may be due to the criteria for finding mutations or to the cause of the M224V
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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mutation (with an unfavorable prognosis) that has been variably included in the P loop
categories. 251
However, only 7 amino acid substitutions (M244V, G250E, Y253F / H, E255K / V (P-
loop), T315I (Imatinib binding site), M315T and F359V (the catalytic domain)) account
for 85% of all mutations associated with therapeutic resistance 247
Although 2nd generation TKIs (Dasatinib and Nilotinib) offer an alternative for most
mutations except T315I, the development of new mutations to the second TKIs is
possible.
Sequential selective pressure of TKI therapy has been evaluated in resistant imatinib
patients who already have kinase domain mutations. They were subsequently treated
by a second TKI or even a third. It has been shown that 83% of patients have relapsed
after the onset of an initial response, due to the appearance of new acquired mutations
252. The T315I mutation was most commonly involved with a frequency of 36% 253.
In sum, the identification of a mutation is relevant depending on the stage of the disease
and the response to therapy, with greater impact when the disease is advanced. The
resistance mechanisms can be overcome with an increase in the dose 253, the transition
to 2nd generation TKI 221 at which the mutant is sensitive but also through non-BCR-
ABL dependent therapies.
5.5 Resistance and mechanisms involved in the persistence of leukemic stem
cells
In 2002, using quiescent HSC identification techniques, the Holyoake team 254 showed
that STI (Imatinib mesylate or IM) did not induce apoptosis of quiescent primitive Ph +
cells which remained active as this has been shown by phosphorylation of CRK-L (
242) Inhibition of tyrosine kinase activity only reduces the proliferation of LSCs.
Similarly, second generation TKIs do not provide better results on this cell fraction
255,256. Indeed, therapeutic TKIs, however targeted, fail in the elimination of all the
LSCs. The microenvironment of the bone marrow thus offers them the niche necessary
to resist the TKIs and to allow the leukemic relapse at the time of the stop of the
treatment 257. It has been demonstrated that the level of expression of BCR-ABL was
low in the LSC, which may be a cause of some resistance to TKIs 159,258. While LSCs
retain some kinase activity they can survive TKIs probably by mechanisms
independent of BCR-ABL kinase activity. Several pathways have already been studied
because they play a major role in the biology of the stem cell and by targeting them,
they can be therapeutic targets for removing LSCs.
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Figure 14. Resistance of LSCs259 to TKI
5.5.1 Activation of leukemia stem cell signaling pathways
Several signaling pathways are involved in maintaining self-renewal and differentiation
potentials of leukemic stem cells. These pathways may be common to both normal and
leukemic cells but in the LSC some are specifically activated that may be the target of
therapeutics.
The Wnt / β catenin, Sonic Hedgehog and Notch pathways play important roles in
maintaining stem cell function and have therefore been extensively studied as
alternative pathways of LSC persistence in CML.
Βcatenin is a protein of the Wnt pathway that is important for the survival and self-
renewal of HSCs 260. When Wnt is bound to its receptor, βcatenin is protected from
ubiquination and thus from degradation. It can therefore translocate into the nucleus
and activate its target genes 261 .Although not essential for the maintenance of HSCs
262 it has been shown that the therapeutic targeting of this signaling pathway in
association with TKIs could delay the onset of disease and eliminate more primitive
cells in mouse models of CML . During the blast crisis, it is suggested that
hematopoietic progenitors could reacquire stem cell properties via activation of β-
catenin 239 Hes1 (Notch target gene) has been shown to maintain LSC self-renewal
and promote the blast crisis transformation 263
The hedgehog pathway is an activated signaling pathway during embryogenesis. At
adulthood this pathway is conserved physiologically in stem cells and pathologically in
tumor cells. This is a dual receiver system, the PTCHD now Smo (Smoothened) in an
inhibited state. In the presence of the Hedgehog ligand, the inhibition is raised and the
Smo receptor activates the transcription factor Gli with the action of the target genes
264. It has been shown that this pathway was constitutively activated in the leukemic
cells of patients with CML and in mouse models of CML. The Smo pathway is more
active in leukemic cells compared to normal HSCs, and inhibition of Smo impairs the
development and maintenance of leukemic stem cells in murine models of CML 265.
In addition, the hematopoietic niche could regulate Smo and Wnt signaling pathways
104,261
The PML tumor suppressor involved in promyelocytic leukemia via the PML-RARα
rearrangement. PML is dysregulated in CML and is highly expressed in the bone
marrow of chronic phase patients. It has also been shown to be necessary to maintain
LSC 266. Indeed, in the murine model of transduction of the BCR-ABL gene in the
invaded mouse for the PML - / - gene, the induction of leukemia is much less efficient
compared to the PML + / + mice. These experiments made it possible to consider the
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use of arsenic derivatives (Arsenic trioxide) to eradicate quiescent stem cells from
CML.
FoxO transcription factors in particular FoxO3 are related to stem cell biology. FoxO3
is one of the targets of PI3K / AKT. In mature cells, Akt mediated signaling is more
important and allows proliferation of leukemic cells giving them a proliferative benefit.
However, in the LSC, the Akt signal is inhibited by PTEN 267 and TGFβ 268. This effect
of BCR-ABL1 induces the inactivation of FoxO3, which allows transcription of BCL6
which favors quiescence and self-renewal 269.
A new role for lipid metabolism in the maintenance of CML stem cells has recently
emerged. Arachidonate 5-lipoxygenase (Alox5) is part of the 5-LO pathway that
synthesizes Leukotriene B4 (LTB4) 269. Alox5 has been shown to be essential for the
survival, proliferation and differentiation of LSC. Alox5 expression is increased in BCR-
ABL gene transduction models in a manner dependent on Bcr-Abl TK activity. Similar
to PML, K / O mice for the Alox5a gene are resistant to leukemia development after
BCR-ABL 270 transduced stem cell transplantation. Alox5 has been shown to depend
on Msr1 itself regulated by BCR-ABL. Msr1 is an important regulator of PI3K / AKT and
β-catenin pathways and thus affecting the functions of LSC 270. Its inhibition only affects
LSCs and not normal HSCs 270.
The AHI 1 (Abelson helper integration site 1) oncogene could also be used as a new
target because it appears to be overexpressed in the LSCs and appears to have an
effect on cell proliferation 271. AHI 1 interacts with BCR-ABL, but also with the JAK2 /
STAT5 pathway.
Figure 15. LSCs resistance pathways 272
5.5.2 Clonal evolution and gene instability
Chromosomal abnormalities added to the Philadelphia chromosome (Ph +) cell
population define clonal evolution. This marks the transition to a more advanced stage
of the disease. The most commonly found chromosomal aberrations are a
supernumerary Ph chromosome, trisomy 8 and an isochromosome 17q (195).
Clonal evolution is associated with an escape from TKI treatment with hematologic
relapse, a lack of cytogenetic response (196) and a reduction in overall survival197. This
evolution reflects the genetic instability of the primitive Ph + highly proliferative
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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population 198. Clonal evolution could be involved in the phenomena of resistance to
TKI more important than mutations (58% of patients for 45%) 199. The incidence of
clonal evolution is even more pronounced in blast phases (73%) compared to the
frequency of kinase domain mutations (30%). 273
5.5.3 Quiescence phenomenon
Despite the good results obtained with Imatinib, tumor cells persist. Indeed, several
clinical trials of Imatinib discontinuation in patients with undetectable residual disease
over time have been performed 274–278. In general, these tests show that the disease
remains undetectable 40-50% of patients while a molecular relapse occurs very early,
in the first 6 months for the majority, in 50 to 60% of patients. The French STIM (Stop
IMatinib) study of 2010 was the first to show these results in patients who had an
undetectable residual disease of grade MR 4.5 for more than 2 years 279. These clinical
trials show the persistence in vivo of LSC leading to a new progressive phase of the
disease that can be avoided by resuming treatment with IM.
Therapeutic failures result from the insensitivity of quiescent cells to Imatinib. The
CD34 + CD38- leukemic cells, which are in phase G0 of the cell cycle and are by
definition quiescent, represent less than 1% of the CD34 + cells 255, it is however this
tumor cell fraction which persists and which is the support of residual disease.
Figure 16. LSCs resistance in the bone marrow niche 272
The phenomenon of quiescence is thus at the same time a cell cycle arrest (G0 phase)
and a basal metabolic state. In CML, most LSCs appear to be in the cell cycle while
HSCs remain quiescent 280. However, the cellular interactions and / or the secretion of
soluble factors can modify this state of rest.
BCR-ABL1 specifically inhibits the expression of CXCR4 in CML cells, this expression
is restored by Imatinib. The cytokine environment of the bone marrow triggers the
expression of CXCR4 by leukemic cells allowing their extravasion and migration
through the vascular wall. Interactions with the stroma trigger activation of integrins
and cell survival pathways. LSCs interacting with stromal cells return to quiescence
and become resistant to TKI 281
However, quiescent stem cell resistance appears to be multifactorial and includes drug
efflux mechanisms 282 and transcription of the larger BCR-ABL gene 255.
Dasatinib, although capable of inducing significant inhibition of Crk1 phophorylation in
CD34 + 38- cells, also remains ineffective on the quiescent cell population 77,283. In the
same way this cell fraction is also resistant to Nilotinib 256.
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Difficulties persist with the concept of quiescent stem cells. Residual disease is usually
detected in most patients, but it is not correlated with the number of quiescent leukemic
stem cells because of the detection limit of the method.
Nevertheless, therapeutics for targeting this potential disease reservoir continue to be
developed and include a cell cycle-inducing growth factor 284 and immunological
approaches 285.
AHR activation by TCDD has been shown to result in depletion of the HSC pool via
quiescent cell cycle entry 286,287. Later, murine KO models confirmed that AHR had a
predominant role in the maintenance of HSC quiescence288,289. The pharmacological
inhibition of AHR by a chemical antagonist in 2014 by the Boitano team showed the
possibility of using this pathway to induce massive expansion of HSCs, possibly for the
purpose of transplantation. These various studies indicate that AHR plays a role in the
proliferative and / or quiescent activity of HSCs 130.
Imatinib Dasatinib Nilotinib Bosunitib Ponatinib
Year of 2001/2001 2006/2006 2007/2007 2012/2013 2012/2013
FDA/EMA
approval
Kinase BCR-ABL, BCR-ABL, BCR-ABL, c- BCR-ABL, Native/mutant
Inhibited PDGF, SCF, SRC, family kit, PDGFR, SRC, family BCR-ABL,
c-kit kinases, c-kit, CSF-IR, DDRI kinases, including T315I,
EPHA2, Minimal VEGFR, EPH
PDGFRβ activity receptors, SRC,
against c kit or family kinases,
PDGFR KIT, RET, TIE2,
FL3
Indication 1st line 1st or 2nd line 1st or 2nd line Resistance or T315I-positive or
and usage in intolerance to when no other
CML prior therapy TKI is indicated
Table 1. TKI comparation in CML treatment 290
6. Allogeneic HSCT in a post-TKI era
Despite being the only proven curative treatment for CML, after the introduction of TKI
treatments, allogeneic transplantation is only recommended for patients who do not
respond well or develop treatment resistance. An allogeneic transplant requires a
highly skilled team, an especial structure and a previous preparation of the patient and
the cells that will be transplanted, which makes the process very expensive. In addition,
many patients develop post-transplantation complications 291.
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Table 3. Relation between TKI sensitiveness vs CML mutations 292.
7. Tumor Heterogeneity: Overview
In the 21st century, one of the major challenges in oncology is the unravelling of cancer
heterogeneity in order to uncover mechanism of progression of cancer cells under the
influence of microenvironment, genetic instability inherent to tumor cells and the effects
of different therapies. Many patients, who previously responded well to treatment, may
develop resistance over time. Patients with the same type of cancer and very close
clinical conditions may not have the same sensitivity to therapy. Intra-tumoral
heterogeneity is one of the reasons for such events. Tremendous technical challenges
are currently being addressed by the development of single cell analyses allowing to
establish the clonal architecture of cancer cells as well as their heterogeneity at the
level of epigenetics, splicing events, transcriptome and event at the level of protein
translation.
Two models, not mutually exclusive, are proposed to explain the origin of this
heterogeneity: 1) The cancer stem cells and 2) The clonal evolution 293,294 .
The concept of cancer stem cells (CSC) is ancient, but still unclear. It’s believed that
CSC have the same abilities as a normal stem cell, such as self-renewal, quiescent
capacity, pluripotency, and capable of regenerate a tumor in health mice when
transplanted. In leukemias this concept is clearer, once it concerns directly the
hematopoietic stem cells. In solid tumors, despite many evidences, it remains
questionable. In both cases, lack of specific biomarkers is one of the greatest barriers
for distinguishing CSC from normal stem cells. The heterogeneity in this model is a
result of the pluripotency ability of this subpopulation to generate whatever type of cell,
providing a cellular diversity 63,295.
Peter Nowell pioneered the first clonal evolution model, in 1976, based on a tumor
growth from a single mutated cell, continuously acquiring mutations along it progress.
Consequently, it give rise to subpopulations that could have their own characteristics
and regulations296. Some of these subclones may behave differently from the bulk
population, being less sensitive or even resistant to drug therapy and dominating the
tumor environment over time 297,298.
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Figure From Ben David et al Nat Rev cancer 2019
Biology of cancer heterogeneity.
Figure 19. Linear clonal expansion vs Branched clonal expansion schemes
7.1 Implications of Heterogeneity
Tumor heterogeneity play an important role from research to clinical application.
Mimicking such diversity in animal experiments is still a challenge. The Patient Derived
Xenograft (PDX) model consists of grafting human tumor cells or tissues into
immunodeficient mice and is considered the best current model for studies that are
directly or indirectly linked to heterogeneity. However, it is not enough to mimic tumor
complexity 299.
The use of biomarkers is also impaired as there may be variance between subclones
during disease evolution and certain biomarkers may be lost or modified. New single
cell technologies have been very useful in finding more specific subpopulation markers
300,301.
Heterogeneity also impairs the therapeutic response. It is believed in the presence of
subclones less sensitive to drug treatment than others. In addition, clonal evolution
may further cause certain subpopulations to lose their previously present sensitivity.
These more resistant clones may eventually dominate the tumor, causing more
aggressive disease progression 302,303.
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7.2 Heterogeneity of leukemic cells in CML
The clinical heterogeneity of CML is a fact known to doctors and scientists There is a
wide variance among patients in terms of disease course, response to treatment and
eventual relapses. In 2003, Gordon et al raised this issue and conducted a comparative
study of clonogenicity of healthy patient and donor cells and concluded that the
difference in response is due to normal biological differences between people304.
However, later, several other studies would show that this variance would go beyond
biological differences and perhaps be associated with a molecular difference305.
Since then, many studies involving transcriptomic differences have been published,
some extremely relevant, such as Condello et a l306. They discovered over 3,000 genes
whose expressions vary as the disease progresses. Wnt pathway, DNA damage,
apoptosis, ribosomal functions and glucose metabolism-related genes change their
expression depending on the stage of the disease 307. Another study has analyzed
CD34+ cells collected at diagnosis from two patients with distinct prognostics, which
one progressed early to a blast crisis while the other succeeded to keep chronic phase
for more than 7 years. 134
Recently, with the evolution of single cell technology, some studies on CML have been
published, two of which deserve special mention. Giustachinni et al managed to
combine high-sensitivity mutation detection with whole-transcriptome analysis single
cell techniques in a study that aimed to unravel potential genes that may be involved
to blast crisis progression and poor response to TKI 308. Another study, by Warfvinge
et al. combined high-throughput immunophenotypic screens with large-scale single-
cell gene expression analysis to check the heterogeneity within the LSC population in
CP-CML patients at diagnosis and following TKI treatment 142. In our study, we
combined single cell analysis with pseudotime and trajectory estimation methods in
order to understand the clonal evolution of CML progenitor cells.
8. Transcriptional heterogeneity
During the last 10 years, the major progress obtained in gene expression analyses,
allowed an unprecedented evaluation of inter-and intra-tumoral heterogeneity of cancer
and leukemia 309 . This heterogeneity which was obvious in terms of phenotype, drug
response, genomic abnormalities has also been found at the level of transcription which
can now be analyzed at the level of single cells 310. The advances obtained in this filed,
can now be used to generate clonal architecture of leukemias and may serve the basis
for future personalized therapies as the transcriptional dysregulation could be at the
origin of drug resistance 311 .
During the last 10 years, the major progress obtained in gene expression analyses,
allowed an unprecedented evaluation of inter- and intra-tumoral heterogeneity of
cancer and leukemia. 309
This heterogeneity, which was obvious in terms of phenotype, drug response, genomic
abnormalities has also been found at the level of transcription. Transcriptional
regulation have been exhaustively studied at le the level of initiation, but recent studies
have been showing the importance of the elongation step as well. 312
8.1 Transcriptional heterogeneity in leukemia
Epigenetic deregulation acts in conjunction with genetic anomalies, facilitating not only
the generation of tumors, but also their maintenance and progress. Studies show that
epigenetic processes in stem cells, such as the most primitive hematopoietic cells, a
group directly affected by CML, are critical for their development and maintenance.
These processes can have direct effects on: (1) transcription regulation, DNA
damage/repair and DNA replication, (2) RNA levels and post-transcription stability and
(3) protein translation or post-translation protein modifications. Mutations by these
mechanisms are relatively rare in the CML at CP. These mutations are more frequently
detected during disease progression and the proportions of leukemic cells carrying
mutations may be directly related to its response to TKI therapy. 313
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Transcriptional regulations play an important role in leukemia heterogeneity.
Transcription factor (e.g. WT1, MLL/ELL, C-MYC in acute myeloid leukemia; PML and
RARα in acute promyelocytic leukemia; PU.1 in erythroid leukemia; EZA/PBX1 and
TELL/AML1 in acute lymphoblastic leukemia)314 play an important role in transcriptional
heterogeneity and their dysregulations can be acquired via mutation (e.g. AML-
associated GATA2 mutations, CML-associated BCR-ABL mutations)315–317, by
translocation (e.g. RUNX1‐RUNX1T1, RUNX1‐EVI1, CBFB‐MYH11 translocations in
AML)8,314, changes in the genes of downstream signaling pathways, among others.
BCR-ABL acting essentially via the phosphorylation of tyrosine containing target
proteins, its involvement of the transcriptional pathways has been less studied. We
have recently shown that BCR-ABL activates ETS1, a major transcriptional regulator
in hematopoietic cells. 318 In a previous study, we transfected the BCR-ABL gene into
UT7 cells319 - an acute megakaryoblastic leukemia cell line - and performed a complete
transcriptome analysis comparing the parental and transfected cells. We found the
transcription factor, ETS1, as the gene most impacted by BCR-ABL expression, having
its expression increased by 35.86-fold compared to the control320. In this study we
demonstrate that ETS1 is a promoter of another EEF2K, also overexpressed in BCR-
ABL positive cells.
9. Eukaryotic Elongation 2 Kinase (EEF2K)
9.1 EEF2K in normal cells
Increased cellular metabolism and resistance to apoptosis are two hallmarks of cell
transformation. To sustain viability, cells regulate their proliferation and growth
according to modifications acquired in their environment, such as availability of
nutrients, for example 321 .
Cells under stress or under nutrient deprivation tend to protect themselves for survival
by reducing energy demand. Eukaryotic elongation factor 2 (EEF2K) is responsible for
one of these cytoprotection mechanisms. EEF2K belongs to a family of atypical kinases
that reversibly blocks the step of elongation of translation by phosphorylation of
elongation factor 2 (eEF2).
9.2 EEF2K in cancer
Cancer is characterized by abnormal expansion of malignant cells. Expansions like this
increase the demand for nutrients, generating excessive energy consumption, causing
an imbalance in the microenvironment, disrupting DNA repair and facilitating the gain
of mutations. Although it seems an unfavorable scenario for cellular expansion progress,
tumor cells manage to find a way to overcome nutrient restriction barriers. The
mechanisms involved are not fully known.
EEF2K is now known to be overexpressed in a wide range of tumors, such as breast,
ovarian, colorectal, for example 322–330. However, nothing is known about the impact of
EEF2K on chronic myeloid leukemia.
EEF2K is regulated by nutrient deprivation, energy depletion, Inadequate growth factor
signaling and/or hypoxia and DNA damage. Therefore, situations easily identified in
tumor environments. The increased expression of a gene that negatively regulates
protein synthesis in highly proliferating cells, such as cancer cells, seems paradoxical.
There are possible explanations demonstrated by some published studies, such that
the suppression of protein synthesis may be selective, that is, prioritizing the synthesis
of certain major proteins. EEF2K depletion in breast cancer was associated with lower
levels of pro-oncogene proteins, such as c-MYC and cyclin D1 and higher levels of cell
cycle inhibitory protein p27kip1 192,196. In glioma cells, siEEF2K cells had increased levels
of apoptosis genes and lower expression of anti-apoptotic proteins. There is also the
possibility that EEF2K stimulates autophagy, where cells can catabolize some of their
macromolecules for acquiring amino acids 332. Autophagy is linked to EEF2K in many
studies, specially involving breast cancer and glioma cells 324,328,330,331,333.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 67
Figure 20. EEF2K mechanism 334
10. Methods of single cell analyses
Genomics analysis have been widely applied in cancer. Genome sequencing, as well
as epigenomics and transcriptomics analysis lead us to better understanding tumor
progression and its mechanisms. Such analyses were generally done in the bulk of
cells or tissues, giving us a general idea of tumor behavior, however, masking the
intratumor heterogeneity due a lack of sensibility and precise “individual” data. Single
cell technologies are providing us an important perspective of tumorigeneses, potential
biomarkers and clonal expansion models, for example. 335
Single cell technologies are progressing at an undeniable speed, allowing the most
variable type of intratumor heterogeneity analysis. It is possible to evaluate it using 4
distinguished approaches: (1) Genome sequence (2) Transcriptomic analysis (3)
Epigenetics analysis (4) Multi-omics analysis. 335
Intra-tumor genomic and transcriptomic heterogeneity can be measured at single cell
level providing ample amounts of DNA/cDNA for subsequent sequencing. Methods
such as degenerate oligonucleotide primed PCR (DOP-PCR) 336, specific-to-allele PCR
FISH (STAR-FISH) 337, smFISH, SeqFISH 338,339 , combines PCR, FISH, single-
cell RNA sequencing and/or qPCR, and have been used in various cancer studies.
Gene expression signatures allowed to identify cancer cell types, such as cancer stem
cells or contributed for clonal evolution and multi-lineage differentiation in colon cancer,
for example 340,341. Epigenetics plays a decisive role in regulating gene expression and
the single cell approach is more complicated at this context. However, multiple methods
have been adapted for single cell purposes (reviewed in 342). Ideally, in order to have a
global perspective of tumor mechanisms, a combination of genomics, transcriptomics,
proteomics and epigenetics analysis should be applied. Despite the technical
difficulties, science is progressing towards it, such as the scTRIO-Seq method, that
simultaneously sequences genome, transcriptome and DNA methylome.
342
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Method type Specific methods Application to cancer genomics
Single-cell whole DOP-PCR, MDA, Used in conjunction with next-
genome MALBAC generation sequencing to detect intra-
amplification tumor CNVs and SNPs.
Single-cell spatial STAR-FISH Detects the spatial distribution of intra-
genomics tumor CNVs and SNPs. Can be
combined with longitudinal analysis to
reveal migratory cells.
Single-cell Smart-seq, Tang et al. Identifies cancer-specific gene
transcriptome method, single-cell expression signatures, cancer cell
amplification qPCR types, alternative-splicing events.
Single-cell spatial smFISH, SeqFISH, Can provide spatially resolved gene
transcriptomics MERFISH, FISSEQ, expression signatures in tumors. Has
TIVA potential applications in tracing cell
migratory paths and locating tumor-like
stem cells.
Single-cell DNA scRRBS, PBAT Enables the discovery of differential
methylomics methylation in cancer cells. Potential
for broadening understanding of
phenotypic plasticity of cancer cells.
Single-cell ATAC-seq, Pico-Seq Can give insight into the differential
chromatin binding of transcription factors in
accessibility cancer cells.
Chromosome Hi-C, ChIP-seq Potential for understanding the
conformation mechanisms of cancer heterogeneity
capture through mapping transcription factor-
regulatory element interactions.
Simultaneous G&T-seq, scTrio-seq, Provides an integrated view of intra-
multiple single-cell Darmanis et al. method tumoral heterogeneity through
omics measuring direct interactions between
genomic, transcriptomic, epigenetic,
and proteomic variation.
Single-cell spatial Seurat, Achim et al. Infers cell location through scRNA-seq
transcriptomic
inference
method data and an in situ RNA reference map
of several landmark genes, enabling
mapping of intra-tumor spatial
heterogeneity.
Pseudo-time Monocle, TSCAN, Projects gene expression values from
ordering Waterfall, SCUBA,
Wanderlust, Wishbone
a single time-point to a continuous
trajectory over cell differentation.
Potential use in understanding
differentiation from stem-like cancer
cell to matured cancer cell.
Rare cell-type RaceID, StemID, Potential use in the detection of
detection GiniClust circulating tumors cells and stem-like
cancer cells.
Clonal evolution
inference
SCITE, OncoNEM Builds lineage trees for understanding
evolutionary events such as the
development of therapy resistance.
Table 3. Relevant single-cell methods 335
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Figure 21. Relevant single-cell applications 335
Figure 22.Single cell analysis of cancer genomes. 343
10.1 Single Cells and Computational analysis
It is becoming increasingly clear that clonal evolution occurs in a non-synchronized and
asymmetric manner, which makes it impossible to accurately analyze this evolution
through time points and cell bulk. Unique cell techniques allow the study of significant
amounts of cells at a single time point. Within this group there are most likely cells in
different states of evolution of the entire continuous process, and their individual
assessment associated with computational analysis enables us today to estimate their
evolutionary trajectories.344,345
10.2 Single cell isolation techniques
Isolation single cell used to be challenging few years ago. Methods such as limiting
dilution and micromanipulation were widely used in the past and still applied in the
present, but they are inefficient and time-consuming. Sorting cells by FACS are
commonly used and very efficient, especially in cases where the marker studied is very
low expressed. However, this method requires a large start volume of cells, which can
be a huge barrier when analyzing rare cells.346,347
With the evolution of single-cell techniques, isolation cells became much easier. The
microfluidic technology has decreased significantly the sample volume required while
improved substantially the quality of analysis. This method can be applied to a variety
of applications, including DNA and protein assays as well as RNA sequencing and
other transcriptomic profile assays. 348 Fluidigm pioneered provided a machine (C1)
that not only sorts single cells using microfluidic technologies, but also is able to
perform cell lysis, RNA extraction and cDNA amplification in a single chip. Their method
is convenient for providing lower false positives and less bias than standard tests.
However, it still requires a considerable concentration of cells (around 2,5.105 cells/ml),
a limited number of cells analyzed in one assay and the homogeneous cell size
mandatory requirement. 349
Another popular technique is the microdroplet-based microfluidics which disperse
single cells into aqueous droplets in an oil phase. The even lower volume required by
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
this method, compare to microfluidic ones, allowing the screening of a huge number of
cells (thousands to millions), consequently enabling the analysis of rare cell types in a
proper heterogeneous environment. On the other hand, this technique is more bias-
susceptible and has lower resolution than C1 350,351
Finally, the isolation of rare circulating tumor cells, precursors of metastasis and
potential targets for advanced personalized therapies can be isolated by enumeration
using magnet conjugated with antibodies by the CellSearch method, the first to be
clinically validated by FDA.349
Applications of single-cell RNA transcriptomic analysis
We can use single cell RNA transcriptomic analysis for many purposes, but there are
3 most important ones:
1) Cell type identification
With the advances of technologies, we can identify cell populations once not notice
due experimental limitations, such as lower sensibility or specificity. Using single cell
analysis, we can access to hidden rare cell populations that were not detected by
conventional methods. 352
2) Inferring regulatory networks
Single cell transcriptome gives us enough information about gene expression a its
intracellular variation. Several algorithms that have been developed for the analysis
this massive amount of data generated can be applied to address the gene regulatory
networks, such as methods based on machine learning, co-expression, modelling or
theoretical approaches. This way is possible to estimate the gene expression curves
and their network inference. 353,354
3) Cell trajectory
Each cell undergoes dynamic process and it is affected by its microenvironment,
changing its behavior and regulations at its own pace. It is experimentally impossible
to time-lapse assay all these changes of a whole cell population, considering their
individual and unique parameters, without artificial intelligence help. Considering that
in single-cell assays cells are unsynchronized, providing us different time-points that
can be used for a trajectory estimation. This method of analysis, called pseudotime,
was first introduced by Monocle algorithm. As the name suggests, the purpose of
pseudotime is not to infer a “real time” trajectory, but to put in evidence the cellular
dynamics and biological evolution along this trajectory. 355–357
10.2.1 Data Analysis
There is a wide list of methods that can be used for single-cell data analysis. The most
common programming languages are R and Phyton. The choice of the analysis tools
may be dependent of its programming language. Popular platforms, such as Seurat
358, Scater 359, and Monocle 360 relies on R programming packages while Scanpy 361
offers a Phyton toolkit.
In this study, we used C1® and Biomark® machines from Fluidigm and analysed our
data using the monocle method.
10.3 C1 from Fluidigm
The Fluidigm C1 equipment uses microfluidic technology to capture and analyze single
cells at the level of their mRNA. Thanks to its integrated fluidic circuit (IFC) chip design
and its cDNA preamplification technology (SMARTer), it is possible to distribute the
cells individually, lyse them and perform reverse transcription through simple reagent
and suspension of cells pipetting. Each IFC has a specific cell size range (5–10, 10–
17, and 17–25 μM) and it is able to capture up to 96 cells. Pre-designed primers are
pooled in a solution and injected into the chip at the same time, in a different well, as
the pool of cells. Cells must be fresh and viable. To complete the qPCR analysis, the
preamplified cDNA must be analyzed by Fluidigm BioMark machine. Results should be
analyzed using bioinformatic methods.
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Fluidigm©
Figure 23. C1 machine, from Fluidigm© and its worlflow.
10.4 Monocle and Pseudotime
Many methods have been developed to infer trajectories from single cell transcripts
(see table x) whose algorithms measure the distance or differences between cells and
position them along a continuous path, placing similar cells close together, thus
creating a continuous path. To achieve the final result, the data goes through several
steps, gene selection, size reduction, clustering, pseudotime inferences, and finally
trajectory built (linear or branched) 362.
