Pre-BCR Signaling Defines a Novel Subgroup in B-Cell Acute Lymphoblastic Leukemia and Can Be Targeted by SYK Inhibition

Pre-BCR signaling defines a novel subgroup in B-cell acute lymphoblastic leukemia and can be targeted by SYK inhibition

Doctoral thesis at the Medical University of Vienna for obtaining the academic degree

Doctor of Philosophy

Submitted by

Dr. med. univ. Stefan Köhrer

Supervisor: Assoc. Prof. Mag. Dr. Katrina Vanura Medical University of Vienna, Department of Medicine I Division of Hematology & Hemostaseology Währinger Gürtel 18-20, 1090 Vienna, Austria

and

UT MD Anderson Cancer Center, Department of Leukemia 1515 Holcombe BLVD, 77030 Houston, Texas, USA

Vienna, 05/2018 Declaration I hereby declare that the work presented in this thesis was performed at the University of Texas MD Anderson Cancer Center Department of Leukemia in the laboratory of Prof. Jan Burger and at the Medical University of Vienna, Department of Medicine I, Division of Hematology & Hemostaseology under the supervision of Assoc. Prof. Mag. Dr. Katrina Vanura. I certify that this thesis is entirely my own original work, with the exception of the animal studies, which were conducted in collaboration with the UCL San Francisco by Christian Hurtz under the supervision of Markus Müschen and in collaboration with Ulm University by Felix Seyfried under the supervision of Lüder- Hinrich Meyer and Klaus-Michael Debatin. The gene expression array studies were conducted by Zhiqiang Wang from R. Eric Davis’ laboratory at MD Anderson Cancer Center Department of Lymphoma.

ii Table of contents Declaration ...... ii Table of contents ...... iii List of figures ...... v List of tables ...... vii Abstract (English)...... viii Abstract (German) ...... ix Publications ...... x Abbreviations...... xi Acknowledgments ...... xiv 1 Introduction ...... 1 1.1 B-lymphopoiesis in the bone marrow ...... 1 1.1.1 General ...... 1 1.1.2 Checkpoints in B-cell development ...... 2 1.1.3 The pre-BCR checkpoint ...... 2 1.1.4 The immature B-cell checkpoint ...... 6 1.2 Inhibitors of pre-BCR/BCR associated kinases ...... 7 1.2.1 SYK ...... 8 1.2.2 BTK ...... 10 1.2.3 PI3K ...... 11 1.3 B-cell acute lymphoblastic leukemia (B-ALL) ...... 13 1.3.1 General ...... 13 1.3.2 Genetic characteristics of B-ALL ...... 13 1.3.3 B-ALL immunophenotype ...... 15 2 Aims of this study ...... 17 3 Results ...... 18 3.1 A subset of B-cell ALL resembles normal pre-BCR+ B-cell progenitors by gene expression profile ...... 18 3.2 Pre-B ALL is characterized by pre-BCR surface expression ...... 19 3.3 Pre-BCR+ ALL requires constitutive pre-BCR signaling for proliferation and survival...... 22 3.4 The dependency of pre-BCR+ ALL on the pre-BCR and SYK can be exploited therapeutically by SYK inhibition ...... 24 3.5 Pre-BCR signaling drives B-ALL by modulating PI3K signaling ...... 27

iii 3.6 The effects of pre-BCR inhibition on pre-BCR+ ALL require the reactivation of the transcription factor FOXO1 ...... 31 3.7 The pre-BCR regulates MYC in a PI3K- and FOXO1-dependent manner ...... 35 4 Discussion ...... 40 5 Material and Methods ...... 44 5.1 Cell lines, xenografts and patient samples ...... 44 5.2 In vivo B-ALL xenograft models ...... 46 5.3 Flow Cytometry ...... 46 5.3.1 Antibodies ...... 46 5.3.2 Intra- and extracellular staining protocols ...... 47 5.3.3 Ca++-Flux measurements ...... 47 5.4 Assessment of cell proliferation and cell viability ...... 47 5.5 Western Blot...... 48 5.5.1 Antibodies ...... 48 5.5.2 Western Blot Protocol ...... 49 5.6 CRISPR/Cas9 mediated target-gene knockout ...... 49 5.6.1 sgRNA design ...... 49 5.6.2 CRISPR/Cas9 plasmids ...... 50 5.6.1 Electroporation ...... 51 5.7 Confocal microscopy ...... 51 5.8 Gene Expression Profiling and Gene Set Enrichment Analysis (GSEA)...... 52 5.9 Statistical Analysis ...... 52 6 References ...... 53 7 Appendix ...... 66

iv List of figures Figure 1. Stages of B-cell development in the bone marrow...... 2 Figure 2. Structures of the pre-BCR and BCR...... 3 Figure 3. The pre-BCR signaling cascade...... 5 Figure 4. Pre-BCR/BCR signaling inhibitors...... 8 Figure 5. Structural composition of SYK family kinases...... 9 Figure 6. BTK domain structure and binding partners...... 10 Figure 7. Domain structure of PI3K regulatory and catalytic subunits...... 12 Figure 8. The genetic subclassification of B-cell ALL...... 14 Figure 9. Genes differentially expressed between pro-B and pre-B cells...... 18 Figure 10. Pre-B ALL is characterized by a GEP highly similar to non-malignant pre- BCR+ B-cell progenitors and exhibits signs of active pre-BCR signaling...... 19 Figure 11. Pre-BCR expression in pre-B ALL cell lines and xenografts...... 20 Figure 12. Pre-BCR+ ALL cells express key pre-BCR signaling molecules...... 20 Figure 13. Pre-BCR+ cells respond to anti-Igµ treatment with Ca++ release...... 21 Figure 14. Stimulation of pre-BCR+ ALL cells with anti-Igµ induces phosphorylation of AKT and ERK...... 21 Figure 15. Stimulation with anti-Igµ does not affect proliferation of pre-BCR+ cells. ... 21 Figure 16. Pre-BCR and SLC expression after Igµ knockout...... 22 Figure 17. Effects of pre-BCR knockout on pre-B ALL proliferation and survival...... 23 Figure 18. Effects of SYK knockout on pre-BCR+ and pre-BCR- cells...... 24 Figure 19. Pre-BCR+ B-ALL cells are selectively sensitive to SYK inhibition...... 25 Figure 20. Pre-BCR+ cells are particularly sensitive to dasatinib...... 25 Figure 21. PRT318 exhibits efficacy in a pre-BCR+ xenograft model of B-ALL...... 26 Figure 22. The SYK inhibitor PRT062607 prolongs survival in an alternative xenograft model of pre-BCR+ ALL...... 27 Figure 23. PRT318 exhibits efficacy in pre-B ALL primary patient samples...... 27 Figure 24. PRT318 selectively inhibits PI3K-AKT pathway signaling activity...... 28 Figure 25. PRT318 blocks PI3K-AKT signaling activity selectively in pre-BCR+ cells. 28 Figure 26. Pre-BCR+ xenografts exhibit higher baseline AKT phosphorylation which is selectively sensitive to PRT318...... 29 Figure 27. Pre-BCR+ ALL cells are sensitive to LY294002...... 29 Figure 28. Pre-BCR inhibition blocks formation of the CD19 signalosome...... 30

v Figure 29. LY294002 reduces pAKT without affecting the CD19 signalosome...... 30 Figure 30. CD19 knockout reduces proliferation selectively of pre-BCR+ cells...... 30 Figure 31. FOXO1 expression at different B-cell developmental stages...... 31 Figure 32. PI3K modulates the transcriptional activity of FOXO1...... 32 Figure 33. FOXO1 is highly phosphorylated in pre-BCR+ ALL...... 32 Figure 34. Inhibition of pre-BCR signaling reduces FOXO1 phosphorylation and increases FOXO1 total protein expression...... 33 Figure 35. Inhibition of pre-BCR signaling results in the shift of FOXO1 from the cytoplasm to the nucleus...... 33 Figure 36. Inhibition of pre-BCR signaling results in the upregulation of FOXO1 transcriptional targets...... 34 Figure 37. Constitutive active FOXO1 mimics the effects of pre-BCR inhibition...... 34 Figure 38. Pre-BCR+ patient samples exhibit evidence for FOXO1 inactivation...... 35 Figure 39. FOXO1 is reactivated in response to pre-BCR knockout...... 36 Figure 40. Gene sets enriched in pre-BCR+ cells after pre-BCR knockout...... 36 Figure 41. Reduction of MYC mRNA and protein levels after pre-BCR knockout...... 37 Figure 42. Downregulation of MYC protein levels in response to SYK and PI3K inhibition...... 37 Figure 43. Knockout of MYC reduces proliferation of pre-BCR+ cells...... 38 Figure 44. Differentially expressed genes in FOXO1-AAA expressing cells show evidence for down modulation of MYC activity...... 38 Figure 45. FOXO1-AAA suppresses MYC...... 39 Figure 46. Pre-BCR signaling serves as therapeutic target in pre-BCR+ ALL...... 40 Figure 47. 48h after transfection of SMS-SB cells with control vector (px458 EV) or igµ- specific CRISPR/Cas9 knockout vector (px458 KO1 and px458 KO2)...... 51

vi List of tables Table 1. B-ALL subtypes according to their developmental stage...... 15 Table 2. Characteristics of cell lines and xenografts used throughout the study...... 46 Table 3. Antibodies for Flow Cytometry ...... 47 Table 4. Antibodies for Western Blot...... 49 Table 5. sgRNA sequences for CRISPR/Cas9 knockout...... 50

vii Abstract (English) Despite recent advances in B-ALL therapy, with overall survival rates reaching up to 90% in recent clinical trials, there is an urgent need for novel more targeted treatment approaches, particularly for patients failing chemotherapy and for patients at risk to develop chemotherapy-related toxicities. However, the identification of subtype specific therapeutic targets requires a thorough understanding of the underlying molecular mechanisms driving the disease. In this thesis, we describe a novel subgroup of B-ALL, characterized by pre-BCR surface expression and the requirement for active pre-BCR signaling for proliferation and survival. Using publicly available GEP datasets of normal pre-BCR+ B-cell progenitors in combination with GEP datasets from over 400 B-ALL patient samples we show that pre-BCR+ ALL cells inherit pre-BCR-dependency from their non-malignant counterparts the cyto-Igµ+, surface IgM- pre-B cell. Furthermore, we employed a comparative approach of genetic pre-BCR pathway interference and pharmacologic inhibition of pre-BCR signaling in order to dissect the underlying molecular mechanisms driving proliferation and survival of pre-BCR+ ALL. This resulted in the identification of a signaling axis, involving the pre-BCR-dependent activation of PI3K, the inactivation of FOXO1 and the upregulation of MYC, which is crucial for the survival of pre-BCR+ ALL cells. Importantly, the inhibition of this signaling axis with SYK inhibitors completely reversed these effects and exhibited promising efficacy in several in vitro and in vivo models of pre-BCR+ ALL. This ultimately provides a rational for the assessment of pre-BCR inhibition as novel therapeutic strategy for subtypes of B-ALL in clinical trials.

viii Abstract (German) Trotz großer Fortschritte bei der Behandlung von Patienten mit akuter lymphoblastischer Leukämie (ALL) versterben immer noch mehr als die Hälfte (60%) aller Erwachsenen an den Folgen dieser Erkrankung. Auch bei Kindern, bei denen die Heilungsrate in den letzten drei Jahrzehnten auf über 85% angestiegen ist, besteht noch Bedarf nach neuen Therapien. Das Ziel dieser ist es Heilungsraten weiter zu verbessern und gleichzeitig das Auftreten von Chemo- und Strahlentherapie-induzierten Folgeerkrankungen (z.B. Malignome und kognitive Beeinträchtigungen) zu verringern. Der Einsatz gezielter Therapien stellt eine Möglichkeit dar, diesen Zielen näher zu kommen. Es werden dabei Substanzen verabreicht, die einen für die Erkrankung spezifischen Prozess gezielt blockieren. Der Erfolg eines solchen Therapieansatzes setzt jedoch genaues Wissen über die molekularbiologischen Veränderungen der Erkrankung voraus. Erkrankungsbedingte, aberrante Signalwege sind jedoch nicht für alle ALL Untergruppen im Detail erforscht. Im Rahmen der vorliegenden Arbeit beschreiben wir eine neue Subgruppe der B-Zell Vorläufer ALL, die durch die Abhängigkeit von Wachstumssignalen des pre-B Zellrezeptor (pre-BCR) gekennzeichnet ist. Der kombinierte Einsatz molekulargenetischer und pharmakologischer Methoden ermöglicht uns außerdem, die entsprechenden molekularen Mechanismen dieser Abhängigkeit (oder diese Prozesses) aufzuklären. Im Zentrum dieser steht die pre-BCR-mediierte Aktivierung der PI3 Kinase und von AKT. Dies setzt eine Signalkaskade in Gang an deren Ende die Inaktivierung des Tumorsuppressors FOXO1 und die Aufhebung der C-MYC Blockierung steht. Die selektive Blockade dieser Signalwege durch Inhibition der pre- BCR-assoziierten SYK Kinase führt zu reduziertem Tumorwachstum und reduzierter Viabilität in verschiedenen in vitro und in vivo Modellen pre-BCR+ Leukämien. Diese Ergebnisse unterstreichen die therapeutische Relevanz der pre-BCR Signalkaskade in B-ALL und bilden die Grundlage für die mögliche weitere Exploration der Effekte von SYK Inhibitoren in klinischen Studien bei Patienten mit pre-BCR+ ALL.

ix Publications The results of the presented work have also been published as part of the manuscript “Pre-BCR Signaling In Precursor B-Cell Acute Lymphoblastic Leukemia regulates PI3K/AKT, FOXO1, and MYC, and can be targeted by SYK inhibition” Köhrer S, Havranek O, Seyfried F, et al. Leukemia 2016; 30(6):1246–1254. Figures that are derived from the manuscript are cited accordingly.

Additional manuscripts that have been published over the course of the PhD program:

Blunt MD#, Köhrer S#, et al. “The dual Syk/JAK inhibitor cerdulatinib antagonizes B-cell receptor and microenvironmental signaling in chronic lymphocytic leukemia“ (2017) Clinical Cancer Res. 23: 2313–2324. #Joint first author

Köhrer S, Burger JA “B-Cell Receptor Signaling in Chronic Lymphocytic Leukemia and Other B-Cell Malignancies” Clinical Advances in Hematology & Oncology 14, Issue 1 (January 2016): 55-65.

