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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�

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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

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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.

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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.

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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

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al con

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du

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bl an

d its ro

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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,


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


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.


Page | 45

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

http://clinicaltrials.gov/show/NCT00471497

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


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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


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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.


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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

Page | 69


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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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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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.

Page | 75


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

Page | 77


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:

http://cole-trapnell-lab.github.io/monocle-release/

http://cole-trapnell-lab.github.io/monocle-release/

Figure 24. Trajectory construction and analysis steps

Page | 79


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/

https://quantdare.com/tsne/

Page | 81


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

Page | 83


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

Page | 85


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

Page | 87


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 simultaneously 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 confirmed 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 diagnosis, 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 simultaneously 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) translocation 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: [email protected]

ABL oncogene, in a very primitive hematopoietic stem cell [1]. 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 (TKIs) has been found to lead to major responses and remissions [2]. The use of imatinib 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.



mailto:[email protected]


2 S. Pagliaro et al. / Experimental Hematology 2020;000:1−10

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 recurrences 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 requiring TKI switch and leading, in some rare cases, to progression 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 eradication of the disease. To uncover the panel of genes that can contribute to this event, we applied the technology 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 discovery 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 therapy 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 simultaneously with Fluidigm.

Single-cell lysis, reverse transcription, and cDNA pre-amplification were performed on the C1 apparatus according to the protocol 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 apparatus from Fluidigm.

Bioinformatics analyses

Single-cell bioinformatics analyses were performed in the R software environment, Version 3.4.3. Unsupervised classification (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 environment 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 performed RT-qPCR using peripheral blood from CML and control samples. For this purpose, CD34+ cells were studied in comparison 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-microglobulin 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


https://www.fluidigm.com/products/c1-system

https://www.fluidigm.com/products/c1-system

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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 )



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 tyrosine 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 analyzed at diagnosis were 100% Ph1+ cells at cytogenetic and fluorescence in situ hybridization (FISH) analyses. These cells were then used to perform Fluidigm experiments followed 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 expression. Cluster 1 (n = 50 cells) included c-MYC and JAK2, two genes highly involved in BCR-ABL−induced leukemogenesis 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 heterogeneity 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 (containing 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 pseudotime analysis, seven possible different cell fate decisions 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 pseudotime 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 previously 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.


http://www.exphem.org/



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 presence of appropriate control cells. As can be seen in Figure 6, CML cells treated with the pan-FGFR inhibitor ponatinib for 48 hours exhibited reduced expression 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 primary 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 different 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.



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 population 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 heterogeneity 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 cancer 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 pseudotime, allowing classification of the genes in a trajectory, 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 transcriptome has been performed. One of the major advantages of this methodology is use of the Monocle algorithm, which provides unprecedented insights into the transcriptome dynamics of data collected at different sequential time points [17]. The question of the heterogeneity of CML LSCs has recently been studied [28,31], allowing identification, in a combined mutational and transcriptome analysis, the presence of multiple 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 identification 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), suggesting 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 population 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 different 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 phosphorylation 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.

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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 showing 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)



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


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


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.


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.


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),


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).


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


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.


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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.


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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


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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).


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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


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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).


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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.


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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.


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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.

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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%.


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.


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.


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


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.

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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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