Table 4. Existing computational methods for constructing cell lineages from single cell
data362
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
For our analysis we used the Monocle method (http://cole-trapnell-
lab.github.io/monocle-release/), that allow one to order single cells by pseudotime in a
trajectory according to their biological process. This order is done by advanced
machine learning techniques based in single cell genomics data. The trajectories are
built in 3 major steps:
Figure 24. Trajectory construction and analysis steps
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
1. Clustering : In this step, the main goal is to identify subtypes of cells and
organize them in different clusters. There are many methods of clustering, such as like
Principal Component Analysis (PCA)363, ISOMAP364 and t-distribution Stochastic
Neighbor Embedding (t-SNE)365. All are based on the reduction of the high dimension
to a low dimension, facilitating the visualization and analysis of the data. In our project,
we use t-SNE, which is the latest and best visual method for composing graphics,
providing more harmonious images for the human eye366.
2. Constructing Trajectories: Pseudotime, as the name suggests, is an abstract
unit of time that calculates the distance of a cellular path based on the transcriptional
changes a cell undergoes 367 Monocle uses pseudotime to organize cells and connect
clusters, creating a branched trajectory that allow us to study the evolution of cell states
368. This method is particularly special due the asynchrony of clonal evolution and cell
differentiation, making it very difficult to study to understand these processes, because
in a single sample there are cells in different states of evolution.
3. Differential Expression Analysis: With the aid of trajectory, we can estimate,
statistically, which genes have a similar path or may be co-regulated. Monocle assists
in the identification of genes that are differentially expressed between trajectory cell
groups and infer the statistical significance of these change
Figure 25. Same data visually represented by PCA, ISOMAP and t-SNE methods.
https://quantdare.com/tsne/
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
11. Single-cell applications in cancer
The transcriptomic analysis of patients derived cells can reveal a pre-existing
heterogeneity. A rare primary tumor subclone could have expanded and dominating
the current population. Using this information, we can identify new cell types and cell
states. The cell types are identified by clustering and they are designated by a highly
distinct cluster among the bulk population. The cell states are related to the dynamics
of cells, such as their progression in cell cycle or metabolic differences. Looking into
the gene expression profile related to the cell type and cell states and crossing this
data with the already published genomic data, it’s possible do distinguish between
potential malignant cells from the non-malignant ones.
In the past years of single-cell studies, it is possible to identify some similar patterns
among different studies and different cancer types. Several studies have demonstrated
an up regulation of cell-cycle related genes and/or genes associate to hypoxia, for
example. These cell cycle and stress states confirm their importance in cancer
progression and contribution to inter and intra tumor heterogeneity.369
Single cell studies have also contributed to uncover nuances previously undetected in
specific types of tumor. Jerby-Aron et al has recently published a study using single
cell RNA sequencing analysis of 33 melanoma samples, where the cancer cell state
that promotes immune evasion and resistance to immune checkpoint inhibitors was
identified 370. Malignant cells from head and neck cancer differ in their epithelial
differentiation and epithelial-to-mesenchymal transition mechanisms, which may
contribute to metastasis.371. These patterns can be used to identify some mechanisms
that can be related to specific cancer due the nature of their cells and environment.
The use of single cell approaches can also be applied for understanding the
phylogenetic relationship of metastases and primary tumors as well as clarifying the
mechanisms of the metastatic process. 372
In addition, these methods can be used to anticipate a prognostic and treatment
response from patients.
Objectives of the Thesis
1—To analyze the heterogeneity of CML stem cells and progenitors using single cell
transcriptome analyses at diagnosis before any therapy in order to determine the
prevalence of the expression of a panel of genes involved in the quiescence, self-
renewal and pluripotency
2- To validate the findings obtained in single cell experiments in CML samples and to
evaluate the possibility of identifying druggable targets.
3- To analyze the role of EEF2K expression in CML cells
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
PAPER 1 – Summary
SINGLE CELL TRANSCRIPTOME IN CHRONIC MYELOID LEUKEMIA (CML):
PSEUDOTIME ANALYSIS REVEALS EVIDENCE OF EMBRYONIC AND
TRANSITIONAL STEM CELL STATES
One of the major reasons of the impossibility of eradication of the most primitive LSC
in CML is the heterogeneity of the disease at diagnosis as well as during potentially
successive TKI therapies. This phenomenon is certainly due to the genetic instability
of BCR-ABL expressing leukemic cells, but it has also been shown that the primitive
LSC are not addict to TK activity of BCR-ABL. This has therefore led during the last 10
years to an extensive research for the identification of novel targets involved in the self-
renewal of LSC, allowing the identification several important druggable targets such as
SMO, ALOX5a, PML and STAT5. However, the identification of these pathways which
have been validated in preclinical models and sometimes already tested in clinical trials
do not consider the heterogeneity of CML stem cells at single cell level. Single cell
transcriptome and more recently single cell RNAseq analyses allow currently
unprecedented insights to be made for the pathophysiology of cancer cells.
Studies analysing the heterogeneity of single CML stem cells are limited and there is
currently no study evaluating sequential gene expression using Pseudotime analysis.
Recent experimental data suggest that the heterogeneity of CML stem cells can be
due not only to their differential BCR-ABL expression but also to the development of
unique molecular events generating functional consequences in terms of their stem
cell potential. Understanding these events at diagnosis of the disease and during
tyrosine kinase inhibitor (TKI) therapies is of major interest as this can explain the
clonal evolution and selection explaining resistance and persistence of LSC. In order
to explore this phenomenon, we have designed a single cell transcriptome study using
Fluidigm technology allowing to evaluate simultaneously the expression of 87 genes.
Highly purified CD34+ cells from 3 CML patients at diagnosis and from two cord blood
samples were included in the study. Individual 70, 93, 93 cells from three patients were
immobilized in microfluidic chips and the expression of 87 genes was evaluated in each
cell. After independent normalization and pre-processing filtration, a merged matrix
was built with 256 valid cells and 87 transcripts.
In this work, our first objective was to determine at the single cell level, the profile of
gene expression in single leukemic cells using a panel of genes involved in the
pathophysiology of CML. We have chosen for this purpose, genes involved in LSC
quiescence self-renewal, drug resistance and apoptosis. As some previous data
reported the potential involvement of pluripotency genes in CML progression we have
included in our analysis, genes such as OCT4, SOX2, KLF4 and NANOG. Single cell
Fluidigm technology was used to analyse the simultaneous expression of these genes.
For the first time to our knowledge, we have analysed these cells using the
bioinformatics tool Pseudotime allowing to class the genes in a trajectory, according to
levels of expression in the population analysed. Pseudotime technology is a unique
bioinformatics tool allowing to analyse the gene expression patterns in single cells in
which a transcriptome has been performed. One of the major advantages of this
methodology is to use the Monocle algorithm that allows unprecedented insights into
the transcriptome dynamics of data collected at different sequential timepoints. The
question of the heterogeneity of CML LSC has been recently studied allowing to identify
in a combined mutational and transcriptome analysis, the presence of multiple
subclones evolving during therapy. To our knowledge, there is no study evaluating the
role of sequential gene expression in single cell in using a panel that we report here.
This analysis identified a group of 13 genes highly connected and enriched in
pluripotency genes such as NANOG, POU5F1, LIN28A, SOX2 a «stem cell-like»
population represent 8.59% of the cells analyzed. Bioinformatics analysis using
corrected matrix and tSNE algorithm were applied for a trajectory inference based on
the identification of four distinct clusters. Seven possibly different cell fate decisions
were estimated by pseudo time and crossed with the 4 clusters we had found, in
order to be validated. Six out of seven cell states overlapped the 4 clusters previously
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
estimated. In in vitro analyses, two genes which were predicted to undergo similar
regulation using pseudotime analysis (ALOX5 and FGFR) were found to be similarly
inhibited by Ponatinib, a FGFR inhibitor. Finally, using an independent cohort of CML
patients we show that pluripotency gene expression is a common feature in CD34+
CML cells at diagnosis. Overall, these experiments allowed to identify individual stem
cells expressing high levels of pluripotency genes at diagnosis, in which a continuum
of transitional states was identified using pseudotime analysis. These results suggest
that LSC persistence in CML is regulated by transitional stem cell states that need to
be targeted simultaneously rather than using a single pathway.
L’ARTICLE 1 – Résumé
TRANSCRIPTOME EN CELLULE UNIQUE DANS LA LEUCÉMIE MYÉLOÏDE
CHRONIQUE (LMC) : L'ANALYSE DE PSEUDO-MOTIFS RÉVÈLE DES SIGNES
D'ÉTATS DE CELLULES SOUCHES EMBRYONNAIRES ET TRANSITOIRES
L'une des principales raisons de l'impossibilité d'éradiquer la CSL la plus primitive dans
la LMC est l'hétérogénéité de la maladie au moment du diagnostic ainsi que pendant
les traitements de la TKI qui peuvent se succéder. Ce phénomène est certainement dû
à l'instabilité génétique des cellules leucémiques exprimant le gène BCR-ABL, mais il a
également été démontré que les CSL primitives ne sont pas dépendantes de l'activité
TK du gène BCR-ABL. C'est pourquoi, au cours des dix dernières années, des
recherches approfondies ont été menées pour identifier de nouvelles cibles impliquées
dans l'auto-renouvellement des CSL, ce qui a permis d'identifier plusieurs cibles
médicamenteuses importantes telles que SMO, ALOX5a, PML et STAT5. Toutefois,
l'identification de ces voies, qui ont été validées dans des modèles précliniques et
parfois déjà testées dans des essais cliniques, ne tient pas compte de l'hétérogénéité
des cellules souches de la LMC au niveau d'une seule cellule. Les analyses du
transcriptome unicellulaire et, plus récemment, de l'ARNseq unicellulaire permettent
d'obtenir des informations sans précédent sur la physiopathologie des cellules
cancéreuses.
Les études analysant l'hétérogénéité des cellules souches uniques de la LMC sont
limitées et il n'existe actuellement aucune étude évaluant l'expression séquentielle des
gènes à l'aide de l'analyse des pseudotimes.
Des données expérimentales récentes suggèrent que l'hétérogénéité des cellules
souches de la LMC peut être due non seulement à leur expression différentielle BCR-
ABL, mais aussi au développement d'événements moléculaires uniques générant des
conséquences fonctionnelles en termes de potentiel des cellules souches. La
compréhension de ces événements au moment du diagnostic de la maladie et pendant
les thérapies par inhibiteurs de la tyrosine kinase (TKI) est d'un intérêt majeur car elle
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Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
peut expliquer l'évolution et la sélection clonale expliquant la résistance et la
persistance de la LSC. Afin d'explorer ce phénomène, nous avons conçu une étude du
transcriptome cellulaire unique utilisant la technologie Fluidigm permettant d'évaluer
simultanément l'expression de 87 gènes. Des cellules CD34+ hautement purifiées
provenant de 3 patients atteints de LMC au moment du diagnostic et de deux
échantillons de sang de cordon ont été incluses dans l'étude. Des cellules individuelles
de 70, 93, 93 provenant de trois patients ont été immobilisées dans des puces
microfluidiques et l'expression de 87 gènes a été évaluée dans chaque cellule. Après
normalisation indépendante et filtration préalable, une matrice fusionnée a été
construite avec 256 cellules valides et 87 transcrits.
Dans ce travail, notre premier objectif était de déterminer, au niveau de la cellule
unique, le profil de l'expression des gènes dans les cellules leucémiques uniques en
utilisant un panel de gènes impliqués dans la physiopathologie de la LMC. Nous avons
choisi à cette fin, les gènes impliqués dans l'auto-renouvellement de la quiescence de
la LSC, la résistance aux médicaments et l'apoptose. Comme certaines données
antérieures faisaient état de l'implication potentielle de gènes de pluripotence dans
l'évolution de la LMC, nous avons inclus dans notre analyse des gènes tels que OCT4,
SOX2, KLF4 et NANOG. La technologie Fluidigm à cellule unique a été utilisée pour
analyser l'expression simultanée de ces gènes. Pour la première fois à notre
connaissance, nous avons analysé ces cellules en utilisant l'outil bioinformatique
Pseudotime permettant de classer les gènes dans une trajectoire, en fonction des
niveaux d'expression dans la population analysée. La technologie Pseudotime est un
outil bioinformatique unique qui permet d'analyser les modèles d'expression des gènes
dans les cellules individuelles dans lesquelles un transcriptome a été effectué. L'un
des principaux avantages de cette méthodologie est d'utiliser l'algorithme Monocle qui
permet d'obtenir des informations sans précédent sur la dynamique du transcriptome
à partir de données recueillies à différents moments de la séquence. La question de
l'hétérogénéité de la CSL LMC a été récemment étudiée, permettant d'identifier, dans
une analyse combinée des mutations et du transcriptome, la présence de multiples
sous-clones évoluant au cours de la thérapie. À notre connaissance, il n'existe pas
d'étude évaluant le rôle de l'expression séquentielle des gènes dans une seule cellule
à l'aide d'un panel dont nous rendons compte ici.
Cette analyse a permis d'identifier un groupe de 13 gènes fortement connectés et
enrichis en gènes de pluripotence tels que NANOG, POU5F1, LIN28A, SOX2 ; une
population de type "cellules souches" représente 8,59% des cellules analysées.
L'analyse bioinformatique avec matrice corrigée et algorithme tSNE a permis
d'identifier quatre groupes distincts et l'analyse pseudo time a confirmé dans les 4
groupes identifiés, la présence de 7 états cellulaires séparées par 3 branches
d'intersection reliant l'expression d'ALOX5 a été associée au groupe de cellules
exprimant les marqueurs de pluripotence. Dans les analyses in vitro, deux gènes dont
on avait prédit qu'ils allaient subir une régulation similaire par l'analyse des pseudos
(ALOX5 et FGFR) se sont révélés être inhibés de manière similaire par le Ponatinib,
un inhibiteur du FGFR. Enfin, en utilisant une cohorte indépendante de patients atteints
de LMC, nous montrons que l'expression des gènes de la pluripotence est une
caractéristique commune des cellules CD34+ de la LMC au moment du diagnostic.
Dans l'ensemble, ces expériences ont permis d'identifier des cellules souches
individuelles exprimant des niveaux élevés de gènes de pluripotence au moment du
diagnostic, dans lesquelles un continuum d'états transitoires a été identifié à l'aide
d'une analyse de pseudo-caractères. Ces résultats suggèrent que la persistance des
CSL dans la LMC est régulée par des états transitoires des cellules souches qui
doivent être ciblées simultanément plutôt que d'utiliser une seule chemin.
ARTICLE IN PRESS
https://doi.org/10.1016/j.exphem.2020.04.005
Experimental Hematology 2020;000:1−10
REGULAR SUBMISSION
Single-Cell Transcriptome in Chronic Myeloid Leukemia: Pseudotime Analysis Reveals Evidence of Embryonic and Transitional Stem
Cell States Sarah Pagliaroa,b,c, Christoph Desterkea,c, Herve Acloquea, Jean Claude Chomeld, Lucas
de Souzaa, Patricia Huguesa, Frank Griscellia,e,f, Adlen Foudig,
Annelise Bennaceur-Griscellia,c,e, and Ali G. Turhana,c,e aINSERM UMR-S 935, Villejuif, France;
bSciencia Sem Fronteiras, CAPES, Brasilia , Brazil;
cUniversite Paris Saclay, France;
dService de
cancerologie Biologique, CHU Poitiers, Poitiers France; eINGESTEM National iPSC Infrastructure, 94800 Villejuif, France;
fUniversite Paris
Descartes, Faculte Sorbonne Paris Cite, Faculte des Sciences Pharmaceutiques et Biologiques, Paris, France; gATIP-Avenir INSERM UMR-S
935, Universite Paris Sud, 94800 Villejuif
(Received 10 March 2020; revised 4 April 2020; accepted 8 April 2020)
Recent experimental data suggest that the heterogeneity of chronic myeloid leukemia (CML) stem cells may be the result of the development of unique molecular events generating func - tional consequences in terms of the resistance and persistence of leukemic stem cells. To explore this phenomenon, we designed a single-cell transcriptome assay evaluating simulta- neously the expression of 87 genes. Highly purified CD34+ cells from three CML patients at diagnosis were immobilized in microfluidic chips, and the expression of 87 genes was evaluated in each cell. This analysis identified a group of 13 highly connected genes including NANOG, POU5F1, LIN28A, and SOX2, representing on average 8.59% of the cell population analyzed. Bioinformatics analysis with the corrected matrix and t-distributed stochastic neighbor embedding (tSNE) algorithm identified four distinct clusters, and the pseudotime analysis con- firmed the presence of seven stem cell states in the four clusters identified. ALOX5 expression was associated with the group of cells expressing the pluripotency markers. In in vitro analy - ses, two genes that were predicted to undergo similar regulation using pseudotime analysis (ALOX5 and FGFR) were found to be similarly inhibited by ponatinib, an FGFR inhibitor. Finally, in an independent cohort of CML patients, we found that pluripotency gene expression is a common feature of CD34+ CML cells at diagnosis. Overall, these experiments allowed identification of individual CD34+ cells expressing high levels of pluripotency genes at diagno- sis, in which a continuum of transitional states were identified using pseudotime analysis. These results suggest that leukemic stem cell persistence in CML needs to be targeted simulta- neously rather than using a single pathway. © 2020 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.
Chronic myeloid leukemia (CML) is a clonal hematopoietic malignancy, characterized by acquisition of the t(9;22) translo- cation leading to Ph1 chromosome and its counterpart BCR-
Offprint requests to: Ali G. Turhan, INGESTEM National IPSC Infrastructure, Avenue Paul Vaillant Couturier, Villejuif 94800, France; E-mail: turviv33@gmail.com
ABL oncogene, in a very primitive hematopoietic stem cell [1]. CML is a model of targeted therapies as the proof of con- cept of the feasibility of targeting the tyrosine kinase (TK) activity BCR-ABL using TK inhibitors (TKIs) has been found to lead to major responses and remissions [2]. The use of ima- tinib and, later, second-generation [3,4] and third-generation [5] TKI therapies has changed the natural history of the disease and prolonged overall survival [6] . Moreover, recent results from CML registries indicate that the overall survival of CML
0301-472X/© 2020 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.exphem.2020.04.005.
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patients responding to TKI therapies could equal that of the general population of the same age and sex [7]. However, the problems currently encountered in the therapy of patients are the TKI resistance of primitive leukemic stem cells (LSCs) [8] and the persistence of LSCs [9], which is thought to be related to the heterogeneity of the stem cells at diagnosis, leading to clonal selection of cells resisting TKI therapies [10].
Persistence of the most primitive LSCs, even in patients in deep molecular responses (DMRs), has been reported [9,11], explaining the occurrence of rapid relapses caused by the long-term persistence of dor- mant CML stem cells. Indeed, rapid molecular recur- rences have been reported to occur after TKI discontinuation in several clinical trials performed to date [10,12].
The second major current problem is the resistance to TKI therapies that occurs in 30% of patients requir- ing TKI switch and leading, in some rare cases, to pro- gression toward aggressive-phase CML, for example, accelerated phases and blast crisis [9].
The heterogeneity of CML stem cells contributes to these two events and is an obstacle to successful eradi- cation of the disease. To uncover the panel of genes that can contribute to this event, we applied the tech- nology of single-cell transcriptome analysis to CML cells using a panel of genes involved in embryogenesis, cell quiescence, stemness, self-renewal and apoptosis. We then applied the technique of trajectory analysis to the gene expression pattern. We report here the discov- ery of transitional stem cell states, including embryonic genes identified in CML cells at diagnosis, which could contribute to LSC resistance and persistence.
Methods Peripheral blood from CML patients at diagnosis before any ther- apy were collected with informed consent from all patients according to the principles of the Declaration of Helsinki. Sin- gle-cell analysis was performed in three patients, and molecular validation in an independent cohort of 10 patients.
Isolation of CD34+ cells
Peripheral blood mononuclear cells (PBMCs) were separated from whole blood by density gradient centrifugation using the Ficoll method, labeled with anti-CD34 antibodies (Ref. 130-090-954, Miltenyi, Bergisch Gladbach, Germany), and sorted using magnetic columns from Miltenyi (Ref. 130-046- 703). The purity was evaluated by flow cytometry (MACS- Quant, Miltenyi), using the anti-CD34+ human (Ref. 130- 090-954) antibody from Miltenyi.
Fluidigm assays
To perform single-cell transcriptome analyses, we designed 87 genes involved in the cell cycle, stem cell quiescence, proliferation, and pluripotency (Table 1). The designed panel was validated for the possibility of amplification simulta- neously with Fluidigm.
Single-cell lysis, reverse transcription, and cDNA pre-amplifi- cation were performed on the C1 apparatus according to the proto- col provided by Fluidigm (https://www.fluidigm.com/products/c1- system). Briefly, the first step of the experiment is on the C1 machine (Fluidigm), using the C1 Single-Cell Preamp IFC (10−17 mm; Ref. 100−5479), where a solution of pooled cells and primers can be added along with other reagents from the kit (C1 Single- Cell Reagent Kit, Ref. 100−5319) and pipetted at the same time, into different wells, for later processing with the equipment. Using a microfluidic system, the C1 machine performs cell lysis, RNA extraction, reverse transcription, and pre-amplification of cDNA. After pre-amplification, samples were sent to the INRA Institute in Toulouse, France, for single-cell quantitative reverse transcription polymerase chain reaction (RT-qPCR) using the Biomark appara- tus from Fluidigm.
Bioinformatics analyses
Single-cell bioinformatics analyses were performed in the R software environment, Version 3.4.3. Unsupervised classifica- tion (Pearson correlation distance and average method) with an expression heatmap was performed with R Bioconductor made4 [13]. Unsupervised principal component analysis was performed with the FactoMineR R package [14].Violinplot expression for expression of markers was performed with ggplot2 graphical definition in R software [15]. Leave-one- out with cross-validation supervised machine learning was realized with the Stanford algorithm in the pamr R package [16]. Receiver operating characteristic (ROC) curves were drawn with the Epi R package, and the decision tree with the rpart R package. Statistical significance between groups was assessed using a two-sided t test with Welch correction. Pseudotime analysis was performed in the R software envi- ronment with monocle package version monocle 2.10.1 [17].
RNA extraction, reverse transcription and RT-qPCR
To validate the expression of genes identified by C1, we per- formed RT-qPCR using peripheral blood from CML and control samples. For this purpose, CD34+ cells were studied in compari- son with peripheral blood mononuclear cells (PBMCs). Isolation of RNA was performed using the PureLink RNA Purification (Thermo Fisher Scientific, Waltham, MA). RNA was quantified with a NanoDrop spectrophotometer (Thermo Scientific). Reverse transcription was carried out using the Applied Biosys- tems High-Capacity cDNA Reverse Transcription Kit (Thermo Scientific). Quantitative real-time PCR was performed using the Stratagene MxPro 3005P system (Agilent Technologies Santa Clara, CA) and SYBR Green PCR Master Mix (Applied Biosys- tems, Foster City, CA). Quantification was performed using the DDCt method. Normalization was performed using b2-microglo- bulin mRNA as a housekeeper. The primers used for quantitative RT-qPCR were obtained from Integrated DNA Technologies (IDT, Coralville, CA) and are listed in Table 2.
Evaluation of FGFR1 and ALOX5 co-regulation using
FGFR1 inhibition
To determine these interactions, CD34+ cells (250 cells/mL) from three CML patients were cultured in the presence of ponatinib for 3 days in Iscove’s modified Dulbecco’s medium (IMDM), 20% BIT 9500 Serum Substitute (STEMCELL
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Table 1. Genes used to design C1 Fluidigm single-cell experiments
Gene symbol Gene NCBI ID Description
ABI1 10006 abl interactor 1
ABL1 25 ABL proto-oncogene 1, non-receptor tyrosine kinase
ACKR1 2532 Atypical chemokine receptor 1 AHI1 54806 Abelson helper integration site 1 AKT1 207 AKT serine/threonine kinase 1
ALOX5 240 Arachidonate 5-lipoxygenase ASH1L 55870 ASH1 like histone lysine methyltransferase
B2M 567 b2-microglobulin BAD 572 BCL2-associated agonist of cell death
BCL2 596 BCL2 apoptosis regulator BMI1 648 BMI1 proto-oncogene, polycomb ring finger CASP3 836 Caspase 3 CD82 3732 CD82 molecule
CD93 22918 CD93 molecule CDH1 999 Cadherin 1 CDK2 1017 Cyclin-dependent kinase 2
CDK6 1021 Cyclin-dependent kinase 6 CDKN1C 1028 Cyclin-dependent kinase inhibitor 1C
CDKN2C 1031 Cyclin-dependent kinase inhibitor 2C CNNM1 26507 Cyclin and CBS domain divalent metal cation transport mediator 1 CTNNB1 1499 Catenin beta-1
CXCL2 2920 C−X−C motif chemokine ligand 2
CXCR2 3579 C−X−C motif chemokine receptor 2 CXCR3 2833 C−X−C motif chemokine receptor 3
CXCR4 7852 C−X−C motif chemokine receptor 4 CYLD 1540 CYLD lysine 63 deubiquitinase CYP1B1 1545 Cytochrome P450 family 1 subfamily B member 1
DACT1 51339 Dishevelled binding antagonist of catenin beta-1 DDX3X 1654 DEAD-box helicase 3 X-linked
DLK1 8788 Delta-like noncanonical Notch ligand 1 DNMT3A 1788 DNA methyltransferase 3 alpha DNMT3B 1789 DNA methyltransferase 3 beta
DPP4 1803 Dipeptidyl peptidase 4 DPPA3 359787 Developmental pluripotency associated 3 DPPA5 340168 Developmental pluripotency associated 5
EEF2K 29904 Eukaryotic elongation factor 2 kinase ERCC2 2068 ERCC excision repair 2, TFIIH core complex helicase subunit ETS1 2113 ETS proto-oncogene 1, transcription factor ETV6 2120 ETS variant transcription factor 6
EZH2 2146 Enhancer of zeste 2 polycomb repressive complex 2 subunit
FBXW7 55294 F-box and WD repeat domain containing 7 FGF5 2250 Fibroblast growth factor 5 FGFR1 2260 Fibroblast growth factor receptor 1
FOS 2353 Fos proto-oncogene, AP-1 transcription factor subunit GAPDH 2597 Glyceraldehyde-3-phosphate dehydrogenase GLI1 2735 GLI family zinc finger 1
HSP90AA1 3320 Heat shock protein 90 alpha family class A member 1 IFNA1 3439 Interferon alpha-1 IGF2R 3482 Insulin like growth factor 2 receptor IL6ST 3572 Interleukin-6 signal transducer
JAK1 3716 Janus kinase 1 JAK2 3717 Janus kinase 2 JUNB 3726 JunB proto-oncogene, AP-1 transcription factor subunit
KIT 3815 KIT proto-oncogene, receptor tyrosine kinase KLF4 9314 Kruppel-like factor 4
KRAS 3845 KRAS proto-oncogene, GTPase LIFR 3977 LIF receptor subunit alpha LIMS1 3987 LIM zinc finger domain containing 1
LIN28A 79727 lin-28 homologue A MAPK1 5594 Mitogen-activated protein kinase 1 MAPK3 5595 Mitogen-activated protein kinase 3
(continued )
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Table 1 (Continued)
Gene symbol Gene NCBI ID Description
MCL1 4170 MCL1 apoptosis regulator, BCL2 family member
MEG3 55384 Maternally expressed 3
MSI2 124540 Musashi RNA binding protein 2 MTOR 2475 Mechanistic target of rapamycin kinase MYC 4609 MYC proto-oncogene, bHLH transcription factor
MYSM1 114803 Myb like, SWIRM and MPN domains 1 NANOG 79923 Nanog homeobox
NOTCH1 4851 Notch receptor 1 PIK3CA 5290 Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha PML 5371 Promyelocytic leukemia
POU5F1 5460 POU class 5 homeobox 1 PRKAA1 5562 Protein kinase AMP-activated catalytic subunit alpha-1 PROM1 8842 Prominin 1
PTCH1 5727 Patched 1 PTEN 5728 Phosphatase and tensin homologue PTK7 5754 Protein tyrosine kinase 7 PTPA 5524 Protein phosphatase 2 phosphatase activator
SIRT1 23411 Sirtuin 1
SKP2 6502 S-Phase kinase-associated protein 2 SMO 6608 Smoothened, frizzled class receptor SOX2 6657 SRY-box transcription factor 2
SPP1 6696 Secreted phosphoprotein 1 STAT3 6774 Signal transducer and activator of transcription 3 STAT5A 6776 Signal transducer and activator of transcription 5A STMN1 3925 Stathmin 1
TFCP2L1 29842 Transcription factor CP2 like 1 TGFB1 7040 Transforming growth factor b1
TP53 7157 Tumor protein p53 ZBTB16 7704 Zinc finger and BTB domain containing 16
ZFP36 7538 ZFP36 ring finger protein
ZFP36L1 677 ZFP36 ring finger protein-like 1
Technologies) containing interleukin (IL)-3, FMS-like tyro- sine kinase 3 ligand (FLT3-L) and stem cell factor (SCF) (100 ng/mL each), and TPO (20 ng/mL). Cells were collected at day +3 and after extraction of RNA. RT-qPCR analysis was performed to evaluate the expression of FGFR1 and ALOX5a.
Results
Patient characteristics
Characteristics of patients included in the initial study are summarized in Table 3.
The three patients at diagnosis (UPN1, UPN2, UPN3) were in the chronic phase of their disease. Blood cells ana- lyzed at diagnosis were 100% Ph1+ cells at cytogenetic and fluorescence in situ hybridization (FISH) analyses. These cells were then used to perform Fluidigm experiments fol- lowed by bioinformatics analyses, as indicated in Figure 1.