Havranek O, Xu J, Köhrer S, Wang Z, et al. (2017) “Tonic B-cell receptor signaling in diffuse large B-cell lymphoma” Blood 130: 995–1006

x Abbreviations BCR B-cell receptor pHSC Pluripotent hematopoietic stem cell E2A Immunoglobulin enhancer binding factors E12/E47 SYK Spleen tyrosine kinase IKAROS Zinc finger protein, subfamily 1A MPP Multipotent progenitors EBF1 Early B-cell factor 1 PAX5 Paired box 5 IgHC Immunoglobulin heavy chain IgLC Immunoglobulin light chain pre-BCR Precursor B-cell receptor B-ALL B-cell acute lymphoblastic leukemia Igα Immunoglobulin alpha (CD79a) Igβ Immunoglobulin beta (CD79b) λ5 Lambda5 VpreB Pre-B lymphocyte 1 IgLC Immunoglobulin light chain SLC Surrogate light chain SRC SRC proto-oncogene, non-receptor tyrosine kinase ITAM Immunoreceptor tyrosine-based activation motif SH2 Src homology 2 domain LYN Lck/Yes-related novel protein tyrosine kinase PI3K Phosphatidylinositol-4,5-Bisphosphate 3-Kinase AKT RAC-alpha serine/threonine-protein kinase MAPK Mitogen-activated protein kinase 1 SLP-65 Src homology 2 domain-containing leukocyte protein of 65 kDa BTK Bruton's tyrosine kinase PLCγ Phospholipase c gamma FOXO Forkhead box protein O p27kip1 Cyclin-dependent kinase inhibitor 1B Arf ADP-ribosylation factor Ink4 Inhibitor of Cyclin-Dependent Kinase 4

xi c-Myc Avian myelocytomatosis virus oncogene cellular homolog Fab Fragment antigen-binding BCAP B-cell adaptor for phosphoinositide 3-kinase BLNK B-cell linker protein XLA X-linked agammaglobulinemia DNA Deoxyribonucleic acid JAK Janus kinase ABL Abelson murine leukemia viral oncogene homolog PDGFRB Platelet-derived growth factor receptor beta CRLF2 Cytokine receptor-like factor 2 EPOR Erythropoietin receptor MLL Mixed lineage leukemia Ph Philadelphia chromosome BCR-ABL Breakpoint cluster region - Abelson murine leukemia viral oncogene homolog RAG1/2 Recombination-activating gene 1/2 TdT Terminal deoxynucleotidyl transferase ATCC American type culture collection FBS Fetal bovine serum NOD/SCID Nonobese diabetic-severe combined immunodeficient BSA Bovine serum albumin FACS Fluorescence-activated cell sorting HBSS Hanks balanced salt solution PI Propidium iodide DiOC6 3,3′-dihexyloxacarbocyanine iodide PVDF Polyvinylidene difluoride PBS Phosphate buffered saline HRP Horseradish peroxidase sgRNA Short-guide ribonucleic acid CRISPR Clustered regularly interspaced short palindromic repeats Cas9 CRISPR associated protein 9 GFP Green fluorescent protein CMFDA 5-chloromethylfluorescein diacetate

xii DAPI 4',6-diamidino-2-phenylindole RNA Ribonucleic acid GSEA Gene set enrichment analysis GEO Gene expression omnibus GEP Gene expression profiling IGHM Immunoglobulin mu heavy chain, gene IGLL1 immunoglobulin lambda-like polypeptide 1, gene MFIR Mean fluorescent intensity ratio Igµ Immunoglobulin mu heavy chain, protein Igγ Immunoglobulin gamma heavy chain, protein ANOVA Analysis of variance EV Empty vector KO Knockout BID Bis in die PB Peripheral blood CNS Central nervous system BM Bone marrow DMSO Dimethyl sulfoxide VAV Vav guanine nucleotide exchange factor ImmGen Immunological genome project BIM Bcl2-interacting mediator of cell death Trail Tumor necrosis factor-related apoptosis inducing ligand BCL6 B-cell leukemia/lymphoma 6 TCF3 Transcription factor 3 PBX1 Pre B-cell leukemia transcription factor 1 ETV6 ETS Variant 6 RUNX1 Runt-related transcription factor 1 FDR False discovery rate µm Micrometer log2 Logarithm of 2

xiii Acknowledgments I would like to thank my thesis supervisor at the Medical University of Vienna Assoc. Prof. Mag. Dr. Katrina Vanura for her continuous support, the numerous critical discussions and her helpful advice throughout the research and writing of this thesis. I would also like to deeply thank Prof. Ulrich Jäger head of the Department of Hematology and Hemostaseology at Medical University of Vienna for the opportunity to conduct my thesis project at his institution, for his continuous support and his critical questions. Equally important for the success of this study was my former boss and mentor Prof. Jan A. Burger at MD Anderson Cancer Center, Department of Leukemia in Houston, Texas. Prof. Burger’s knowledge and experience in the field of experimental hematology were highly beneficial for the work presented here and have also aided me in bringing me closer to my own scientific goals. I would like to thank Dr. med. R. Eric Davis, whose expertise in novel genome editing technologies has been key for all CRISPR/Cas9 related work presented in this study. Moreover, his knowledge and experience in bioinformatic analyses was highly beneficial for the gene expression profiling analyses conducted throughout the study. I would like to thank my coworkers in Prof. Burger’s laboratory Ekaterina Kim, Mariela Sivina, Shubhchintan Randhawa, Julia Hoellenriegel, Nathalie Rosin, Elisa ten Hacken, Christopher Jäger and Deepesh Lad for the friendly atmosphere, the countless highly productive discussions and the support in dire times. Several members of Dr. med. R. Eric Davis laboratory have also contributed significantly to the success my thesis project: Ondrej Havranek, who spent precious time from his own research projects to share his extensive knowledge in molecular biology with me. By doing so he taught me the basics of molecular cloning and raised in me an ever- growing interest in genome editing technologies; and Zhiqiang Wang, whose technical experience with gene expression profiling experiments has greatly improved the quality of the obtained data. Furthermore, I would like to thank Prof. Hassan Jumaa from the Department of Immunology at Ulm University and Prof. Markus Müschen from University of California, San Francisco for the critical discussions and proof reading of the final manuscript for publication.

xiv 1 Introduction

1.1 B-lymphopoiesis in the bone marrow

1.1.1 General

The initial steps of B-cell development take place in the bone marrow and are tightly linked to the generation of a functional B-cell receptor (BCR) (LeBien, 2000). It proceeds under the sole premise of generating mature B-cells with distinct B-cell receptors that are capable of recognizing foreign pathogens while sparing host tissues. During maturation B-cells undergo several developmental stages each characterized by a distinct set of cell surface molecules and a unique gene expression signature (Hystad et al, 2007) that allow for the robust identification of individual B-cell precursor subsets in the bone marrow (Hardy & Hayakawa, 2001). Ensuing their initial appearance in pluripotent hematopoietic stem cells (pHSC), the concerted actions of the transcription factors E2A, PU.1 and IKAROS prime multipotent progenitors (MPP) for B-cell development followed by their commitment to the B-cell lineage through the induction of the B-cell specific transcription factors EBF1 and PAX5 (Melchers, 2015). Following lineage commitment B-cell development is mainly driven by the generation of signaling competent BCRs through random reshuffling of the BCR-associated gene loci in a process referred to as somatic recombination (Figure 1). This involves the random recombination of variable (V), diversity (D) and joining (J) gene segments of the immunoglobulin heavy chain gene (IgHC) locus at the pro-B cell stage and the succeeding recombination of V and J gene segments of the immunoglobulin light chain gene (IgLC) locus at the pre-B cell stage. Ultimately, this results in the formation of immature B-cells with distinct BCRs, that exit the bone marrow and continue maturation in the secondary lymphoid tissues of the spleen and lymph nodes.

Figure 1. Stages of B-cell development in the bone marrow. IgHC rearrangement takes place at the pro-B cell stage, resulting in the expression of the pre-BCR at the pre-B cell stage and consequently the induction of IgLC rearrangement. Immature B-cells are characterized by successful rearrangements at both Ig loci and the surface expression of a mature BCR.

1.1.2 Checkpoints in B-cell development

The lifelong generation of B-cells, each capable of forming BCRs against an infinite number of antigens requires several developmental checkpoints that ensure BCR functionality while preventing the expansion of B-cell progenitors with self-reactive BCR specificities. Consequently, only a small fraction of the progenitor cells initially entering the B-cell developmental program exit the bone marrow as immature B-cells. The remainders are primed to undergo apoptosis, either due to the lack of functional immunoglobulin rearrangements or as a result of the expression of auto-reactive BCRs. Failure to eliminate these potentially harmful B-cell progenitors underlies several B-cell- mediated disorders, such as autoimmune diseases and precursor B-cell malignancies. During B-cell development in the bone marrow two developmental checkpoints are of particular importance: the precursor B-cell receptor (pre-BCR) checkpoint at the pro-B to pre-B transition and the immature B-cell checkpoint on the verge of immature B-cells exiting the bone marrow to populate the secondary lymphoid tissues.

1.1.3 The pre-BCR checkpoint

Surface expression of a functional pre-BCR complex constitutes the first checkpoint of B-cell development in the bone marrow. The structure of the pre-BCR closely resembles the structure of the mature BCR (Figure 2). It consists of two IgHC and the signal transducing heterodimer of Igα and Igβ (Mårtensson et al, 2010). However, instead of a rearranged light chain (LC) the pre-BCR contains the surrogate light chain (SLC), a heterodimer of the two invariant proteins λ5 and VpreB. Surface expression of the pre- BCR at the pre-B cell stage serves several purposes: (1) it indicates the successful

2 rearrangement of the IgHC locus; (2) it probes the capability of the newly synthesized IgHC for subsequent IgLC binding by pairing with the SLC; and (3) it induces somatic recombination at the IgLC gene loci (Mårtensson et al, 2010). Successful assembly of the pre-BCR triggers an initial burst of pre-B cell proliferation in order to increase the number of productive IgHC rearrangements prior to the onset of IgLC rearrangement. In contrast, B-cell progenitors failing to assemble a functional pre-BCR are rapidly removed from the pool of developing B-cells via apoptosis.

Figure 2. Structures of the pre-BCR and BCR. The structure of the pre-BCR closely resembles the structure of the BCR. Both consist of two immunoglobulin heavy chains and the signal transducing heterodimers of Igα and Igβ. However, whereas the IgHC of the BCR pairs with the IgLC, the IgHC of the pre-BCR complexes with the SLC.

The mechanisms underlying pre-BCR activation are still discussed controversially. Studies by groups of Fritz Melchers and Hassan Jumaa have shown that aggregation of pre-BCRs on the surface of pre-B cells is sufficient to generate a cell-autonomous signal that drives pre-B cell proliferation and differentiation (Kazuo Ohnishi & Fritz Melchers, 2003). According to their model pre-BCR aggregation requires the non-Ig portion of λ5 of one pre-BCR to non-covalently bind a highly conserved region in the IgHC constant region of another pre-BCR in the vicinity (Rudolf Übelhart et al, 2010). Consistently, targeted mutations of either binding partner disrupt pre-BCR signaling and block pre-B cell proliferation in vitro. The structural prerequisites for pre-BCR aggregation have recently been uncovered by Bankovich et al., who used human pre- BCR fab-like fragments and electron microscopy to highlight the importance of the non- Ig portion of λ5 for pre-BCR dimerization (Bankovich et al, 2007). Pre-BCR aggregation ultimately results in the activation of the pre-BCR signaling cascade and induces the rapid internalization of the pre-BCR oligomers. Due to this constant receptor

3 internalization, pre-BCR expression in pre-B cells is markedly lower than BCR expression on mature B-cells. Fueled by the observation that human pre-BCRs are also capable of binding bone marrow stromal cells in vitro Gauthier et al. proposed an alternative model of pre-BCR activation, involving the λ5-dependent binding of Galectin-1 expressed on the surface of bone marrow stromal cells (Gauthier et al, 2002). Along these lines, pre-BCR+ cells in healthy mice are preferentially found in close proximity to Galectin-1-expressing stromal cells (Mourcin et al, 2011) and pre-B cells in the bone marrow of Galectin-1- deficient mice are characterized by a decrease in their proliferative capacity (Espeli et al, 2009). Subsequent structural analyses of the Galectin-1-pre-BCR interaction revealed that the λ5 residues involved in Galectin-1 binding are distinct from those required for cell-autonomous signaling (Elantak et al, 2012), thereby raising the possibility, that cell-autonomous and ligand-induced pre-BCR signaling might both be required for optimal pre-BCR activation. Upon pre-BCR activation SRC kinases, such as LYN phosphorylate the immunoreceptor tyrosine-based activation motives (ITAMs) of Igα and Igβ (Figure 3) (Guo et al, 2000). Subsequently spleen tyrosine kinase (SYK), a non-receptor tyrosine kinase, is recruited to the plasma membrane by binding the phosphorylated ITAM residues through its tandem SH2 domains (Fütterer et al, 1998). ITAM binding, phosphorylation through LYN and auto-phosphorylation concertedly activate SYK kinase function (Keshvara et al, 1997; Mócsai et al, 2010). SYK in turn activates several downstream cascades including PI3K-AKT-, MAPK- and SLP-65/BTK/PLCγ2 pathways. PI3K- and BTK signaling cascades serve two distinct purposes downstream of the pre- BCR. PI3K signaling has been shown to be required for pre-BCR-dependent cell proliferation via the inhibition of the FOXO-dependent upregulation of cell cycle inhibitors, including p27kip1, ARF and INK4 and the upregulation of c-MYC (Kitamura et al, 1992; Tsukada et al, 2012; Pearl et al, 1978). In contrast signal transduction through SLP-65, BTK and PLCγ2 downregulates pre-BCR expression (Parker et al, 2005), blocks cell proliferation and induces pre-B cell differentiation via the induction of the somatic recombination machinery including the upregulation of RAG1/2 protein expression (Geier & Schlissel, 2006) and an increase in the accessibility of the kappa light chain gene locus (Thompson et al, 2007). The molecular mechanisms underlying the switch from pro-proliferative to pro-differentiating pre-BCR signaling are still ill-

4 defined but at least in part seem to rely on the PI3K-AKT dependent upregulation of SLP-65 expression levels.

Figure 3. The pre-BCR signaling cascade. Pre-BCR activation triggers a signaling cascade involving several signal-propagating kinases including LYN, SYK and BTK as well as adapter proteins such as CD19, BCAP and BLNK, which bring kinases and their respective substrates in close proximity.

The crucial role of pre-BCR signaling for B-cell development is underscored by mouse models and human B-cell disorders characterized by deficiencies in pre-BCR pathway components. Failure to express the pre-BCR in mice or humans, either through disruption of Igµ surface expression (Yel et al, 1996; Kitamura et al, 1991) or due to mutations in the SLC component λ5 (Minegishi & Coustan-Smith, 1998; Kitamura et al, 1992) blocks B-cell development at the pro-B to pre-B transition and results in an almost complete absence of peripheral blood B-cells. Similarly, B-cells deficient for pre-BCR downstream signaling molecules including Igα or BTK fail to develop past the pre-BCR+ stage. In humans X-linked agammaglobulinemia (XLA), an inherited immunodeficiency syndrome caused by deleterious mutations in the BTK gene serves as posterchild for the consequences of altered pre-BCR signaling on B-cell development. XLA patients are characterized by the almost complete absence of mature B-cells in the peripheral blood (Tsukada et al, 2012) despite normal pre-B cell numbers in the bone marrow, consistent with a block at the pre-BCR+ stage of B-cell development (Pearl et al, 1978). Along these lines mice deficient for SYK (Cheng et al, 1995; Turner et al, 1995) or the

5 catalytic components of PI3K (Ramadani et al, 2010) exhibit severe defects in B-cell development at the pro-B to pre-B cell transition.