Identification of pluripotency genes in CML CD34+
cells
The first step of the analysis included the building of a matrix using 256 cells. We identified a population of
22 cells (8.59% of general population) that highly
Table 2. Genes analyzed by standard quantitative reverse transcription polymerase chain reaction validate C1 results
Gene RefSeq sequence Amplification length (pb)
Forward primer sequence Reverse primer sequence
ALOX5 NM_001320861.2 86 AgCTgCAggACTTCgTgAA CTCTTgACCgACTTggggAA
FGFR1 NM_023110.3 89 ACACTgCgCTggTTgAAAA ATgATgCTCCAggTggCATA
JAK2 NM_004972.4 77 ACTTCTgCAgTACACATCTCA CCAgATCCCTgTggATATACC LIN28A NM_024674.6 84 CATgCAgAAgCgCAgATCAA ggTggCAgCTTgCATTCC MYC NM_002467.6 104 CCTggTgCTCCATgAggA CCTgCCTCTTTTCCACAgAAA PIK3CA NM_006218.4 82 CTgCAgTTCAACAgCCACAC ACAggTCAATggCTgCATCA
SOX2 NM_003106.4 83 CATgAAggAgCACCCggATTA CgggCAgCgTgTACTTATCC TGFB1 NM_000660.7 85 CTACTACgCCAAggAggTCAC gCTgTgTgTACTCTgCTTgAAC FGF5 NM_001291812.1 107 gAgTggTATgTggCCCTgAA gACTgCTTgAATCTTggCAgAAA
LIMS1 NM_001193483.3 76 ACTgCTTTgCCTgTTCTACC ACTggCTTCATgTCAAACTCC
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Table 3. Characteristics of three patients included in the study
Patient Sex Age Sokal index WBC DG Disease phase at diagnosis Outcome Cells used for Fluidigm
UPN1 M 57 Low 56 g/L - DMR +2 y Diagnosis CD34+ cells
UPN2 F 62 Int 89 g/L CP DMR +2 y Diagnosis CD34+ cells
UPN3 F 63 Int 286 g/L CP DMR +5 y Diagnosis CD34+ cells
CP=Chronic phase; DMR=deep molecular response; F=female; M=male; WBC=white blood cell; DG=Diagnosis.
expressed a specific group of 12 genes, among them pluripotency genes such as LIN28a NANOG, OCT4, and SOX2 (Figure 2). Interestingly, similar numbers of cells expressed the pluripotency genes (eight cells in UPN1, seven cells in UPN2, seven cells in UPN3). As can be seen in Figure 2, expression of the pluripotency genes was restricted to this small population that we designated stem-like cells.
Estimation of gene trajectory using pseudotime analysis Using t-distributed stochastic neighbor embedding (tSNE) and unsupervised classification, we next analyzed the gene trajectory of the cells, allowing determination of four principal clusters based on gene expression. For this analysis, we combined the results obtained in 256 cells from three patients. As can be seen in Figure 3, the major cluster represented the cells with an overall reduced gene expression compared with the other three clusters. As opposed to this major cluster, the other three clusters had a distinct pattern of increased gene expres- sion. Cluster 1 (n = 50 cells) included c-MYC and JAK2, two genes highly involved in BCR-ABL−induced leuke- mogenesis and CML pathophysiology. Cluster 4 (n = 35 cells) included essentially ALOX5 and LIN28A involved, respectively, in self-renewal and persistence of LSC (ALOX5) and CML progression (LIN28A). Cluster 3 (n = 41 cells) included MYC, LIMS1, and TGFB (Figure 3).
Starting from such a data set, our aim was to use the trajectory inference methods to order cells with respect to an underlying dynamic process that explains the het- erogeneity of the sample. The structure of the dynamic
process can be either linear or nonlinear, generating a branching process such as during cell differentiation or in cells in a state of cycling or quiescence.
Using these computational techniques, we estimated a branched trajectory based on their gene expression. As illustrated in Figure 4, the trajectory had three major hubs: hub 1 delimited the major cluster (contain- ing lower expression genes) from the clusters with more highly expressed genes. The last and most inter- esting hub 2 provided evidence of an important split between two different cell states.
Cell fate decisions and trajectory validation
To validate our trajectory, we estimated, using pseudo- time analysis, seven possible different cell fate deci- sions and crossed them with the four clusters we previously found (Supplementary Figure E1, online only, available at www.exphem.org).
Subsequently, we used the stem-like genes identified on the heatmap matrix (Supplementary Figure E2, online only, available at www.exphem.org) to perform the pseu- dotime analysis with the cell fate decisions we have seen. As illustrated in Figure 2, we found that the results highlighted some of the stem-like genes that we had pre- viously identified, and it aggregates with ALOX5. Given the fact that the ALOX5 cluster belongs to hub 2 of our trajectory and considering that cells can decide to evolve toward ALOX5, the LIN28a cluster, or the cluster of MYC, LIMS1, and TGFB, we attempted to identify genes that can be involved in its regulation.
We performed further bioinformatics analyses to determine the genes involved in this trajectory split. As
Figure 1. Schematic of the experimental strategy. The analysis at diagnosis included CD34+ cells purified at diagnosis, with a purity >98%.
CML-CP=chronic myeloid leukemia-chronic phase.
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Figure 2. Identification of stem cells with pluripotency gene expression by single-cell analysis.
Figure 3. t-Distributed stochastic neighbor embedding (tSNE) and unsupervised classification allowed the identification of four principal clusters based on the genes of expression. The major cluster contains cells with a lower overall gene expression compared with the three other clusters.
Figure 4. Pseudotime branching analysis of the single-cell transcriptome in three patients.
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Figure 5. Pseudotime relative expression of FGFR1 and PIK3CA as compared with ALOX5 stem cell marker and ABL1 used as control of expression.
can be seen in Supplementary Figure E2, mathematical analysis of gene expression revealed that from branch 2, cells could evolve in terms of gene expression either toward the ALOX5/LIN28A cluster or toward the MYC/LIMS/TGFP cluster. As can be seen in the figure, the gene most likely decisional in this event was STAT5A, a druggable gene highly involved in the pathophysiology of CML.
To identify genes that may be involved in ALOX5 regulation, we determined the wave of expression of the genes belonging to the same group. As can be seen in Figure 5, PI3KCA and FGFR1 genes had a wave shape similar to that of ALOX5, suggesting that these genes may be co-regulated.
Functional evaluation of a common regulatory pathway for
ALOX5 and FGFR1 To determine and validate the possible functional role of ALOX5 and FGFR co-regulation, we designed an
in vitro experiment consisting of the inhibition of FGFR expression, and to determine the potential impact of this inhibition, we evaluated the expression of ALOX5, FGFR1, FGF5, and PIK3CA in the pres- ence of appropriate control cells. As can be seen in Figure 6, CML cells treated with the pan-FGFR inhib- itor ponatinib for 48 hours exhibited reduced expres- sion of ALOX5 and FGFR1 gene, whereas expression of FGF5 and PIK3CA was not modified (Figure 6). These experiments suggested that the pattern isolated by pseudotime analysis could be demonstrated in pri- mary CML cells.
Validation of pluripotency gene expression in primary
CML cells We next asked whether the genomic pattern identified using Fluidigm analyses could be validated in a differ- ent cohort of CML progenitors. For this purpose, we analyzed CD34+ cells from 10 patients as compared
Figure 6. Validation of gene signature trajectory with ponatinib functional experiments performed CML CD34+ cells.
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Figure 7. Validation of embryonic stem cell signature in CD34+ cells from CML patients: RT-qPCR relative expression were normalized with the b2-microglobulin housekeeping gene. Results of t-test: *p < 0.05, **p value between 0.05 and 0.005, ***p < 0.005, NS = nonsignificant.
with bulk PBMC mRNA expression. We analyzed the expression of genes including ALOX5, FGFR1, P1K3CA, TGFB1, LIMS1, SOX2, C-MYC, LIN28a, and SOX2 (Figure 7).
This analysis revealed increased levels of expression for FGFR1, PIK3CA, LIMS1, TGFB1, SOX2, c-MYC, LIN28a, and JAK2 RNA in the CML CD34+ cell popu- lation as compared with their PBMCs. Interestingly, despite the possibility of a common regulation in single cells, ALOX5 and FGFR expression was found to be discordant in bulk CD34+ cells, probably because the analysis in bulk cells does not capture the transitional states and there is probably an inter-patient heterogene- ity in the expression of both genes. It is also of interest to note that the overexpression of SOX2 in human stem cells (HSCs) was reported in a previous work, whereas OCT4 overexpression was not detected in the same population [18] (Supplementary Figure E3, online only, available at www.exphem.org).
Discussion One of the major reasons it is impossible to eradicate the most primitive LSCs in CML is the heterogeneity of the disease at diagnosis as well as during potentially successive TKI therapies [1,9]. This phenomenon is certainly due to the genetic instability of BCR-ABL −expressing leukemic cells [19], but it has also been found that the primitive LSCs are not addict to the TK activity of BCR-ABL [20]. This led, during the last 10 years, to extensive research in the identification of novel targets involved in the self-renewal of LSCs, allowing identification of several important druggable targets such as SMO [21,22], ALOX5a [23], PML [24], and STAT5 [25]. However, the identification of these pathways, which have been validated in preclinical models [23] and, in some cases, have already been tested in clinical trials, do not take into account the heterogeneity of CML stem cells at the single-cell level.
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Single-cell transcriptome and, more recently, single- cell RNAseq analyses allow us to gain currently unprecedented insights into the pathophysiology of can- cer cells [26−30].
Studies analyzing the heterogeneity of single CML stem cells are limited [28,31], and there is currently no study evaluating sequential gene expression using pseudotime analysis. In this work, our first objective was to determine, at the single-cell level, the profile of gene expression in single leukemic CD34+ cells using a panel of genes involved in the pathophysiology of CML. We chose, for this purpose, genes involved in LSC quiescence self- renewal, drug resistance, and apoptosis (Table 1).
The potential involvement of pluripotency genes in CML progression has previously been reported [32]. In our analysis, we included genes such as OCT4, SOX2, KLF4, and NANOG. Single-cell Fluidigm technology was used to analyze the simultaneous expression of these genes. For the first time to our knowledge, we analyzed these cells using the bioinformatics tool pseu- dotime, allowing classification of the genes in a trajec- tory, according to the levels of expression in the population analyzed [17]. Pseudotime technology is a unique bioinformatics tool allowing analysis of gene expression patterns in single cells in which a transcrip- tome has been performed. One of the major advantages of this methodology is use of the Monocle algorithm, which provides unprecedented insights into the tran- scriptome dynamics of data collected at different sequential time points [17]. The question of the hetero- geneity of CML LSCs has recently been studied [28,31], allowing identification, in a combined muta- tional and transcriptome analysis, the presence of mul- tiple subclones evolving during therapy. To our knowledge, there is no study has evaluated the role of sequential gene expression in single cells using the panel we report here.
One of the major findings of this study was the identifi- cation of a small population of single cells expressing genes involved in pluripotency, such as OCT4, SOX2, and NANOG. In the three patients studied, the numbers of such cells were low but consistent (8% of cells), suggest- ing their relevance to the pathophysiology of CML. Analy- sis of the expression of some these genes also revealed their high expression at diagnosis (Figure 7F).
The significance of these findings is not established at present, as analysis of these genes, detectable in CD34+ cells (and not in the total blood RNA used for diagnosis), is not performed routinely. One limitation of our analysis that we could not analyze normal adult CD34+ cells with this technology. All three patients analyzed had small numbers of single cells expressing pluripotency genes and, at the time of analysis, had excellent responses to TKI therapies. It would be of major interest to determine in a large cohort of patients
if the expression of these cells in the CD34+ cell popu- lation correlates with functional stem cell detection tests and outcome and resistance to TKI therapies.
We also reported here that there is a sequential expression of key genes involved in the pathogenesis of CML, and according to the levels of expression of some genes such as STAT5A, cells are engaged in dif- ferent transcriptional pathways. STAT5 plays a major role in the pathogenesis of CML [33,34], and it has been reported that STAT5A, in particular, can play a major role in the TKI resistance of CML cells [35]. STAT5 can represent a potential therapeutic target as it has been shown that Pimozide, a STAT5 phosphoryla- tion inhibitor exhibits high synergistic activity with Imatinib against CML progenitors [25,36−38].
We also reported that the findings obtained using pseudotime wave analysis can be validated in primary CML samples, as this was seen in the experiments using FGFR inhibition by ponatinib. Indeed, we found that FGFR inhibition by ponatinib can lead to reduction of the expression of ALOX5 and FGFR1, suggesting co-regulation, which needs to be confirmed in further studies. Similarly, further studies on the use of a more specific FGFR1 inhibitor are needed, because ponatinib also inhibits other receptors and pathways such as PDGFR, SRC, RET, KIT, and FLT1.
One of the major findings of our study is that CML stem cells do not have a static transcriptional status but express different genes with a dynamic pattern and according to some key decisional points (Supplemen- tary Figure E4, online only, available at www.exphem. org). As can be seen in this figure, depending on the STAT5A levels, cells can be involved in two different transcriptional pathways, and predominance of the ALOX5/LIN28 pathway could lead to their resistance, persistence, and progression [9]. Interestingly, zileuton, an inhibitor of ALOX5, is being tested in clinical trials in combination with imatinib [39]. However, recent studies have provided evidence that this effect may not be transferable into human cells [40,41].
The major consequence of the findings reported here could be that CML CD34+ cells express a highly dynamic transcriptional pattern and should be targeted not by a single pathway, but by several pathways involving several drugs. Indeed, recent work suggests that CML LSCs could be best targeted by the combined use of JAK/STAT and TKI inhibitors [42]. Further studies using the druggable genes active in CML LSCs could possibly lead to a cure for CML.
References 1. Houshmand M, Simonetti G, Circosta P, et al. Chronic myeloid
leukemia stem cells. Leukemia. 2019;33:1543–1556. 2. Deininger MW, O’Brien Druker BJ. International randomized
study of interferon vs STI571 (IRIS) 8-year follow up: sustained
10 S. Pagliaro et al. / Experimental Hematology 2020;000:1−10
survival and low risk for progression or events in patients with newly diagnosed chronic myeloid leukemia in chronic phase (CML-CP) treated with imatinib. Blood. 2009;114:1126.
3. Kantarjian H, Giles F, Wunderle L, et al. Nilotinib in imatinib- resistant CML and Philadelphia chromosome-positive ALL. N Engl J Med. 2006;354:2542–2551.
4. Talpaz M, Shah NP, Kantarjian H, et al. Dasatinib in imatinib- resistant Philadelphia chromosome-positive leukemias. N Engl J Med. 2006;354:2531–2541.
5. Cortes JE, Kantarjian H, Shah NP, et al. Ponatinib in refractory Philadelphia chromosome-positive leukemias. N Engl J Med. 2012;367:2075–2088.
6. Kantarjian HM, Shah NP, Cortes JE, et al. Dasatinib or imatinib in newly diagnosed chronic-phase chronic myeloid leukemia: 2- year follow-up from a randomized phase 3 trial (DASISION). Blood. 2012;119:1123–1129.
7. Bower H, Bj€orkholm M, Dickman PW, H€oglund M, Lambert PC, Andersson TML. Life expectancy of patients with chronic mye- loid leukemia approaches the life expectancy of the general pop- ulation. J Clin Oncol. 2016;34:2851–2857.
8. Graham SM, Jørgensen HG, Allan E, et al. Primitive, quiescent, Philadel- phia-positive stem cells from patients with chronic myeloid leukemia are insensitive to STI571 in vitro. Blood. 2002;99:319–325.
9. Chomel JC, Turhan AG. Chronic myeloid leukemia stem cells in the era of targeted therapies: resistance, persistence and long- term dormancy. Oncotarget. 2011;2:713–727.
10. Etienne G, Guilhot J, Rea D, et al. Long-term follow-up of the French Stop Imatinib (STIM1) Study in patients with chronic myeloid leukemia. J Clin Oncol. 2017;35:298–305.
11. Chomel JC, Sorel N, Guilhot J, Guilhot F, Turhan AG. BCR- ABL expression in leukemic progenitors and primitive stem cells of patients with chronic myeloid leukemia. Blood. 2012;119:2964–2965. author reply 2965−2966.
12. Ross DM, Branford S, Seymour JF, et al. Safety and efficacy of imatinib cessation for CML patients with stable undetectable minimal residual disease: results from the TWISTER study. Blood. 2013;122:515–522.
13. Culhane AC, Thioulouse J, Perriere G, Higgins DG. MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics. 2005;21:2789–2790.
14. Le S, Josse J, Husson F. FactoMineR: an R package for multi- variate analysis. J Stat Softw. 2008;25(1).
15. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer; 2009.
16. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA. 2002;99:6567–6572.
17. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotem- poral ordering of single cells. Nat Biotechnol. 2014;32:381–386.
18. Cramer-Morales K, Nieborowska-Skorska M, Scheibner K, et al. Personalized synthetic lethality induced by targeting RAD52 in leukemias identified by gene mutation and expression profile. Blood. 2013;122:1293–1304.
19. Perrotti D, Jamieson C, Goldman J, Skorski T. Chronic myeloid leukemia: mechanisms of blastic transformation. J Clin Invest. 2010;120:2254–2264.
20. Corbin AS, Agarwal A, Loriaux M, Cortes J, Deininger MW, Druker BJ. Human chronic myeloid leukemia stem cells are insensitive to imatinib despite inhibition of BCR-ABL activity. J Clin Invest. 2011;121:396–409.
21. Katagiri S, Tauchi T, Okabe S, et al. Combination of ponatinib with hedgehog antagonist vismodegib for therapy-resistant BCR-ABL1- positive leukemia. Clin Cancer Res. 2013;19:1422–1432.
22.
Zhao C, Chen A, Jamieson CH, et al. Hedgehog signalling is essential for maintenance of cancer stem cells in myeloid leu- kaemia. Nature. 2009;458:776–779.
23. Nelson EA, Walker SR, Weisberg E, et al. The STAT5 inhibitor pimozide decreases survival of chronic myelogenous leukemia cells resistant to kinase inhibitors. Blood. 2011;117:3421–3429.
24. Ito K, Bernardi R, Morotti A, et al. PML targeting eradicates quies- cent leukaemia-initiating cells. Nature. 2008;453:1072–1078.
25. Chen Y, Hu Y, Zhang H, Peng C, Li S. Loss of the Alox5 gene impairs leukemia stem cells and prevents chronic myeloid leuke- mia. Nat Genet. 2009;41:783–792.
26. Cobo I, Martinelli P, Flandez M, et al. Transcriptional regulation by NR5A2 links differentiation and inflammation in the pan- creas. Nature. 2018;554:533–537.
27. Darmanis S, Sloan SA, Croote D, et al. Single-cell RNA-Seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 2017;21:1399–1410.
28. Giustacchini A, Thongjuea S, Barkas N, et al. Single-cell tran- scriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat Med. 2017;23:692–702.
29. Hovestadt V, Smith KS, Bihannic L, et al. Resolving medullo- blastoma cellular architecture by single-cell genomics. Nature. 2019;572:74–79.
30. Ma X, Liu Y, Liu Y, et al. Pan-cancer genome and transcrip- tome analyses of 1,699 paediatric leukaemias and solid tumours. Nature. 2018;555:371–376.
31. Warfvinge R, Geironson L, Sommarin MNE, et al. Single-cell molec- ular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood. 2017;129:2384–2394.
32. Viswanathan SR, Powers JT, Einhorn W, et al. Lin28 promotes transformation and is associated with advanced human malig- nancies. Nat Genet. 2009;41:843–848.
33. Ahmed M, Dusanter-Fourt I, Dugray A, et al. BCR-ABL fails to inhibit apoptosis in U937 myelomonocytic cells expressing a carboxyl -termi- nal truncated STAT5. Leuk Lymphoma. 2001;42:445–455.
34. Walz C, Ahmed W, Lazarides K, et al. Essential role for Stat5a/ b in myeloproliferative neoplasms induced by BCR-ABL1 and JAK2(V617F) in mice. Blood. 2012;119:3550–3560.
35. Casetti L, Martin-Lanneree S, Najjar I, et al. Differential contri- butions of STAT5A and STAT5B to stress protection and tyro- sine kinase inhibitor resistance of chronic myeloid leukemia stem/progenitor cells. Cancer Res. 2013;73:2052–2058.
36. Turhan AG. STAT5 as a CML target: STATinib therapies? Blood. 2011;117:3252–3253.
37. Valent P. Targeting the JAK2-STAT5 pathway in CML. Blood. 2014;124:1386–1388.
38. Warsch W, Walz C, Sexl V. JAK of all trades: JAK2-STAT5 as novel therapeutic targets in BCR-ABL1+ chronic myeloid leuke- mia. Blood. 2013;122:2167–2175.
39. O’Hare T, Zabriskie MS, Eiring AM, Deininger MW. Pushing the limits of targeted therapy in chronic myeloid leukaemia. Nat Rev Cancer. 2012;12:513–526.
40. Dolinska M, Piccini A, Wong WM, et al. Leukotriene signaling via ALOX5 and cysteinyl leukotriene receptor 1 is dispensable for in vitro growth of CD34
+CD38− stem and progenitor cells in chronic
myeloid leukemia. Biochem Biophys Res Commun. 2017;490:378–384.
41. Lucas CM, Harris RJ, Giannoudis A, McDonald E, Clark RE. Low leukotriene B4 receptor 1 leads to ALOX5 downregulation at diagnosis of chronic myeloid leukemia. Haematologica. 2014;99:1710–1715.
42. Gallipoli P, Cook A, Rhodes S, et al. JAK2/STAT5 inhibition by nilotinib with ruxolitinib contributes to the elimination of CML CD34+ cells in vitro and in vivo. Blood. 2014;124:1492–1501.
ARTICLE IN PRESS
S. Pagliaro et al. / Experimental Hematology 2020;000:1−10 10.e1
Supplemental Figures
Supplementary Figure. 1. Cluster stratification of the states identified during pseudotime analysi s performed on single cell experiments of CML CD34+ cells. Seven states were highlighted with different colors on pseudotime trajectory for each four cluster
Supplementary Figure. 2. Pseudotime heatmap highlighting the fact that ALOX5 trajectory is shared with that of a gene cluster comprising pluripotentcy genes except MYC.
10.e2 S. Pagliaro et al. / Experimental Hematology 2020;000:1−10
Supplementary Figure. 3. Pseudotime analysis focus branching point which discriminated ALOX5 and MYC clusters from major clus ter: branching 2 allowed to separate MYC and ALOX5 clusters from major cluster: regulated genes on this branching are represented by pseudotime heatmap with best significance of STAT5A (negative log10 of p-value on barplot)
Supplementary Figure. 4. Bioinformatics analysis of expression of OCT4 (POU5F1) and Nanog in purified hematopietic cell populations show- ing absence of overexpression of OCT4 in the “bulk” HSC (4A) whereas an overexpression of Nanog is detected in the HSC (4B). (Data from Cramer-Morales K et al. Blood. 2013: 122:1293-304)
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 103
PAPER 2 – Summary
EUKARYOTIC ELONGATION FACTOR 2 KINASE (EEF2K) IS A
TRANSCRIPTIONAL TARGET OF BCR-ABL AND IS IMPLICATED IN THE
SURVIVAL OF LEUKEMIC CELLS
In previous studies, we demonstrated a major upregulation of ETS1 mRNA expression
in UT7-cells expressing BCR-ABL as compared to UT7 control cells. As EEF2K was
identified as a transcriptional target of ETS1 gene in BCR-ABL expressing cells, we
checked the EEF2K gene expression in peripheral blood (total blood) of CML patients
at diagnosis (n = 59) as compared to health donors (n = 32). Our results showed a
major increase in EEF2K expression in CML patients compared to controls, with a p-
value < 2.2e-16.
To determine the type of cells in which EEF2K is overexpressed, we tested its
expression in purified CD34+, CD34- and PBMC from 3 CML patients. Our analyses
revealed higher expression of EEF2K in more differentiated, light density cells and
CD34- cells, as compared to CD34+ cells, suggesting that EEF2K expression appears
to be increased in cells committed to differentiation. This result was further confirmed
using single-cell analysis of CD34+ cells isolated from the peripheral blood of patients
with CML showing that the expression of EEF2K was significantly lower (p = 0.006978)
in stem cell-like cells.
Western blot analyses in UT7 cells expressing BCR-ABL detected higher levels of
EEF2K and its target EF2 protein expression compared to parental UT7. Considering
that EEF2K expression can be directly regulated by the presence or scarcity of
nutrients in cancer cells, we analyzed the impact of a nutrient deprivation on EEF2K
expression in the context of BCR-ABL, To perform nutrient deprivation experiments,
cells were cultured for 4 days in three different conditions: normal conditions (NC), no
FBS condition (NF) and no glucose condition (NG) .
The results suggest that, as shown in other cell culture systems, EEF2K expression in
normal culture conditions oscillates according to nutrients levels in order to stop
elongation, allowing to protect cells from excess of unnecessary intracellular proteins.
However, in BCR-ABL expressing cells, the expression of EEF2K do not appear to be
regulated according to nutrient levels, suggesting that the EEF2K gene is regulated
differently in BCR-ABL-positive and negative cells.
To evaluate further the potential effects of EEF2K expression in the context of BCR-
ABL expression, we have designed knockdown experiments using shRNA strategy.
For this purpose, two different shRNA targeting EEF2K in the presence of a scramble
control were used.
Under normal conditions, the proliferation rates of UT7 shEEF2K were significantly
decreased as compared to the scramble controls whereas in the same conditions UT7
-11-shEEF2K cells have proliferated. Under nutrient deprivation – glucose and/or FBS-
free conditions - both UT7 and UT7 p210 decreased their proliferation rates. However,
in UT7-11-shEEFK2 cells a recover of the proliferation as compared to scramble
controls suggesting that EEF2K invalidation in BCR-ABL positive cells allows normal
proliferation despite nutrient deprivation EEF2K appears to have protect UT7-11 cells
from the effect of nutrient deprivation (FBS and glucose) while same did not occur in
UT7 cells in which it was noticed an opposite effect, namely, an escalation of
decreasing indexes of proliferation in cultures without FBS. EEF2K inhibition in UT7
cells had no impact at proliferation rates under glucose deprivation, compared to
scramble. In addition, EEF2K inhibition had no effect on imatinib response in vitro
neither under normal nor nutrient deprivation conditions, compared to scramble.
For this purpose, we have performed a metabolomics assay using the Energy
Phenotype test kit from SeaHorse technology UT7 and ECAR levels in UT7 cells were
impacted by shEEF2K in UT7 cells as compared to scramble controls, with cells not
responding to stress conditions. On the other hand, UT7-11 cells had increased ECAR
levels with or without EEF2K invalidation, suggesting the absence of control exerted
by the EEF2K pathway in BCR-ABL-transformed cells.
The transcriptome in basal UT7 cell line comparing EEF2K shRNA and its scramble
shRNA allowed to identify 402 differential expressed genes, revealing a positive
induction of granulocyte migration and regulated secreted pathway. These results
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 105
showed that EEF2K play an important repressive role in invasive process regulated by
myeloid cell at basal level.
In context of UT7-11 cell line, EEF2K shRNA induced a less differential expressed
genes: only 136 genes were found regulated with 76 up regulated genes and 60 down
regulated genes. This analysis also revealed an increase of positive regulation in
intrinsic apoptosis A positive regulation of E2Fs targets genes was also found
significant in p210 BCR-ABL UT7 cell lines under action of EEF2K shRNA. Repressed
signatures by EEF2K shRNA in basal and p210 BCR-ABL conditions were found
similar quantitatively with respectively 64 and 60 down regulated genes that allowed
us to identify significant repression of cell cycle under action of EEF2K shRNA in basal
condition. A significant repression of oxidative phosphorylation was found in basal
condition under EEF2K shRNA. These results showed that in UT7 basal condition
EEF2K play a role in the promotion of cell cycle and oxidative phosphorylation
metabolism.
L’ARTICLE 2 – Résumé
LA KINASE DU FACTEUR D'ÉLONGATION EUCARYOTE 2 (EEF2K) EST UNE
CIBLE TRANSCRIPTIONNELLE DE BCR-ABL ET EST IMPLIQUÉE DANS LA
SURVIE DES CELLULES LEUCÉMIQUES
Dans des études précédentes, nous avons démontré une augmentation importante de
l'expression de l'ARNm ETS1 dans les cellules UT7 exprimant la BCR-ABL par rapport
aux cellules témoins UT7. Comme EEF2K a été identifié comme une cible
transcriptionnelle du gène ETS1 dans les cellules exprimant BCR-ABL, nous avons
vérifié l'expression du gène EEF2K dans le sang périphérique (sang total) des patients
atteints de LMC au moment du diagnostic (n = 59) par rapport aux donneurs de santé
(n = 32). Nos résultats ont montré une augmentation majeure de l'expression du gène
EEF2K chez les patients atteints de LMC par rapport aux témoins, avec une valeur p
< 2,2e-16.
Pour déterminer le type de cellules dans lesquelles EEF2K est surexprimé, nous avons
testé son expression dans les cellules CD34+, CD34- et PBMC purifiées de 3 patients
atteints de LMC. Nos analyses ont révélé une expression plus élevée d'EEF2K dans
des cellules plus différenciées, de densité lumineuse et des cellules CD34-, par rapport
aux cellules CD34+, ce qui suggère que l'expression d'EEF2K semble être accrue dans
les cellules engagées dans la différenciation. Ce résultat a été confirmé par l'analyse
d'une seule cellule de cellules CD34+ isolées du sang périphérique de patients atteints
de LMC, montrant que l'expression de EEF2K était significativement plus faible (p =
0,006978) dans les cellules apparentées aux cellules souches.
Les analyses par Western blot dans les cellules UT7 exprimant le BCR-ABL ont permis
de détecter des niveaux plus élevés d'EEF2K et de l'expression de la protéine EF2 qui
lui sert de cible, par rapport à l'UT7 parentale. Considérant que l'expression d'EEF2K
peut être directement régulée par la présence ou la rareté des nutriments dans les
cellules cancéreuses, nous avons analysé l'impact d'une privation de nutriments sur
l'expression d'EEF2K dans le contexte de BCR-ABL. Pour réaliser des expériences de
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 107
privation de nutriments, les cellules ont été cultivées pendant 4 jours dans trois
conditions différentes : conditions normales (NC), pas de condition FBS (NF) et pas de
condition glucose (NG) .
Les résultats suggèrent que, comme le montrent d'autres systèmes de culture
cellulaire, l'expression de EEF2K dans des conditions de culture normales oscille en
fonction des niveaux de nutriments afin de stopper l'élongation, ce qui permet de
protéger les cellules contre l'excès de protéines intracellulaires inutiles. Cependant,
dans les cellules exprimant le gène BCR-ABL, l'expression de EEF2K ne semble pas
être régulée en fonction des niveaux de nutriments, ce qui suggère que le gène EEF2K
est régulé différemment dans les cellules BCR-ABL positives et négatives.
Pour évaluer plus avant les effets potentiels de l'expression de EEF2K dans le contexte
de l'expression de BCR-ABL, nous avons conçu des expériences de knockdown en
utilisant la stratégie de l'ARNsh. À cette fin, deux ARNsh différents ciblant EEF2K en
présence d'un contrôle de brouillage ont été utilisés.
Dans des conditions normales, les taux de prolifération des cellules UT7 shEEF2K ont
été significativement réduits par rapport aux contrôles de brouillage alors que dans les
mêmes conditions, les cellules UT7 -11-shEEF2K ont proliféré. Dans des conditions
de privation de nutriments - sans glucose et/ou FBS - les taux de prolifération des
cellules UT7 et UT7 p210 ont tous deux diminué. Cependant, dans les cellules UT7-
11-shEEFK2, une reprise de la prolifération par rapport aux témoins de brouillage
suggère que l'invalidation EEF2K dans les cellules positives BCR-ABL permet une
prolifération normale malgré la privation de nutriments. EEF2K semble avoir protégé
les cellules UT7-11 de l'effet de la privation de nutriments (FBS et glucose) alors que
cela ne s'est pas produit dans les cellules UT7 dans lesquelles on a remarqué un effet
inverse, à savoir une augmentation des indices décroissants de prolifération dans les
cultures sans FBS. L'inhibition de l'EEF2K dans les cellules UT7 n'a pas eu d'impact
sur les taux de prolifération en cas de privation de glucose, par rapport au brouillage.
En outre, l'inhibition d'EEF2K n'a eu aucun effet sur la réponse de l'imatinib in vitro, ni
dans des conditions normales ni dans des conditions de privation de nutriments, par
rapport à la culture brouillée.
À cette fin, nous avons effectué un test de métabolomique en utilisant le kit de test
Energy Phenotype de la technologie SeaHorse UT7 et les niveaux ECAR dans les
cellules UT7 ont été influencés par shEEF2K dans les cellules UT7 par rapport aux
témoins scrables, les cellules ne répondant pas aux conditions de stress. D'autre part,
les cellules UT7-11 ont augmenté les niveaux d'ECAR avec ou sans invalidation par
EEF2K, ce qui suggère l'absence de contrôle exercé par la voie EEF2K dans les
cellules transformées par BCR-ABL.
Le transcriptome de la lignée cellulaire UT7 basale comparant l'ARNsh EEF2K et son
ARNsh brouillé a permis d'identifier 402 gènes différentiels exprimés, révélant une
induction positive de la migration des granulocytes et une voie sécrétée régulée. Ces
résultats ont montré que EEF2K joue un rôle répressif important dans le processus
invasif régulé par la cellule myéloïde au niveau basal.
Dans le contexte de la lignée cellulaire UT7-11, l'ARNsh EEF2K a induit des gènes
exprimés de manière moins différentielle : seuls 136 gènes ont été trouvés régulés,
avec 76 gènes régulés vers le haut et 60 gènes régulés vers le bas. Cette analyse a
également révélé une augmentation de la régulation positive dans l'apoptose
intrinsèque. Une régulation positive des gènes cibles E2Fs a également été trouvée
significative dans les lignées cellulaires UT7 p210 BCR-ABL sous l'action de l'ARNm
EEF2K. Des signatures réprimées par l'ARNsh EEF2K dans des conditions basales et
p210 BCR-ABL ont été trouvées de manière quantitativement similaire avec
respectivement 64 et 60 gènes régulés à la baisse.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 109
IN PREPARATION
EUKARYOTIC ELONGATION FACTOR 2 KINASE (eEF2K) IS A
TRANSCRIPTIONAL TARGET OF BCR-ABL AND IS IMPLICATED IN THE
SURVIVAL OF LEUKEMIC CELLS
S. Pagliaro1,5,6, L de Souza, C. Desterke, J.C. Chomel, P Hugues , F. Griscelli1,2,4
A. Foudi, A. Bennaceur-Griscelli1,2 A.G. Turhan1,2
1INSERM UMR-S 935, Université Paris Sud, 94800 Villejuif, France and ESTeam
Paris Sud, Université Paris Sud, 94800 Villejuif, France.
2INGESTEM National iPSC Infrastructure, 94800 Villejuif, France
3ATIP-Avenir INSERM UMR-S 935, Université Paris Sud, 94800 Villejuif
4Université Paris Descartes, Faculté Sorbonne Paris Cité, Faculté des Sciences
Pharmaceutiques et Biologiques, Paris, France
5 Université Paris-Sud, Faculté de Pharmacie, 92296 Châtenay-Malabry
6 Science without Borders, CAPES, Brasilia, Brazil
ABSTRACT
The oncoprotein BCR-ABL is the constitutively active tyrosine kinase produced by the
chimeric BCR-ABL gene in chronic myeloid leukemia (CML). The transcriptional targets
of BCR-ABL in leukemic cells have not been extensively studied. In transcriptome
experiments using the hematopoietic UT7 cell line expressing BCR-ABL, we have
identified ETS1 as a major upregulated gene. We report here that ETS1 controls the
expression of eukaryotic elongation factor kinase 2 (eEF2K) which plays a major role
in the survival of cells upon nutrient deprivation. eEEF2K expression was increased in
the BCR-ABL expressing UT7-11 cells as compared to parental cells, with evidence of
increased phosphorylation of the eEF2. Using q-RT-PCR assays we have evaluated
the expression of eEF2 kinase in primary CML samples and this expression was found
to be highly increased in CML samples (n = 59) as compared to controls (n=32). To
determine the potential effects of eEF2K overexpression in the context of BCR-ABL
expression, we inhibited eEF2K by shRNA strategy in UT7-11 cells as compared to
parental cells and tested the growth and Imatinib sensitivity in the context of nutrient
deprivation. In UT7-BCR-ABL cell with eEF2K inhibition, there was a significantly
higher growth rate in glucose-deprivation and serum-deprivation conditions suggesting
that, as opposed to data reported in solid tumors, eEF2K overexpression renders
leukemic cells sensitive to nutrient-deprivation conditions. To determine the potential
oncogenic role of eEF2K in BCR-ABL-expressing cells, a transcriptome analysis has
been performed in UT7-11 cells expressing lower levels of eEF2K as compared to sh-
scramble controls. These studies clearly indicate the positive correlation between
inactivation of eEF2K and oxidative phosphorylation and genes involved in ROS as
well as genes involved in peroxisome and IFN-gamma pathways. Overall, our data
suggest that overexpression of eEF2K in CML is associated with an increased
sensitivity to nutrient-deprivation.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 111
Introduction
Protein synthesis is the cellular process that consumes the most of energy, demanding
about 30-35% of ATPs or equivalents(Buttgereit and Brand, 1995). Since proteins play
crucial roles and are essential for cellular homeostasis maintenance, the regulation of
its synthesis is of fundamental importance for cell survival. One of the forms of
regulation is through eukaryotic elongation factor 2 kinase (eEF2K), a kinase that, when
active, phosphorylates and inhibits the action of elongation factor 2 (eEF2), blocking
the elongation phase of translation(Ryazanov et al., 1988). EEF2 plays an essential
role in the translocation of ribosomes carrying amino acids to the peptide- chain
formation(Browne and Proud, 2002; Dever and Green, 2012; Kaul et al., 2011; Mauro
and Matsuda, 2016). In normal cells, this mechanism is regulated, among other factors,
by stress conditions, such as nutrient shortages (Browne et al., 2004; Browne and
Proud, 2004; Pavur et al., 2000). Tumor cells are highly proliferative and grow rapidly
and anarchically. Along with this rapid and abnormal cell growth, there is an increased
energy/nutrient demand in tumor microenvironment. These factors led the scientific
community to investigate the role of EEF2K in tumor cells. EEF2K is now known to be
overexpressed in a wide range of tumors, such as breast (Bayraktar et al., 2018, p. 2;
Guan et al., 2018, p. 2; Kabil et al., 2018, p. 2; Pan et al., 2018, p. 2), ovarian (Shi et
al., 2018), colorectal (Ng et al., 2019). However, nothing is known about the impact of
EEF2K on chronic myeloid leukemia.
The oncoprotein BCR-ABL is the constitutively active tyrosine kinase produced by the
BCR-ABL fusion-gene in chronic myeloid leukemia (CML). In a previous study, we
transfected the BCR-ABL gene into UT7 cells (Issaad et al., 2000) - an acute
megakaryoblastic leukemia cell line - and performed a complete transcriptome analysis
comparing the parental and transfected cells. The results have shown that BCR-ABL
activates ETS1, a major transcriptional regulator in hematopoietic cells (Desterke et
al., 2018).One of the targets of ETS1 is the EEF2Kinase, a unique kinase controlling
the protein elongation. We report here that eEF2K expression is deregulated in CML
via ETS1 induction and eEF2K could play a role in the metabolism of leukemic cells, in
particular in the pathway of oxidative stress. In this study we demonstrate that ETS1 is
a promoter of another EEF2K, also overexpressed in BCR-ABL positive cells. In this
study, our findings demonstrate that eEF2K inhibition affects differently, almost
oppositely, UT7 expressing BCR-ABL comparing to UT7 parental.
Materials and Methods
Cell Lines
UT7 is a GM-CSF-dependent cell line (generously provided by Dr Komatsu(Komatsu
et al., 1991)) from which we have generated UT7 cells expressing BCR-ABL (UT7-11)
as previously described (Issaad et al., 2000). Parental UT7 cells were grown in RPMI
1640 medium (Thermo Fisher) containing 10% FBS (Gibco) and 10 ng/ml of rhGM-
CSF whereas UT7-11 cells were growth-factor-independent. Cells were split three
times per week at the concentration of 105 cells/ml.
Nutrient Deprivation Assay
UT7 and UT7 p210 cells were cultured at initial concentration as 100,000 cells/ml in
glucose-less (RPMI 1640 without glucose, 10% FBS, 1% PenStrep,Glutamax), FBS-
less (RPMI 1640, 1% PenStrep, 1% Glutamax) or normal conditions. GM-CSF cytokine
was added in UT7 cells medium at 10 ng/ml concentration. Cells were collected at day
0, 2 and 4 for counting, RNA extraction and RT-qPCR analysis.
Lentiviral vectors and infection
The shRNA constructs targeting human EEF2K-1 (shRNA TRCN0000234847) and
EEF2K-2 (shRNA TRCN0000234845) were obtained from the Integrated DNA
Technologies (IDT). The constructs were in the pLKO.GFP-puro lentiviral vector. As
controls, the lentiviral vectors pLKO-GFP-puro scrambles (the empty vector with a 1.2
kb stuffer element) were used .The sequences of the shRNAs used in this study are…
Human kidney 293T cells plated onto 15 cm dishes with 24μg of EEF2K backbone,
1.2μg of tat, 1,2μg of rev, 1.2μg gag/pol and 2.4 μg vsv-g plasmid and 90μl of
TransIT®-293 (Mirus). Supernatants were collected at 36 to 48 h after transfection.
Concentrated vector preparations were made by ultracentrifugation, filtered (0.45 μm),
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 113
divided into aliquots and stored. Virus titration was performed using NIH 3T3 cells. UT7
and UT7 p210 cells were mixed with viral supernatant (MOI = 2) in the presence of 8
μg/ml polybrene in a 24-well plate at a density of 300,000 cells per well. The cells were
left in viral supernatant overnight. The medium was replaced the morning after
infection. Selection of infected cells was started 24 hours after infection with 2 μg/ml
puromycin. The transduction efficiency was assessed by measuring the percentage of
GFP+ cells by flow cytometry 48 hours post infection. Typically, the transduction
efficiency was >80%.
Western Blots.
Cells were harvested at indicated time points, washed twice in PBS, resuspended in
RIPA lysis buffer (NuPAGE, Thermo Scientific) supplemented with 1x Complete Mini
EDTA-Free (Roche), 50 mM NaF and 10mM Na3VO4, vortexed and incubated for 30
min on ice, vortexing samples each 10 min. After centrifugation at 14,000 rpm for 10
min at 4°C to remove cellular debris, the remaining supernatant was transferred to a
new tube. Protein dosage was performed using Bradford (Sigma Aldrich) method.
Equal amounts of proteins were supplemented with 1x NuPAGE LDS sample buffer,
1% NuPAGE Sample Reducing Agent (Thermo Fisher Scientific) and incubated for 5
min at 100°C. Proteins were separated by SDS gel electrophoresis using the XCell™
Module system, NOVEX™ WedgeWell™ 4-20% or 10 % Tris-Glycine gel and Tris-
Glycine Running Buffer (Thermo Scientific). Subsequently, proteins were transferred
onto a NOVEX® PVDF 0.45µM pore size membrane (Thermo Scientific) using NOVEX®
Transfer Buffer (Thermo Scientific). Membranes were blocked with 5% Bovine Serum
Albumin (Sigma Aldrich) TBS buffer (Thermo Scientific), 0.1% Tween® 20 (Sigma
Aldrich) for 1h and probed with mouse monoclonal antibody anti-EEF2 (#131202,
Abcam) at a 1:500 dilution, rabbit monoclonal antibody anti-EEF2K antibody (#45168,
Abcam) at a 1:500 dilution, rabbit polyclonal antibody anti-EEF2 [phospho T56]
antibody (#53114, Abcam) at a 1:500 dilution, mouse monoclonal anti-c-ABL antibody
(#OP20, Millipore), rabbit polyclonal anti-p–ABL (#7-788, Millipore), mouse monoclonal
anti-β-Actin−Peroxidase antibody (#3854, Sigma) at a 1:10000 dilution in 5% BSA-
TBST for 1 h at room temperature or overnight at 4°C. Membranes were washed four
times with TBST, incubated with anti-mouse IgG (#0168, Sigma) or anti-
rabbit IgG (#0545, Sigma) to 1:10000 dilution in 5% BSA-TBST for 1h at room
temperature, washed three times with TBST, and revealed with SuperSignal® West
Dura (Thermo Scientific).
RT-qPCR
Isolation of RNA was performed using the PureLink RNA Purification (Thermo Fisher
Scientific). RNA was quantified by a NanoDrop spectrophotometer (Thermo Scientific).
Reverse transcription was carried out using the Applied Biosystems™ High-Capacity
cDNA Reverse Transcription Kit (Thermo Scientific). Quantitative Real-time PCR was
performed using Strategene MxPro 3005P system (Agilent Tecnhologies) and SYBR
Green PCR Master Mix (Applied Biosystems). Quantification was performed using the
ΔΔCt method. Normalization was performed using β-2-Microglobulin mRNA as a
housekeeper. The primers used for quantitative RT-PCR were obtained by IDT.
(EEF2K1: TCCCTTGGACCACCCATAAAT, EEF2K2:
CATGGCCACTCATACAGTAAT)
Transcriptome analysis
Total RNA from UT7 cells with or without transfection of p210 BCR-ABL and in
presence of shRNAs targeting EEF2K or scramble control was used to perform
transcriptome experiment on ClariomS Human Chip (ThermoFisher Scientific).
Microarray experiments were processed with WT plus chemistry (ThermoFisher
Scientific) dedicated to cell line treatment starting with 50 to 500 ng range of total RNA.
CEL raw files were normalized with robust microarray analysis (RMA) method (Irizarry
et al., 2003) with Transcriptome Analysis Console (TAC) version 4.0 software
(ThermoFisher Scientific). Supervised analyses between each specific shRNA and
their respective scramble control were performed with Significance Analysis for
Microarray (SAM) algorithm with fold change threshold over 2 and false discovery rate
(FDR) threshold less than 0.05 (Tusher et al., 2001). Bioinformatics analysis were
performed in R software environment version 3.4.3. Expression heatmap were drawn
made4 R Bioconductor package (Culhane et al., 2005, p. 4) and functional enrichment
analysis was performed with Gene set enrichment analysis standalone software
(GSEA) (Subramanian et al., 2005) with MSigDb version 6.2 (Liberzon et al., 2015).
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 115
Sea Horse Assays
Real-time oxygen consumption rate (OCR) and extracellular acidification rate (ECAR)
of live cells were measured using a 8-well plate by SeaHorse® XF Technology. Sensor
Cartridge (SeaHorse Bioscience) were previously hydrated with XF Calibrant reagent
(SeaHorse Bioscience) overnight at 37°C, non-CO2 conditions. UT7 and UT7 p210 –
scrambles and shEEF2Ks – were seeded at 2x105 cells/well in XF Base Medium
(SeaHorse Bioscience) added with 10mM Glucose solution (Sigma), 1mM Pyruvate
solution (Sigma) and 2mM Glutamine solution (Sigma), plates were centrifugated to
allow cells to seed at the bottom and let incubated at 37°C, non-CO2 conditions for 30-
60 min to equilibrate the temperature before the final assay. A stressor mix solution -
olygomycin (1µM) and FCCP (0.5µM) – was added to the plate and the assay was
followed according to the fabricant protocol. Cells were counted from each well after
running to normalize the data set. Results were analyzed using the SeaHorse XF Cell
Energy Phenotype Test Generator software, provided by the fabricant. Data are
expressed as mean±S.E.M.
RESULTS
High Levels of eEF2K expression in UT7 cells expressing BCR-ABL.
As eEF2K was identified as a transcriptional target of ETS1 gene in BCR-ABL
expressing cells (Supplementary Figure 1), we analyzed its expression in these two
cell contexts. As can be seen in Figure 1 there was a major increase of ETS1 RNA
expression in UT7-cells expressing BCR-ABL as compared to control cells (Figure 1).
We then projected to determine if eEF2K overexpression was a feature that we could
identify in primary CML cells.
Figure 1: Overexpression of EEF2K in transcriptome of UT7 cell transfected with BCR-
ABL: Boxplot of EEF2K expression found to be significantly overexpressed (fold
change =1.92) in UT7-11 (BCR-ABL positive) as compared to the UT7 cell control non-
transduced (2 sided ttest p-value=0.00113).
EEF2K is overexpressed in CML patients
To assess the true relevance of the eEF2K gene in CML, we evaluated its expression
by Q-RT-PCR in peripheral blood (total blood) of CML patients at diagnosis (n = 59) as
compared to healthy donors (n = 32). As can be seen in Figure 2, Our results showed
a major increase in eEF2K expression in CML patients compared to controls, with a p-
value < 2.2e-16(Figure 2).
To determine the type of cells in which eEF2K is overexpressed, we tested its
expression in purified CD34+ cells as compared to light-density cells separated by
Ficoll. Using samples from 3 patients, we analyzed the expression in the light density
cells as well as the CD34+ and CD34- fractions. These analyses revealed higher
expression of eEF2K in more differentiated, light density cells and CD34- cells, as
compared to CD34+ cells, suggesting that eEF2K expression appears to be increased
in cells committed to differentiation (Figure 3A). This result was further confirmed in
another study (Pagliaro et al., 2020) using single-cell analysis of CD34+ cells isolated
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 117
from the peripheral blood of patients with CML (Figure 3b). In this previous study, we
performed a single cell transcriptomic profile using CD34+ cells from 3 CML patients,
where a subpopulation expressing multiple pluripotent genes (stem cell-like cells). And
which the expression of eEF2K was significantly lower (p = 0.006978) Thus, these
results suggested that the expression of eEF2K was increased in differentiated myeloid
components.
Figure 2: Over expression of EEF2K in peripheral blood of CML patients as compared
to healthy donors: boxplot of EEF2K mRNA quantified by QRT-PCR at diagnostic time
in peripheral blood of CML patients as compared to healthy donors. Total RNA was
extract from peripheral blood mononuclear cells.
Figure 3. EEF2K is increased in CD34- cells: PBMC, CD34+ and CD34- cells from 3
CP-CML patients were isolated from whole blood using Ficoll (PBMC) followed from
magnetic column separation (CD34+ and CD34-). 3a: RT-qPCR analysis show that
mRNA eEF2K expression were 2.3x higher in CD34- cells than in CD34+ cells.
Calculations were performed using ΔΔCt formula normalized by B2M mRNA
expression. 3b: Violin plot from single-cell (C1) analysis of 3 CP-CML CD34+ cells
show that eEEF2K expression is predominant in the non-stem cell-like population.
Evaluation of the expression of eEF2K protein and its target eEF2.
To determine the possibility of a direct link between BCR-ABL expression and that of
eEFK, at the protein level, we have performed Western blot analyses in UT7 cells
expressing BCR-ABL. We have asked whether the target of eEF2K underwent a
phosphorylation in this context.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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As can be seen in Figure 4, BCR-ABL expressing UT7 cells (UT7-p210) had higher
levels of eEF2K as compared to parental controls and in these cells, the levels of
phosphorylated eEF2 which is the only target of eEF2K, were found to be increased
(Figure 4)
Figure 4. eEF2K and p-eEF2K proteins levels are overexpressed in UT7-p210 cells.
Western Blot comparing UT7 and UT7-p210 (BCR-ABL positive) cells shows that
eEF2K and p-eEF2K protein levels are overexpressed in UT7 BCR-ABL positive cells
compared to UT7.
Evaluation of eEF2K mRNA levels in response to nutrient deprivation conditions
The expression of eEF2K can be directly regulated by the presence or scarcity of
nutrients in cancer cells and cell lines. To analyze the impact of a nutrient deprivation
on eEF2K expression in the context of BCR-ABL, we have used UT7 and UT7-p210
cell lines. To perform nutrient deprivation experiments, cells were cultured in three
conditions including in the regular medium (complete RPMI, 10% FBS, 10% PenStrep)
or in RPMI medium without glucose or without serum for 4 days. Cells from each group
were collected on days 0, 2 and 4 counted and analyzed for eEF2K expression by RT-
qPCR.
As can be seen in Figure 6a, both UT7 and UT7-p210 had their cell proliferation rates
reduced when cultured without glucose or without serum (n = 3 experiments). In UT7
cells, there was an increase of eEF2K expression throughout the culture time under
normal conditions, while this oscillation was significantly lower when cultured in the
absence of glucose and serum. Interestingly, in UT7 p210 cells, none of the conditions
(normal or nutrient deprivation) had an impact on eEF2K levels. (Figure 6b).
These results suggest that, as shown in other studies (reviewed in Wang et al., 2017),
eEF2K expression in normal culture conditions oscillates in response to nutrient
deprivation in order to regulate elongation, saving cells from high energy consumption
by blocking protein synthesis. However, in BCR-ABL positive cells, the expression of
eEF2K does not appear to be regulated according to nutrient levels, suggesting that
the gene may be regulated differently in BCR-ABL-positive compare to negative cells.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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Figure 5. Nutrient Deprivation assay scheme. UT7 and UT7-210 cells were cultured
under normal conditions and nutrient deprivation conditions. Normal conditions
(control): complete RPMI 1640, 10% FBS, 10% PenStrep. Glucose deprivation: RPMI
1640 without glucose, 10% FBS, 10% PenStrep. Serum deprivation: complete RPMI
1640, 10% PenStrep, no serum (FBS) added. Cells were seeded at 105cells/ml and
collected at day 0, 2 and 4 for cell counting, RNA extraction and RT-qPCR.
Figure 6. EEF2K expression appears to not be regulated by nutrient deprivation in cells
expressing BCR-ABL. In 6a, cells analyzed from nutrient deprivation assay (see Fig 5)
show a cell growth decrease in the nutrient deprivation group for both cell lines (UT7
and UT7-p210). Glucose deprivation starts to show a significant decrease at day 4,
while serum deprivation is impacting cell growth from day 2, comparing to the control
group cultured under normal conditions. In 6b, EEF2K expression (RT-qPCR)
oscillated according to nutrient deprivation in UT7 cells, but not in UT7-210, where the
expression remained constant.
Evaluation of the inhibition of eEF2K expression in UT7 and UT7-BCR-ABL cells.
To further evaluate the potential effects of eEF2K expression in the context of BCR-
ABL expression, we designed two different shRNA targeting eEF2K in the presence of
a scramble control. After subcloning into the pLKO-GFP vector, defective lentivirus
supernatants were produced, tittered and used to infect UT7 and UT7-p210 cells. The
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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efficiency of transduction was evaluated by analyzing GFP expression in both cell lines
(Supplementary Figure 3).
The efficiency of the inhibition of eEF2K expression was also evaluated using Western
blot analyses. As can be seen in Figure 7A, in two different clones with different shRNA
targets, eEF2K expression was reduced (Figure 7a) as compared to scramble controls.
The inhibition was confirmed by RT-qPCR (Figure 7b)
We then tested the proliferation rates of these cells in conditions of nutrient deprivation
as well as their response to TKI (Imatinib) in vitro.
Figure 7. EEF2K knock down validation. UT7 and UT7 p210 were transduced with
shEEF2K and scramble. EEF2K gene expression decrease were confirmed by western
blot (7a) and rt-qPCR (7b).
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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Proliferation rates under normal conditions were altered in UT7 shEEF2K, but
not in UT7 BCR-ABL shEEF2K cells
Under normal conditions (Figure 8a), the proliferation rates of UT7 shEEF2K were
significantly decreased (p < 0.002) as compared to the scramble controls whereas in
the same conditions UT7-p210-shEEF2K cells proliferated (Figure 8A).
Under nutrient deprivation – glucose (Figure 8B) and/or FBS-free conditions - both UT7
and UT7 p210 decreased their proliferation rates. However, in UT7-p210-sh-eEFK cells
we have observed a significant proliferation as compared to the scramble controls
suggesting that eEF2K invalidation in BCR-ABL expressing cells allows normal
proliferation despite nutrient deprivation. It appeared that eEF2K had protect UT7-p210
cells from the effect of nutrient deprivation (FBS and glucose), as it was observed a
recovery of cellular proliferation compared to scramble under the same conditions. An
opposite effect was noticed in UT7 cells, which presented an escalation of decreasing
indexes of proliferation in cultures without FBS and no effect under Glucose deprivation
compared to scramble.
EEF2K inhibition had no effect on imatinib response in vitro neither under normal nor
nutrient deprivation conditions, compared to scramble (Supplementary Figure 4).
We next wished to evaluate the metabolic behavior of the UT7 and UT7-p210 cells in
cells in which eEF2K expression was reduced by shRNA strategy.
Figure 8. EEF2K knockdown seems to protect cells under nutrient deprivation in UT7-
210 cells but not in UT7. UT7 and UT-p210 transduced with scramble and shEEF2K (1
and 2) were submitted to the same experimental scheme from figure 5. Using the
scramble as the internal control and Normal Conditions (8a) as an experimental control,
the results show that EEF2K knockdown has protected UT7-p210 cells from nutrient
deprivation compared to scramble. UT7-p210 shEEF2K cells managed to recover their
growth under nutrient deprivation (glucose and serum; 8b), while UT7 shEEF2K
haven’t change under glucose deprivation and increased their growth loss under serum
deprivation.
Evaluation of glucose and oxygen consumption in UT7 and UT7-11 after eEF2K
invalidation.
For this purpose, we have performed a metabolomics assay using the Energy
Phenotype test kit from SeaHorse technology. UT7 and UT7 11cells, before and after
invalidation of eeF2K expression, and in the presence of scramble controls were
analyzed. The Extracellular Acidification Rate (ECAR) and the Oxygen Consumption
Rate (OCR) were measured in basal and stressed conditions. Oligomycin and FCCP
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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(Carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone) were used as metabolic
stressors. The first blocks oxidative phosphorylation, stimulating anaerobic
metabolism, thus increasing glycolysis levels; the second triggers oxidative
phosphorylation, boosting oxygen consumption.
As can be seen in Figure 9, ECAR levels in UT7 cells were impacted by shEEF2K in
UT7 cells as compared to scramble controls, with cells not responding to stress
conditions. On the other hand, UT7-p210 cells had increased ECAR levels with or
without eEF2K invalidation, suggesting the absence of control exerted by the eEF2K
pathway in BCR-ABL-transformed cells. On the other hand, oxygen consumption rates
were not found to be influenced consistently in UT7 p210 cells despite the fact it was
found to be reduced in one UT7 -p210-shEEF2K cells (Figure 9B).
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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Figure 9. Energy Phenotype (SeaHorse). UT7 and UT7 p210 cells – scramble vs
shEEF2ks – Extracellular Acidification Rate (ECAR) and Oxygen Consumption Rate
(OCR) were analyzed in basal and stress conditions. Oligomycin and FCCP were used
as stressors ECAR levels were impacted by shEEF2K in UT7 cells, compared to
scramble, but was not modified in UT7 p210 cells. Oxygen consumption rate (OCR)
was reduced in one clone of UT7 p210 shEEF2K and didn’t change in UT7 cells.
Identification of the gene expression profile of UT7-11 cells with low levels of
eEF2K
The previous results showed that EEF2K knockdown had different impacts on UT7
cells compared to UT7-p210. In order to understand better, we performed a
transcriptome, using the Clariom S technique (ThermoFisher) comparing the UT7 and
UT7-p210 cells transduced with shEEF2K and their respective scrambles. Supervised
analysis performed in basal UT7 cell line comparing EEF2K shRNA (double shRNA in
duplicates) and its scramble shRNA allowed to identified 402 differential expressed
genes with a majority of up regulated genes (338 up regulated genes and 64 down
regulated genes, supplementaryfigures5A(all) and 6A(only down regulated)). Gene set
enrichment analysis of UT7 shEEF2K performed with Gene Ontology biological
process database revealed a positive induction of granulocyte migration (normalized
enrichment score (NES) = +2.61, p-value <0.001) with induction of chemokines
molecules such CCL3, CCL4L2, CCL2, CCL3L3 (supplementary figure 6B), also a
positive induction of regulated secreted pathway (NES = +2.32, p-value <0.001,
supplementary figure 6B), and induction of vasculature development (NES = +2.54, p-
value<0.001, supplementary figure 6B) especially by secreted molecules such as
angiopoietin 2, FGF2, HGF, PDGFD, IL1B, C3, THBS1 (thrombospondin 1) and UTS2
(urotensin). These results showed that EEF2K play an important repressive role in
invasive process regulated by myeloid cell at basal level. In context of UT7-p210 cell
line, EEF2K shRNA induced a less differential expressed genes: only 136 genes were
found regulated with 76 up regulated genes and 60 down regulated genes
(supplementary figures 5B (all) and 7A (down regulated)). This analysis also revealed
an increase of positive regulation in intrinsic apoptosis signaling (NES = +2.13, P-
value<0.001, supplementary figure 7B) especially with up regulation of clusterin alias
SP-40. An positive regulation of E2Fs targets genes was also found significant in p210
BCR-ABL UT7 cell lines under action of EEF2K shRNA (NES=+1.69, p-value<0.001,
supplementary figure 7B), among these target genes some polymerases like POL2D,
POLE, some transcription factors like E2F8, ETS1 and MYC, EZH2 epigenetic
regulator but also BRCA2 repair gene were found concerned.