1.1.4 The immature B-cell checkpoint

The switch from pro-proliferative to pro-differentiative pre-BCR signaling is succeeded by the suppression of SLC gene expression, the disappearance of pre-BCRs from the cell surface and the upregulation of the somatic recombination machinery to commence IgLC gene rearrangement at the two IgLC loci, κ and λ (Nemazee, 2017; Melchers, 2015). Morphologically this is reflected by the transition from large-cycling to small resting pre-B cells. IgLC gene recombination starts at the κ and proceeds to the λ gene loci until it yields an in-frame V to D gene segment rearrangement resulting in an IgLC capable of pairing with the preformed IgHC to assemble a mature BCR. Once expressed on the cell surface the BCRs antigen specificity determines the future fate of the developing B-cell. Due to the stochastic nature of IgHC and IgLC rearrangements a considerable amount of newly formed BCRs cross-react with self-antigens (Nemazee David, 2008). Binding of these autoreactive BCRs to their cognate autoantigen in the bone marrow typically elicits a strong receptor response followed by rapid receptor internalization, which signals the self-reactive B-cell to reinitiate somatic recombination at its IgLC gene loci. This process of receptor editing continues until it yields a non-self- reactive BCR specificity or until the death of the affected B-cells via apoptosis. Given the high frequency of autoreactive BCRs in the initial pool of BCR specificities (Wardemann et al, 2003) receptor editing contributes significantly to the final composition of the B-cell repertoire. In keeping with the fate of autoreactive B-cell progenitors that fail receptor editing, pre-B-cells lacking BCRs of any specificity are also removed from the pool of developing B-cells. Ultimately, the survival and positive selection of immature B-cells at the immature B-cell checkpoint requires a minimum signaling activity from the BCR, below the threshold for autoreactivity. This type of low- level BCR activation has been referred to as tonic BCR signaling. The nature of these signals, particularly as to whether they emerge as a consequence of the interaction of the BCR with low-affinity self-antigens or as a result of the random association and activation of BCR molecules on the cell surface or a combination of both remains to be determined (Monroe, 2006; Jianying Yang & Michael Reth, 2010). On the molecular level the differential outcomes of autoreactive and tonic BCR signaling at the immature B-cell stage relies on the same pathways and signaling molecules that

6 distinguish pro-proliferative from pro-differentiative pre-BCR activation at the pre-B cell stage (Köhler et al, 2008). Tonic BCR signaling requires the SRC kinase- and SYK- dependent phosphorylation of CD19 and BCAP (Aiba et al, 2008), which results in the activation of PI3K and the PI3K-mediated suppression of RAG1/2 gene expression (Llorian et al, 2007; Verkoczy et al, 2007). The importance of PI3K for tonic BCR activation and the survival of immature B-cells in the bone marrow was shown in seminal studies by Srinivasan et al. and Okkenhaug et al. (Srinivasan et al, 2009; Okkenhaug et al, 2002). In contrast, it is assumed that strong BCR activation in immature B-cells i.e. after exposure to self-antigens actively suppresses PI3K activity via SLP65 and commences receptor editing through the activation of the BTK-SLP65-PLCγ2 signaling axis, the disinhibition of FOXO1 and consequently the upregulation of the somatic recombination machinery (Herzog et al, 2008). It is estimated that only 10% to 15% of the hematopoietic progenitors that commit to the B-cell lineage successfully complete B-cell development in the bone marrow and populate secondary lymphoid tissues (Rolink et al, 1998). An even smaller fraction of these will encounter their cognate antigen and persist as long-lived plasma- or memory B-cells. The importance of rigorously removing non-reactive or auto-reactive BCRs is underscored by the fact that autoreactive B-cell specificities persisting past the immature B-cell checkpoint have been implicated in several autoimmune conditions, such as systemic lupus erythematosus (SLE) (Yurasov et al, 2005; Lamoureux et al, 2007), rheumatoid arthritis (RA) or diabetes mellitus (DM) Type 1 (Henry-Bonami et al, 2013). Indeed, several mechanisms, such as anergy or receptor desensitization, collectively referred to as peripheral tolerance mechanisms are in place to inactivate autoreactive B-cells even after their egress from the bone marrow. However, in contrast to bone marrow checkpoints the outcomes of these peripheral tolerance mechanisms are typically reversible in nature. The pre-BCR and immature B-cell checkpoints therefore serve unique roles in B-cell development by shaping the B-cell repertoire and by reducing the frequencies of autoreactive BCRs.

1.2 Inhibitors of pre-BCR/BCR associated kinases

The recent discovery of the importance of the mature BCR for the development and pathophysiology of B-cell-derived malignancies, such as chronic lymphocytic leukemia (CLL), subsets of diffuse large b-cell lymphoma (DLBCL), mantle cell lymphoma (MCL)

7 and Waldenström’s macroglobulinemia (WM) has fueled the development of several small molecule inhibitors of BCR associated kinases. So far, efforts have predominantly focused on the development of inhibitors against the three upstream BCR kinases SYK, BTK and PI3K (Figure 4).

Figure 4. Pre-BCR/BCR signaling inhibitors. The discovery of BCR signaling as valuable therapeutic target for B-cell malignancies has led to the development of a plethora of BCR signaling inhibitors. So far the efforts have mainly focused on three BCR-associated kinases: SYK, BTK and PI3K.

1.2.1 SYK

SYK constitutes a 72 kDa non-receptor tyrosine kinase and together with the ζ-chain- associated protein kinase of 70 kDa (ZAP70) forms the SYK family of tyrosine kinases (Figure 5). It consists of two tandem Src homology 2 (SH2) domains and a kinase domain, separated by the Interdomains A and B (Mócsai et al, 2010). SYK is predominantly expressed in cells of the hematopoietic system, specifically in B-cells and in myeloid cells but not in T-cells and has been implicated in signal transduction from an array of cell surface receptors, including the pre-BCR/BCR, Integrin receptors, FC receptors and Toll-like receptors. In resting cells SYK adopts an inactive confirmation mediated by the interaction between the two Interdomains and the kinase domain (Arias-Palomo et al, 2007). Upon receptor engagement the concerted action of ITAM- binding, transphosphorylation by SRC-kinases and auto-phosphorylation allow SYK to adopt an active confirmation and recruit it to the inner leaflet of the plasma membrane in close proximity to its substrates. Consequently, SYK activates downstream signaling cascades either directly via phosphorylation of kinases such as PI3K, BTK and PLCγ2

8 or indirectly via phosphorylation of adapter proteins which in turn serve as docking sites for kinase proteins.

Figure 5. Structural composition of SYK family kinases. Tandem SH2 domains promote ITAM binding and consequently kinase activation. AA: amino acids. Modified from (Mócsai et al, 2010)

Fostamatinib disodium, previously also known as R406, was among the first inhibitors of SYK in clinical development (Braselmann et al, 2006). It constitutes an ATP- competitive inhibitor of SYK with preclinical activity in B-cell malignancies, including CLL (Quiroga et al, 2009; Gobessi et al, 2008), MCL and DLBCL (Chen et al, 2008, 2013). In several in vitro model systems fostamatinib was shown to selectively suppress BCR- derived survival signals, such as the BCR-dependent calcium release from the endoplasmic reticulum and the antigen-induced activation of PI3K- and MAPK pathways (Quiroga et al, 2009). Moreover, pretreatment with fostamatinib prevented the migration of CLL cells towards a gradient of tissue-homing cytokines, thereby depriving them of pro-survival signals from the surrounding tissue-microenvironment. In a clinical Phase 1/2 trial in a heavily pretreated cohort of non-Hodgkin lymphoma patients these effects translated into objective response rates in 55% of CLL and 11% of MCL patients (Friedberg et al, 2010). These encouraging initial results have fueled the recent development of the novel more potent and more specific SYK inhibitors entospletinib (GS-9973), cerdulatinib (PRT062070) and the Portola compounds PRT318 and PRT2761. Collectively these substances all belong to the group of ATP-competitive SYK tyrosine kinase inhibitors and mimic the in vitro effects observed with fostamatinib, albeit at lower compound concentrations and reduced off target effects (Hoellenriegel et al, 2012; Sharman et al, 2015). In a clinical phase 1/2 trial in CLL patients entospletinib was reported to achieve an objective response rate of 61% with an acceptable toxicity profile (Sharman et al, 2015). The clinical assessment of cerdulatinib in BCR-dependent malignancies is currently ongoing (NCT01994382).

9 1.2.2 BTK

BTK belongs to the non-receptor tyrosine kinases (non-RTK) and is predominantly expressed in cells of the hematopoietic lineage, particularly in B-cells (Smith et al, 1994). Together with the five closely related kinases TEC, ITK, RLK/TXK and BMX, BTK forms the group of TEC family tyrosine kinases (TFK) (Figure 6). TFKs share their core structure with the group of non-RTKs, including the C-terminal kinase domain and two Src homology domains (SH2 and SH3) (Smith et al, 2001). The SH2 and SH3 domains primarily function as docking sites for downstream targets as well as for upstream regulators of BTK kinase activity. SH2 binding partners include B-cell linker protein (BLNK) (Hashimoto et al, 1999) and VAV whereas SYK, WASP and c-CBL rely on the SH3 domain for BTK binding (Qiu & Kung, 2000). Besides SH2 and SH3 domains TFKs also possess several unique regions that distinguishing them from other non-RTK. These include the N-terminal pleckstrin homology (PH) domain which is involved in membrane-tethering of BTK prior to its activation and the Tec homology (TH) domain which consists of the BTK domain and several proline rich regions (PRR). By binding Zn++ ions (BTK domain) and through the formation of intra- and intermolecular interactions (PRRs) both subdomains are essential for optimal stability and BTK kinase function (Qiu & Kung, 2000).

Figure 6. BTK domain structure and binding partners. PKC: protein kinase C, PIP5K: phosphatidylinositol-4-phosphate 5-kinase, WASP: Wiskott–Aldrich syndrome protein, SLP65: SH2 domain-containing leukocyte protein of 65 kDa. Modified from (Hendriks et al, 2014)

Activation of BTK requires the concerted action of phosphatidylinositol 3-Kinase (PI3K), SRC family kinases and SYK and typically follows a two-step process, consisting of the translocation of cytoplasmic BTK to the plasma membrane and its activation through phosphorylation. Upon pre-BCR/BCR receptor engagement, increasing levels of the

PI3K product phosphatidylinositol (3,4,5) trisphosphate (PIP3) tether BTK to the plasma membrane via its PH-domain, in close proximity to SRC kinases and SYK, which in turn

10 activate BTK through trans-phosphorylation of Y551 in the kinase domain (Rawlings et al, 1996)(Baba et al, 2001) In contrast, Y223 is a target for BTK autophosphorylation and while its biologic function remains elusive (Park et al, 1996) Y223 phosphorylation levels serve as widely accepted measure for BTK kinase activity. Ibrutinib (PCI-32765) constitutes the clinically most advanced BTK inhibitor and the FDA has recently granted its approval for the treatment of CLL, MCL and WM. Ibrutinib exerts its effects by covalently binding a highly-conserved cysteine residue (Cys-481) in the kinase domain of BTK, thereby irreversibly blocking its activity (Honigberg et al, 2010). In BCR-dependent malignancies ibrutinib was shown to prevent BCR-derived survival signals (Herman et al, 2011) and the BCR-mediated interaction with the leukemia microenvironment through downregulation of the T-cell attracting cytokines CCL3 and CCL4 (Ponader et al, 2012). In DLBCL and MCL ibrutinib was also shown to inhibit BCR-derived NFκB activation (Davis et al, 2010; Rahal et al, 2013). In clinical trials ibrutinib elicited substantial and durable responses in a wide variety of BCR-dependent malignancies. In CLL phase 2 and phase 3 trials ibrutinib consistently achieved overall response rates between 70% and 90% irrespective of established clinical and genomic risk factors (Byrd et al, 2014; O’Brien et al, 2014a; Farooqui et al, 2015). Comparable results were observed in WM patients under ibrutinib therapy (Treon et al, 2015). These exciting results have formed the foundation for the development of alternative second- generation inhibitors of BTK kinase activity. These include Spebrutinib (CC-292), Acalabrutinib (ACP-196) and ONO-4059, which are currently under preclinical and clinical evaluation.

1.2.3 PI3K

Various cell types rely on PI3K signaling to regulate their growth, proliferation and survival. Sequence homology and varying substrate specificities allow the distinction of three classes of PI3Ks: Class 1 PI3Ks, which are particularly involved in cancer (Vivanco & Sawyers, 2002); Class 2, which are required for glucose transport, insulin signaling and cell migration and Class 3, which have been implicated in the regulation of autophagy and the endosome-lysosome maturation (Jean & Kiger, 2014). Class 1 PI3Ks typically consist of a regulatory subunit and a catalytic subunit (Figure 7). Based on the combination of distinct regulatory and catalytic subunits Class 1 PI3Ks can be further classified into PI3Ka, PI3Kb, PI3Kg, and PI3Kd. PI3Ka and PI3Kb exhibit a broad tissue distribution, whereas PI3Kg and PI3Kd expression is restricted to the hematopoietic

11 system, where they are involved in signal transduction from a wide array of immune cell receptors, including the pre-BCR/BCR signaling complex (Jou et al, 2002). Along these lines, B-cells from mice lacking functional PI3Kd poorly respond to immunization and exhibit defective BCR and CD40 signaling (Okkenhaug et al, 2002; Clayton et al, 2002). Following pre-BCR/BCR engagement, the regulatory subunit facilitates the recruitment of the PI3K heterodimer to the plasma membrane in close proximity to its substrate phosphatidylinositol-4, 5-bisphosphate (PIP2). This in turn activates its catalytic domain and facilitates the conversion of PIP2 in phosphatidylinositol-3, 4, 5-trisphosphate (PIP3).

PIP3 serves as membrane anchor for several PH-domain containing proteins, including BTK, PLCγ2 and AKT.

Figure 7. Domain structure of PI3K regulatory and catalytic subunits. P: Proline rich region, BH: BCR homology domain, C2:, PIK: phosphatidylinositol kinase homology domain. Modified from (Engelman et al, 2006)

Idelalisib (CAL-101) constitutes a selective PI3Kd inhibitor with preclinical and clinical activity in several BCR-dependent malignancies (Lannutti et al, 2011). Clinical trials have yielded encouraging results for idelalisib in CLL, WM and follicular lymphoma, which resulted in its FDA-approval for the treatment of relapsed/refractory CLL and follicular lymphoma patients (Furman et al, 2014; Brown et al, 2014; Gopal et al, 2014). Based on these results several second-generation PI3K inhibitors are currently in preclinical and clinical development for use in hematologic malignancies (O’Connor et al, 2015; O’Brien et al, 2014b). Inhibitors of BCR signaling have been proven game changers for the treatment of B-cell malignancies, producing durable responses even in heavily pretreated patient populations. In keeping with the essential role of the pre-BCR for normal B-cell development it is tempting to speculate whether these encouraging results in mature B- cell derived disorders can be recapitulated in subgroups of B-cell precursor malignancies. Besides the exceptional clinical activity of BCR inhibitors, their unique mechanism of action is associated with a distinct side effect profile, including diarrhea, fatigue and hypertension. This is in stark contrast to the predominantly

12 myelosuppressive side effects observed with established chemotherapeutic agents and makes them valuable candidates for combination chemotherapy regiments.

1.3 B-cell acute lymphoblastic leukemia (B-ALL)

1.3.1 General

An estimate of 6000 US citizens develop acute lymphoblastic leukemia (ALL) every year (Siegel et al, 2012). In 80% of these cases the leukemia cells express B lymphocyte markers alongside early progenitor antigens (Armstrong, 2005), consistent with the malignant transformation of conventional precursor B lymphocytes during their maturation in the bone marrow (Zhou et al, 2012). Over the last 50 years, the introduction of combination chemotherapy, improved risk stratification and enhanced supportive care led to a steady increase in the survival rates of ALL patients. Today, state of the art therapy cures an average of 85% of childhood and 40% of adult ALL cases (Pui et al, 2009; Hunger et al, 2012; Sive et al, 2012). However, ALL treatment remains challenging. The extensive exposure of children to cytotoxic agents causes side effects including cognitive impairment and second malignancies even years after completing therapy (Conklin et al, 2012; Mody et al, 2008; Nottage et al, 2011). Moreover, despite strong efforts 60% of adults still succumb to their disease. It is widely accepted that further improvements of ALL therapy will have to rely on novel more targeted treatment approaches. This includes the specific inhibition of kinases that promote the clonal expansion of B-ALL cells, using small molecule kinase inhibitors. However, the successful application of such targeted agents requires a profound knowledge of the biological mechanisms driving malignant proliferation.

1.3.2 Genetic characteristics of B-ALL

The genetic alterations underlying B-ALL include changes in chromosome number, structural chromosomal abnormalities and submicroscopic lesions including point mutations, DNA copy number variations and deletions (Mullighan, 2012). Whereas initial discoveries of genetic abnormalities were mainly driven by the need for markers for risk stratification (Secker-Walker et al, 1978) it has become increasingly clear that the identification of genomic abnormalities is also prerequisite for deciphering the mechanisms of leukemia initiation, maintenance and clonal evolution. According to the latest WHO classification of acute leukemia’s B lymphoblastic leukemia/lymphoma can

13 be stratified according to chromosome number into hypodiploid (<44 Chr) or hyperdiploid (>50 Chr) B-ALL, or according to the presence of recurrent structural chromosome abnormalities in t(12;21)+-, t(1;19)+-, MLL-rearranged-, or Philadelphia chromosome+ (Ph+) B-ALL (Arber et al, 2016; Vardiman et al, 2009). Besides these well- established recurrent genomic lesions the recent emergence of next-generation sequencing technologies has led to the discovery of additional genomic aberrations involving for instance the lymphocyte-specific transcriptions factors PAX5 and IKAROS (Mullighan et al, 2007, 2008), JAK and ABL tyrosine kinases (Roberts et al, 2012, 2014) as well as growth-factor receptors such as platelet-derived growth factor receptor beta (PDGFRB) (Roberts et al, 2012), Cytokine Receptor-Like Factor 2 (CRLF2) (Mullighan et al, 2009) or the Erythropoietin receptor (EPOR) (Iacobucci et al, 2016). The integration of these newly identified alterations in the established diagnostic workup of B-ALL currently allows the robust genetic classification of ~90% of B-cell ALL cases (Lilljebjörn & Fioretos, 2017) (Figure 8).