Repressed signatures by EEF2K shRNA in basal and p210 BCR-ABL conditions were
found similar quantitatively with respectively 64 and 60 down regulated genes. Venn
diagram showed that these two repressed signatures are specific with poor overlap
(supplementary figure 5C). Unsupervised classification allowed to confirm specific
repression of genes in basal condition under EEF2K shRNA action (supplementary
figure 6A). In this context, Gene set enrichment analysis allowed to identified a
significant repression of cell cycle (NES < -2.17, p-value < 0.001, supplementary figure
6C) under action of EEF2K shRNA in basal condition especially with centromeric
molecules CENPA and CENPF, with CDC25A cell division molecule, with CCND1 and
CCNB2 cyclins and divers E2F transcription factors. A significant repression of
oxidative phosphorylation was found in basal condition under EEF2K shRNA (figure
2B, NES < -2.17, p-value < 0.001, supplementary figure 6C) with divers mitochondrial
metabolic actors associated to a significant down regulation of reactive oxygen species
(ROS) pathway (NES < -2.87, p-value < 0.001, supplementary figure 6C) especially
with peroxiredoxin molecule family comprising PRDX2 and PRDX1, catalase, SOD2
superoxide dismutase 2 and NQO1 NADPH dehydrogenase quinone 1. These results
showed that in UT7 basal condition EEF2K play a role in the promotion of cell cycle
and oxidative phosphorylation metabolism.
A down regulation of peroxisome was also found significant in p210 BCR-ABL context
(NES = -1.39, p-value < 0.001, supplementary figure 7C); interferon gamma mediated
pathway was also found repressed in this context (NES = -2.16, p-value < 0.001,
supplementary figure 7C) with repression of HLA class I molecules such as HLA-E,
HLA-A, HLA-C and interferon regulatory factors IRF1 and IRF2, SP100 nuclear
antigen, JAK2 signaling molecule.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 131
Discussion
Eukaryotic elongation factor 2 kinase (eEF2 kinase) is a major protein controlling the
adaptation of tumor cells to nutrient deprivation conditions (NDC) (Browne and Proud,
2002). The activation of this kinase leads to the phosphorylation of its only target eEF2,
which is the rate limiting enzyme of translation elongation (Johanns et al., 2017). In
eukaryotic cells, this mechanism allows avoiding of the accumulation of excessive
protein production which could lead to cell death (Browne and Proud, 2002).
Therefore, the activation of eEF2K in NDC is a mechanism of cytoprotection in normal
cells. On the other hand, eEF2K pathway activation has been shown to be a
mechanism of adaptation to hostile microenvironment conditions in cancer cells
(Leprivier et al., 2013a). Indeed, cancer cells undergo apoptosis in acute nutrient
deprivation conditions and the inactivation of eEF2K allows to restore resistance to cell
death.
The role of eEF2K has also been studied in colon cancer cells and its inactivation has
been shown to induce autophagy via activation of AMPK-ULK1 pathways (Xie et al.,
2014).
We report here for the first time to our knowledge, the implication of eEF2K in the
context of CML.
We first show that eEF2K expression is increased in UT7 cells expressing BCR-ABL
as well as in the leukemic cells in a large cohort of patient with CML as compared to
healthy controls. This expression leads to the phosphorylation of eEF2K which is the
only target of eEF2K, allowing the limitation of mRNA translation. The reasons for the
increased expression of eEF2K could be due to the increased activation of several
signaling pathways by BCR-ABL, including c--MYC, which has been shown to increase
levels of EEF2K in neuroblastoma cells allowing them to survive in starvation
conditions (Delaidelli et al., 2017). Our experiments showed a significantly higher
growth rate of UT7-11 sheEF2K cells as compared to scramble controls in the
conditions of serum or glucose deprivation, suggesting strongly that eEF2K impairs the
survival of leukemic cells. These findings are in contrast with the data reported in
neuroblastoma (Delaidelli et al., 2017) glioblastoma (Leprivier et al., 2013b) or lung
cancer (Moore et al., 2016) cell lines.
Our data in UT7-p210 suggest that, as opposed to what has been reported in other
cancer cell lines and tumors, regulation of EEF2K is altered in CML cells in which
eEF2K overexpression allows a functional role like that found in normal cells. Contrary
to what we expected, EEF2K knock-down by shRNA, allows leukemic cells to survive
and proliferate in nutrient deprivation conditions.
The significance of the major overexpression of eEF2K in CML cells is unclear at the
present time. It is known that eEF2K activates a large number of proteins involved in
the adhesion and migration of cancer cells (Xie et al., 2018). Further studies will be
required to determine the potential role of eEF2K in the adhesion and migration
abnormalities known to occur in CML (Chomel and Turhan, 2011).
In this work, we have also studied the transcriptomic network which might be involved
in BCR-ABL-expressing leukemic cells with and without eEF2K invalidation at the level
of RNA and protein.
We showed that in specific context of p210 BCR-ABL, UT7 could repress E2F target
genes such as transcription factor ETS1 that was found previously implicated in
pathophysiology of chronic myelogenous leukemia (Desterke et al., 2018).
EEF2K plays an important repressive function in basal condition of UT7 by acting in
repression of invasion processes like granulocyte migration and promotion of vascular
development through liberation of extracellular angiogenic ligands such as angiopoietin
2.
In basal condition of UT7, EEF2K specifically induced cell cycle and mitochondrial
response to the oxidative phosphorylation.
These studies clearly indicate the positive correlation between inactivation of eEF2K
and oxidative phosphorylation and genes involved in ROS as well as genes involved
in peroxisome and IFN-gamma pathways.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 133
Overall, we report for the first time to our knowledge, that eEF2K, a target of ETS1
gene overexpressed in CML, is involved in the pathophysiology of CML. This unique
control mechanism of translation elongation appears to not be regulated in CML cells
in response to NDC and might be involved in the myeloproliferative phenotype of CML
in an uncontrolled manner. The inactivation experiments clearly showed that in the
context of CML, the reduced expression of eEF2K allows leukemic cells to grow in
extreme NDC. Further studies are required to determine the role of eEF2K in self-
renewal and quiescence of primary leukemic cells in patients with CML.
References
Bayraktar, R., Ivan, C., Bayraktar, E., Kanlikilicer, P., Kabil, N.N., Kahraman, N.,
Mokhlis, H.A., Karakas, D., Rodriguez-Aguayo, C., Arslan, A., Sheng, J., Wong, S.,
Lopez-Berestein, G., Calin, G.A., Ozpolat, B., 2018. Dual Suppressive Effect of miR-
34a on the FOXM1/eEF2-Kinase Axis Regulates Triple-Negative Breast Cancer
Growth and Invasion. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 24, 4225–
4241. https://doi.org/10.1158/1078-0432.CCR-17-1959
Browne, G.J., Finn, S.G., Proud, C.G., 2004. Stimulation of the AMP-activated Protein
Kinase Leads to Activation of Eukaryotic Elongation Factor 2 Kinase and to Its
Phosphorylation at a Novel Site, Serine 398. J. Biol. Chem. 279, 12220–12231.
https://doi.org/10.1074/jbc.M309773200
Browne, G.J., Proud, C.G., 2004. A Novel mTOR-Regulated Phosphorylation Site in
Elongation Factor 2 Kinase Modulates the Activity of the Kinase and Its Binding to
Calmodulin. Mol. Cell. Biol. 24, 2986–2997. https://doi.org/10.1128/MCB.24.7.2986-
2997.2004
Browne, G.J., Proud, C.G., 2002. Regulation of peptide-chain elongation in mammalian
cells. Eur. J. Biochem. 269, 5360–5368. https://doi.org/10.1046/j.1432-
1033.2002.03290.x
Buttgereit, F., Brand, M.D., 1995. A hierarchy of ATP-consuming processes in
mammalian cells. Biochem. J. 312 ( Pt 1), 163–167. https://doi.org/10.1042/bj3120163
Chomel, J.-C., Turhan, A.G., 2011. Chronic myeloid leukemia stem cells in the era of
targeted therapies: resistance, persistence and long-term dormancy. Oncotarget 2,
713–727. https://doi.org/10.18632/oncotarget.333
Culhane, A.C., Thioulouse, J., Perrière, G., Higgins, D.G., 2005. MADE4: an R
package for multivariate analysis of gene expression data. Bioinforma. Oxf. Engl. 21,
2789–2790. https://doi.org/10.1093/bioinformatics/bti394
Delaidelli, A., Leprivier, G., Sorensen, P.H., 2017. eEF2K protects MYCN-amplified
cells from starvation. Cell Cycle Georget. Tex 16, 1633–1634.
https://doi.org/10.1080/15384101.2017.1355180
Desterke, C., Voldoire, M., Bonnet, M.-L., Sorel, N., Pagliaro, S., Rahban, H.,
Bennaceur-Griscelli, A., Cayssials, E., Chomel, J.-C., Turhan, A.G., 2018.
Experimental and integrative analyses identify an ETS1 network downstream of BCR-
ABL in chronic myeloid leukemia (CML). Exp. Hematol. 64, 71-83.e8.
https://doi.org/10.1016/j.exphem.2018.04.007
Dever, T.E., Green, R., 2012. The elongation, termination, and recycling phases of
translation in eukaryotes. Cold Spring Harb. Perspect. Biol. 4, a013706.
https://doi.org/10.1101/cshperspect.a013706
Guan, Y., Jiang, S.-L., Yu, P., Wen, M., Zhang, Y., Xiao, S.-S., Xu, X.-J., Cheng, Y.,
2018. Suppression of eEF-2K-mediated autophagy enhances the cytotoxicity of
raddeanin A against human breast cancer cells in vitro. Acta Pharmacol. Sin. 39, 642–
648. https://doi.org/10.1038/aps.2017.139
Irizarry, R.A., Bolstad, B.M., Collin, F., Cope, L.M., Hobbs, B., Speed, T.P., 2003.
Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15.
Issaad, C., Ahmed, M., Novault, S., Bonnet, M.L., Bennardo, T., Varet, B.,
Vainchenker, W., Turhan, A.G., 2000. Biological effects induced by variable levels of
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 135
BCR-ABL protein in the pluripotent hematopoietic cell line UT-7. Leukemia 14, 662.
https://doi.org/10.1038/sj.leu.2401730
Johanns, M., Pyr Dit Ruys, S., Houddane, A., Vertommen, D., Herinckx, G., Hue, L.,
Proud, C.G., Rider, M.H., 2017. Direct and indirect activation of eukaryotic elongation
factor 2 kinase by AMP-activated protein kinase. Cell. Signal. 36, 212–221.
https://doi.org/10.1016/j.cellsig.2017.05.010
Kabil, N., Bayraktar, R., Kahraman, N., Mokhlis, H.A., Calin, G.A., Lopez-Berestein, G.,
Ozpolat, B., 2018. Thymoquinone inhibits cell proliferation, migration, and invasion by
regulating the elongation factor 2 kinase (eEF-2K) signaling axis in triple-negative
breast cancer. Breast Cancer Res. Treat. 171, 593–605.
https://doi.org/10.1007/s10549-018-4847-2
Kaul, G., Pattan, G., Rafeequi, T., 2011. Eukaryotic elongation factor-2 (eEF2): its
regulation and peptide chain elongation. Cell Biochem. Funct. 29, 227–234.
https://doi.org/10.1002/cbf.1740
Komatsu, N., Nakauchi, H., Miwa, A., Ishihara, T., Eguchi, M., Moroi, M., Okada, M.,
Sato, Y., Wada, H., Yawata, Y., 1991. Establishment and characterization of a human
leukemic cell line with megakaryocytic features: dependency on granulocyte-
macrophage colony-stimulating factor, interleukin 3, or erythropoietin for growth and
survival. Cancer Res. 51, 341–348.
Leprivier, G., Remke, M., Rotblat, B., Dubuc, A., Mateo, A.-R.F., Kool, M., Agnihotri,
S., El-Naggar, A., Yu, B., Prakash Somasekharan, S., Faubert, B., Bridon, G., Tognon,
C.E., Mathers, J., Thomas, R., Li, A., Barokas, A., Kwok, B., Bowden, M., Smith, S.,
Wu, X., Korshunov, A., Hielscher, T., Northcott, P.A., Galpin, J.D., Ahern, C.A., Wang,
Y., McCabe, M.G., Collins, V.P., Jones, R.G., Pollak, M., Delattre, O., Gleave, M.E.,
Jan, E., Pfister, S.M., Proud, C.G., Derry, W.B., Taylor, M.D., Sorensen, P.H., 2013a.
The eEF2 Kinase Confers Resistance to Nutrient Deprivation by Blocking Translation
Elongation. Cell 153, 1064–1079. https://doi.org/10.1016/j.cell.2013.04.055
Leprivier, G., Remke, M., Rotblat, B., Dubuc, A., Mateo, A.-R.F., Kool, M., Agnihotri,
S., El-Naggar, A., Yu, B., Somasekharan, S.P., Faubert, B., Bridon, G., Tognon, C.E.,
Mathers, J., Thomas, R., Li, A., Barokas, A., Kwok, B., Bowden, M., Smith, S., Wu, X.,
Korshunov, A., Hielscher, T., Northcott, P.A., Galpin, J.D., Ahern, C.A., Wang, Y.,
McCabe, M.G., Collins, V.P., Jones, R.G., Pollak, M., Delattre, O., Gleave, M.E., Jan,
E., Pfister, S.M., Proud, C.G., Derry, W.B., Taylor, M.D., Sorensen, P.H., 2013b. The
eEF2 kinase confers resistance to nutrient deprivation by blocking translation
elongation. Cell 153, 1064–1079. https://doi.org/10.1016/j.cell.2013.04.055
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J.P., Tamayo, P.,
2015. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell
Syst. 1, 417–425. https://doi.org/10.1016/j.cels.2015.12.004
Mauro, V.P., Matsuda, D., 2016. Translation regulation by ribosomes: Increased
complexity and expanded scope. RNA Biol. 13, 748–755.
https://doi.org/10.1080/15476286.2015.1107701
Moore, C.E.J., Wang, X., Xie, J., Pickford, J., Barron, J., Regufe da Mota, S., Versele,
M., Proud, C.G., 2016. Elongation factor 2 kinase promotes cell survival by inhibiting
protein synthesis without inducing autophagy. Cell. Signal. 28, 284–293.
https://doi.org/10.1016/j.cellsig.2016.01.005
Ng, T.H., Sham, K.W.Y., Xie, C.M., Ng, S.S.M., To, K.F., Tong, J.H.M., Liu, W.Y.Z.,
Zhang, L., Chan, M.T.V., Wu, W.K.K., Cheng, C.H.K., 2019. Eukaryotic elongation
factor-2 kinase expression is an independent prognostic factor in colorectal cancer.
BMC Cancer 19, 649. https://doi.org/10.1186/s12885-019-5873-0
Pagliaro, S., Desterke, C., Acloque, H., Chomel, J.C., de Souza, L., Hugues, P.,
Griscelli, F., Foudi, A., Bennaceur-Griscelli, A., Turhan, A.G., 2020. Single-Cell
Transcriptome in Chronic Myeloid Leukemia: Pseudotime Analysis Reveals Evidence
of Embryonic and Transitional Stem Cell States. Exp. Hematol.
https://doi.org/10.1016/j.exphem.2020.04.005
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 137
Pan, Z., Chen, Y., Liu, J., Jiang, Q., Yang, S., Guo, L., He, G., 2018. Design, synthesis,
and biological evaluation of polo-like kinase 1/eukaryotic elongation factor 2 kinase
(PLK1/EEF2K) dual inhibitors for regulating breast cancer cells apoptosis and
autophagy. Eur. J. Med. Chem. 144, 517–528.
https://doi.org/10.1016/j.ejmech.2017.12.046
Pavur, K.S., Petrov, A.N., Ryazanov, A.G., 2000. Mapping the Functional Domains of
Elongation Factor-2 Kinase. Biochemistry 39, 12216–12224.
https://doi.org/10.1021/bi0007270
Ryazanov, A.G., Davydova, E.K., 1989. Mechanism of elongation factor 2 (EF-2)
inactivation upon phosphorylation. Phosphorylated EF-2 is unable to catalyze
translocation. FEBS Lett. 251, 187–190. https://doi.org/10.1016/0014-5793(89)81452-
8
Ryazanov, A.G., Natapov, P.G., Shestakova, E.A., Severin, F.F., Spirin, A.S., 1988a.
Phosphorylation of the elongation factor 2: the fifth Ca2+/calmodulin-dependent
system of protein phosphorylation. Biochimie 70, 619–626.
Ryazanov, A.G., Shestakova, E.A., Natapov, P.G., 1988b. Phosphorylation of
elongation factor 2 by EF-2 kinase affects rate of translation. Nature 334, 170–173.
https://doi.org/10.1038/334170a0
Shi, N., Chen, X., Liu, R., Wang, D., Su, M., Wang, Q., He, A., Gu, H., 2018. Eukaryotic
elongation factors 2 promotes tumor cell proliferation and correlates with poor
prognosis in ovarian cancer. Tissue Cell 53, 53–60.
https://doi.org/10.1016/j.tice.2018.05.014
Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A.,
Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.P., 2005. Gene set
enrichment analysis: a knowledge-based approach for interpreting genome-wide
expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545–15550.
https://doi.org/10.1073/pnas.0506580102
Tusher, V.G., Tibshirani, R., Chu, G., 2001. Significance analysis of microarrays
applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 98, 5116–
5121. https://doi.org/10.1073/pnas.091062498
Wang, X., Xie, J., Proud, C., 2017. Eukaryotic Elongation Factor 2 Kinase (eEF2K) in
Cancer. Cancers 9, 162. https://doi.org/10.3390/cancers9120162
Xie, C.-M., Liu, X.-Y., Sham, K.W.Y., Lai, J.M.Y., Cheng, C.H.K., 2014. Silencing of
EEF2K (eukaryotic elongation factor-2 kinase) reveals AMPK-ULK1-dependent
autophagy in colon cancer cells. Autophagy 10, 1495–1508.
https://doi.org/10.4161/auto.29164
Xie, J., Shen, K., Lenchine, R.V., Gethings, L.A., Trim, P.J., Snel, M.F., Zhou, Y.,
Kenney, J.W., Kamei, M., Kochetkova, M., Wang, X., Proud, C.G., 2018. Eukaryotic
elongation factor 2 kinase upregulates the expression of proteins implicated in cell
migration and cancer cell metastasis. Int. J. Cancer 142, 1865–1877.
https://doi.org/10.1002/ijc.31210
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
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Supplementary figure 1: Proximal Promoter binding events for EEF2K gene with
ETS1 fixation in K562 by CHIP-sequencing: proximal events found in promoter (-3000
pb upstream Transcription starting site) of EE2K gene locus after analysis of ETS1
K562 CHIP-sequencing. Peaks were visualized with Integrative Genome Viewer on
HG19 human genome.
Supplementary Figure 2. Lentiviral vector pLKO-GFP-puro shEEF2K. shEEF2K-1
(shRNA TRCN0000234847) and shEEF2K-2 (shRNA TRCN0000234845) were
obtained from the Integrated DNA Technologies (IDT). Scramble (empty vector with a
1.2 kb stuffer element) were used as control. Virus were produced in human kidney
293T. Concentrated vector preparations were made by ultracentrifugation, filtered
(0.45 μm), divided into aliquots and stored. Virus titration was performed using NIH 3T3
cells. UT7 and UT7 p210 cells were transduced with viral supernatant (MOI = 2).
Supplementary Figure 3. EEF2K transduction efficiency. Selection of infected cells
was started 24 hours after infection with 2 μg/ml puromycin. The infection efficiency
infected cells were assessed by measuring the frequency of GFP+ cells by flow
cytometry 48 hours post infection. Typically, the frequency of GFP+ cells for primary
were >80%.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 141
Supplementary Figure 4. Sensitivity to Imatinib. EEF2K knock down didn’t affect the
sensitive to imatinib. Cells were cultured (105 cells/ml) under normal conditions (RPMI
1640, 10%FBS, 10% PenStrep) with or without imatinib (1 and 2µM) and counted at
day 2. No significant changes were observed.
Supplementary Figure 5: Effect of EEF2K shRNA on cross expression profiles
between UT7 basal and UT7-11 BCR-ABL: A/ expression heatmap of differential
expressed genes induced by EEK2K shRNA in UT7 basal (Pearson distances); B/
expression heatmap of differential expressed genes induced by EEK2K shRNA in UT7
11 (BCR-ABL+) (Pearson distances); C/Venn diagram quantifying effect of EEF2K
shRNA on cross expression profile between UT7 basal and UT7-11 BCR-ABL cell
lines.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 143
Supplementary Figure 6: UT7 basal expression profile regulated by EEF2K shRNA:
A/ Expression heatmap of genes found down regulated by EEF2K shRNA in UT7
basal cell line (Pearson distances), abbreviations for B and C: NES: normalized
enrichment score); B/ Gene set enrichment scores found significantly increased by
EEF2K shRNA in UT7 basal cell line; C/ Gene set enrichment scores found
significantly repressed by EEF2K shRNA in UT7 basal cell line
GENERAL DISCUSSION
CML is one of the major models of oncogenesis and the first major success story for
the use of targeted therapies. Indeed, the use of TKI therapies has now transformed
the natural history of the disease for which the median survival was 3-5 years until 20
years ago. The bone marrow transplantation has first been shown to be the first
curative therapy in early 70’s but is was applicable to a minority of patients. With the
use of TKI’s introduced since 1999-2000, the survival of patients responding to
treatment is now equivalent to that of patients with the same age and the same sex .373
The disease can now be followed by using molecular monitoring and deep molecular
responses can be obtained in 30-40% of the patients, allowing to design treatment-
free remission (TFR) strategies with discontinuation of TKI. However, TKI
discontinuation lead to rapid molecular recurrences in 50-60% of the patients,
suggesting the persistence of LSC in the bone marrow as this has been shown
previously by our team159. As discussed in the introduction section, the characteristics
of these cells remain elusive as there are very few differences between CML-LSC and
normal HSC in terms of cell surface markers. Besides, these cells seem to escape TKI
action either by their reduced BCR-ABL expression 159 or by their interaction with the
bone marrow leukemic niche which can trigger in the LSC self-renewal or quiescence
signaling pathways. Conversely, it has been shown that the bone marrow niche itself
could be abnormal in CML, as it has been shown that MSC from CML patients express
high levels of transforming genes such as podoplanin and the pluripotency gene
Nanog. These finds were noticed even in patients in deep molecular responses,
suggesting “memory” persistence in the components of LSC niche 374.
From this regard, one of the major insights into the CML LSC persistence has been the
demonstration of the fact that they are not addicted to the activity of BCR-ABL
oncogene.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 145
This finding suggested that it was possible to target CML stem cells by using other
druggable pathways, combined with TKI, in order to obtain a status of deep molecular
response, allowing to safely stop TKI. This concept is illustrated in the Figure 26.
Figure 26 – TKI Treatment alone Vs Combined treated
During the last 10 years, several signaling pathways which could be a druggable target
has been identified in CML (Figure 27)
Figure 27 - Potential pathways to target CML stem cells Figure from Houshmand et
al Leukemia 2019
The exploitation of these pathways led to the identification of drugs, some of which
(such as Zileuton) have been introduced into the clinical trials. However, this approach
needs to be carefully evaluated as the goal is to avoid toxicity in patients which remain
for several years on TKI therapy with a satisfactory tolerance.
Persistence and resistance of LSC in patients on TKI therapy clearly indicates that
these cells could be selected out during TKI administration or they are present at
diagnosis with variable frequency. This would then suggest a heterogeneity at
diagnosis, and we wished to analyze this phenomenon at the single cell level using
transcriptome analysis.
One important result of my work is the identification of the expression of embryonic
pluripotency genes in 9% of the CD34+ cells at diagnosis. This analysis was performed
in a small number of patients, but these findings were consistent and clearly
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 147
different from controls. The second important result was the identification of several
“stem cell states” using Pseudo time technology, suggesting that a CML stem cell could
undergo during self-renewal divisions, several transcriptional modifications with
successive expression of signaling pathways (Figure 27). These findings need to be
confirmed in larger series of patients, but they suggest that targeting a single pathway
will not be enough to eliminate LSC in some patients. Interestingly we have confirmed
the expression of several pluripotency genes in a group of CML patients and currently
we are analyzing the expression of this panel of genes in CML cells resistant to TKI.
In the second part of my work I have analyzed the role of EEF2K gene which we have
reported for the first time as a transcriptional target of ETS1 gene 318
One important finding of this work was the fact that EEF2K expression is highly
increased in the whole blood of patients with CML at diagnosis. In cell separation
experiments, this overexpression was found to be essentially in the more differentiated
CD34- fraction. Our data in UT7-p210 suggest that, as opposed to what has been
reported in other cancer cell lines and tumors, regulation of EEF2K is altered in CML
cells in which eEF2K overexpression allows a functional role like that found in normal
cells. Contrary to what we expected, EEF2K knock-down by shRNA, allows leukemic
cells to survive and proliferate in nutrient deprivation conditions.
Thus, this unique control mechanism of translation elongation appears to be not
regulated in CML cells in response to nutrient depletion conditions and might be
involved in the myeloproliferative phenotype of CML in an uncontrolled manner. Further
studies are required to determine the role of eEF2K in self-renewal and quiescence of
primary leukemic cells in patients with CML. These studies will include, the lentivirus-
mediated transfer of sh-eEF2K gene in normal and CML stem cells followed by
functional stem cell assays using LTC-IC and NGS-mouse repopulating assays. These
works are currently pursued in the Inserm Unit U935.
Overall, the work presented on this Thesis allowed to underline the important
heterogeneity of CML stem cells at the level of single cell transcriptome analysis and
at the level of the potential transcriptional targets of ETS1 genes such as EEF2K.
REFERENCES
1. Mughal TI, Goldman JM. Chronic myeloid leukemia: why does it evolve from
chronic phase to blast transformation? Front Biosci J Virtual Libr. 2006;11:198-208.
doi:10.2741/1791
2. Bennett JH, University of Glasgow. Library. Case of Hypertrophy of the Spleen
and Liver, Which Death Took Place from Suppuration of the Blood [Electronic
Resource]. [Edinburgh] : [Printed by Stark and Company]; 1845.
http://archive.org/details/b21470388. Accessed October 11, 2019.
3. Silver GA. Virchow, the heroic model in medicine: health policy by accolade. Am
J Public Health. 1987;77(1):82-88. doi:10.2105/ajph.77.1.82
4. Donne A. De l’origine des globules du sang, de leur mode de formation, de leur
fin. CR Acad Sci.
5. Geary CG. The story of chronic myeloid leukaemia. Br J Haematol.
2000;110(1):2-11.
6. Nowell PC. The minute chromosome in human chronic granulocytic leukemia.
Science. 1962;8:65-66. doi:10.1007/bf01630378
7. Rowley JD. Letter: A new consistent chromosomal abnormality in chronic
myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining.
Nature. 1973;243(5405):290-293.
8. De Klein A, Hagemeijer A, Bartram CR, et al. bcr rearrangement and
translocation of the c-abl oncogene in Philadelphia positive acute lymphoblastic
leukemia. Blood. 1986;68(6):1369-1375.
9. Goldman JM, Grosveld G, Baltimore D, Gale RP. Chronic myelogenous
leukemia--the unfolding saga. Leukemia. 1990;4(3):163-167.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 149
10. Groffen J, Stephenson JR, Heisterkamp N, de Klein A, Bartram CR, Grosveld G.
Philadelphia chromosomal breakpoints are clustered within a limited region, bcr, on
chromosome 22. Cell. 1984;36(1):93-99.
11. Canaani E, Gale RP, Steiner-Saltz D, Berrebi A, Aghai E, Januszewicz E. Altered
transcription of an oncogene in chronic myeloid leukaemia. Lancet Lond Engl.
1984;1(8377):593-595.
12. Shtivelman E, Lifshitz B, Gale RP, Canaani E. Fused transcript of abl and bcr
genes in chronic myelogenous leukaemia. Nature. 1985;315(6020):550-554.
13. Nowell PC, Hungerford DA. Chromosome studies in human leukemia. II. Chronic
granulocytic leukemia. J Natl Cancer Inst. 1961;27:1013-1035.
14. Rowley JD. Ph1-positive leukaemia, including chronic myelogenous leukaemia.
Clin Haematol. 1980;9(1):55-86.
15. Rozman C, Urbano-Ispizua A, Cervantes F, et al. Analysis of the clinical
relevance of the breakpoint location within M-BCR and the type of chimeric mRNA in
chronic myelogenous leukemia. Leukemia. 1995;9(6):1104-1107.
16. Harrison CJ. Philadelphia Chromosome. In: Brenner S, Miller JH, eds.
Encyclopedia of Genetics. New York: Academic Press; 2001:1449-1450.
doi:10.1006/rwgn.2001.0991
17. Verhest A, Monsieur R. Philadelphia chromosome-positive thrombocythemia
with leukemic transformation. N Engl J Med. 1983;308(26):1603.
doi:10.1056/NEJM198306303082620
18. Melo JV. The diversity of BCR-ABL fusion proteins and their relationship to
leukemia phenotype. Blood. 1996;88(7):2375-2384.
19. Zhao X, Ghaffari S, Lodish H, Malashkevich VN, Kim PS. Structure of the Bcr-
Abl oncoprotein oligomerization domain. Nat Struct Biol. 2002;9(2):117-120.
doi:10.1038/nsb747
20. Mughal TI, Goldman JM. Chronic myeloid leukemia: why does it evolve from
chronic phase to blast transformation? Front Biosci J Virtual Libr. 2006;11:198-208.
21. Li S, Ilaria RL, Million RP, Daley GQ, Van Etten RA. The P190, P210, and P230
Forms of the BCR/ABL Oncogene Induce a Similar Chronic Myeloid Leukemia–like
Syndrome in Mice but Have Different Lymphoid Leukemogenic Activity. J Exp Med.
1999;189(9):1399-1412. doi:10.1084/jem.189.9.1399
22. Van Etten RA. The molecular pathogenesis of the Philadelphia-positive
leukemias: implications for diagnosis and therapy. Cancer Treat Res. 1993;64:295-
325.
23. Chan LC, Karhi KK, Rayter SI, et al. A novel abl protein expressed in Philadelphia
chromosome positive acute lymphoblastic leukaemia. Nature. 1987;325(6105):635-
637. doi:10.1038/325635a0
24. Kurzrock R, Shtalrid M, Talpaz M, Kloetzer WS, Gutterman JU. Expression of c-
abl in Philadelphia-positive acute myelogenous leukemia. Blood. 1987;70(5):1584-
1588.