MEF2D-rearranged, Other, 10.0% 0.5% ZNF384-rearranged, 1.0%

ETV6-RUNX1-like, High Hyperdiploid, 3.0% 30.0% DUX4-rearranged, 4.0%

iAMP21, 0.5%

BCR-ABL-like, 8.0%

Hypodiploid, 1.0%

BCR-ABL, 3.0%

TCF3-PBX1, 7.0%

ETV6-RUNX1, MLL-rearranged, 25.0% 7.0%

Figure 8. The genetic subclassification of B-cell ALL. Numbers in Brackets indicate % of total B-ALL patient population. Applying state of the art diagnostic procedures leaves 10% of B-ALL cases without a distinct genetic alteration. Modified from (Lilljebjörn & Fioretos, 2017)

14 These genetic aberrations induce malignant transformation through various mechanisms. In t(12;21)+, t(1;19)+ or MLL-rearranged B-cell ALL the rearrangements result in the expression of chimeric transcription factors with novel oncogenic properties that disrupt regular B-cell development. In contrast, rearrangements involving kinases or cell surface receptors typically result in the aberrant activation of signaling cascades which facilitate malignant transformation and drive proliferation and survival of leukemia cells. The latter are of particular interest since they offer the opportunity to selectively inhibit these signaling pathways for B-ALL therapy. This has proven particularly successful in the case of Ph+ B-ALL were the addition of BCR-ABL kinase inhibitors to standard B-ALL chemotherapy has resulted in improved remission rates and prolonged survival (Bernt & Hunger, 2014; Zwaan et al, 2013).

1.3.3 B-ALL immunophenotype

In the majority of cases acute lymphoblastic leukemia cells are characterized by a precursor B-cell immunophenotype. Combined assessment of cell surface and intracellular markers facilitates the stratification of B-ALL cells according to their maturational stage into pro-B, common-B, pre-B and mature-B ALL, in line with their emergence from consecutive stages of B-cell development (Table 1) (Ratei et al, 1998).

CD19 CD34 CD10 cyto-Igµ pre-BCR BCR Prevalence Pro-B + + - - - - 8% Common-B + +/- + - - - 71% Pre-B + +/- + + + - 20% Mature B + - + + - + 1% Table 1. B-ALL subtypes by developmental stage.

Pro-B ALL accounts for roughly 10% of B-ALL cases and blast cells typically express CD19, CD34, CD22, TdT and cytoplasmic CD79a but lack CD10 expression (Chiaretti et al, 2014; Ratei et al, 1998). Common-B cell ALL constitutes the most frequent B-ALL subtype and is distinguished from pro-B ALL by the additional expression of CD10. Expression of a functional IgHC marks pre-B ALL cells and indicates their emergence from B-cell progenitors harboring a successful IgHC rearrangement. Mature-B ALL, also referred to as Burkitt’s leukemia/lymphoma, is characterized by the expression of a functional BCR, suggesting the malignant transformation of immature-B cells in the bone

15 marrow or secondary lymphoid tissues. In line with its importance for B-ALL biology certain immunophenotypes are typically associated with specific genomic abnormalities. For instance, blast cells harboring an MLL-AF4 rearrangement typically exhibit the pro- B phenotype, t(1;19)+ blasts almost exclusively express pre-B ALL features and Burkitt leukemia cells are typically of the mature-B-ALL phenotype. However, despite the importance of the immunophenotype for the diagnosis of B-ALL, risk stratification nowadays almost exclusively relies on clinical features, treatment response and the presence or absence of certain cytogenetic or molecular abnormalities at diagnosis (Faderl et al, 2010).

16 2 Aims of this study In the past, ALL research has largely focused on chemotherapy and chemotherapy resistance mechanisms. Today, there is an urgent need to identify leukemia driving pathways that ultimately serve as targets for kinase inhibitors. These agents will allow us to tailor ALL therapy to the specific needs of certain leukemia subtypes and thereby help to further improve outcome and to reduce chemotherapy-associated side effects. Recent treatment advances in Philadelphia chromosome positive ALL (Ph+ ALL), accomplished by the addition of highly specific BCR-ABL kinase inhibitors to standard chemotherapy, is the most prominent example of the validity of this approach and it underscores the importance of selective kinase inhibition in ALL. We are hypothesizing that pre-BCR signaling serves as one of these novel targets for B-ALL therapy. The pre-BCR’s cell-autonomous mechanism of activation as well as its growth promoting role in B-lymphopoiesis make it an ideal candidate oncogenic driver in subgroups of B-ALL. Indeed, in roughly 20% of cases B-ALL blast cells are arrested at the pre-B cell stage of B-cell development and are therefore likely to express the pre- BCR. In line with our hypothesis Bicocca et al. recently discovered a link between pre- BCR signaling and cell viability in a small subgroup of pre-B ALL cases and Bertrand et al. observed increased cell proliferation in response to pre-BCR stimulation in a pre-B ALL cell line (Bicocca et al, 2012; Bertrand et al, 2000). However, the direct contribution of pre-BCR signaling to B-ALL biology as well as its potential role as novel therapeutic target have not been evaluated yet. To address these questions, we formulated three main aims for this study:

1. To evaluate the expression and functionality of the pre-BCR in B-ALL cells and to assess their dependence on active pre-BCR signaling.

2. To delineate the underlying molecular mechanisms of pre-BCR-dependency in pre-BCR+ ALL cells.

3. To study the effects of pre-BCR signaling inhibitors on proliferation and survival in in vitro and in vivo models of pre-BCR+ ALL.

17 3 Results

3.1 A subset of B-cell ALL resembles normal pre-BCR+ B-cell progenitors by gene expression profile

Due to the essential role of the pre-BCR for proliferation and survival of non-malignant B-cell progenitors we hypothesized that pre-BCR-dependent ALL cells might resemble normal pre-BCR+ B-cell progenitors by gene expression profile (GEP). Using publicly available GEP data of non-malignant B-cell progenitors we identified the genes most differentially expressed between pro-B (pre-BCR-) and pre-B (pre-BCR+) lymphocytes (Figure 9 and Table A1) and applied this gene signature to cluster publicly available B- ALL GEP data sets from St. Jude Children’s Hospital comprising a total of 483 B-ALL cases.

Figure 9. Genes differentially expressed between pro-B and pre-B cells. The heatmap depicts the 83 most differently expressed genes between non-malignant pre-BCR- pro-B cells and pre-BCR+ pre-B cells (GSE45460, Lee ST et al. 2012). The expression values for each gene were log2-transformed and mean-centered. Genes in red are upregulated, genes in green are downregulated in the respective sub-groups. Modified from Koehrer et al.(Köhrer et al, 2016).

This resulted in the identification of a distinct subgroup of 16,4% of B-ALL cases with a GEP signature highly similar to normal pre-BCR+ B-cell progenitors, thereafter referred to as pre-B ALL (Figure 10a). This cluster comprised 79 (16.4%) cases; half of these (49.4%) harbored t(1;19), followed by cases with non-recurrent cytogenetic abnormalities (27.8%), MLL-rearrangements (16.5%) and hypodiploidy (6.3%) (Figure 10b-c).

18 a b

d IGHM IGLL1 VPREB1 SYK **** **** ** **** 2 2 2 2 0 0 0 0 -2 -2 -2 -2 (mean centered) (mean centered) (mean centered) (mean (mean centered) (mean -4 -4 -4 log2 expression values values expression log2 values expression log2 values expression log2 log2 expression values values expression log2 -4 pre-B other pre-B other pre-B other pre-B other Figure 10. Pre-B ALL is characterized by a GEP highly similar to non-malignant pre-BCR+ B-cell progenitors and exhibits signs of active pre-BCR signaling. (a) The heatmap depicts 483 B-ALL cases clustered according to the gene set described in Figure 9. GEP values were log2-transformed, mean-centered and clustered using average-linkage clustering. This resulted in the identification of (b) 16.4% pre-B ALL cases with a GEP signature highly similar to non- malignant pre-B cells. (c) Frequency of the pre-B ALL phenotype by cytogenetic subgroup (d) Comparison of the log2-transformed gene expression values of the indicated genes between pre-B and non-pre-B (other) cases. Modified from Koehrer et al. (Köhrer et al, 2016).

Suggesting the presence of active pre-BCR signaling in this subgroup, expression of components of the pre-BCR and the pre-BCR signaling cascade, including IGHM, IGLL1, VPREB1 and SYK was significantly higher in pre-B ALL than in non-pre-B ALL cases (Figure 10d).

3.2 Pre-B ALL is characterized by pre-BCR surface expression

To assess the status of pre-BCR expression in pre-B ALL (cyto-Igµ+, surface IgM-) we consequently screened a panel of pre-B ALL cell lines and xenograft cells for pre-BCR surface expression using the pre-BCR-specific monoclonal antibody HSL2 (Tsuganezawa et al, 1998). Suggesting the importance of the pre-BCR for pre-B-ALL

19 we detected pre-BCR expression in the majority of pre-B ALL cases but not in pro-B or mature-B ALL (Figure 11).

pro-B-ALL pre-B-ALL mature-B-ALL REH RCH-ACV SMS-SB Nalm-6 697 X018 X134 BALL1 2.64 3.3 2.6 16.7 2.21 2.95 rel. cellrel. number pre-BCR fluorescence intensity (log) Figure 11. Pre-BCR expression in pre-B ALL cell lines and xenografts. Pro-B, pre-B and mature-B ALL cells were screened for pre-BCR surface expression. Numbers indicate mean fluorescent intensity ratios (MFIR). Histograms are representative for three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Pre-BCR expression levels were low, as assessed by mean fluorescent intensity ratios (MFIR), suggesting that the pre-BCR is constitutively active in pre-B ALL cells. Further supporting the importance of the pre-BCR for pre-BCR+ ALL, immunoblot analysis confirmed the expression of key pre-BCR signaling molecules in the majority of pre-B ALL cells (Figure 12).

2 6 - -ACV -SB - RCH SMS Nalm Kasumi697 Igα LYN

SYK

SHP-1

BLNK

BTK PLCγ2

VAV1 GAPDH Figure 12. Pre-BCR+ ALL cells express key pre-BCR signaling molecules. Immunoblot analysis of the indicated proteins in pre-BCR+ ALL cells. Western blots are representative of three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

In order to evaluate the functionality of these molecules within the pre-BCR signaling cascade we assessed their activation after exogenous pre-BCR stimulation with anti- Igµ specific F(ab’)2 fragments. Exposure of pre-BCR+ ALL cells to anti-Igµ induced a rapid cytoplasmic Ca++ release (Figure 13) and resulted in the phosphorylation of AKT and ERK, all likely indicators for the presence of an intact pre-BCR signaling cascade in pre-BCR+ ALL cases (Figure 14).

20 50 Sample % Gate 40 SMS-SB_10yg IgM 93.1 Viable Cells RCH-ACV_10yg Igm 92.9 Viable Cells 30 anti-Igµ Nalm-6_10ygIgm 92.3 Viable Cells 697_10ygIgM 91.8 Viable Cells 20 FL1-H: Fluo-3AM 10

0 50 100 150 sec

RCH-ACV Nalm-6 SMS-SB 697 Kinetics Figure 13. Pre-BCR+ cells respond to anti-Igµ treatment with Ca++ release. Pre-BCR+ cells were stained with the calcium dye Fluo-3AM according to protocol, followed by the exposure to 10 µg/ml anti-Igµ and the assessment of Fluo-3AM fluorescence via flow cytometry. Anti-Igµ stimulation resulted in a rapid increase in cytoplasmic Ca++ levels in all pre-BCR+ cells tested. The black arrow indicates the addition of anti-Igµ to the cell suspension. The graphs are representative of two independent experiments.

RCH-ACV SMS-SB Nalm-6 697 Ctl Igµ Igγ Ctl Igµ Igγ Ctl Igµ Igγ Ctl Igµ Igγ pAKT(S473) tAKT pERK tERK GAPDH Figure 14. Stimulation of pre-BCR+ ALL cells with anti-Igµ induces phosphorylation of AKT and ERK. Pre-BCR+ cells were seeded at equal numbers followed by the stimulation with 10 µg/ml anti-Igµ or 10 µg/ml anti-Igγ control stimulation for 10 minutes. Anti-Igµ stimulation resulted in a significant increase in pAKT and pERK levels. Immunoblots are representative for three independent experiments.

However, exogenous pre-BCR stimulation did not influence proliferation of pre-BCR+ ALL cells, neither in optimal growth conditions nor in conditions of serum starvation (Figure 15). RCH-ACV Nalm6 5 RCH-ACV Nalm-6 1.4×10 1.8×105 Control (20% FBS) 1.2×105 1.5×105 10µg/ml anti-Igµ (20% FBS) 1.0×105 1.2×105 10µg/ml anti-Igγ (20% FBS) 8.0×104 9.0×104 6.0×104 Control (1% FBS) 4 4 6.0×10 10µg/ml anti-Igµ (1% FBS)

Live Cells / Well / Cells Live 4.0×10 Live Cells / Well / Cells Live 4 10µg/ml anti-Igγ (1% FBS) 2.0×104 3.0×10 0.0 0.0 0 1 2 3 0 1 2 3 Days Days Figure 15. Stimulation with anti-Igµ does not affect proliferation of pre-BCR+ cells. The pre-BCR+ cells RCH-ACV (left panel) and Nalm-6 (right panel) were seeded at equal numbers in medium supplemented with 20% FBS (blue) or under conditions of serum starvation (1% FBS, red) followed by the addition of 10 µg/ml anti-Igµ or anti-Igγ. The number of viable cells was assessed at the indicated time points as described previously. Graphs depict mean values ± standard deviations of three independent experiments.

21 3.3 Pre-BCR+ ALL requires constitutive pre-BCR signaling for proliferation and survival

Since exogenous pre-BCR activation had no impact on proliferation of pre-BCR+ cells we consequently evaluated the contribution of constitutive pre-BCR signaling in the maintenance of pre-BCR+ ALL. To this end we generated stable pre-BCR knockout (pre- BCR-KO) cell lines, using CRISPR/Cas9 genome editing with Igµ-specific short guide RNAs (sgRNAs) targeting the expressed Igµ chain of the pre-BCR+ cells RCH-ACV and SMS-SB. This resulted in the complete loss of Igµ protein expression in both cell lines and consequently the eradication of pre-BCR surface expression. In line with the exclusive expression of the SLC as part of the pre-BCR complex, Igµ-KO cells were also negative for λ5 and VpreB surface expression (Figure 16a and b).

Figure 16. Pre-BCR and SLC expression after Igµ knockout. (a) Knockout of the expressed Igµ heavy chain with CRISPR/Cas9 gene editing results in the stable loss of Igµ protein expression in RCH-ACV and SMS-SB cells as assessed by immunoblot analysis. Western blots are representative for two independent experiments (b) Loss of pre-BCR and SLC surface expression in Igµ KO cells. KO1 and KO2 indicate two distinct Igµ specific sgRNAs. Histograms are representative for three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

To assess functional consequences of the pre-BCR-KO we compared cell proliferation and viability of Igµ- cells with their wildtype counterparts (Figure 17a and b).