25. Pane F, Frigeri F, Sindona M, et al. Neutrophilic-chronic myeloid leukemia: a
distinct disease with a specific molecular marker (BCR/ABL with C3/A2 junction).
Blood. 1996;88(7):2410-2414.
26. Chen SJ, Flandrin G, Daniel MT, et al. Philadelphia-positive acute leukemia:
lineage promiscuity and inconsistently rearranged breakpoint cluster region. Leukemia.
1988;2(5):261-273.
27. Hirsch-Ginsberg C, Childs C, Chang KS, et al. Phenotypic and molecular
heterogeneity in Philadelphia chromosome-positive acute leukemia. Blood.
1988;71(1):186-195.
28. Schaefer-Rego K, Arlin Z, Shapiro LG, Mears JG, Leibowitz D. Molecular
heterogeneity of adult Philadelphia chromosome-positive acute lymphoblastic
leukemia. Cancer Res. 1988;48(4):866-869.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 151
29. Hantschel O. Structure, Regulation, Signaling, and Targeting of Abl Kinases in
Cancer. Genes Cancer. 2012;3(5-6):436-446. doi:10.1177/1947601912458584
30. Daley GQ, Van Etten RA, Baltimore D. Induction of chronic myelogenous
leukemia in mice by the P210bcr/abl gene of the Philadelphia chromosome. Science.
1990;247(4944):824-830. doi:10.1126/science.2406902
31. Kelliher MA, McLaughlin J, Witte ON, Rosenberg N. Induction of a chronic
myelogenous leukemia-like syndrome in mice with v-abl and BCR/ABL. Proc Natl Acad
Sci U S A. 1990;87(17):6649-6653. doi:10.1073/pnas.87.17.6649
32. Bedi A, Zehnbauer BA, Barber JP, Sharkis SJ, Jones RJ. Inhibition of apoptosis
by BCR-ABL in chronic myeloid leukemia. Blood. 1994;83(8):2038-2044.
33. Hariharan IK, Adams JM, Cory S. bcr-abl oncogene renders myeloid cell line
factor independent: potential autocrine mechanism in chronic myeloid leukemia.
Oncogene Res. 1988;3(4):387-399.
34. Wetzler M, Talpaz M, Etten RAV, Hirsh-Ginsberg C, Beran M, Kurzrock R.
Subcellular localization of Bcr, Abl, and Bcr-Abl proteins in normal and leukemic cells
and correlation of expression with myeloid differentiation. J Clin Invest.
1993;92(4):1925-1939. doi:10.1172/JCI116786
35. Taagepera S, McDonald D, Loeb JE, et al. Nuclear-cytoplasmic shuttling of C-
ABL tyrosine kinase. Proc Natl Acad Sci U S A. 1998;95(13):7457-7462.
doi:10.1073/pnas.95.13.7457
36. Chai SK, Nichols GL, Rothman P. Constitutive activation of JAKs and STATs in
BCR-Abl-expressing cell lines and peripheral blood cells derived from leukemic
patients. J Immunol Baltim Md 1950. 1997;159(10):4720-4728.
37. Samanta A, Perazzona B, Chakraborty S, et al. Janus kinase 2 regulates Bcr-
Abl signaling in chronic myeloid leukemia. Leukemia. 2011;25(3):463-472.
doi:10.1038/leu.2010.287
38. Spiekermann K, Pau M, Schwab R, Schmieja K, Franzrahe S, Hiddemann W.
Constitutive activation of STAT3 and STAT5 is induced by leukemic fusion proteins
with protein tyrosine kinase activity and is sufficient for transformation of hematopoietic
precursor cells. Exp Hematol. 2002;30(3):262-271. doi:10.1016/s0301-
472x(01)00787-1
39. de Groot RP, Raaijmakers JA, Lammers JW, Koenderman L. STAT5-Dependent
CyclinD1 and Bcl-xL expression in Bcr-Abl-transformed cells. Mol Cell Biol Res
Commun MCBRC. 2000;3(5):299-305. doi:10.1006/mcbr.2000.0231
40. Hoelbl A, Schuster C, Kovacic B, et al. Stat5 is indispensable for the maintenance
of bcr/abl-positive leukaemia. EMBO Mol Med. 2010;2(3):98-110.
doi:10.1002/emmm.201000062
41. Walz C, Ahmed W, Lazarides K, et al. Essential role for Stat5a/b in
myeloproliferative neoplasms induced by BCR-ABL1 and JAK2(V617F) in mice. Blood.
2012;119(15):3550-3560. doi:10.1182/blood-2011-12-397554
42. Hantschel O, Warsch W, Eckelhart E, et al. BCR-ABL uncouples canonical JAK2-
STAT5 signaling in chronic myeloid leukemia. Nat Chem Biol. 2012;8(3):285-
293. doi:10.1038/nchembio.775
43. Zhao JJ, Cheng H, Jia S, et al. The p110alpha isoform of PI3K is essential for
proper growth factor signaling and oncogenic transformation. Proc Natl Acad Sci U S
A. 2006;103(44):16296-16300. doi:10.1073/pnas.0607899103
44. Sheng Z, Ma L, Sun JE, Zhu LJ, Green MR. BCR-ABL suppresses autophagy
through ATF5-mediated regulation of mTOR transcription. Blood. 2011;118(10):2840-
2848. doi:10.1182/blood-2010-12-322537
45. ten Hoeve J, Arlinghaus RB, Guo JQ, Heisterkamp N, Groffen J. Tyrosine
phosphorylation of CRKL in Philadelphia+ leukemia. Blood. 1994;84(6):1731-1736.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 153
46. Birge RB, Kalodimos C, Inagaki F, Tanaka S. Crk and CrkL adaptor proteins:
networks for physiological and pathological signaling. Cell Commun Signal CCS.
2009;7:13. doi:10.1186/1478-811X-7-13
47. Seo J-H, Wood LJ, Agarwal A, et al. A specific need for CRKL in p210BCR-ABL-
induced transformation of mouse hematopoietic progenitors. Cancer Res.
2010;70(18):7325-7335. doi:10.1158/0008-5472.CAN-10-0607
48. Steelman LS, Franklin RA, Abrams SL, et al. Roles of the Ras/Raf/MEK/ERK
pathway in leukemia therapy. Leukemia. 2011;25(7):1080-1094.
doi:10.1038/leu.2011.66
49. Puil L, Liu J, Gish G, et al. Bcr-Abl oncoproteins bind directly to activators of the
Ras signalling pathway. EMBO J. 1994;13(4):764-773.
50. Baum KJ, Ren R. Effect of Ras inhibition in hematopoiesis and BCR/ABL
leukemogenesis. J Hematol OncolJ Hematol Oncol. 2008;1:5. doi:10.1186/1756-8722-
1-5
51. Kim LC, Song L, Haura EB. Src kinases as therapeutic targets for cancer. Nat
Rev Clin Oncol. 2009;6(10):587-595. doi:10.1038/nrclinonc.2009.129
52. Danhauser-Riedl S, Warmuth M, Druker BJ, Emmerich B, Hallek M. Activation of
Src kinases p53/56lyn and p59hck by p210bcr/abl in myeloid cells. Cancer Res.
1996;56(15):3589-3596.
53. Wilson MB, Schreiner SJ, Choi H-J, Kamens J, Smithgall TE. Selective pyrrolo-
pyrimidine inhibitors reveal a necessary role for Src family kinases in Bcr-Abl signal
transduction and oncogenesis. Oncogene. 2002;21(53):8075-8088.
doi:10.1038/sj.onc.1206008
54. Klejman A, Schreiner SJ, Nieborowska-Skorska M, et al. The Src family kinase
Hck couples BCR/ABL to STAT5 activation in myeloid leukemia cells. EMBO J.
2002;21(21):5766-5774. doi:10.1093/emboj/cdf562
55. Warmuth M, Simon N, Mitina O, et al. Dual-specific Src and Abl kinase inhibitors,
PP1 and CGP76030, inhibit growth and survival of cells expressing imatinib mesylate-
resistant Bcr-Abl kinases. Blood. 2003;101(2):664-672. doi:10.1182/blood-2002-01-
0288
56. Hu Y, Liu Y, Pelletier S, et al. Requirement of Src kinases Lyn, Hck and Fgr for
BCR-ABL1-induced B-lymphoblastic leukemia but not chronic myeloid leukemia. Nat
Genet. 2004;36(5):453-461. doi:10.1038/ng1343
57. Neshat MS, Raitano AB, Wang HG, Reed JC, Sawyers CL. The survival function
of the Bcr-Abl oncogene is mediated by Bad-dependent and -independent pathways:
roles for phosphatidylinositol 3-kinase and Raf. Mol Cell Biol. 2000;20(4):1179-1186.
doi:10.1128/mcb.20.4.1179-1186.2000
58. Mughal TI, Radich JP, Deininger MW, et al. Chronic myeloid leukemia:
reminiscences and dreams. Haematologica. 2016;101(5):541-558.
doi:10.3324/haematol.2015.139337
59. Orkin SH, Zon LI. Hematopoiesis: an evolving paradigm for stem cell biology.
Cell. 2008;132(4):631-644. doi:10.1016/j.cell.2008.01.025
60. Eaves CJ. Hematopoietic stem cells: concepts, definitions, and the new reality.
Blood. 2015;125(17):2605-2613. doi:10.1182/blood-2014-12-570200
61. Copelan EA. Hematopoietic stem-cell transplantation. N Engl J Med.
2006;354(17):1813-1826. doi:10.1056/NEJMra052638
62. Akashi K, Traver D, Miyamoto T, Weissman IL. A clonogenic common myeloid
progenitor that gives rise to all myeloid lineages. Nature. 2000;404(6774):193-197.
doi:10.1038/35004599
63. Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer
stem cells. Nature. 2001;414(6859):105-111. doi:10.1038/35102167
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 155
64. Upadhaya S, Sawai CM, Papalexi E, et al. Kinetics of adult hematopoietic stem
cell differentiation in vivo. J Exp Med. 2018;215(11):2815-2832.
doi:10.1084/jem.20180136
65. Sun J, Ramos A, Chapman B, et al. Clonal dynamics of native haematopoiesis.
Nature. 2014;514(7522):322-327. doi:10.1038/nature13824
66. Busch K, Klapproth K, Barile M, et al. Fundamental properties of unperturbed
haematopoiesis from stem cells in vivo. Nature. 2015;518(7540):542-546.
doi:10.1038/nature14242
67. Bhatia R, Holtz M, Niu N, et al. Persistence of malignant hematopoietic
progenitors in chronic myelogenous leukemia patients in complete cytogenetic
remission following imatinib mesylate treatment. Blood. 2003;101(12):4701-4707.
doi:10.1182/blood-2002-09-2780
68. Chapple RH, Tseng Y-J, Hu T, et al. Lineage tracing of murine adult
hematopoietic stem cells reveals active contribution to steady-state hematopoiesis.
Blood Adv. 2018;2(11):1220-1228. doi:10.1182/bloodadvances.2018016295
69. Biasco L, Pellin D, Scala S, et al. In Vivo Tracking of Human Hematopoiesis
Reveals Patterns of Clonal Dynamics during Early and Steady-State Reconstitution
Phases. Cell Stem Cell. 2016;19(1):107-119. doi:10.1016/j.stem.2016.04.016
70. Notta F, Zandi S, Takayama N, et al. Distinct routes of lineage development
reshape the human blood hierarchy across ontogeny. Science.
2016;351(6269):aab2116. doi:10.1126/science.aab2116
71. Perié L, Duffy KR, Kok L, de Boer RJ, Schumacher TN. The Branching Point in
Erythro-Myeloid Differentiation. Cell. 2015;163(7):1655-1662.
doi:10.1016/j.cell.2015.11.059
72. Rodriguez-Fraticelli AE, Wolock SL, Weinreb CS, et al. Clonal analysis of lineage
fate in native haematopoiesis. Nature. 2018;553(7687):212-216.
doi:10.1038/nature25168
73. Carrelha J, Meng Y, Kettyle LM, et al. Hierarchically related lineage-restricted
fates of multipotent haematopoietic stem cells. Nature. 2018;554(7690):106-111.
doi:10.1038/nature25455
74. Lee Y, Decker M, Lee H, Ding L. Extrinsic regulation of hematopoietic stem cells
in development, homeostasis and diseases. Wiley Interdiscip Rev Dev Biol. 2017;6(5).
doi:10.1002/wdev.279
75. Naik SH, Perié L, Swart E, et al. Diverse and heritable lineage imprinting of early
haematopoietic progenitors. Nature. 2013;496(7444):229-232.
doi:10.1038/nature12013
76. Till JE, McCulloch EA. A Direct Measurement of the Radiation Sensitivity of
Normal Mouse Bone Marrow Cells. :1.
77. Magli MC, Iscove NN, Odartchenko N. Transient nature of early haematopoietic
spleen colonies. Nature. 1982;295(5849):527-529. doi:10.1038/295527a0
78. Siminovitch L, Till JE, McCulloch EA. Decline in colony-forming ability of marrow
cells subjected to serial transplantation into irradiated mice. J Cell Comp Physiol.
1964;64(1):23-31. doi:10.1002/jcp.1030640104
79. Dick J, Magli M, Huszar D, Phillips R, Bernstein A. Introduction of a selectable
gene into primitive stem cells capable of long-term reconstitution of the hemopoietic
system of W/Wv mice. Cell. 1985;42(1):71-79. doi:10.1016/S0092-8674(85)80102-1
80. van de Rijn M, Heimfeld S, Spangrude GJ, Weissman IL. Mouse hematopoietic
stem-cell antigen Sca-1 is a member of the Ly-6 antigen family. Proc Natl Acad Sci.
1989;86(12):4634-4638. doi:10.1073/pnas.86.12.4634
81. Yilmaz ÖH, Kiel MJ, Morrison SJ. SLAM family markers are conserved among
hematopoietic stem cells from old and reconstituted mice and markedly increase their
purity. Blood. 2006;107(3):924-930. doi:10.1182/blood-2005-05-2140
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 157
82. Sidney LE, Branch MJ, Dunphy SE, Dua HS, Hopkinson A. Concise Review:
Evidence for CD34 as a Common Marker for Diverse Progenitors: CD34 as a Common
Marker for Diverse Progenitors. STEM CELLS. 2014;32(6):1380-1389.
doi:10.1002/stem.1661
83. Civin CI, Trischmann T, Kadan NS, et al. Highly purified CD34-positive cells
reconstitute hematopoiesis. J Clin Oncol. 1996;14(8):2224-2233.
84. Larochelle A, Vormoor J, Hanenberg H, et al. Identification of primitive human
hematopoietic cells capable of repopulating NOD/SCID mouse bone marrow:
Implications for gene therapy. Nat Med. 1996;2(12):1329-1337. doi:10.1038/nm1296-
1329
85. Vogel W, Scheding S, Kanz L, Brugger W. Clinical applications of CD34(+)
peripheral blood progenitor cells (PBPC). Stem Cells Dayt Ohio. 2000;18(2):87-92.
doi:10.1634/stemcells.18-2-87
86. Civin CI, Strauss LC, Brovall C, Fackler MJ, Schwartz JF, Shaper JH. Antigenic
analysis of hematopoiesis. III. A hematopoietic progenitor cell surface antigen defined
by a monoclonal antibody raised against KG-1a cells. J Immunol. 1984;133(1):157-
165.
87. Ciraci E, Bella SD, Salvucci O, et al. Adult human circulating
CD34−Lin−CD45−CD133− cells can differentiate into hematopoietic and endothelial
cells. Blood. 2011;118(8):2105-2115. doi:10.1182/blood-2010-10-316596
88. Cheung AMS, Leung D, Rostamirad S, et al. Distinct but phenotypically
heterogeneous human cell populations produce rapid recovery of platelets and
neutrophils after transplantation. Blood. 2012;119(15):3431-3439. doi:10.1182/blood-
2011-12-398024
89. Glimm H, Eisterer W, Lee K, et al. Previously undetected human hematopoietic
cell populations with short-term repopulating activity selectively engraft NOD/SCID-
beta2 microglobulin-null mice. J Clin Invest. 2001;107(2):199-206.
doi:10.1172/JCI11519
90. Craig W, Kay R, Cutler RL, Lansdorp PM. Expression of Thy-1 on human
hematopoietic progenitor cells. J Exp Med. 1993;177(5):1331-1342.
doi:10.1084/jem.177.5.1331
91. Terstappen LW, Huang S, Safford M, Lansdorp PM, Loken MR. Sequential
generations of hematopoietic colonies derived from single nonlineage-committed
CD34+CD38- progenitor cells. Blood. 1991;77(6):1218-1227.
92. Dexter TM, Allen TD, Lajtha LG. Conditions controlling the proliferation of
haemopoietic stem cells in vitro. J Cell Physiol. 1977;91(3):335-344.
doi:10.1002/jcp.1040910303
93. Hao QL, Shah AJ, Thiemann FT, Smogorzewska EM, Crooks GM. A functional
comparison of CD34 + CD38- cells in cord blood and bone marrow. Blood.
1995;86(10):3745-3753.
94. Sutherland HJ, Eaves CJ, Eaves AC, Dragowska W, Lansdorp PM.
Characterization and partial purification of human marrow cells capable of initiating
long-term hematopoiesis in vitro. Blood. 1989;74(5):1563-1570.
95. Ploemacher RE, van der Sluijs JP, Voerman JS, Brons NH. An in vitro limiting-
dilution assay of long-term repopulating hematopoietic stem cells in the mouse. Blood.
1989;74(8):2755-2763.
96. Shultz LD, Schweitzer PA, Christianson SW, et al. Multiple defects in innate and
adaptive immunologic function in NOD/LtSz-scid mice. J Immunol Baltim Md 1950.
1995;154(1):180-191.
97. Ploemacher RE, van der Sluijs JP, van Beurden CA, Baert MR, Chan PL. Use of
limiting-dilution type long-term marrow cultures in frequency analysis of marrow-
repopulating and spleen colony-forming hematopoietic stem cells in the mouse. Blood.
1991;78(10):2527-2533.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 159
98. Rieger MA, Schroeder T. Hematopoiesis. Cold Spring Harb Perspect Biol.
2012;4(12). doi:10.1101/cshperspect.a008250
99. Bradley TR, Metcalf D. The growth of mouse bone marrow cells in vitro. Aust J
Exp Biol Med Sci. 1966;44(3):287-299. doi:10.1038/icb.1966.28
100. Yang H, Acker JP, Cabuhat M, Letcher B, Larratt L, McGann LE. Association of
post-thaw viable CD34+ cells and CFU-GM with time to hematopoietic engraftment.
Bone Marrow Transplant. 2005;35(9):881-887. doi:10.1038/sj.bmt.1704926
101. Schofield R. The relationship between the spleen colony-forming cell and the
haemopoietic stem cell. Blood Cells. 1978;4(1-2):7-25.
102. Jones DL, Wagers AJ. No place like home: anatomy and function of the stem
cell niche. Nat Rev Mol Cell Biol. 2008;9(1):11-21. doi:10.1038/nrm2319
103. Kopp H-G, Avecilla ST, Hooper AT, Rafii S. The bone marrow vascular niche:
home of HSC differentiation and mobilization. Physiol Bethesda Md. 2005;20:349-356.
doi:10.1152/physiol.00025.2005
104. Calvi LM, Adams GB, Weibrecht KW, et al. Osteoblastic cells regulate the
haematopoietic stem cell niche. Nature. 2003;425(6960):841-846.
doi:10.1038/nature02040
105. Calvi LM, Link DC. The hematopoietic stem cell niche in homeostasis and
disease. Blood. 2015;126(22):2443-2451. doi:10.1182/blood-2015-07-533588
106. Visnjic D, Kalajzic Z, Rowe DW, Katavic V, Lorenzo J, Aguila HL. Hematopoiesis
is severely altered in mice with an induced osteoblast deficiency. Blood.
2004;103(9):3258-3264. doi:10.1182/blood-2003-11-4011
107. Huber TL, Kouskoff V, Fehling HJ, Palis J, Keller G. Haemangioblast
commitment is initiated in the primitive streak of the mouse embryo. Nature.
2004;432(7017):625-630. doi:10.1038/nature03122
108. Rafii S, Shapiro F, Pettengell R, et al. Human bone marrow microvascular
endothelial cells support long-term proliferation and differentiation of myeloid and
megakaryocytic progenitors. Blood. 1995;86(9):3353-3363.
109. Hess DA, Meyerrose TE, Wirthlin L, et al. Functional characterization of highly
purified human hematopoietic repopulating cells isolated according to aldehyde
dehydrogenase activity. Blood. 2004;104(6):1648-1655. doi:10.1182/blood-2004-02-
0448
110. Avecilla ST, Hattori K, Heissig B, et al. Chemokine-mediated interaction of
hematopoietic progenitors with the bone marrow vascular niche is required for
thrombopoiesis. Nat Med. 2004;10(1):64-71. doi:10.1038/nm973
111. Rafii S, Mohle R, Shapiro F, Frey BM, Moore MA. Regulation of hematopoiesis
by microvascular endothelium. Leuk Lymphoma. 1997;27(5-6):375-386.
doi:10.3109/10428199709058305
112. Heissig B, Hattori K, Dias S, et al. Recruitment of stem and progenitor cells from
the bone marrow niche requires MMP-9 mediated release of kit-ligand. Cell.
2002;109(5):625-637. doi:10.1016/s0092-8674(02)00754-7
113. Kiel MJ, Morrison SJ. Uncertainty in the niches that maintain haematopoietic
stem cells. Nat Rev Immunol. 2008;8(4):290-301. doi:10.1038/nri2279
114. Kiel MJ, Yilmaz ÖH, Iwashita T, Yilmaz OH, Terhorst C, Morrison SJ. SLAM
Family Receptors Distinguish Hematopoietic Stem and Progenitor Cells and Reveal
Endothelial Niches for Stem Cells. Cell. 2005;121(7):1109-1121.
doi:10.1016/j.cell.2005.05.026
115. Sugiyama T, Kohara H, Noda M, Nagasawa T. Maintenance of the hematopoietic
stem cell pool by CXCL12-CXCR4 chemokine signaling in bone marrow stromal cell
niches. Immunity. 2006;25(6):977-988. doi:10.1016/j.immuni.2006.10.016
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 161
116. Arai F, Hirao A, Ohmura M, et al. Tie2/angiopoietin-1 signaling regulates
hematopoietic stem cell quiescence in the bone marrow niche. Cell. 2004;118(2):149-
161. doi:10.1016/j.cell.2004.07.004
117. Hosokawa K, Arai F, Yoshihara H, et al. Knockdown of N-cadherin suppresses
the long-term engraftment of hematopoietic stem cells. Blood. 2010;116(4):554-563.
doi:10.1182/blood-2009-05-224857
118. Nilsson SK, Johnston HM, Whitty GA, et al. Osteopontin, a key component of the
hematopoietic stem cell niche and regulator of primitive hematopoietic progenitor cells.
Blood. 2005;106(4):1232-1239. doi:10.1182/blood-2004-11-4422
119. Lapidot T. Mechanism of human stem cell migration and repopulation of
NOD/SCID and B2mnull NOD/SCID mice. The role of SDF-1/CXCR4 interactions. Ann
N Y Acad Sci. 2001;938:83-95. doi:10.1111/j.1749-6632.2001.tb03577.x
120. Yin T, Li L. The stem cell niches in bone. J Clin Invest. 2006;116(5):1195-1201.
doi:10.1172/JCI28568
121. Kaushansky K. Lineage-specific hematopoietic growth factors. N Engl J Med.
2006;354(19):2034-2045. doi:10.1056/NEJMra052706
122. Schäfer R, DeBaun MR, Fleck E, et al. Quantitation of progenitor cell populations
and growth factors after bone marrow aspirate concentration. J Transl Med.
2019;17(1):115. doi:10.1186/s12967-019-1866-7
123. Kaushansky K. Lineage-specific hematopoietic growth factors. N Engl J Med.
2006;354(19):2034-2045. doi:10.1056/NEJMra052706
124. Buza-Vidas N, Antonchuk J, Qian H, et al. Cytokines regulate postnatal
hematopoietic stem cell expansion: opposing roles of thrombopoietin and LNK. Genes
Dev. 2006;20(15):2018-2023. doi:10.1101/gad.385606
125. Mackarehtschian K, Hardin JD, Moore KA, Boast S, Goff SP, Lemischka IR.
Targeted disruption of the flk2/flt3 gene leads to deficiencies in primitive hematopoietic
progenitors. Immunity. 1995;3(1):147-161. doi:10.1016/1074-7613(95)90167-1
126. Maurer A-M, Zhou B, Han ZC. Roles of platelet factor 4 in hematopoiesis and
angiogenesis. Growth Factors Chur Switz. 2006;24(4):242-252.
doi:10.1080/08977190600988225
127. Taichman RS, Reilly MJ, Matthews LS. Human osteoblast-like cells and
osteosarcoma cell lines synthesize macrophage inhibitory protein 1alpha in response
to interleukin 1beta and tumour necrosis factor alpha stimulation in vitro. Br J Haematol.
2000;108(2):275-283. doi:10.1046/j.1365-2141.2000.01873.x
128. Sauvageau G, Thorsteinsdottir U, Eaves CJ, et al. Overexpression of HOXB4 in
hematopoietic cells causes the selective expansion of more primitive populations in
vitro and in vivo. Genes Dev. 1995;9(14):1753-1765. doi:10.1101/gad.9.14.1753
129. Buske C, Feuring-Buske M, Abramovich C, et al. Deregulated expression of
HOXB4 enhances the primitive growth activity of human hematopoietic cells. Blood.
2002;100(3):862-868. doi:10.1182/blood-2002-01-0220
130. Boitano AE, Wang J, Romeo R, et al. Aryl hydrocarbon receptor antagonists
promote the expansion of human hematopoietic stem cells. Science.
2010;329(5997):1345-1348. doi:10.1126/science.1191536
131. Fang B, Zheng C, Liao L, et al. Identification of human chronic myelogenous
leukemia progenitor cells with hemangioblastic characteristics. Blood.
2005;105(7):2733-2740. doi:10.1182/blood-2004-07-2514
132. Bhatia R, Holtz M, Niu N, et al. Persistence of malignant hematopoietic
progenitors in chronic myelogenous leukemia patients in complete cytogenetic
remission following imatinib mesylate treatment. Blood. 2003;101(12):4701-4707.
doi:10.1182/blood-2002-09-2780
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 163
133. Zhu J, Emerson SG. Hematopoietic cytokines, transcription factors and lineage
commitment. Oncogene. 2002;21(21):3295-3313. doi:10.1038/sj.onc.1205318
134. Herrmann H, Cerny-Reiterer S, Gleixner KV, et al. CD34(+)/CD38(-) stem cells
in chronic myeloid leukemia express Siglec-3 (CD33) and are responsive to the CD33-
targeting drug gemtuzumab/ozogamicin. Haematologica. 2012;97(2):219-226.
doi:10.3324/haematol.2010.035006
135. Landberg N, von Palffy S, Askmyr M, et al. CD36 defines primitive chronic
myeloid leukemia cells less responsive to imatinib but vulnerable to antibody-based
therapeutic targeting. Haematologica. 2018;103(3):447-455.
doi:10.3324/haematol.2017.169946
136. Valent P, Sadovnik I, Ráčil Z, et al. DPPIV (CD26) as a novel stem cell marker
in Ph+ chronic myeloid leukaemia. Eur J Clin Invest. 2014;44(12):1239-1245.
doi:10.1111/eci.12368
137. Bocchia M, Sicuranza A, Abruzzese E, et al. Residual Peripheral Blood CD26+
Leukemic Stem Cells in Chronic Myeloid Leukemia Patients During TKI Therapy and
During Treatment-Free Remission. Front Oncol. 2018;8:194.
doi:10.3389/fonc.2018.00194
138. Landberg N, Hansen N, Askmyr M, et al. IL1RAP expression as a measure of
leukemic stem cell burden at diagnosis of chronic myeloid leukemia predicts therapy
outcome. Leukemia. 2016;30(1):253-257. doi:10.1038/leu.2015.135
139. Sadovnik I, Herrmann H, Eisenwort G, et al. Expression of CD25 on leukemic
stem cells in BCR-ABL1+ CML: Potential diagnostic value and functional implications.
Exp Hematol. 2017;51:17-24. doi:10.1016/j.exphem.2017.04.003
140. Herrmann H, Sadovnik I, Cerny-Reiterer S, et al. Dipeptidylpeptidase IV (CD26)
defines leukemic stem cells (LSC) in chronic myeloid leukemia. Blood.
2014;123(25):3951-3962. doi:10.1182/blood-2013-10-536078
141. Matteucci E, Giampietro O. Dipeptidyl peptidase-4 (CD26): knowing the function
before inhibiting the enzyme. Curr Med Chem. 2009;16(23):2943-2951.
doi:10.2174/092986709788803114
142. Warfvinge R, Geironson L, Sommarin MNE, et al. Single-cell molecular analysis
defines therapy response and immunophenotype of stem cell subpopulations in CML.
Blood. 2017;129(17):2384-2394. doi:10.1182/blood-2016-07-728873
143. Houshmand M, Soleimani M, Atashi A, Saglio G, Abdollahi M, Nikougoftar Zarif
M. Mimicking the Acute Myeloid Leukemia Niche for Molecular Study and Drug
Screening. Tissue Eng Part C Methods. 2017;23(2):72-85.
doi:10.1089/ten.TEC.2016.0404
144. Méndez-Ferrer S, Michurina TV, Ferraro F, et al. Mesenchymal and
haematopoietic stem cells form a unique bone marrow niche. Nature.
2010;466(7308):829-834. doi:10.1038/nature09262
145. Wilson A, Trumpp A. Bone-marrow haematopoietic-stem-cell niches. Nat Rev
Immunol. 2006;6(2):93-106. doi:10.1038/nri1779
146. Sánchez-Aguilera A, Méndez-Ferrer S. The hematopoietic stem-cell niche in
health and leukemia. Cell Mol Life Sci CMLS. 2017;74(4):579-590.
doi:10.1007/s00018-016-2306-y
147. Banavali SD, Hulette BC, Preisler HD, Silvestri FF, Civin CI. Isolation and
characterization of the cd34+ hematopoietic progenitor cells from the peripheral blood
of patients with chronic myeloid leukemia. Int J Cell Cloning. 1991;9(5):474-490.
doi:10.1002/stem.1991.5530090505
148. Elefanty AG, Hariharan IK, Cory S. bcr-abl, the hallmark of chronic myeloid
leukaemia in man, induces multiple haemopoietic neoplasms in mice. EMBO J.