22 a b 100

2.2×105 6.2×105 90 RCH-ACV SMS-SB 1.7×105 4.7×105 80 1.2×105 3.2×105 4 5 **** 7.0×10 **** 70 1.7×10 **** Viable Cells / Well **** Viable Cells / Well 2.0×104 2.0×104 **** **** **** **** Viability (% of Control) 60 0 2 4 6 0 2 4 6 Time (Days) Time (Days) 50 RCH-ACV SMS-SB EV KO1 EV KO2gRNA1 gRNA2 EVC KO1 KO2KO2 Figure 17. Effects of pre-BCR knockout on pre-B ALL proliferation and survival. (a) Pre-BCR knockout cells (KO1 and KO2) proliferate less than their wildtype counterparts (EV) and (b) exhibit signs of reduced viability. For (a) displayed are means ± standard deviations of three independent experiments. ****p<0.001, two-way ANOVA and Sidak’s test for multiple comparisons. (b) depicts means ± standard deviations of three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Interestingly, pre-BCR KO cells exhibited significantly reduced proliferation and slightly reduced viability, consistent with the importance of constitutive pre-BCR signaling for the maintenance of pre-BCR+ ALL. To assess whether the depletion of pre-BCR downstream signaling molecules recapitulates the pre-BCR KO phenotype we used CRISPR/Cas9 gene editing to selectively knockout SYK in the B-ALL cell lines REH (pro-B), Nalm-21 (pro-B; BCR-ABL+), RCH-ACV and SMS-SB (pre-B; pre-BCR- dependent). Despite successful elimination of SYK in all 4 cell lines, only RCH-ACV and SMS-SB cells required SYK for optimal growth and proliferation (Figure 18a and b). Collectively these results suggest that the tumor-promoting role of SYK is restricted to B-ALL cells with constitutively active pre-BCR signaling.

23 Baseline Day 2 Day 6 Day 8 b SYK KO 61.1% 56.1% SYK WT RCH-ACV a 30.9% 120 16.2% 100 80 60 SMS-SB 29.1% 24.6% 19.4% 14.9% 40 20 % SYK knockout cells

0 REH 0 2 4 6 8 34.8% 34.1% 34.3% 36.1% Time (Days)

REH Nalm-21

RCH-ACV SMS-SB cellrel. number Nalm21 33.9% 33.4% 32.7% 34.2%

SYK Fluorescence Intensity (log) Figure 18. Effects of SYK knockout on pre-BCR+ and pre-BCR- cells. (a) Fraction of SYK knockout cells at consecutive time points following transfection with SYK-specific CRISPR/Cas9 plasmids. pre-BCR- cells (red), pre-BCR+ cells (green), data indicates means ± standard deviations of three independent experiments each conducted with a distinct SYK-specific sgRNA. (b) Histograms depicting representative raw data from (a). Cells were stained for SYK in the cytoplasm via flow cytometry. Modified from Koehrer et al. (Köhrer et al, 2016).

3.4 The dependency of pre-BCR+ ALL on the pre-BCR and SYK can be exploited therapeutically by SYK inhibition

The selective dependence of pre-BCR+ cells on SYK was of particular interest to us since the recent development of BCR inhibitors for mature B-cell malignancies has spurred the development of several highly selective SYK tyrosine kinase inhibitors, suitable for clinical application (Friedberg et al, 2010; Hoellenriegel et al, 2012). To evaluate the potential role of SYK as therapeutic target in pre-BCR+-ALL we consequently treated a panel of pre-BCR+ and pre-BCR- ALL cell lines and xenograft cells with increasing concentrations of the SYK-specific small molecular inhibitor PRT318. In line with our previous results pre-BCR+ cells were particularly sensitive to PRT318 (Figure 19).

24 110 REH ICN12 90 RS4;11 Nalm-6 70 SFO3 RCH-ACV

50 TOM-1 SMS-SB Nalm-20 Tanoue 30

10 Metabolically active cells (%)

- Control 10-8 10-7 10-6 10-5 10-4 PRT318 Concentration (M)

Figure 19. Pre-BCR+ B-ALL cells are selectively sensitive to SYK inhibition. Pro-B (red), pre-B (green) and mature-B-ALL (brown) cells were incubated with increasing concentrations of the SYK inhibitor PRT318, followed by the assessment of cell proliferation using XTT assays. Pre-B ALL cells were particularly sensitive to SYK inhibition. Modified from Koehrer et al. (Köhrer et al, 2016).

We also hypothesized that if pre-BCR+ cells require SYK for proliferation and survival they must be equally sensitive to inhibitors targeting upstream constituents of the pre- BCR signaling cascade, such as LYN. We therefore treated pre-BCR+ and pre-BCR- cells with increasing concentrations of the SRC family kinase (SFK) inhibitor dasatinib. Confirming our hypothesis pre-BCR- and SYK-dependent B-ALL cells were also highly sensitive to treatment with dasatinib (Figure 20).

120 RS4;11 100 REH 80 RCH-ACV 60 SMS-SB 40 ICN12 20 Kasumi-2 Metabolically active cells (%) 0 0 10 20 30 40 50 Dasatinib (nM) Figure 20. Pre-BCR+ cells are particularly sensitive to dasatinib. Pre-BCR+ (green) and pre-BCR- (red) cells were incubated with the indicated concentrations of dasatinib followed by the assessment of its effects by XTT assay.

Consequently, we tried to confirm these results in an in vivo model of pre-BCR+ ALL. We therefore transplanted NOD/SCID mice with pre-BCR+ or pre-BCR- ALL xenograft cells and following engraftment, as indicated by the presence of > 5% leukemia cells in the peripheral blood, started treatment with 30mg/kg PRT318 BID or control (5% Captisol). After 10 days the mice were sacrificed and subjected to quantification of leukemia cell burden in the peripheral blood (PB), central nervous system (CNS) and

25 bone marrow (BM). Confirming our in vitro findings PRT318 selectively reduced tumor cell burden in mice with pre-BCR+ ALL, with significant reductions of leukemia cell infiltration in all three compartments (Figure 21).

pre-BCR+ ALL (X018)

7 8 7 ** 80 1.2×10 ** 8×10 1.2×10 ** 8 60 6×10 6 8.0×106 8.0×10 cells (abs.) + 8 cells (abs.) cells (abs.)

*** 4×10 +

40 + 4.0×106 4.0×106 8 20 2×10 PB huCD19 [%] 0 0.0 0 0.0 BM hCD19 CNS hCD19 CNS 0 7 10 PRT318 Captisol Spleen hCD19 PRT318 Captisol PRT318 Captisol Time (days) PRT318 Captisol pre-BCR- ALL (X089) ns ns ns 6 8080 1×107 1×108 2.0×10 7 1.5×106 6060 nsns 8×10 8×106 cells (abs.) cells (abs.) 7 6

6×10 + + 1.0×10 cells (abs.)

4040 + 4×107 5 nsns 6×106 5.0×10 20 20 2×107 PB huCD19 [%] PB huCD19 [%] 0.0 00 6 0

4×10 hCD19 CNS BM hCD19 PRT318 Captisol 00 77 1010 PRT318 Captisol Spleen hCD19 PRT318 Captisol TimeTime (days) (days) PRT318PRT318 CaptisolCaptisol Figure 21. PRT318 exhibits efficacy in a pre-BCR+ xenograft model of B-ALL. Following engraftment with pre-BCR+ (upper panel) or pre-BCR- (lower panel) xenograft cells mice were treated with 30mg/kg PRT318 or 5% Captisol. Pre-BCR+ xenografts were particularly sensitive to PRT318 treatment as indicated by reduced leukemia cell burden in peripheral blood (PB), bone marrow (BM), spleen (SP) and central nervous system (CNS). For PB huCD19 time course two-way ANOVA and Sidak’s test for multiple comparisons were used (***p<0.005). Group comparisons were performed using Mann- Whitney tests (**p<0.01). Modified from Koehrer et al. (Köhrer et al, 2016).

To proof these results in an additional model of pre-BCR+ ALL we treated NOD/SCID mice engrafted with pre-BCR+ ICN12 xenograft cells with the alternative SYK-selective tyrosine kinase inhibitor PRT062607. In this setting PRT062607 prolonged survival of leukemia-bearing mice significantly in comparison to control treatment (Figure 22).

26 100

*** Percent survival p<0.001 0 0 30 40 50 60 70 Time (Days) Control PRT062607 Figure 22. The SYK inhibitor PRT062607 prolongs survival in an alternative xenograft model of pre-BCR+ ALL. ***p<0.001 Mantel-Cox survival analysis. Modified from Koehrer et al. (Köhrer et al, 2016).

Similarly, we noted that primary B-ALL cells that were isolated directly from patient samples and that had a pre-B immunophenotype (cyto-Igμ+ and surface IgM−) were more sensitive to SYK inhibition with PRT318 than B-ALL cells with a pro-B immunophenotype (cyto-Igμ−, surface IgM−) (Figure 23).

110 100 pro-B ALL 1 90 80 pro-B ALL 2 70 pre-B ALL 1 60 50 pre-B ALL 2 Viability (% of Control) 40 0 1 2 3 4 5 PRT318 (µM)

Figure 23. PRT318 exhibits efficacy in pre-B ALL primary patient samples. Pre-B (cyto-Igµ+/surface IgM-) and pro-B (cyto-Igµ-/surface IgM-) ALL primary patient samples were treated with increasing concentrations of PRT318 for 48h followed by the assessment of cell viability. Modified from Koehrer et al. (Köhrer et al, 2016).

3.5 Pre-BCR signaling drives B-ALL by modulating PI3K signaling

In order to study the molecular requirements for pre-BCR dependency in pre-BCR+-ALL we assessed changes in the activity of the pre-BCR downstream molecules AKT, BTK and ERK after knockout of the pre-BCR as well as after exposure to PRT318. The inhibition of pre-BCR signaling resulted in the selective reduction of AKT phosphorylation, whereas BTK and ERK phosphorylation were not affected, suggesting the selective importance of PI3K-AKT signaling for the maintenance of pre-BCR+ ALL (Figure 24).

27 a SMS-SB b SMS-SB Nalm-6 RCH-ACV EV KO PRT318(µM) 0 0.5 1 2.5 5 0 0.5 1 2.5 5 0 0.5 1 2.5 5 pAKT(S473) pAKT(S473) tAKT tAKT pBTK (Y223) pBTK (Y223) tBTK tBTK pERK pERK tERK tERK GAPDH GAPDH Figure 24. PRT318 selectively inhibits PI3K-AKT pathway signaling activity. (a) SMS-SB cells were transfected with IgHC-specific CRISPR/Cas9 plasmids followed by flow cytometry based cell sorting and immunoblot analysis of the indicated proteins (b) Pre-BCR+ cells SMS-SB, Nalm- 6 and RCH-ACV were incubated with increasing concentrations of PRT318 for 2 hours followed by immunoblot analysis of the pre-BCR downstream signaling molecules AKT, BTK and ERK. Modified from Koehrer et al. (Köhrer et al, 2016).

In order to confirm that the effects of SYK inhibition on PI3K-pathway activity were specific for pre-BCR-dependent B-ALL we next compared the impact of PRT318 on AKT phosphorylation in pre-BCR+ and pre-BCR- ALL cells. In line with the results of the functional assays PRT318 selectively reduced pAKT in pre-BCR+ cells (Figure 25).

pre-BCR- ALL pre-BCR+ ALL REH RCH-ACV PRT318(µM) 0 0.1 0.25 0.5 1 0 0.1 0.25 0.5 1 pAKT(S473) tAKT GAPDH RS4;11 SMS-SB PRT318(µM) 0 0.1 0.25 0.5 1 0 0.1 0.25 0.5 1 pAKT(S473) tAKT GAPDH Nalm-6 PRT318(µM) 0 0.1 0.25 0.5 1 pAKT(S473) tAKT GAPDH Figure 25. PRT318 blocks PI3K-AKT signaling activity selectively in pre-BCR+ cells. Pre-BCR- (REH and RS4;11) and pre-BCR+ (RCH-ACV, SMS-SB, Nalm-6) cells were incubated with the indicated concentrations of PRT318 for 2h followed by immunoblot analysis of AKT phosphorylation. Immunoblots are representative for three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Along these lines pre-BCR+ xenograft cells exhibited higher baseline pAKT levels than pre-BCR- cells which were selectively sensitive to treatment with PRT318 (Figure 26).

28 a b 8 p=0.056 110 ** 100 7 90

6 80

(% Control) (% 70 5 pAKT (S473) MFIR pAKT (S473) MFIR 60

4 50 pre-BCR pre-BCR pre-BCR pre-BCR negative positive negative positive Figure 26. Pre-BCR+ xenografts exhibit higher baseline AKT phosphorylation which is selectively sensitive to PRT318. (a) MFIR values of baseline pAKT levels as assessed by flow cytometry. (b) Reduction of pAKT MFIR levels after exposure to PRT318 for 2h. Values were normalized to DMSO treated control cells. (a), (b) **p<0.01 Mann-Whitney test Modified from Koehrer et al. (Köhrer et al, 2016).

Further highlighting the importance of PI3K-AKT downstream of the pre-BCR, pre-BCR+ cells were also more sensitive to treatment with the pan-PI3K inhibitor LY294002 than their pre-BCR- counterparts (Figure 27).

110 90 70 50 30 10 Metabolically active cells (%) cells active Metabolically Control 1 5 10 15 20 LY294002 (µM) REH RCH-ACV ICN12 SMS-SB Figure 27. Pre-BCR+ ALL cells are sensitive to LY294002. Pre-BCR- and pre-BCR+ cells were treated with increasing concentrations of the pan-PI3K inhibitor LY294002 for 72 h followed by the quantification of its effects by XTT assays. Modified from Koehrer et al. (Köhrer et al, 2016).

Finally, we intended to investigate the nature of the connection between the pre-BCR and PI3K. In B cells activation of the PI3K/AKT pathway in response to BCR engagement is preceded by the formation of the CD19 signalosome, a complex including CD19, VAV, and the PI3K regulatory subunit p85. Both Igµ-KO or PRT318 treatment disrupted the formation of this complex, as indicated by reduced phosphorylation of CD19 and VAV1 (Figure 28a and b).

29 a b SMS-SB SMS-SB EV KO PRT318(µM) 0 0.5 1 2.5 5 pCD19 (Y531) pCD19 (Y531) tCD19 tCD19 pVAV1(Y174) pVAV1(Y174)

tVAV1 tVAV1 GAPDH GAPDH Figure 28. Pre-BCR inhibition blocks formation of the CD19 signalosome. (a) Pre-BCR KO cells or (b) PRT318-treated cells were probed for phosphorylation of the indicated proteins. Western Blots are representative of two independent experiments.

LY294002 reduced pAKT levels but without affecting CD19 or VAV1 phosphorylation, supporting the notion that PI3K primarily functions downstream of CD19 (Figure 29).

RCH-ACV SMS-SB LY294002(µM) 0 1 10 20 0 1 10 20 pCD19 (Y531) tCD19 pVAV1(Y174) tVAV1 pAKT(S473) tAKT GAPDH Figure 29. LY294002 reduces pAKT without affecting the CD19 signalosome. RCH-ACV and SMS-SB cells were treated with increasing concentrations of LY294002 for 2h followed by the assessment of CD19, VAV1 and AKT phosphorylation by immunoblot analysis. Western blots are representative of two independent experiments.

The importance of CD19 was further emphasized by its knockout which predominantly reduced proliferation of pre-BCR-dependent ALL cells while, to our surprise, increased proliferation of pro-B-ALL cells (Figure 30).

150

120 REH

90 Nalm-21 697 60 RCH-ACV

30 % CD19 knockout cells

0 0 2 4 6 8 Time (Days) Figure 30. CD19 knockout reduces proliferation selectively of pre-BCR+ cells.

30 Pre-BCR+ (green) and pre-BCR- (red) cells were transfected with either one of three distinct CD19-specfic sgRNAs followed by the identification of CD19 KO cells via flow cytometry. Fractions of CD19 knockout cells were reassessed at the indicated time points.

Collectively, these results establish the importance of the PI3K-AKT signaling module in the maintenance of pre-BCR-dependent ALL and point towards a distinct role of CD19 in regulating PI3K activity downstream of the pre-BCR.

3.6 The effects of pre-BCR inhibition on pre-BCR+ ALL require the reactivation of the transcription factor FOXO1

To further dissect the molecular means of pre-BCR dependency we assessed effects of the pre-BCR on the activity of FOXO transcription factors in pre-BCR+ ALL cells. The FOXO family of transcription factors comprises four isoforms, FOXO1, FOXO3a, FOXO4 and FOXO6 all characterized by a distinct tissue specific expression pattern. Highlighting the importance of the FOXO TF family for B-cell development, a regulatory network consisting of FOXO1, E2A and EBF1 was recently identified to orchestrate early pro-B cell development and to ensure B-cell commitment through upregulation of PAX5 (Yin C Lin et al, 2010). Similarly, in pre-BCR+ pre-B cells signals originating from the pre-BCR were shown to modulate FOXO1 and FOXO3A transcriptional activity in order to sustain cell proliferation while delaying light chain gene rearrangement (Herzog et al, 2008). Using publicly available gene expression profiling data from the immunological genome project (Immgen, www.immgen.org) we first compared expression levels of the different FOXO transcription factors at various stages of B-cell development. In line with its importance for lineage-committed B-cell progenitors FOXO1 expression levels predominate from the pro-B cell to the immature-B cell stage (Figure 31).

2000

1500

1000

500 GEP Values Raw Values GEP

MLP

proB CLPproB Fr.A preB Fr.CpreB Fr.D proB Fr.BC imm. B Fr.E FOXO1 FOXO3A FOXO4 FOXO6 Figure 31. Expression levels of FOXO family members at different stages of B-cell development.

31 Data were derived from the immunological genome project. The histogram depicts means of three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Regulation of FOXO1 transcriptional activity involves a plethora of posttranslational modifications including phosphorylation, ubiquitination, acetylation and methylation thereby allowing FOXO1 to integrate signals from a variety of signaling cascades. As part of the PI3K signaling axis FOXO1 becomes phosphorylated by AKT at three highly conserved amino acid residues T24, S256 and S319, marking it for nuclear export and ubiquitin-mediated cytoplasmic degradation. Unphosphorylated FOXO1 typically resides in the nucleus and induces the expression of cell cycle regulators, such as p27 or p21, and pro-apoptotic proteins such as BIM or TRAIL (Figure 32).

Figure 32. PI3K modulates the transcriptional activity of FOXO1. (Left panel) Following its phosphorylation by AKT at three distinct amino acid residues FOXO1 shuffles from the nucleus to the cytoplasm, where it is ubiquitinated and degraded. (Right panel) Inhibition of PI3K and AKT results in the dephosphorylation of FOXO1 and its retention in the nucleus. Nuclear FOXO1 is transcriptionally active and induces transcription of a variety of proapoptotic (e.g. BIM, TRAIL) and anti- proliferative (e.g. p27, p21) genes.

Consistent with its continuous inactivation in pre-BCR+ ALL, FOXO1 was highly phosphorylated in pre-BCR+ ALL cells (Figure 33).

-ACV-SB -6 RCH SMSNalm pFOXO1(Thr24) tFOXO1 GAPDH Figure 33. FOXO1 is highly phosphorylated in pre-BCR+ ALL. Immunoblots are representative of three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

32 Importantly, SYK inhibition as well as pre-BCR KO resulted in the reduction of FOXO1 phosphorylation and an increase in FOXO1 protein expression selectively in pre-BCR+ cells (Figure 34a and b).

proB-ALL preB-ALL a b RCH-ACV RS4;11 RCH-ACV SMS-SB Nalm-6 PRT318 (µM) 0 .1 .25 .5 1 0 .1 .25 .5 1 0 .1 .25 .5 1 0 .1 .25 .5 1 EV KO pFOXO1 pFOXO1 tFOXO1 tFOXO1 GAPDH GAPDH Figure 34. Inhibition of pre-BCR signaling reduces FOXO1 phosphorylation and increases FOXO1 total protein expression. (a) Pro-B (RS4;11) and pre-B (RCH-ACV, SMS-SB, Nalm-6) ALL cells were incubated with the indicated concentrations of PRT318 followed by Immunoblot analysis of pFOXO1 and tFOXO1. PRT318 reduced pFOXO1- and upregulated tFOXO1 levels selectively in pre-BCR+ ALL cells. (b) Pre-BCR knockout RCH- ACV cells and wildtype control cells were stained for pFOXO1 and tFOXO1. Immunoblots in (a) are representative for three independent experiments in (b) for two independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Moreover, treatment of pre-BCR+ cells with PRT318 resulted in the redistribution of FOXO1 from the cytoplasm to the nucleus, suggesting the reactivation of FOXO1 transcriptional activity in response to the inhibition of pre-BCR signaling (Figure 35a and b).

a Control PRT318 LY294002

RCH- ACV

SMS- SB

DAPI FOXO1 MERGE

b RCH-ACV SMS-SB 2.5 **** 2.5 **** **** **** 2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0 Foxo1 Nuclear/Cytoplasmic Ratio Nuclear/Cytoplasmic Foxo1 Control PRT318 LY294002 Ratio Nuclear/Cytoplasmic Foxo1 Control PRT318 LY294002 Figure 35. Inhibition of pre-BCR signaling results in the shift of FOXO1 from the cytoplasm to the nucleus.

33 (a) RCH-ACV and SMS-SB cells were serum starved for 12h followed by 6h exposure to PRT318 or LY294002. Consecutively cells were fixed, permeabilized and stained according to protocol. Cells were stained with DAPI (blue) to stain nuclear DNA and a FOXO1-specific antibody (red). Pictures are representatives of three independent experiments. (b) Nuclear- and cytoplasmic levels of FOXO1 were quantified for single cells to compare FOXO1 nuclear to cytoplasmic ratios for each treatment. Each dot represents a single cell. In total 120 cells per condition were analyzed (40 cells/experiment). ****p<0.0001. Kruskal-Wallis- and Dunn’s multiple comparison test. Modified from Koehrer et al. (Köhrer et al, 2016).

Along these lines we observed upregulation of the FOXO1 transcriptional targets BLNK and p27 after pre-BCR inhibition (Figure 36a and b).

a PRT318 LY294002 b RCH-ACV SMS-SB RCH-ACV SMS-SB RCH-ACV Time(h) 0 24 72 0 24 72 0 24 72 0 24 72 C KO p27 p27 BLNK BLNK GAPDH GAPDH Figure 36. Inhibition of pre-BCR signaling results in the upregulation of FOXO1 transcriptional targets. (a) RCH-ACV and SMS-SB cells were treated with either 1µM PRT318 or 20µM LY294002 for the indicated time points, followed by the assessment of p27 and BLNK levels by Immunoblot analysis. (b) Pre-BCR knockout RCH-ACV cells were probed for p27 and BLNK levels via Western Blot analysis. Immunoblots in (a) are representative for 3 independent experiments and (b) for 2 independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

To proof that the reactivation of FOXO1 transcriptional activity is required for the effects of pre-BCR inhibition in pre-BCR+ ALL we transiently expressed a constitutively active form of FOXO1 (FOXO1-AAA), in which the amino acid residues typically phosphorylated by AKT are replaced with alanine residues, in RCH-ACV cells. This resulted in reduced proliferation of RCH-ACV cells and in the upregulation of FOXO1 transcriptional targets, thereby confirming the importance of FOXO1 reactivation for the effects of pre-BCR inhibition on pre-BCR+ ALL (Figure 37a and b).

a 1.5×105 *** 1.2×105 b -AAA

9.0×104 GFP FOXO1 6.0×104 p27

Live Cells / Well BLNK 3.0×104 GAPDH 0.0 GFP FOXO1-AAA Figure 37. Constitutive active FOXO1 mimics the effects of pre-BCR inhibition. (a) RCH-ACV cells were transfected with a GFP-tagged FOXO1-AAA containing expression vector or an empty vector control. 24h after transfection GFP+ cells were sorted, seeded at equal numbers and incubated for 72h at normal growth conditions before assessing the number of viable cells/well via flow cytometry. ***p<0.001 Unpaired t test with Welch’s correction. (b) After sorting GFP+ cells were incubated

34 for 24h before analyzing p27 and BLNK protein levels. Immunoblots are representative for 2 independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Finally, we also wanted to provide evidence for the continuous inactivation of FOXO1 transcriptional activity in pre-BCR+ patient samples. To identify genes regulated by FOXO1 in pre-BCR+ ALL we performed microarray-based gene expression profiling of RCH-ACV cells expressing FOXO1-AAA or a GFP-containing control vector, followed by the selection of differentially expressed genes. Using a minimum absolute fold- change threshold of ≥ 2 we identified 58 genes significantly upregulated by FOXO1- AAA (Table A2). Consequently, gene set enrichment analysis (GSEA) revealed the significant enrichment of these FOXO1-AAA-regulated genes in the list of genes differentially expressed in pre-B and non-pre-B ALL cases from the St. Jude GEP dataset (Figure 38, Table A3).

a FOXO1-3A_UP b

FDR=0.001, p<0.001

Figure 38. Pre-BCR+ patient samples exhibit evidence for FOXO1 inactivation. (a) GSEA of FOXO1-target genes in differentially expressed genes of pre-B and non-pre-B ALL cases. FDR, False discovery rate. (b) Genes upregulated by FOXO1-AAA in RCH-ACV cells are downregulated in pre-B ALL cases of the St. Jude B-ALL GEP dataset. Modified from Koehrer et al. (Köhrer et al, 2016).

3.7 The pre-BCR regulates MYC in a PI3K- and FOXO1-dependent manner

In order to identify additional targets of pre-BCR signaling in B-ALL we assessed global gene expression changes of the pre-BCR+ cells RCH-ACV and SMS-SB in response to pre-BCR KO, followed by GSEA (Table A4). Confirming FOXO1 as target of the pre- BCR in these cells, the previously established FOXO1-3A_UP gene set was highly enriched in the expected direction in the list of pre-BCR regulated genes (Figure 39).

35 FOXO1-3A_UP

FDR>0.001; p>0.001 Figure 39. FOXO1 is reactivated in response to pre-BCR knockout. The previously established FOXO1-3A_UP gene set was probed for its enrichment in the list of pre-BCR regulated genes. Modified from Koehrer et al.(Köhrer et al, 2016).

We also probed the list of pre-BCR-dependent genes for the enrichment of gene sets provided by the Molecular Signatures Database (MSigDB v4.0). Among the most significantly enriched (FDR< 0.001) were gene sets associated with hypoxia, rapamycin treatment and the suppression of MYC (Figure 40a and b). In view of its importance for cell proliferation and survival and its frequent deregulation in other B-cell malignancies, the potential involvement of MYC in pre-BCR-dependent ALL was of particular interest to us.

b SCHUMACHER_MYC _TARGETS_UP a

MYC_UP.V1_UP

Figure 40. Gene sets enriched in pre-BCR+ cells after pre-BCR knockout. (a) Gene sets associated with the downmodulation of MYC activity that are significantly enriched after pre-BCR knockout (FDR<0.005; NOM p-Value < 0.005). Gene sets were derived from the molecular signature database (MSigDB v4.0) provided by the Broad Institute. (b) Enrichment blots of the indicated gene sets from (a). Modified from Koehrer et al. (Köhrer et al, 2016).

MYC expression levels were comparable among pre-BCR+ and pre-BCR- ALL cells (Figure 41a). However, in line with the GSEA data we observed reduced MYC mRNA

36 and protein levels in response to pre-BCR KO in RCH-ACV and SMS-SB cells (Figure 41b and c).

a pre-BCR--ALL pre-BCR+-ALL

2 - -8 -ACV-SB -6 RS4;11REH RCH SMS NalmKasumi697 KOPN MYC GAPDH

* * b 1.0 c

0.8 RCH-ACV SMS-SB EV G1 EV G1 0.6 MYC 0.4 GAPDH (rel. to control) to (rel. 0.2 MYC mRNA expression expression mRNA MYC

0.0 EV Igµ-KO EV Igµ-KO

RCH-ACV SMS-SB Figure 41. Reduction of MYC mRNA and protein levels after pre-BCR knockout. (a) Baseline MYC expression levels in pre-BCR+ and pre-BCR- ALL cells. (b) Reduction of MYC mRNA levels as assessed by microarray-based GEP. (c) Suppression of MYC protein levels in pre-BCR knockout RCH-ACV and SMS-SB cells. Immunoblots in (a) and (b) are representative for 2 independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

Similarly, treatment with PRT318 as well as with LY294002 resulted in reduced MYC protein levels suggesting that the pre-BCR regulates MYC in a SYK- and PI3K- dependent manner (Figure 42).

PRT318 LY294002 RCH-ACV SMS-SB RCH-ACV SMS-SB Time(h) 0 24 72 0 24 72 0 24 72 0 24 72 MYC GAPDH Figure 42. Downregulation of MYC protein levels in response to SYK and PI3K inhibition. Cells were treated with either 1µM PRT318 or 20µM LY294002 for the indicated time points followed by the quantification of MYC protein levels via immunoblot analysis. Immunoblots are representative for three independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

To investigate whether the downmodulation of MYC is prerequisite for the effects of pre- BCR inhibition on pre-BCR+ ALL, we generated MYC knockout RCH-ACV and SMS-SB cells (Figure 43a). Confirming the importance of MYC in modulating the pre-BCR- dependent proliferation of ALL cells, knockout of MYC resulted in a significant reduction in cell proliferation (Figure 43b).

37 a RCH-ACV SMS-SB

EV KO1 KO2 KO3 EV KO1 KO2 KO3 MYC GAPDH

b 4×105 5 SMS-SB 6.0×10 RCH-ACV 3×105 4.5×105 2×105 3.0×105 5 1×10 1.5×105 **** **** Viable Cells / Well

Viable Cells / Well * 0 0.0 **** 0 2 4 6 0 2 4 6 Time (Days) Time (Days) Control MYC-KOEV KO1 KO2 Figure 43. Knockout of MYC reduces proliferation of pre-BCR+ cells. (a) RCH-ACV and SMS-SB cells were transfected with GFP-tagged CRISPR/Cas9 plasmids containing sgRNAs specific for MYC (KO1-3) or empty vector control (EV), followed by Flow cytometry-based sorting of GFP+ cells and the confirmation of MYC knockout via immunoblot analysis. Immunoblots are representatives for three independent experiments. (b) After sorting GFP+ cells were seeded at equal numbers (2*104/100µl) and the number of viable cells was assessed at the indicated time points via Flow cytometry. The graphs depict three independent experiments each conducted with a distinct MYC specific sgRNA. *p<0.05 ****p<0.0001 Two-way ANOVA and Sidak’s test for multiple comparisons. Modified from Koehrer et al. (Köhrer et al, 2016).

Next, we wanted to assess whether the reactivation of FOXO1 was also involved in the downregulation of MYC in pre-BCR+ ALL cells. This hypothesis was supported by several lines of evidence. First, Gan et al. described the modulation of MYC activity through the FOXO-dependent activation of Mxi-1 and mir-145 and second, the nature of genes differentially regulated in FOXO1-AAA expressing RCH-ACV cells showed evidence for the FOXO1-AAA-mediated downmodulation of MYC transcriptional activity (Figure 44a and b) (Boyi Gan et al, 2010). a b

ODONNELL_TARGETS_OF_ MYC_AND_TFRC_UP

Figure 44. Differentially expressed genes in FOXO1-AAA expressing cells show evidence for downmodulation of MYC activity.

38 (a) Gene sets suggesting the down modulation of MYC activity are significantly enriched in the list of genes differentially regulated in RCV-ACV cells expressing FOXO1-AAA. Gene sets were derived from the MSigDB v4.0 provided by the Broad Institute. (b) Enrichment plot from the most significant enriched gene set from (a). Modified from Koehrer et al. (Köhrer et al, 2016).