1990;9(4):1069-1078.
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 165
149. Koschmieder S, Göttgens B, Zhang P, et al. Inducible chronic phase of myeloid
leukemia with expansion of hematopoietic stem cells in a transgenic model of BCR-
ABL leukemogenesis. Blood. 2005;105(1):324-334. doi:10.1182/blood-2003-12-4369
150. Schemionek M, Elling C, Steidl U, et al. BCR-ABL enhances differentiation of
long-term repopulating hematopoietic stem cells. Blood. 2010;115(16):3185-3195.
doi:10.1182/blood-2009-04-215376
151. Schemionek M, Spieker T, Kerstiens L, et al. Leukemic spleen cells are more
potent than bone marrow-derived cells in a transgenic mouse model of CML.
Leukemia. 2012;26(5):1030-1037. doi:10.1038/leu.2011.366
152. Dazzi F, Capelli D, Hasserjian R, et al. The kinetics and extent of engraftment of
chronic myelogenous leukemia cells in non-obese diabetic/severe combined
immunodeficiency mice reflect the phase of the donor’s disease: an in vivo model of
chronic myelogenous leukemia biology. Blood. 1998;92(4):1390-1396.
153. Wang JC, Lapidot T, Cashman JD, et al. High level engraftment of NOD/SCID
mice by primitive normal and leukemic hematopoietic cells from patients with chronic
myeloid leukemia in chronic phase. Blood. 1998;91(7):2406-2414.
154. Antonelli A, Noort WA, Jaques J, et al. Establishing human leukemia xenograft
mouse models by implanting human bone marrow-like scaffold-based niches. Blood.
2016;128(25):2949-2959. doi:10.1182/blood-2016-05-719021
155. Eisterer W, Jiang X, Christ O, et al. Different subsets of primary chronic myeloid
leukemia stem cells engraft immunodeficient mice and produce a model of the human
disease. Leukemia. 2005;19(3):435-441. doi:10.1038/sj.leu.2403649
156. Hawkins ED, Duarte D, Akinduro O, et al. T-cell acute leukaemia exhibits
dynamic interactions with bone marrow microenvironments. Nature.
2016;538(7626):518-522. doi:10.1038/nature19801
157. Lozzio CB, Lozzio BB. Human chronic myelogenous leukemia cell-line with
positive Philadelphia chromosome. Blood. 1975;45(3):321-334.
158. Issaad C, Ahmed M, Novault S, et al. Biological effects induced by variable levels
of BCR-ABL protein in the pluripotent hematopoietic cell line UT-7. Leukemia.
2000;14(4):662-670. doi:10.1038/sj.leu.2401730
159. Chomel J-C, Sorel N, Guilhot J, Guilhot F, Turhan AG. BCR-ABL expression in
leukemic progenitors and primitive stem cells of patients with chronic myeloid leukemia.
Blood. 2012;119(12):2964-2965; author reply 2965-2966. doi:10.1182/blood-2011-12-
396226
160. Gentil M, Hugues P, Desterke C, et al. Aryl hydrocarbon receptor (AHR) is a
novel druggable pathway controlling malignant progenitor proliferation in chronic
myeloid leukemia (CML). PloS One. 2018;13(8):e0200923.
doi:10.1371/journal.pone.0200923
161. Takahashi, Yamanaka. Induction of Pluripotent Stem Cells from Mouse
Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell. 2006.
doi:10.1016/j.cell.2006.07.024
162. Yu J, Vodyanik MA, Smuga-Otto K, et al. Induced pluripotent stem cell lines
derived from human somatic cells. Science. 2007;318(5858):1917-1920.
doi:10.1126/science.1151526
163. Park I-H, Arora N, Huo H, et al. Disease-specific induced pluripotent stem cells.
Cell. 2008;134(5):877-886. doi:10.1016/j.cell.2008.07.041
164. Carette JE, Pruszak J, Varadarajan M, et al. Generation of iPSCs from cultured
human malignant cells. Blood. 2010;115(20):4039-4042. doi:10.1182/blood-2009-07-
231845
165. Turhan A, Foudi A, Hwang JW, Desterke C, Griscelli F, Bennaceur-Griscelli A.
Modeling malignancies using induced pluripotent stem cells: from chronic myeloid
leukemia to hereditary cancers. Exp Hematol. 2019;71:61-67.
doi:10.1016/j.exphem.2019.01.003
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 167
166. Jabbour E, Kantarjian H. Chronic myeloid leukemia: 2018 update on diagnosis,
therapy and monitoring. Am J Hematol. 2018;93(3):442-459. doi:10.1002/ajh.25011
167. Hochhaus A, Saussele S, Rosti G, et al. Chronic myeloid leukaemia: ESMO
Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann Oncol.
2017;28(suppl_4):iv41-iv51. doi:10.1093/annonc/mdx219
168. Pallera A, Altman J, Radich J. NCCN Guidelines Insights: Chronic Myeloid
Leukemia, Version 1.2017 in: Journal of the National Comprehensive Cancer Network
Volume 14 Issue 12 (2016). J Natl Compr Canc Netw. 2017.
https://jnccn.org/view/journals/jnccn/14/12/article-p1505.xml?print&print. Accessed
September 12, 2019.
169. Apperley JF. CML and tyrosine kinase inhibition: the hope becomes reality.
Lancet Haematol. 2015;2(5):e176-177. doi:10.1016/S2352-3026(15)00072-1
170. Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to the World Health
Organization classification of myeloid neoplasms and acute leukemia. Blood.
2016;127(20):2391-2405. doi:10.1182/blood-2016-03-643544
171. Hehlmann R, Hochhaus A, Baccarani M, European LeukemiaNet. Chronic
myeloid leukaemia. Lancet Lond Engl. 2007;370(9584):342-350. doi:10.1016/S0140-
6736(07)61165-9
172. Clarke CJ, Holyoake TL. Preclinical approaches in chronic myeloid leukemia:
from cells to systems. Exp Hematol. 2017;47:13-23.
doi:10.1016/j.exphem.2016.11.005
173. Haddow A, Timmis G. Myleran in chronic myeloid leukaemia; chemical
constitution and biological action. Lancet Lond Engl. 1953. doi:10.1016/s0140-
6736(53)90884-8
174. Hematologic remission and cytogenetic improvement induced by recombinant
human interferon alpha A in chronic myelogenous leukemia. - PubMed - NCBI.
https://www-ncbi-nlm-nih-gov.gate2.inist.fr/pubmed/3457264. Accessed September
12, 2019.
175. Hehlmann R, Heimpel H, Heinzel B. ThRandomized comparison of interferon-
alpha with busulfan and hydroxyurea in chronic myelogenous leukemia. Blood. 1994.
doi:7994025
176. Kirken RA, Erwin RA, Taub D, et al. Tyrphostin AG-490 inhibits cytokine-
mediated JAK3/STAT5a/b signal transduction and cellular proliferation of antigen-
activated human T cells. J Leukoc Biol. 1999;65(6):891-899. doi:10.1002/jlb.65.6.891
177. Druker B. Imatinib (Gleevec®) as a Paradigm of Targeted Cancer Therapies.
Keio J Med. 2010;59(1):1-3. doi:10.2302/kjm.59.1
178. Gambacorti-Passerini C. Part I: Milestones in personalised medicine—imatinib.
Lancet Oncol. 2008;9(6):600. doi:10.1016/S1470-2045(08)70152-9
179. Edwin E, Osgood MD, Mathews MD. Aplastic anemia treated daily transfusions
intravenous marrow case report. Ann Intern Med. 1939. doi:10.7326/0003-4819-13-2-
357
180. Koval, Shamrai. Effect of bone marrow transplantation on the activity of lethally
irradiated animals. Vrach Delo. 1975. doi:766405
181. Barnes, Corp, Neal. Treatment of murine leukaemia with X rays and homologous
bone marrow; preliminary communication. Br Med J. 1956.
doi:10.1136/bmj.2.4993.626
182. Georges Mathé / Histoire de l’Inserm. https://histoire.inserm.fr/les-femmes-et-
les-hommes/georges-mathe. Accessed March 5, 2020.
183. Thomas E, Lochte H, Ferrebee J. Intravenous infusion of bone marrow in patients
receiving radiation and chemotherapy. N Engl J Med. 1957.
doi:10.1056/NEJM195709122571102
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 169
184. Uchida N, Tsukamoto A, He D, Friera AM, Scollay R, Weissman IL. High doses
of purified stem cells cause early hematopoietic recovery in syngeneic and allogeneic
hosts. J Clin Invest. 1998;101(5):961-966. doi:10.1172/JCI1681
185. Thomas E, Buckner C, Weiden P. One hundred patients with acute leukemia
treated by chemotherapy, total body irradiation, and allogeneic marrow transplantation.
Blood. 1977. doi:14751
186. Loren Gragert, Martin Maiers, Mari Eapen. HLA Match Likelihoods for
Hematopoietic Stem-Cell Grafts in the U.S. Registry | NEJM. N Engl J Med. 2014.
doi:10.1056/NEJMsa1311707
187. Worldwide Network for Blood & Marrow Transplantation (WBMT) [General
information, Bylaws, Presentations]. https://www.wbmt.org/general-information-
bylaws-presentations. Accessed September 13, 2019.
188. Speck B, Bortin MM, Champlin R, et al. Allogeneic bone-marrow transplantation
for chronic myelogenous leukaemia. Lancet Lond Engl. 1984;1(8378):665-668.
doi:10.1016/s0140-6736(84)92179-2
189. Apperley JF, Jones L, Hale G, et al. Bone marrow transplantation for patients
with chronic myeloid leukaemia: T-cell depletion with Campath-1 reduces the incidence
of graft-versus-host disease but may increase the risk of leukaemic relapse. Bone
Marrow Transplant. 1986;1(1):53-66.
190. Schattenberg A, Preijers F, Mensink E, et al. Survival in first or second remission
after lymphocyte-depleted transplantation for Philadelphia chromosome-positive CML
in first chronic phase. Bone Marrow Transplant. 1997;19(12):1205-1212.
doi:10.1038/sj.bmt.1700824
191. Sehn LH, Alyea EP, Weller E, et al. Comparative outcomes of T-cell-depleted
and non-T-cell-depleted allogeneic bone marrow transplantation for chronic
myelogenous leukemia: impact of donor lymphocyte infusion. J Clin Oncol Off J Am
Soc Clin Oncol. 1999;17(2):561-568. doi:10.1200/JCO.1999.17.2.561
192. Mahon F-X, Réa D, Guilhot J, et al. Discontinuation of imatinib in patients with
chronic myeloid leukaemia who have maintained complete molecular remission for at
least 2 years: the prospective, multicentre Stop Imatinib (STIM) trial. Lancet Oncol.
2010;11(11):1029-1035. doi:10.1016/S1470-2045(10)70233-3
193. Etienne MC, Lagrange JL, Dassonville O, et al. Population study of
dihydropyrimidine dehydrogenase in cancer patients. J Clin Oncol Off J Am Soc Clin
Oncol. 1994;12(11):2248-2253. doi:10.1200/JCO.1994.12.11.2248
194. Hehlmann R, Heimpel H, Hasford J, et al. Randomized comparison of busulfan
and hydroxyurea in chronic myelogenous leukemia: prolongation of survival by
hydroxyurea. The German CML Study Group. Blood. 1993;82(2):398-407.
195. Guilhot F, Chastang C, Michallet M, et al. Interferon alfa-2b combined with
cytarabine versus interferon alone in chronic myelogenous leukemia. French Chronic
Myeloid Leukemia Study Group. N Engl J Med. 1997;337(4):223-229.
doi:10.1056/NEJM199707243370402
196. Talpaz M, Kantarjian HM, McCredie K, Trujillo JM, Keating MJ, Gutterman JU.
Hematologic Remission and Cytogenetic Improvement Induced by Recombinant
Human Interferon AlphaA in Chronic Myelogenous Leukemia. N Engl J Med.
1986;314(17):1065-1069. doi:10.1056/NEJM198604243141701
197. Larson RA. Is there a best TKI for chronic phase CML? Blood.
2015;126(21):2370-2375. doi:10.1182/blood-2015-06-641043
198. Deininger MW, O’Brien, Druker BJ. International Randomized Study of Interferon
Vs STI571 (IRIS) 8-Year Follow up: Sustained Survival and Low Risk for Progression
or Events in Patients with Newly Diagnosed Chronic Myeloid Leukemia in Chronic
Phase (CML-CP) Treated with Imatinib. Blood. 2009.
doi:10.1182/blood.V114.22.1126.1126
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 171
199. Monnereau A, Troussard X, Belot A, et al. Unbiased estimates of long-term net
survival of hematological malignancy patients detailed by major subtypes in France. Int
J Cancer. 2013;132(10):2378-2387. doi:10.1002/ijc.27889
200. Druker BJ, Tamura S, Buchdunger E, et al. Effects of a selective inhibitor of the
Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med. 1996;2(5):561-
566. doi:10.1038/nm0596-561
201. Nagar B, Bornmann WG, Pellicena P, et al. Crystal structures of the kinase
domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib
(STI-571). Cancer Res. 2002;62(15):4236-4243.
202. Deininger MW, Goldman JM, Lydon N, Melo JV. The tyrosine kinase inhibitor
CGP57148B selectively inhibits the growth of BCR-ABL-positive cells. Blood.
1997;90(9):3691-3698.
203. O’Brien SG, Guilhot F, Larson RA, et al. Imatinib compared with interferon and
low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N
Engl J Med. 2003;348(11):994-1004. doi:10.1056/NEJMoa022457
204. Hughes T, Saglio G, Hochhaus A. Early molecular response predicts outcomes
in patients with chronic myeloid leukemia in chronic phase treated with frontline nilotinib
or imatinib. Blood. 2014. doi:10.1182/blood-2013-06-510396
205. Walz C, Sattler M. Novel targeted therapies to overcome imatinib mesylate
resistance in chronic myeloid leukemia (CML). Crit Rev Oncol Hematol.
2006;57(2):145-164. doi:10.1016/j.critrevonc.2005.06.007
206. Lombardo LJ, Lee FY, Chen P, et al. Discovery of N-(2-chloro-6-methyl- phenyl)-
2-(6-(4-(2-hydroxyethyl)- piperazin-1-yl)-2-methylpyrimidin-4- ylamino)thiazole-5-
carboxamide (BMS-354825), a dual Src/Abl kinase inhibitor with potent antitumor
activity in preclinical assays. J Med Chem. 2004;47(27):6658-6661.
doi:10.1021/jm049486a
207. Muller, Cortes J, Kin D. Dasatinib treatment of chronic-phase chronic myeloid
leukemia: analysis of responses according to preexisting BCR-ABL mutations. Blood.
2009. doi:10.1182/blood-2009-04-214221
208. Kantarjian HM, Shah, Baccarani M. Dasatinib versus imatinib in newly diagnosed
chronic-phas... - Google Scholar. N Engl J Med. 2010. doi:10.1056/NEJMoa1002315
209. Talpaz M, Shah NP, Kantarjian H, et al. Dasatinib in imatinib-resistant
Philadelphia chromosome-positive leukemias. N Engl J Med. 2006;354(24):2531-
2541. doi:10.1056/NEJMoa055229
210. Milojkovic D, Apperley JF, Gerrard G, et al. Responses to second-line tyrosine
kinase inhibitors are durable: an intention-to-treat analysis in chronic myeloid leukemia
patients. Blood. 2012;119(8):1838-1843. doi:10.1182/blood-2011-10-383000
211. Miura M. Therapeutic Drug Monitoring of Imatinib, Nilotinib, and Dasatinib for
Patients with Chronic Myeloid Leukemia. Biol Pharm Bull. 2015;38(5):645-654.
doi:10.1248/bpb.b15-00103
212. Shah N, Guilhot F, Saglio G. Long-term outcome with dasatinib after imatinib
failure in chronic-phase chronic myeloid leukemia: follow-up of a phase 3 study. Blood.
2014. doi:10.1182/blood-2013-10-532341
213. Jabbour E, Kantarjian HM, Hochhaus A. Early response with dasatinib or imatinib
in chronic myeloid leukemia: 3-year follow-up from a randomized phase 3 trial
(DASISION). Blood. 2014. doi:10.1182/blood-2013-06-511592
214. Kantarjian HM, Giles F, Gattermann N, et al. Nilotinib (formerly AMN107), a
highly selective BCR-ABL tyrosine kinase inhibitor, is effective in patients with
Philadelphia chromosome-positive chronic myelogenous leukemia in chronic phase
following imatinib resistance and intolerance. Blood. 2007;110(10):3540-3546.
doi:10.1182/blood-2007-03-080689
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 173
215. Kantarjian H, Giles F, Wunderle L, et al. Nilotinib in imatinib-resistant CML and
Philadelphia chromosome-positive ALL. N Engl J Med. 2006;354(24):2542-2551.
doi:10.1056/NEJMoa055104
216. Giles FJ, le Coutre PD, Pinilla-Ibarz J, et al. Nilotinib in imatinib-resistant or
imatinib-intolerant patients with chronic myeloid leukemia in chronic phase: 48-month
follow-up results of a phase II study. Leukemia. 2013;27(1):107-112.
doi:10.1038/leu.2012.181
217. Hochhaus A, Saglio G, Hughes TP. Nilotinib is associated with a reduced
incidence of BCR-ABL mutations vs imatinib in patients with newly diagnosed chronic
myeloid leukemia in chronic phase. Blood. 2013. doi:10.1182/blood-2012-04-423418
218. Larson RA, Kim D-W, Hughes TP. Efficacy and Safety of Nilotinib (NIL) vs
Imatinib (IM) in Patients (pts) With Newly Diagnosed Chronic Myeloid Leukemia in
Chronic Phase (CML-CP): Long-Term Follow-Up (f/u) of ENESTnd. Blood. 2014.
doi:10.1182/blood.V124.21.4541.4541
219. Larson RA, Kim D-W, Hughes TP. ENESTnd 5-year (y) update: Long-term
outcomes of patients (pts) with chronic myeloid leukemia in chronic phase (CML-CP)
treated with frontline nilotinib (NIL) versus imatinib (IM). J Clin Oncol. 2014. doi:014
32:15_suppl, 7073-7073
220. Khoury HJ, Cortes JE, Kantarjian HM, et al. Bosutinib is active in chronic phase
chronic myeloid leukemia after imatinib and dasatinib and/or nilotinib therapy failure.
Blood. 2012;119(15):3403-3412. doi:10.1182/blood-2011-11-390120
221. Redaelli S, Piazza R, Rostagno R, et al. Activity of bosutinib, dasatinib, and
nilotinib against 18 imatinib-resistant BCR/ABL mutants. J Clin Oncol Off J Am Soc
Clin Oncol. 2009;27(3):469-471. doi:10.1200/JCO.2008.19.8853
222. Gambacorti-Passerini C, Brümmendorf TH, Kim D-W, et al. Bosutinib efficacy
and safety in chronic phase chronic myeloid leukemia after imatinib resistance or
intolerance: Minimum 24-month follow-up. Am J Hematol. 2014;89(7):732-742.
doi:10.1002/ajh.23728
223. Miller GD, Bruno BJ, Lim CS. Resistant mutations in CML and Ph(+)ALL - role of
ponatinib. Biol Targets Ther. 2014;8:243-254. doi:10.2147/BTT.S50734
224. Wehrle J, von Bubnoff N. Ponatinib: A Third-Generation Inhibitor for the
Treatment of CML. Recent Results Cancer Res Fortschritte Krebsforsch Progres Dans
Rech Sur Cancer. 2018;212:109-118. doi:10.1007/978-3-319-91439-8_5
225. Cortes J, Kim D-W, Kantarjian HM. A phase 2 trial of ponatinib in Philadelphia
chromosome-positive leukemias. N Engl J Med. 2013. doi:10.1056/NEJMoa1306494
226. Cortes J, Kantarjian HM, Turner. Ponatinib in Refractory Philadelphia
Chromosome–Positive Leukemias. N Engl J Med. 2012.
doi:10.1056/NEJMoa1205127
227. Kuwazuru Y, Yoshimura A, Hanada S, et al. Expression of the multidrug
transporter, P-glycoprotein, in chronic myelogenous leukaemia cells in blast crisis. Br
J Haematol. 1990;74(1):24-29. doi:10.1111/j.1365-2141.1990.tb02533.x
228. Mahon F-X, Belloc F, Lagarde V, et al. MDR1 gene overexpression confers
resistance to imatinib mesylate in leukemia cell line models. Blood. 2003;101(6):2368-
2373. doi:10.1182/blood.V101.6.2368
229. Thomas J, Wang L, Clark RE, Pirmohamed M. Active transport of imatinib into
and out of cells: implications for drug resistance. Blood. 2004;104(12):3739-3745.
doi:10.1182/blood-2003-12-4276
230. Crossman LC, Druker BJ, Deininger MWN, Pirmohamed M, Wang L, Clark RE.
hOCT 1 and resistance to imatinib. Blood. 2005;106(3):1133-1134; author reply 1134.
doi:10.1182/blood-2005-02-0694
231. White DL, Saunders VA, Dang P, et al. Most CML patients who have a
suboptimal response to imatinib have low OCT-1 activity: higher doses of imatinib may
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 175
overcome the negative impact of low OCT-1 activity. Blood. 2007;110(12):4064-4072.
doi:10.1182/blood-2007-06-093617
232. Hiwase DK, Saunders V, Hewett D, et al. Dasatinib cellular uptake and efflux in
chronic myeloid leukemia cells: therapeutic implications. Clin Cancer Res Off J Am
Assoc Cancer Res. 2008;14(12):3881-3888. doi:10.1158/1078-0432.CCR-07-5095
233. White DL, Saunders VA, Dang P, et al. OCT-1-mediated influx is a key
determinant of the intracellular uptake of imatinib but not nilotinib (AMN107): reduced
OCT-1 activity is the cause of low in vitro sensitivity to imatinib. Blood.
2006;108(2):697-704. doi:10.1182/blood-2005-11-4687
234. Geahlen RL, Handley MD, Harrison ML. Molecular interdiction of Src-family
kinase signaling in hematopoietic cells. Oncogene. 2004;23(48):8024-8032.
doi:10.1038/sj.onc.1208078
235. Donato NJ, Wu JY, Stapley J, et al. BCR-ABL independence and LYN kinase
overexpression in chronic myelogenous leukemia cells selected for resistance to
STI571. Blood. 2003;101(2):690-698. doi:10.1182/blood.V101.2.690
236. Hu Y, Swerdlow S, Duffy TM, Weinmann R, Lee FY, Li S. Targeting multiple
kinase pathways in leukemic progenitors and stem cells is essential for improved
treatment of Ph+ leukemia in mice. Proc Natl Acad Sci U S A. 2006;103(45):16870-
16875. doi:10.1073/pnas.0606509103
237. Schindler T, Bornmann W, Pellicena P, Miller WT, Clarkson B, Kuriyan J.
Structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science.
2000;289(5486):1938-1942. doi:10.1126/science.289.5486.1938
238. Mahon F-X, Hayette S, Lagarde V, et al. Evidence that resistance to nilotinib may
be due to BCR-ABL, Pgp, or Src kinase overexpression. Cancer Res.
2008;68(23):9809-9816. doi:10.1158/0008-5472.CAN-08-1008
239. Jamieson CHM, Ailles LE, Dylla SJ, et al. Granulocyte-macrophage progenitors
as candidate leukemic stem cells in blast-crisis CML. N Engl J Med. 2004;351(7):657-
667. doi:10.1056/NEJMoa040258
240. Gorre ME, Mohammed M, Ellwood K, et al. Clinical resistance to STI-571 cancer
therapy caused by BCR-ABL gene mutation or amplification. Science.
2001;293(5531):876-880. doi:10.1126/science.1062538
241. Hochhaus A, Kreil S, Corbin AS, et al. Molecular and chromosomal mechanisms
of resistance to imatinib (STI571) therapy. Leukemia. 2002;16(11):2190-2196.
doi:10.1038/sj.leu.2402741
242. Barnes DJ, Palaiologou D, Panousopoulou E, et al. Bcr-Abl expression levels
determine the rate of development of resistance to imatinib mesylate in chronic myeloid
leukemia. Cancer Res. 2005;65(19):8912-8919. doi:10.1158/0008-5472.CAN-05-
0076
243. Quintás-Cardama A, Kantarjian HM, Cortes JE. Mechanisms of primary and
secondary resistance to imatinib in chronic myeloid leukemia. Cancer Control J Moffitt
Cancer Cent. 2009;16(2):122-131. doi:10.1177/107327480901600204
244. Nicolini FE, Corm S, Lê Q-H, et al. Mutation status and clinical outcome of 89
imatinib mesylate-resistant chronic myelogenous leukemia patients: a retrospective
analysis from the French intergroup of CML (Fi(phi)-LMC GROUP). Leukemia.
2006;20(6):1061-1066. doi:10.1038/sj.leu.2404236
245. Jabbour E, Kantarjian H, Jones D, et al. Characteristics and outcomes of patients
with chronic myeloid leukemia and T315I mutation following failure of imatinib mesylate
therapy. Blood. 2008;112(1):53-55. doi:10.1182/blood-2007-11-123950
246. Nicolini FE, Hayette S, Corm S, et al. Clinical outcome of 27 imatinib mesylate-
resistant chronic myelogenous leukemia patients harboring a T315I BCR-ABL
mutation. Haematologica. 2007;92(9):1238-1241. doi:10.3324/haematol.11369
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 177
247. Soverini S, Colarossi S, Gnani A, et al. Contribution of ABL kinase domain
mutations to imatinib resistance in different subsets of Philadelphia-positive patients:
by the GIMEMA Working Party on Chronic Myeloid Leukemia. Clin Cancer Res Off J
Am Assoc Cancer Res. 2006;12(24):7374-7379. doi:10.1158/1078-0432.CCR-06-
1516
248. Brandford S, Rudzki Z, Hughes T. Detection of BCR-ABL mutations in patients
with CML treated with imatinib is virtually always accompanied by clinical resistance,
and mutations in the ATP phosphate-binding loop (P-loop) are associated with a poor
prognosis. Blood. 2003. doi:10.1182/blood-2002-09-2896
249. Griswold IJ, MacPartlin M, Bumm T, et al. Kinase domain mutants of Bcr-Abl
exhibit altered transformation potency, kinase activity, and substrate utilization,
irrespective of sensitivity to imatinib. Mol Cell Biol. 2006;26(16):6082-6093.
doi:10.1128/MCB.02202-05
250. Soverini S, Martinelli G, Rosti G, et al. ABL mutations in late chronic phase
chronic myeloid leukemia patients with up-front cytogenetic resistance to imatinib are
associated with a greater likelihood of progression to blast crisis and shorter survival:
a study by the GIMEMA Working Party on Chronic Myeloid Leukemia. J Clin Oncol Off
J Am Soc Clin Oncol. 2005;23(18):4100-4109. doi:10.1200/JCO.2005.05.531
251. Khorashad JS, de Lavallade H, Apperley JF, et al. Finding of kinase domain
mutations in patients with chronic phase chronic myeloid leukemia responding to
imatinib may identify those at high risk of disease progression. J Clin Oncol Off J Am
Soc Clin Oncol. 2008;26(29):4806-4813. doi:10.1200/JCO.2008.16.9953
252. Soverini S, Gnani A, Colarossi S, et al. Philadelphia-positive patients who already
harbor imatinib-resistant Bcr-Abl kinase domain mutations have a higher likelihood of
developing additional mutations associated with resistance to second- or third-line
tyrosine kinase inhibitors. Blood. 2009;114(10):2168-2171. doi:10.1182/blood-2009-
01-197186
253. Mahon FX, Deininger MW, Schultheis B, et al. Selection and characterization of
BCR-ABL positive cell lines with differential sensitivity to the tyrosine kinase inhibitor
STI571: diverse mechanisms of resistance. Blood. 2000;96(3):1070-1079.
254. Graham SM, Jørgensen HG, Allan E, et al. Primitive, quiescent, Philadelphia-
positive stem cells from patients with chronic myeloid leukemia are insensitive to
STI571 in vitro. Blood. 2002;99(1):319-325. doi:10.1182/blood.v99.1.319
255. Copland M, Hamilton A, Elrick LJ, et al. Dasatinib (BMS-354825) targets an
earlier progenitor population than imatinib in primary CML but does not eliminate the
quiescent fraction. Blood. 2006;107(11):4532-4539. doi:10.1182/blood-2005-07-2947
256. Jørgensen HG, Allan EK, Jordanides NE, Mountford JC, Holyoake TL. Nilotinib
exerts equipotent antiproliferative effects to imatinib and does not induce apoptosis in
CD34+ CML cells. Blood. 2007;109(9):4016-4019. doi:10.1182/blood-2006-11-057521
257. Corbin AS, Agarwal A, Loriaux M, Cortes J, Deininger MW, Druker BJ. Human
chronic myeloid leukemia stem cells are insensitive to imatinib despite inhibition of
BCR-ABL activity. J Clin Invest. 2011;121(1):396-409. doi:10.1172/JCI35721
258. Kumari A, Brendel C, Hochhaus A, Neubauer A, Burchert A. Low BCR-ABL
expression levels in hematopoietic precursor cells enable persistence of chronic
myeloid leukemia under imatinib. Blood. 2012;119(2):530-539. doi:10.1182/blood-
2010-08-303495
259. Houshmand M, Simonetti G, Circosta P, et al. Chronic myeloid leukemia stem
cells. Leukemia. 2019;33(7):1543-1556. doi:10.1038/s41375-019-0490-0
260. Zhao C, Blum J, Chen A, et al. Loss of beta-catenin impairs the renewal of normal
and CML stem cells in vivo. Cancer Cell. 2007;12(6):528-541.
doi:10.1016/j.ccr.2007.11.003
261. Fleming HE, Janzen V, Lo Celso C, et al. Wnt signaling in the niche enforces
hematopoietic stem cell quiescence and is necessary to preserve self-renewal in vivo.