In order to unravel a potential contribution of FOXO1 in the downregulation of MYC expression we also assessed MYC protein levels in RCH-ACV cells transfected with FOXO1-AAA. Indeed, in line with our hypothesis the expression of constitutively active FOXO1 resulted in the down modulation of MYC protein levels, thereby confirming that pre-BCR signaling deregulates MYC activity at least in part through the inactivation of FOXO1 (Figure 45).

-AAA

GFP FOXO1 MYC GAPDH Figure 45. FOXO1-AAA suppresses MYC. RCH-ACV cells were transfected with FOXO1-AAA or control vector as described previously. 24h after flow sorting MYC levels were assessed via immunoblot analysis. Immunoblots are representative for two independent experiments. Modified from Koehrer et al. (Köhrer et al, 2016).

39 4 Discussion In this study, we identified a novel subgroup of B-ALL characterized by pre-BCR expression and the dependence on constitutive signals from the pre-BCR for proliferation and survival. These signals involve the activation of SYK and PI3K ultimately leading to the inactivation of the transcriptional activity of FOXO1 and the deregulation of MYC activity (Figure 46).

Figure 46. Pre-BCR signaling serves as therapeutic target in pre-BCR+ ALL. (Left Panel) Constitutive signals from the pre-BCR activate SYK, which in turn activates PI3K and AKT. AKT ultimately phosphorylates FOXO1 at multiple residues resulting in its nuclear export and cytoplasmic degradation. Inactivation of FOXO1 unleashes MYC activity. (Right Panel) Targeting pre-BCR signaling with PRT318 blocks SYK and consequently PI3K and AKT activation. Unphosphorylated FOXO1 remains in the nucleus, induces the expression of anti-proliferative molecules and suppresses MYC activity.

Inhibition of constitutive pre-BCR signaling either by targeting the pre-BCR for knockout or through the pharmacological inhibition of the pre-BCR-associated kinases LYN, SYK or PI3K interfered with ALL cell proliferation and survival in several in vitro and in vivo models of pre-BCR+ ALL, thereby providing evidence for the efficacy of pre-BCR inhibition as therapeutic approach for this subgroup of B-ALL. As indicated by the highly congruent GEP the dependency of pre-BCR+ ALL on pre- BCR signaling is most likely derived from its cell of origin, the pre-BCR+ pre-B cell

40 progenitor. Previous reports have highlighted the importance of the pre-BCR or its downstream signaling molecules for normal B-cell development. Knockout mice deficient for key components of the pre-BCR signaling cascade such as SYK, PI3K, CD19 and MYC share a common phenotype in respect to B-lymphopoiesis characterized by reduced numbers of mature-B cells and a developmental block at the pro-B to pre-B cell transition (Habib et al, 2007; Suzuki et al, 1999; Aiba et al, 2008; Ramadani et al, 2010; Turner et al, 1995). Our results suggest that pre-BCR+ ALL cells retain the dependency on pre-BCR signaling despite their malignant transformation. Regarding the different modes of pre-BCR activation our results point towards a predominant role for constitutive pre-BCR signaling. Despite its significant effects on signaling activity by Ca++-Flux and immunoblot analyses, exogenous pre-BCR activation via anti-Igµ stimulation did not increase proliferation or survival of pre-BCR+ ALL cells. In contrast, loss of pre-BCR signaling through pre-BCR pathway inhibition immediately blocked cell proliferation and viability, thus pointing towards the dependence of pre- BCR+-ALL on constitutive signals from the pre-BCR. The genetic abnormalities underlying pre-BCR-dependency seem to be heterogeneous. According to the St. Jude GEP data set the group of pre-BCR+ ALL comprises TCF3- PBX1+, MLL-rearranged and hypodiploid B-ALL cases. However, whereas almost all TCF3-PBX1+ cases (98%) belong to the group of pre-B ALL only a fraction of MLL- rearranged (45%) and hypodiploid (26%) cases exhibit the pre-B phenotype, suggesting that additional factors influence disease biology in the latter two subtypes. Further confirming the importance of the pre-BCR in TCF3-PBX1+ ALL, Geng et al. (Geng et al, 2015) recently discovered a positive feedback loop consisting of TCF3-PBX1, the pre- BCR and BCL6 that is essential for the transformation of TCF3-PBX1+ ALL cells. In contrast, BCR-ABL+ ALL cases belong exclusively to the group of non-pre-B ALL cases. This is in line with work by Feldhahn et al. (Feldhahn et al, 2005) and Trageser et al. (Trageser et al, 2009), who observed that expression of BCR-ABL in precursor B-cells solely transforms pre-BCR- B-cell progenitors and that BCR-ABL directly suppresses expression of pre-BCR signaling molecules, such as SYK and Igβ. A practical question arising from our data is how to identify patients with pre-BCR- dependent and SYK inhibitor-sensitive B-ALL in the clinical setting. Previous reports suggested that pre-BCR signaling is primarily involved in the maintenance of B-ALL harboring t(1;19) which is characterized by the expression of the TCF3-PBX1 fusion protein (Bicocca et al, 2012; van der Veer et al, 2014). Indeed, 95% of TCF3-PBX1+

41 cases exhibit the pre-B-ALL phenotype and this group might therefore also contain a substantial amount of pre-BCR-dependent B-ALL cases (Hunger, 1996). On the other hand, only 25% of cytoplasmic Igµ expressing B-ALL harbors the TCF3-PBX1 fusion protein. Therefore, it appears that the use of t(1;19) as sole marker to identify pre-BCR- dependent ALL cases lacks sensitivity and specificity. Instead, we propose an alternative model to identify pre-BCR-dependent ALL. B-ALL cells must be arrested at the pre-B-cell stage (cytoplasmic Igµ+, surface-IgM-) in order to express a functional pre- BCR. We therefor propose the pre-B immunophenotype as minimum requirement to identify pre-BCR-dependent B-ALL cases in the clinics. Ideally this should be combined with direct assessment of pre-BCR surface expression levels to further refine this selection. In respect to the sensitivity of B-ALL to SYK inhibitors our results challenge recent reports in which the authors assess the efficacy of SYK kinase inhibitors in a variety of patient samples and xenograft models of B-ALL and come to the conclusion that SYK serves as valuable target irrespective of immunophenotyped or cytogenetic abnormalities (Perova et al, 2014; Uckun et al, 2010, 2013). In contrast, our data suggest that there is a selective involvement of SYK only in pre-BCR-dependent ALL. In our hands neither pro-B nor mature-B derived ALL cell lines or xenograft expanded primary patient samples responded to PRT318. The differential response to SYK inhibition was accompanied by a selective reduction of AKT phosphorylation and a selective reactivation of FOXO1-dependent transcription in sensitive B-ALL cases. Our data corroborate the findings of Wossning et al., in which the authors describe that the transforming capacity of wild type SYK strongly depends on the concurrent expression of the pre-BCR and MYC [81]. Altogether these results fuel several interesting hypotheses on the possible genetic background of pre-B-ALL. Apart from t(1;19) no recurrent cytogenetic abnormalities are known that entail the arrest of B-ALL blasts at the pre-B-cell stage of lymphocyte development. The importance of the pre-BCR and pre-BCR downstream signaling suggests, however, that genetic lesions leading to pre-B-ALL might specifically interfere with pre-BCR signaling in order to sustain leukemia. Recent efforts in the genomic profiling of B-ALL cases have identified several recurrent genetic lesions, involving for example the lymphoid transcription factors IKAROS and PAX5 (Mullighan et al, 2007; Holmfeldt et al, 2013). However, determining their contribution to the generation of pre- BCR-dependent ALL is challenging, mainly because the outcome of certain genetic

42 alterations also depends on the stage of B-lymphocyte development at which they were acquired. For example, loss of IKAROS in early B-cell progenitors arrests B-cell development at the pro-B cell stage, whereas its inactivation at later time points prevents the shutdown of pre-BCR signaling and arrests B-cell maturation at an aberrant pre- BCR+ stage [83, 84]. Similarly, loss of IKAROS function in B-ALL might be involved in the generation of pre-BCR-dependent ALL if the genetic lesions were acquired at the pre-B-cell stage. However, since the majority of genomic profiling studies in B-ALL have been analyzed irrespective of the underlying blast cell immunophenotype we cannot draw definite conclusions on the genetic background of pre-BCR-dependent B-ALL yet. Collectively, we provide evidence for the existence of a pre-BCR-dependent subset of B-ALL. In light of the susceptibility of pre-BCR-dependent ALL to targeted therapy with SYK inhibitors we propose the further investigation of this subgroup, with a main focus on the identification of pre-BCR-dependent patients and on the genetic background underlying pre-BCR-dependency.

43 5 Material and Methods

5.1 Cell lines, xenografts and patient samples

REH, RS4;11 and Nalm-6 cells were obtained from the American Type Culture Collection (ATCC; Manassas, VA). Nalm-20, Nalm-21, Tom-1, RCH-ACV, Kasumi-2, 697, KOPN-8 cells were obtained from the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany). SMS-SB cells were a kind gift from Dr. Ralph B. Arlinghaus (The University of Texas MD Anderson Cancer Center, USA). BALL1 and Tanoue were purchased from the RIKEN BioResource Center (RIKEN, Japan) and HPB-NULL was kindly provided by Dr. Hassan Jumaa (University of Ulm, Germany). Cell lines were maintained in RPMI 1640 (Hyclone, Logan, UT, USA) supplemented with 2 mmol/l L-glutamine, 100 µl/ml penicillin, 100 µg/ml streptomycin (CellGro, Manassas, VA, USA) and 10% (REH, RS4;11, Nalm-6, SMS-SB, BALL-1, Tanoue and HPB-NULL) or 20% (Nalm-20, Nalm- 21, Tom-1, RCH-ACV, Kasumi-2, 697, KOPN-8) fetal bovine serum (FBS, Hyclone). Xenograft leukemia samples derived from untreated de novo B cell precursor ALL (B- ALL) patients were established using the NOD/SCID/huALL xenograft model as previously described (Meyer et al, 2011). Patient samples were obtained after informed consent and in accordance with the institution’s ethical review board at the University of Ulm, Germany. For ex vivo experiments, xenograft-expanded BCP-ALL cells were isolated from leukemia bearing recipients and cultured in RPMI 1640 medium supplemented with 10% fetal calf serum and 1% L-glutamine (Gibco Life Technologies,

Darmstadt, Germany) at 37°C in humidified air / 5% CO2 as described earlier (Hasan et al, 2015; Queudeville et al, 2012). All xenograft samples contained more than 90% of BCP-ALL cells as estimated by flow cytometry staining for human CD19 and / or CD45. ICN12 xenograft-expanded cells were kindly provided by Dr. Markus Müschen (University of California, San Francisco). Cells were propagated in Alpha MEM supplemented with 20% fetal bovine serum, 100 µl/ml penicillin, 100 µg/ml streptomycin, GlutaMAX (Life Technologies) and Sodium-Pyruvate. PRT318 (PRT060318) was kindly provided by Portola Pharmaceuticals (San Francisco, CA). LY294002, Dasatinib and PRT062607 were purchased from Selleck Chemicals (Houston, TX).

44 B-ALL patient samples for in vitro analysis of PRT318 efficacy were obtained, after informed consent from patients fulfilling diagnostic and immunophenotypic criteria for B- ALL at the Leukemia Department at MD Anderson Cancer Center. Patient consent for samples used in this study was obtained in accordance with the Declaration of Helsinki on protocols that were reviewed and approved by the Institutional Review Board at MD Anderson Cancer Center.

Name Fusiongenes* Cytoplasmic-Igµ Surface IgM pre-BCR Developmental Stage

Cell Lines

REH TEL/AML1 - - - pro-B

RS4;11 MLL/AF4 - - - pro-B

SFO3 - - - - pro-B Nalm-20 BCR/ABL - - - pro-B Nalm-21 BCR/ABL - - - pro-B Tom-1 BCR/ABL - - - pro-B RCH-ACV TCF3/PBX1 + - + pre-B

ICN12 TCF3/PBX1 + - + pre-B

SMS-SB none + - + pre-B Nalm-6 t(5;12) + - + pre-B

Kasumi-2 TCF3/PBX1 + - - pre-B

697 TCF3/PBX1 + - + pre-B

BALL1 unknown + + - mature-B Tanoue unknown + + - mature-B Xenografts

X002 none - - - pro-B X089 none - - - pro-B X112 none - - - pro-B X068 none - - - pro-B SFO3 - - - - pro-B X018 none + - + pre-B X116 none + - + pre-B X006 none + - + pre-B

45 X120 none + - + pre-B X134 none + - + pre-B

X135 BCR/ABL + - + pre-B

ICN12 TCF3/PBX1 + - + pre-B

Table 2. Characteristics of cell lines and xenografts used throughout the study.

5.2 In vivo B-ALL xenograft models

In vivo xenograft experiments were conducted according to the national animal welfare law and approved by the appropriate authority (Regierungspräsidium Tübingen, Germany). Xenograft leukemia cells were transplanted onto female NOD/SCID mice (Jackson Laboratory/Charles River, MA) with a median age of 12 weeks. Following successful engraftment as indicated by more than 5% human CD19+ cells in the peripheral blood, mice were randomized to receive either 30 mg/kg PRT318 or 5% Captisol twice daily via intraperitoneal injection. Mice were killed 10 days after beginning of therapy and the amount of leukemia was assessed in bone marrow of left and right femur and tibia, peripheral blood, spleen, and central nervous system via flow cytometry. For survival analysis, sub-lethally irradiated NOD/SCID mice were inoculated with 106 patient-derived leukemia cells (ICN12). After engraftment mice were randomized into groups of seven receiving either 100 mg/kg PRT062607 dissolved in H20 or H20 alone daily for 5 weeks. Mice exhibiting signs of overt leukemia were killed and the presence of leukemia was confirmed via flow cytometric analysis of hCD45+ and hCD19+ B-ALL blast cells in the spleen and bone marrow. Experiments were performed in agreement with the appropriate authority at the University of California, San Francisco.

5.3 Flow Cytometry

5.3.1 Antibodies

Target Fluorophore Company pre-BCR PE BioLegend, San Diego, CA VpreB PE BioLegend, San Diego, CA λ5 PE BioLegend, San Diego, CA Igµ R-PE, APC SouthernBiotech, Birmingham, AL SYK APC eBioscience, San Diego, CA

46 CD19 PE BD Pharmingen, San Diego, CA AKT (pS473) PE BD Phosphoflow, San Jose, CA CD45 PerCP BD Pharmingen, San Diego, CA Table 3. Antibodies used for flow cytometry

5.3.2 Intra- and extracellular staining protocols

To detect cell surface antigens 5*105 cells were stained with the respective antibody diluted in 100 µl FACS-Buffer (0.5% bovine serum albumin (BSA) w/v in RPMI1640) for 30 min on 4°C protected from light. To measure intracellular antigens cells were fixed, permeabilized and stained using the BD Cytofix/Cytoperm™ Fixation/Permeabilization Solution Kit (BD Bioscience, San Jose, CA). For each antibody respective isotype antibodies were used as controls. After staining cells were washed once and mean fluorescence was assessed on a FACSCalibur (BD, Franklin Lakes, NJ).

5.3.3 Ca++-Flux measurements

A total of 107 cells were resuspended in cell culture medium at a concentration of 107 cells/ml, followed by the addition of 4 µl Fluo3-AM (Life technologies, Grand Island, NY) and 20 µl Pluronic F127 (Sigma-Aldrich, St. Louis, MO) for a final concentration of 4 µM Fluo3-AM and 0.02% Pluronic F127. For optimal staining cell suspensions were incubated for 30 min at 37°C. This was followed by the addition of 1 ml cell culture medium to further dilute the cell suspension to 5*106 cells/ml and an additional 10 min incubation at 37°C. Cells were washed twice with HBSS (Life Technologies) supplemented with 5% FBS followed by their resuspension in HBSS 5% FBS at a concentration of 5*106. For each condition 40 µl of this suspension were diluted in 460 µl staining buffer. Ca++-Flux was assessed on a FACSCalibur. 20 seconds after starting the measurement 10 µg/ml anti-Igµ F(ab’)2 fragments (MP Biomedicals, Santa Ana, CA) or 10 µg/ml anti-human IgG (Jackson ImmunoResearch Laboratories, West Grove, PA) were added to induce pre-BCR signaling.