Cell Stem Cell. 2008;2(3):274-283. doi:10.1016/j.stem.2008.01.003
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 179
262. Cobas M, Wilson A, Ernst B, et al. Beta-catenin is dispensable for hematopoiesis
and lymphopoiesis. J Exp Med. 2004;199(2):221-229. doi:10.1084/jem.20031615
263. Nakahara F, Sakata-Yanagimoto M, Komeno Y, et al. Hes1 immortalizes
committed progenitors and plays a role in blast crisis transition in chronic myelogenous
leukemia. Blood. 2010;115(14):2872-2881. doi:10.1182/blood-2009-05-222836
264. Briscoe J, Thérond PP. The mechanisms of Hedgehog signalling and its roles in
development and disease. Nat Rev Mol Cell Biol. 2013;14(7):416-429.
doi:10.1038/nrm3598
265. Zhao C, Chen A, Jamieson CH, et al. Hedgehog signalling is essential for
maintenance of cancer stem cells in myeloid leukaemia. Nature. 2009;458(7239):776-
779. doi:10.1038/nature07737
266. Ito K, Bernardi R, Morotti A, et al. PML targeting eradicates quiescent leukaemia-
initiating cells. Nature. 2008;453(7198):1072-1078. doi:10.1038/nature07016
267. Hurtz C, Hatzi K, Cerchietti L, et al. BCL6-mediated repression of p53 is critical
for leukemia stem cell survival in chronic myeloid leukemia. J Exp Med.
2011;208(11):2163-2174. doi:10.1084/jem.20110304
268. Naka K, Hoshii T, Muraguchi T, et al. TGF-beta-FOXO signalling maintains
leukaemia-initiating cells in chronic myeloid leukaemia. Nature. 2010;463(7281):676-
680. doi:10.1038/nature08734
269. Rådmark O, Samuelsson B. Regulation of the activity of 5-lipoxygenase, a key
enzyme in leukotriene biosynthesis. Biochem Biophys Res Commun. 2010;396(1):105-
110. doi:10.1016/j.bbrc.2010.02.173
270. Chen Y, Hu Y, Zhang H, Peng C, Li S. Loss of the Alox5 gene impairs leukemia
stem cells and prevents chronic myeloid leukemia. Nat Genet. 2009;41(7):783-792.
doi:10.1038/ng.389
271. Zhou LL, Zhao Y, Ringrose A, et al. AHI-1 interacts with BCR-ABL and modulates
BCR-ABL transforming activity and imatinib response of CML stem/progenitor cells. J
Exp Med. 2008;205(11):2657-2671. doi:10.1084/jem.20072316
272. Arrigoni E, Del Re M, Galimberti S, et al. Concise Review: Chronic Myeloid
Leukemia: Stem Cell Niche and Response to Pharmacologic Treatment. Stem Cells
Transl Med. 2018;7(3):305-314. doi:10.1002/sctm.17-0175
273. Jabbour E, Kantarjian H, Jones D, et al. Frequency and clinical significance of
BCR-ABL mutations in patients with chronic myeloid leukemia treated with imatinib
mesylate. Leukemia. 2006;20(10):1767-1773. doi:10.1038/sj.leu.2404318
274. Cortes J, O’Brien S, Kantarjian H. Discontinuation of imatinib therapy after
achieving a molecular response. Blood. 2004;104(7):2204-2205. doi:10.1182/blood-
2004-04-1335
275. Mauro MJ, Druker BJ, Maziarz RT. Divergent clinical outcome in two CML
patients who discontinued imatinib therapy after achieving a molecular remission. Leuk
Res. 2004;28 Suppl 1:S71-73. doi:10.1016/j.leukres.2003.10.017
276. Merante S, Orlandi E, Bernasconi P, Calatroni S, Boni M, Lazzarino M. Outcome
of four patients with chronic myeloid leukemia after imatinib mesylate discontinuation.
Haematologica. 2005;90(7):979-981.
277. Rousselot P, Huguet F, Rea D, et al. Imatinib mesylate discontinuation in patients
with chronic myelogenous leukemia in complete molecular remission for more than 2
years. Blood. 2007;109(1):58-60. doi:10.1182/blood-2006-03-011239
278. Verma D, Kantarjian H, Jain N, Cortes J. Sustained complete molecular response
after imatinib discontinuation in a patient with chronic myeloid leukemia not previously
exposed to interferon alpha. Leuk Lymphoma. 2008;49(7):1399-1402.
doi:10.1080/10428190802043903
279. Mahon F-X, Réa D, Guilhot J, et al. Discontinuation of imatinib in patients with
chronic myeloid leukaemia who have maintained complete molecular remission for at
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 181
least 2 years: the prospective, multicentre Stop Imatinib (STIM) trial. Lancet Oncol.
2010;11(11):1029-1035. doi:10.1016/S1470-2045(10)70233-3
280. Eaves AC, Barnett MJ, Ponchio L, Cashman JD, Petzer AL, Eaves CJ.
Differences between normal and CML stem cells: potential targets for clinical
exploitation. Stem Cells Dayt Ohio. 1998;16 Suppl 1:77-83; discussion 89.
doi:10.1002/stem.5530160809
281. Jin L, Tabe Y, Konoplev S, et al. CXCR4 up-regulation by imatinib induces
chronic myelogenous leukemia (CML) cell migration to bone marrow stroma and
promotes survival of quiescent CML cells. Mol Cancer Ther. 2008;7(1):48-58.
doi:10.1158/1535-7163.MCT-07-0042
282. Jiang X, Zhao Y, Smith C, et al. Chronic myeloid leukemia stem cells possess
multiple unique features of resistance to BCR-ABL targeted therapies. Leukemia.
2007;21(5):926-935. doi:10.1038/sj.leu.2404609
283. Konig H, Copland M, Chu S, Jove R, Holyoake TL, Bhatia R. Effects of dasatinib
on SRC kinase activity and downstream intracellular signaling in primitive chronic
myelogenous leukemia hematopoietic cells. Cancer Res. 2008;68(23):9624-9633.
doi:10.1158/0008-5472.CAN-08-1131
284. Holtz M, Forman SJ, Bhatia R. Growth factor stimulation reduces residual
quiescent chronic myelogenous leukemia progenitors remaining after imatinib
treatment. Cancer Res. 2007;67(3):1113-1120. doi:10.1158/0008-5472.CAN-06-2014
285. Copland M, Fraser AR, Harrison SJ, Holyoake TL. Targeting the silent minority:
emerging immunotherapeutic strategies for eradication of malignant stem cells in
chronic myeloid leukaemia. Cancer Immunol Immunother CII. 2005;54(4):297-306.
doi:10.1007/s00262-004-0573-1
286. Singh KP, Wyman A, Casado FL, Garrett RW, Gasiewicz TA. Treatment of mice
with the Ah receptor agonist and human carcinogen dioxin results in altered numbers
and function of hematopoietic stem cells. Carcinogenesis. 2009;30(1):11-19.
doi:10.1093/carcin/bgn224
287. Sakai R, Kajiume T, Inoue H, et al. TCDD treatment eliminates the long-term
reconstitution activity of hematopoietic stem cells. Toxicol Sci Off J Soc Toxicol.
2003;72(1):84-91. doi:10.1093/toxsci/kfg002
288. Singh KP, Bennett JA, Casado FL, Walrath JL, Welle SL, Gasiewicz TA. Loss of
aryl hydrocarbon receptor promotes gene changes associated with premature
hematopoietic stem cell exhaustion and development of a myeloproliferative disorder
in aging mice. Stem Cells Dev. 2014;23(2):95-106. doi:10.1089/scd.2013.0346
289. Singh KP, Garrett RW, Casado FL, Gasiewicz TA. Aryl hydrocarbon receptor-
null allele mice have hematopoietic stem/progenitor cells with abnormal characteristics
and functions. Stem Cells Dev. 2011;20(5):769-784. doi:10.1089/scd.2010.0333
290. Pophali P, Patnaik M. The Role of New Tyrosine Kinase Inhibitors in Chronic
Myeloid Leukemia. Cancer J. 2016. doi:10.1097/PPO.0000000000000165.
291. Grigg A, Hughes T. Role of Allogeneic Stem Cell Transplantation for Adult
Chronic Myeloid Leukemia in the Imatinib Era. Biol Blood Marrow Transplant.
2006;12(8):795-807. doi:10.1016/j.bbmt.2006.03.012
292. Talati C, Pinilla-Ibarz J. Resistance in chronic myeloid leukemia: definitions and
novel therapeutic agents. Curr Opin Hematol. 2018;25(2):154-161.
doi:10.1097/MOH.0000000000000403
293. Shlush, Hershkovitz. Clonal evolution models of tumor heterogeneity. Am Soc
Clin Oncol Educ Book. 2015. doi:10.14694/EdBook_AM.2015.35.e662
294. Campbell LL, Polyak K. Breast Tumor Heterogeneity: Cancer Stem Cells or
Clonal Evolution? Cell Cycle. 2007;6(19):2332-2338. doi:10.4161/cc.6.19.4914
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 183
295. Clarke MF, Dick JE, Dirks PB, et al. Cancer Stem Cells—Perspectives on Current
Status and Future Directions: AACR Workshop on Cancer Stem Cells. Cancer Res.
2006;66(19):9339-9344. doi:10.1158/0008-5472.CAN-06-3126
296. The clonal evolution of tumor cell populations | Science.
https://science.sciencemag.org/content/194/4260/23. Accessed September 16, 2019.
297. Swanten C. Intratumor heterogeneity: evolution through space and time. -
PubMed - NCBI. Cancer Res. 2012. doi:10.1158/0008-5472.CAN-12-2217
298. Cancer as an evolutionary and ecological process | Nature Reviews Cancer.
https://www.nature.com/articles/nrc2013. Accessed September 17, 2019.
299. Cassidy J, Caldas C, Bruna A. Maintaining Tumor Heterogeneity in Patient-
Derived Tumor Xenografts. Cancer Res. 2015. doi:10.1158/0008-5472.CAN-15-0727
300. Auman, McLeod. Colorectal Cancer Cell Lines Lack the Molecular Heterogeneity
of Clinical Colorectal Tumors. Clin Colorectal Cancer. 2010.
doi:10.3816/CCC.2010.n.005
301. Intra-tumour heterogeneity: a looking glass for cancer? | Nature Reviews Cancer.
https://www.nature.com/articles/nrc3261. Accessed September 18, 2019.
302. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer
therapies. Nat Rev Clin Oncol. 2018;15(2):81-94. doi:10.1038/nrclinonc.2017.166
303. The causes and consequences of genetic heterogeneity in cancer evolution.
https://www.nature.com/articles/nature12625. Accessed September 17, 2019.
304. Gordon MY, Marley SB, Goldman JM. Clinical heterogeneity in chronic myeloid
leukaemia reflecting biological diversity in normal persons. Br J Haematol. 2003.
doi:10.1046/j.1365-2141.2003.04451.x
305. Goldman JM, Gordon M, Bazeos A, Marin D. Biology of CML stem cells: the basis
for clinical heterogeneity? Leuk Suppl. 2012;1(S2):S43-S45.
doi:10.1038/leusup.2012.23
306. Condello C, Lemmin T, Stöhr J, et al. Structural heterogeneity and intersubject
variability of Aβ in familial and sporadic Alzheimer’s disease. Proc Natl Acad Sci U S
A. 2018;115(4):E782-E791. doi:10.1073/pnas.1714966115
307. Oehler V, Yeung K, Radich J. The derivation of diagnostic markers of chronic
myeloid leukemia progression from microarray data. Blood. 2009. doi:10.1182/blood-
2009-03-212969
308. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells
in chronic myeloid leukemia. - PubMed - NCBI. https://www-ncbi-nlm-nih-
gov.gate2.inist.fr/pubmed/28504724. Accessed September 30, 2019.
309. Chen J, Odenike O, Rowley JD. Leukaemogenesis: more than mutant genes.
Nat Rev Cancer. 2010;10(1):23-36. doi:10.1038/nrc2765
310. Lenstra TL, Rodriguez J, Chen H, Larson DR. Transcription Dynamics in Living
Cells. Annu Rev Biophys. 2016;45(1):25-47. doi:10.1146/annurev-biophys-062215-
010838
311. Gonda TJ, Ramsay RG. Directly targeting transcriptional dysregulation in cancer.
Nat Rev Cancer. 2015;15(11):686-694. doi:10.1038/nrc4018
312. Core LJ, Waterfall JJ, Lis JT. Nascent RNA Sequencing Reveals Widespread
Pausing and Divergent Initiation at Human Promoters. Science. 2008;322(5909):1845-
1848. doi:10.1126/science.1162228
313. Koschmieder S, Vetrie D. Epigenetic dysregulation in chronic myeloid leukaemia:
A myriad of mechanisms and therapeutic options. Semin Cancer Biol. 2018;51:180-
197. doi:10.1016/j.semcancer.2017.07.006
314. Crans HN, Sakamoto KM. Transcription factors and translocations in lymphoid
and myeloid leukemia. Leukemia. 2001;15(3):313-331. doi:10.1038/sj.leu.2402033
315. Cuartero S, Innes AJ, Merkenschlager M. Towards a Better Understanding of
Cohesin Mutations in AML. Front Oncol. 2019;9:867. doi:10.3389/fonc.2019.00867
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 185
316. Takei H, Kobayashi SS. Targeting transcription factors in acute myeloid
leukemia. Int J Hematol. 2019;109(1):28-34. doi:10.1007/s12185-018-2488-1
317. Sportoletti P, Celani L, Varasano E, et al. GATA1 epigenetic deregulation
contributes to the development of AML with NPM1 and FLT3-ITD cooperating
mutations. Leukemia. 2019;33(7):1827-1832. doi:10.1038/s41375-019-0399-7
318. Desterke C, Voldoire M, Bonnet M-L, et al. Experimental and integrative analyses
identify an ETS1 network downstream of BCR-ABL in chronic myeloid leukemia (CML).
Exp Hematol. 2018;64:71-83.e8. doi:10.1016/j.exphem.2018.04.007
319. Dugray A, Geay JF, Foudi A, et al. Rapid generation of a tetracycline-inducible
BCR-ABL defective retrovirus using a single autoregulatory retroviral cassette.
Leukemia. 2001;15(10):1658-1662.
320. Desterke C, Voldoire M, Bonnet M-L, et al. Experimental and integrative analyses
identify an ETS1 network downstream of BCR-ABL in chronic myeloid leukemia (CML).
Exp Hematol. 2018;64:71-83.e8. doi:10.1016/j.exphem.2018.04.007
321. Plas DR, Thompson CB. Cell metabolism in the regulation of programmed cell
death. Trends Endocrinol Metab TEM. 2002;13(2):75-78. doi:10.1016/s1043-
2760(01)00528-8
322. Ng TH, Sham KWY, Xie CM, et al. Eukaryotic elongation factor-2 kinase
expression is an independent prognostic factor in colorectal cancer. BMC Cancer.
2019;19(1):649. doi:10.1186/s12885-019-5873-0
323. Gu H, Chen J, Song Y, Shao H. Gastric Adenocarcinoma Predictive Long
Intergenic Non-Coding RNA Promotes Tumor Occurrence and Progression in Non-
Small Cell Lung Cancer via Regulation of the miR-661/eEF2K Signaling Pathway. Cell
Physiol Biochem Int J Exp Cell Physiol Biochem Pharmacol. 2018;51(5):2136-2147.
doi:10.1159/000495831
324. Zhao Y-Y, Tian Y, Liu L, et al. Inhibiting eEF-2 kinase-mediated autophagy
enhanced the cytocidal effect of AKT inhibitor on human nasopharyngeal carcinoma.
Drug Des Devel Ther. 2018;12:2655-2663. doi:10.2147/DDDT.S169952
325. Shi N, Chen X, Liu R, et al. Eukaryotic elongation factors 2 promotes tumor cell
proliferation and correlates with poor prognosis in ovarian cancer. Tissue Cell.
2018;53:53-60. doi:10.1016/j.tice.2018.05.014
326. Qadir F, Aziz MA, Sari CP, et al. Transcriptome reprogramming by cancer
exosomes: identification of novel molecular targets in matrix and immune modulation.
Mol Cancer. 2018;17(1):97. doi:10.1186/s12943-018-0846-5
327. Kabil N, Bayraktar R, Kahraman N, et al. Thymoquinone inhibits cell proliferation,
migration, and invasion by regulating the elongation factor 2 kinase (eEF-2K) signaling
axis in triple-negative breast cancer. Breast Cancer Res Treat. 2018;171(3):593-605.
doi:10.1007/s10549-018-4847-2
328. Pan Z, Chen Y, Liu J, et al. Design, synthesis, and biological evaluation of polo-
like kinase 1/eukaryotic elongation factor 2 kinase (PLK1/EEF2K) dual inhibitors for
regulating breast cancer cells apoptosis and autophagy. Eur J Med Chem.
2018;144:517-528. doi:10.1016/j.ejmech.2017.12.046
329. Ji C, Xu Q, Guo L, et al. eEF-2 Kinase-targeted miR-449b confers radiation
sensitivity to cancer cells. Cancer Lett. 2018;418:64-74.
doi:10.1016/j.canlet.2018.01.014
330. Guan Y, Jiang S-L, Yu P, et al. Suppression of eEF-2K-mediated autophagy
enhances the cytotoxicity of raddeanin A against human breast cancer cells in vitro.
Acta Pharmacol Sin. 2018;39(4):642-648. doi:10.1038/aps.2017.139
331. Wu H, Zhu H, Liu DX, et al. Silencing of elongation factor-2 kinase potentiates
the effect of 2-deoxy-D-glucose against human glioma cells through blunting of
autophagy. Cancer Res. 2009;69(6):2453-2460. doi:10.1158/0008-5472.CAN-08-
2872
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 187
332. Zhang Y, Cheng Y, Zhang L, et al. Inhibition of eEF-2 kinase sensitizes human
glioma cells to TRAIL and down-regulates Bcl-xL expression. Biochem Biophys Res
Commun. 2011;414(1):129-134. doi:10.1016/j.bbrc.2011.09.038
333. Cheng Y, Ren X, Zhang Y, et al. eEF-2 kinase dictates cross-talk between
autophagy and apoptosis induced by Akt Inhibition, thereby modulating cytotoxicity of
novel Akt inhibitor MK-2206. Cancer Res. 2011;71(7):2654-2663. doi:10.1158/0008-
5472.CAN-10-2889
334. Leprivier G, Remke M, Rotblat B, et al. The eEF2 Kinase Confers Resistance to
Nutrient Deprivation by Blocking Translation Elongation. Cell. 2013;153(5):1064-1079.
doi:10.1016/j.cell.2013.04.055
335. Tsoucas D, Yuan G-C. Recent progress in single-cell cancer genomics. Curr
Opin Genet Dev. 2017;42:22-32. doi:10.1016/j.gde.2017.01.002
336. Baslan T, Kendall J, Rodgers L, et al. Genome-wide copy number analysis of
single cells. Nat Protoc. 2012;7(6):1024-1041. doi:10.1038/nprot.2012.039
337. Janiszewska M, Liu L, Almendro V, et al. In situ single-cell analysis identifies
heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast
cancer. Nat Genet. 2015;47(10):1212-1219. doi:10.1038/ng.3391
338. Femino AM, Fay FS, Fogarty K, Singer RH. Visualization of single RNA
transcripts in situ. Science. 1998;280(5363):585-590.
doi:10.1126/science.280.5363.585
339. Dalerba P, Kalisky T, Sahoo D, et al. Single-cell dissection of transcriptional
heterogeneity in human colon tumors. Nat Biotechnol. 2011;29(12):1120-1127.
doi:10.1038/nbt.2038
340. Patel AP, Tirosh I, Trombetta JJ, et al. Single-cell RNA-seq highlights
intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396-
1401. doi:10.1126/science.1254257
341. Dalerba P, Kalisky T, Sahoo D, et al. Single-cell dissection of transcriptional
heterogeneity in human colon tumors. Nat Biotechnol. 2011;29(12):1120-1127.
doi:10.1038/nbt.2038
342. Hou Y, Guo H, Cao C, et al. Single-cell triple omics sequencing reveals genetic,
epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res.
2016;26(3):304-319. doi:10.1038/cr.2016.23
343. Van Loo P, Voet T. Single cell analysis of cancer genomes. Curr Opin Genet
Dev. 2014;24:82-91. doi:10.1016/j.gde.2013.12.004
344. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell
heterogeneity. Nat Rev Immunol. 2018;18(1):35-45. doi:10.1038/nri.2017.76
345. Hedlund E, Deng Q. Single-cell RNA sequencing: Technical advancements and
biological applications. Mol Aspects Med. 2018;59:36-46.
doi:10.1016/j.mam.2017.07.003
346. Brehm-Stecher BF, Johnson EA. Single-cell microbiology: tools, technologies,
and applications. Microbiol Mol Biol Rev MMBR. 2004;68(3):538-559, table of contents.
doi:10.1128/MMBR.68.3.538-559.2004
347. Guo F, Li L, Li J, et al. Single-cell multi-omics sequencing of mouse early
embryos and embryonic stem cells. Cell Res. 2017;27(8):967-988.
doi:10.1038/cr.2017.82
348. Whitesides GM. The origins and the future of microfluidics. Nature.
2006;442(7101):368-373. doi:10.1038/nature05058
349. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and
bioinformatics pipelines. Exp Mol Med. 2018;50(8):96. doi:10.1038/s12276-018-0071-
8
Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity
Page | 189
350. Utada AS, Lorenceau E, Link DR, Kaplan PD, Stone HA, Weitz DA.
Monodisperse double emulsions generated from a microcapillary device. Science.
2005;308(5721):537-541. doi:10.1126/science.1109164
351. Thorsen T, Roberts RW, Arnold FH, Quake SR. Dynamic pattern formation in a
vesicle-generating microfluidic device. Phys Rev Lett. 2001;86(18):4163-4166.
doi:10.1103/PhysRevLett.86.4163
352. Buettner F, Natarajan KN, Casale FP, et al. Computational analysis of cell-to-cell
heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of
cells. Nat Biotechnol. 2015;33(2):155-160. doi:10.1038/nbt.3102
353. Liang S, Fuhrman S, Somogyi R. Reveal, a general reverse engineering
algorithm for inference of genetic network architectures. Pac Symp Biocomput Pac
Symp Biocomput. 1998:18-29.
354. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A.
Reverse engineering of regulatory networks in human B cells. Nat Genet.
2005;37(4):382-390. doi:10.1038/ng1532
355. Setty M, Tadmor MD, Reich-Zeliger S, et al. Wishbone identifies bifurcating
developmental trajectories from single-cell data. Nat Biotechnol. 2016;34(6):637-645.
doi:10.1038/nbt.3569
356. Bendall SC, Davis KL, Amir ED, et al. Single-Cell Trajectory Detection Uncovers
Progression and Regulatory Coordination in Human B Cell Development. Cell.
2014;157(3):714-725. doi:10.1016/j.cell.2014.04.005
357. Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion Pseudotime
Robustly Reconstructs Lineage Branching. Bioinformatics; 2016. doi:10.1101/041384
358. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell
transcriptomic data across different conditions, technologies, and species. Nat
Biotechnol. 2018;36(5):411-420. doi:10.1038/nbt.4096
359. McCarthy DJ, Campbell KR, Lun ATL, Wills QF. Scater: pre-processing, quality
control, normalization and visualization of single-cell RNA-seq data in R. Bioinforma
Oxf Engl. 2017;33(8):1179-1186. doi:10.1093/bioinformatics/btw777
360. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell
fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol.
2014;32(4):381-386. doi:10.1038/nbt.2859
361. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression
data analysis. Genome Biol. 2018;19(1):15. doi:10.1186/s13059-017-1382-0
362. Chen, Renia, Ginhoux. Constructing cell lineages from single-cell
transcriptomes. Mol Asp Med. 2018. doi:10.1016/j.mam.2017.10.004
363. Análisis de componentes principales ⋆ Quantdare. Quantdare. March 2014.
https://quantdare.com/analisis-de-componentes-principales/. Accessed September
26, 2019.
364. Stock classification with ISOMAP ⋆ Quantdare. Quantdare. January 2016.
https://quantdare.com/stock-classification-with-isomap/. Accessed September 26,
2019.
365. Visualizing Fixed Income ETFs with T-SNE ⋆ Quantdare. Quantdare. July 2016.
https://quantdare.com/tsne/. Accessed September 26, 2019.
366. Maaten L van der, Hinton G. Visualizing Data Using T-SNE.; 2008.
367. Reid JE, Wernisch L. Pseudotime estimation: deconfounding single cell time
series. Bioinformatics. 2016;32(19):2973-2980. doi:10.1093/bioinformatics/btw372
368. Monocle. http://cole-trapnell-lab.github.io/monocle-release/docs/#constructing-
single-cell-trajectories. Accessed September 19, 2019.
369. Suvà ML, Tirosh I. Single-Cell RNA Sequencing in Cancer: Lessons Learned and
Emerging Challenges. Mol Cell. 2019;75(1):7-12.
doi:10.1016/j.molcel.2019.05.003
370. Jerby-Arnon L, Shah P, Cuoco MS, et al. A Cancer Cell Program Promotes T Cell
Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984- 997.e24.
doi:10.1016/j.cell.2018.09.006
371. Puram SV, Tirosh I, Parikh AS, et al. Single-Cell Transcriptomic Analysis of Primary and
Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 2017;171(7):1611-1624.e24.
doi:10.1016/j.cell.2017.10.044
372. Hunter KW, Amin R, Deasy S, Ha N-H, Wakefield L. Genetic insights into the morass
of metastatic heterogeneity. Nat Rev Cancer. 2018;18(4):211-223. doi:10.1038/nrc.2017.126
373. Bower H, Björkholm M, Dickman PW, Höglund M, Lambert PC, Andersson TM-
L. Life Expectancy of Patients With Chronic Myeloid Leukemia Approaches the Life
Expectancy of the General Population. J Clin Oncol Off J Am Soc Clin Oncol.
2016;34(24):2851-2857. doi:10.1200/JCO.2015.66.2866
374. Aggoune D, Sorel N, Bonnet M-L, et al. Bone marrow mesenchymal stromal cell (MSC)
gene profiling in chronic myeloid leukemia (CML) patients at diagnosis and in deep molecular
response induced by tyrosine kinase inhibitors (TKIs). Leuk Res. 2017;60:94-102.
doi:10.1016/j.leukres.2017.07.007
Titre : Contrôle transcriptionnel induit par Bcr-Abl et son rôle dans l'hétérogénéité des cellules souches leucémiques.
Mots clés : Chromosome Philadelphie, Analyse sur cellule unique, Cellules souches hématopoïétiques
La leucémie myéloïde chronique est une hématopoïèse
maligne clonale, caractérisée par l'acquisition de la
translocation t (9;22) conduisant au chromosome Ph1 et à
son homologue l'oncogène BCR-ABL, dans une cellule
souche hématopoïétique très primitive. La LMC est un
modèle de thérapies ciblées, car il a été démontré que la
preuve de la faisabilité du ciblage de l'activité tyrosine kinase
(TK) BCR-ABL à l'aide d'inhibiteurs de TK (TKI) entraîne des
réponses et des rémissions majeures. Cependant, les
problèmes actuels rencontrés dans ces thérapies sont la
résistance des cellules souches leucémiques primitives et
leur persistance qui serait liée à l'hétérogénéité des cellules
souches au moment du diagnostic, ce qui conduit à la
sélection clonale de cellules résistant aux thérapies TKI. J'ai
appliqué la technologie de l'analyse du transcriptome des
cellule uniques aux cellules de la LMC en utilisant un panel
de gènes impliqués dans différentes voies, combinée à
l'analyse d'inférence de trajectoire au modèle d'expression
des gènes.
La leucémie myéloïde chronique est une hématopoïèse
maligne clonale, caractérisée par l'acquisition de la
translocation t (9;22) conduisant au chromosome Ph1 et à
son homologue l'oncogène BCR-ABL, dans une cellule
souche hématopoïétique très primitive. La LMC est un
modèle de thérapies ciblées, car il a été démontré que la
preuve de la faisabilité du ciblage de l'activité tyrosine
kinase (TK) BCR-ABL à l'aide d'inhibiteurs de TK (TKI)
entraîne des réponses et des rémissions majeures.
Cependant, les problèmes actuels rencontrés dans ces
thérapies sont la résistance des cellules souches
leucémiques primitives et leur persistance qui serait liée à
l'hétérogénéité des cellules souches au moment du
diagnostic, ce qui conduit à la sélection clonale de cellules
résistant aux thérapies TKI. J'ai appliqué la technologie de
l'analyse du transcriptome des cellule uniques aux cellules
de la LMC en utilisant un panel de gènes impliqués dans
différentes voies, combinée à l'analyse d'inférence de
trajectoire au modèle d'expression des gènes.
Title : Transcriptional control induced by Bcr-Abl and its role in leukemic stem cell heterogeneity.
Keywords : Philadelphia Chromosome, Single Cell Analysis, Hematopoietic Stem Cells
Chronic myeloid leukemia is a clonal hematopoietic
malignancy, characterized by the acquisition of the t (9;22)
translocation leading to Ph1 chromosome and its
counterpart BCR-ABL oncogene, in a very primitive
hematopoietic stem cell. CML is a model of targeted
therapies as the proof of concept of the feasibility of
targeting the tyrosine kinase (TK) activity BCR-ABL using TK
inhibitors (TKI) has been shown to lead to major responses
and remissions. However, the current problems encountered
in these therapies are primitive leukemic stem cells
resistance and their persistence which is thought to be
related to the heterogeneity of the stem cells at diagnosis
leading to clonal selection of cells resisting to TKI therapies.
I have applied the technology of single cell transcriptome
analysis to CML cells using a panel of genes involved in
different pathways combined with trajectory inference
analysis to the gene expression pattern.
Chronic myeloid leukemia is a clonal hematopoietic
malignancy, characterized by the acquisition of the t (9;22)
translocation leading to Ph1 chromosome and its
counterpart BCR-ABL oncogene, in a very primitive
hematopoietic stem cell. CML is a model of targeted
therapies as the proof of concept of the feasibility of
targeting the tyrosine kinase (TK) activity BCR-ABL using
TK inhibitors (TKI) has been shown to lead to major
responses and remissions. However, the current problems
encountered in these therapies are primitive leukemic
stem cells resistance and their persistence which is
thought to be related to the heterogeneity of the stem
cells at diagnosis leading to clonal selection of cells
resisting to TKI therapies. I have applied the technology of
single cell transcriptome analysis to CML cells using a
panel of genes involved in different pathways combined
with trajectory inference analysis to the gene expression
pattern.
Université Paris-Saclay Espace Technologique / Immeuble Discovery Route de l’Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France
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