5.4 Assessment of cell proliferation and cell viability

For in vitro efficacy studies of PRT318 and LY294002 2*104 B-ALL cells were seeded in 96-well plates in cell culture medium without phenol red and treated with PRT318 (0.1 µM, 0.5 µM, 1 µM, 5 µM, 10 µM) or LY294002 (1 µM, 5 µM, 10 µM, 15 µM, 20 µM) for

47 72 h. Proliferation was assessed via XTT assays according to manufacturers instructions (XTT Cell Proliferation Assay, Trevigen, Gaithersburg, MD, USA). To assess cell proliferation after pre-BCR-KO, MYC-KO, FOXO1-AAA expression and pre-BCR stimulation, 2*104 cells were seeded in 100 µl into 96 well plates. At the indicated time points cells were transferred into FACS tubes followed by the addition of 300 µl viability staining solution (see below) and 10 µl CountBright™ counting beads solution (LifeTechnologies). Cells were analyzed via flow cytometry and absolute numbers of live cells per well were calculated based on the ratio of acquired beads and total numbers of beads/tube. To measure viability cells were stained with 300 µl FACS buffer containing 60 nmol/l DiOC6 (Invitrogen, Grand Island, NY, USA) and 2 µg/ml propidium iodid (PI) for 20 min on 37°C followed by 10 min on room temperature as previously described (Hu & Kipps, 1999). Viable and dead cells were quantified via flow cytometry.

5.5 Western Blot

5.5.1 Antibodies

Target Species Company Igα Rabbit Cell Signaling Technologies, Danvers, MA SYK Rabbit Cell Signaling Technologies, Danvers, MA pZAP-70 (T319)/pSYK Rabbit Cell Signaling Technologies, Danvers, MA (Y352) SHP1 Rabbit Cell Signaling Technologies, Danvers, MA BLNK Rabbit Cell Signaling Technologies, Danvers, MA BTK Rabbit Cell Signaling Technologies, Danvers, MA PLCγ2 Rabbit Cell Signaling Technologies, Danvers, MA VAV1 Rabbit Cell Signaling Technologies, Danvers, MA GAPDH Rabbit Cell Signaling Technologies, Danvers, MA AKT Rabbit Cell Signaling Technologies, Danvers, MA pAKT Rabbit Cell Signaling Technologies, Danvers, MA ERK1/2 Rabbit Cell Signaling Technologies, Danvers, MA CD19 Rabbit Cell Signaling Technologies, Danvers, MA pCD19 (Y531) Rabbit Cell Signaling Technologies, Danvers, MA

48 FOXO1 Rabbit Cell Signaling Technologies, Danvers, MA pFOXO1(T24)/pFOXO3a Rabbit Cell Signaling Technologies, Danvers, MA (T32)/pFOXO4 (T28) p27 Rabbit Cell Signaling Technologies, Danvers, MA MYC Rabbit Cell Signaling Technologies, Danvers, MA LYN Mouse Abcam, Cambridge, MA pLYN (Y396) Mouse Abcam, Cambridge, MA IgM Rabbit Abcam, Cambridge, MA pBTK (Y223) Rabbit Abcam, Cambridge, MA pVAV1 (Y174) Rabbit Abcam, Cambridge, MA Table 4. Antibodies used for western blot.

5.5.2 Western Blot Protocol

Cells were lysed in RIPA Buffer (Sigma Aldrich) containing PhosSTOP Phosphatase Inhibitor Cocktail and cOmplete Protease Inhibitor Cocktail (Roche, Indianapolis, IN) and loaded onto NuPAGE® Novex® 4%-12% gradient gels (Life Technologies). After electrophoresis protein was transferred onto PVDF membranes (EMD Millipore, Darmstadt, Germany). Membranes were blocked for 1 h at room temperature with PBS containing 0.1% Tween (PBS-T) and either 5% BSA (phoshoproteins) or 5% Milk (total protein). Primary antibodies were diluted in blocking solution at the recommended concentrations. Membranes were incubated overnight at 4 °C. The following day membranes were washed three times for 10 min with PBS-T and incubated with species-specific HRP-linked secondary antibody (GE Healthcare) (diluted 1:10000) for 1 h at room temperature. Protein was visualized via ECL detection (Pierce, Rockford, IL) according to supplier’s instructions. Densitometric analysis of pAKT and AKT was performed with ImageJ 1.48v (NIH).

5.6 CRISPR/Cas9 mediated target-gene knockout

5.6.1 sgRNA design

The CRISPR design tool provided by the Zhang Lab (http://crispr.mit.edu/) was used to design target specific CRISPR/Cas9 short guide RNAs (sgRNA). To ensure target specificity only high-scoring (>50) sgRNA sequences were selected. To further reduce

49 chances of off-target effects at least two distinct gRNAs were used per target gene. The sgRNA sequences used throughout the study are summarized in Table A3.

Target Cell Line gRNA1 (forward) gRNA2 (forward) gRNA3 (forward) target region

Igµ RCH- GTGGATGGGA GTGCTATGCAT - VDJ-Region ACV TGGATCAACGC TGGGTGCGCC

Igµ SMS-SB GCTCTGTGACC GTTGGGAGTAT - VDJ-Region GCCGCAGACA CTATCATAGT

SYK - GTTTCGGCAAC GACCATCGAGC GAAGATTACCT Exon 1 ATCACCCGGG GGGAGCTGAA GGTCCAGGG

MYC - GTATTTCTACT GCCGTATTTCT GCTGCACCGA Exon 2 GCGACGAGG ACTGCGACG GTCGTAGTCG

CD19 - GTGGAATGTTT GCCTCCGTGTC GACCGCCCTGA Exon 3 CGGACCTAGG TCCCACCGA GATCTGGGA Table 5. sgRNA sequences for CRISPR/Cas9 knockouts.

5.6.2 CRISPR/Cas9 plasmids

Depending on the nature of the target proteins sgRNAs were cloned into either one of two hSpCas9 containing backbone vectors following the protocol by Ran and colleagues (F Ann Ran et al, 2013). pX330-U6-Chimeric_BB-CBh-hSpCas9 backbone vector (px330) was used for targets where knockout could be verified via flow cytometry (SYK and CD19). sgRNA sequences targeting Igµ and MYC were cloned into the pSpCas9(BB)-2A-GFP (PX458) backbone. Both vectors were a kind gift from Feng Zhang (Addgene plasmid #42230 and #48138) (Cong et al, 2013). Knockout of CD19 and SYK was confirmed via flow Cytometry. To verify knockout of MYC GFP+ cells were sorted on FACSJazz cell sorter (BD Bioscience) 48 h after electroporation and subjected to immunoblot analysis of MYC protein. Low Igµ surface expression levels interfered with the verification of successful Igµ-KO and a combination of GFP and surface Igµ staining was used to distinguish Igµ+ and Igµ-KO cells. Briefly, cells were stained for surface Igµ 48 h after electroporation and assessed via flow cytometry. Gating on GFP+ and Igµ- cells allowed the selection of Igµ- KO cells (Figure 4). A similar approach was applied to sort knockout cells for functional and molecular assays. For all knockout experiments cells transfected with the respective backbone vector without target gRNA (Empty Vector = EV) served as control.

50 px458 EV px458 KO1 px458 KO2

igµ

GFP

Figure 47. 48h after transfection of SMS-SB cells with control vector (px458 EV) or igµ-specific CRISPR/Cas9 knockout vector (px458 KO1 and px458 KO2). The cells were stained for surface Igµ expression. GFP+ cells express px458. GFP+ Cells transfected with px458 control are Igµ+; GFP+ cells transfected with px458 KO1 or KO2 loose Igµ expression. Histograms depict the gates from the blots in the top row. The black line represents the GFP+/Igµ+ gate in px458 EV; Dark filled histograms represent GFP+/Igµ- gates in the px458 gRNA1/2 blots. Grey dotted line represents Isotype control staining. Modified from Koehrer et al. (Köhrer et al, 2016).

5.6.1 Electroporation

Cells were transfected with the NEON™ Transfection System (Life Technologies). Briefly, cells were washed once with PBS and resuspended in R-Buffer at a concentration of 106 cells/100 µl. Cells were electroporated using predetermined conditions and seeded into 6-well plates containing cell culture medium without antibiotics.

5.7 Confocal microscopy

Cells were synchronized through serum starvation for 12 h followed by treatment with 1 µM PRT318 or 20 µM LY294002 for 6 h. Cells were then stained with CellTracker™ Green CMFDA (Molecular Probes®, Life Technologies) according to manufacturer’s instructions and seeded onto coverslips (Ted Pella, Redding, CA) precoated with Poly- L-Ornithine (Sigma Aldrich). After 2 h Cells were fixed with 4% paraformaldehyde (Electron Microscopy Science, Hatfield, PA) for 10 min, washed once with PBS and then

51 permeabilized for 30 min at room temperature with blocking/permeabilization solution (in PBS: 0.1% BSA w/v, 10% FBS, 0.3% v/v TRITON® X-100 (BIORAD, Hercules, CA)). Cells were incubated with FOXO1 antibody (Cell Signaling Technologies) diluted 1:100 in blocking buffer (in PBS: 0.1% BSA w/v, 10% FBS) overnight at 4 °C. Following two washing steps cells were incubated with Alexa Fluor® 647-conjugated species specific secondary antibody (Molecular Probes®, Life Technologies) for 1.5 hours, washed again with PBS and mounted on coverslips with ProLong® Gold Antifade Mountant containing DAPI (Life Technologies). Cells were visualized on an Olympus FV1000 Laser Confocal Microscope. To determine the cellular distribution of FOXO1 captured images were analyzed with Slidebook 5.5v (Intelligent Imaging Innovations, Denver, CO)

5.8 Gene Expression Profiling and Gene Set Enrichment Analysis (GSEA)

Total RNA was extracted with the RNAqueous kit (Ambion, Austin, TX). After confirmation of RNA quality (RIN > 6.6, median 7.2) using a Bioanalyzer 2100 instrument (Agilent), 300 ng of total RNA was amplified and biotin-labeled through an Eberwine procedure using an Illumina TotalPrep RNA Amplification kit (Ambion) and hybridized to Illumina HT12 version 4 human whole-genome arrays. Data processing was performed as previously described (Ma et al, 2010). The B-cell progenitor gene expression profiling dataset (GSE45460) (Lee et al, 2012) and St. Jude B-ALL dataset (GSE33315) (Zhang et al, 2012) were obtained from the NCBI GEO gene expression database and processed using the Gene Pattern Server provided by the Broad Institute (Cambridge, MA). GSEA was performed using the GSEA software provided by the Broad Institute (Subramanian et al, 2005; Mootha et al, 2003). GEP data was clustered using Cluster 3.0 (University of Tokyo, Human Genome Center). Heatmaps and dendrograms were generated via the Java TreeView (v1.1.6r4) software.

5.9 Statistical Analysis

Statistical analyses were performed with GraphPad Prism v6.0

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65 7 Appendix Table A1. Depicted are the genes most differentially expressed between pro-B and pre-B cells. GEP data was obtained from the NCBI GEO GEP database (GSE45460, Lee ST et al.). Expression data for pro-B and pre-B cells was averaged, followed by selection of the most differentially expressed genes (Cutoff: ±1.5 log2).

Up in pre-B Pre-B – Pro B (Ave.) Down in pre-B Pre-B – Pro B (Ave.)

TCL1A 3.409484875 DNTT -3.970801625 ADAM23 3.39671975 CD34 -3.36188325 CAMK2D 2.814519375 ERG -3.2757345 IRF4 2.776612625 MYO5C -2.669143875 TNFRSF17 2.75213025 TCF7L2 -2.322865125 KLRK1 2.642934625 GBP4 -2.247708875 KMO 2.631260875 GIMAP4 -2.246645 FCRLA 2.63115675 TSPAN7 -2.158570875 IKZF3 2.55654725 MPO -2.05548725 MYO3A 2.53473575 ELK3 -1.972691 C13orf18 2.516602625 CCND2 -1.94018325 CAMK4 2.373186875 NPY -1.92724375 ADARB1 2.345757875 HPGDS -1.883255125 IGKC 2.32370975 SOCS2 -1.8701565 LYN 2.31681725 IFITM3 -1.842949375 IGF2 2.28297525 MS4A3 -1.8368695 KIAA1407 2.227569125 PECAM1 -1.82905075 PLEKHA2 2.1536035 CLC -1.823461375 PLEKHG1 2.148306625 SPINK2 -1.81431 ACSM3 2.145306625 CD99 -1.807950375 C20orf103 2.143419 LST1 -1.79281475 FCRL1 2.039112125 AIF1 -1.791898375 PARP15 2.008830375 SEMA6A -1.791159 P2RY10 2.007818 SPG20 -1.7818535 MPEG1 2.006921375 GNG11 -1.748639375 BEST3 2.00088925 IRAK3 -1.74363775 IGKC 1.997911625 LRRC70 -1.73812075 SNX25 1.9600115 NPCDR1 -1.663513875 IGKV3D-11 1.91861575 PLEK -1.657183125 FBXO25 1.917923125 ATP2B4 -1.63642575 ATXN7L1 1.87686725 C1orf54 -1.62824175

66 ABCB4 1.86574025 TFPI -1.624913 VNN2 1.86509025 CD109 -1.597598875 CCDC112 1.85018125 PRG2 -1.591376625 IGK@ 1.834047375 TNFRSF1A -1.589034375 ROR1 1.83379075 MYC -1.58497575 WASF1 1.833325625 CPA3 -1.57857225 KLHL14 1.825049125 RNASE2 -1.57695775 GCET2 1.802085875 PRKCH -1.57691225 MTSS1 1.79681825 P2RY14 -1.571509625 GPR160 1.784055 SRGN -1.56066175 LOC100133207 1.777806375 LCP2 -1.5517245 CD180 1.774211 NIPSNAP3B -1.54061325 LOC652493 1.765745375 IGF2BP2 -1.53788275 HCK 1.75581725 MEF2C -1.517344375 SLAMF6 1.753883125 FAM134B -1.500649625

TCL6 1.7506425

KCNA3 1.7410005

C6orf192 1.691661875

TMSB15A 1.685059875

IKZF2 1.66435725

BMP3 1.656715875

MAP3K1 1.639192625

CHP 1.637050625

S1PR1 1.620007625

VLDLR 1.607713875

RNF144B 1.596045125

ANXA2 1.59383625

TFDP2 1.5924425

IGHM 1.5602

PCDH9 1.54592575

MTMR1 1.540679

VSIG6 1.533327

KLRC4 1.526390125

BIRC3 1.519319375

AFF3t 1.505205625

67 Table A2. FOXO1-3A specific gene set used to assess activity of FOXO1 in pre-B ALL cases of the St. Jude GEP dataset and to assess reactivation of FOXO1 in response to pre-BCR knockout in B-ALL cell lines. FOXO1-3A_UP gene set (Cutoff: 2) CD247 DUSP10 CDH15 TXNIP TM6SF1 PLA2G4C LOC730101 KIAA1683 GNG11 IFIT2 MERTK TP63 ARPP-21 FNDC5 ACTA2 LRRC3B ATP9A KLHL2 CSMD1 IL1B ANGPT2 DDIT3 OSCAR CCPG1 LOC285501 CYTL1 PLS3 ANG CASP1 LOC100131707 MEI1 TMEM71 LITAF LY6H MUC6 NRG3 TGFB1I1 LDHD BTG2 PADI4 NR3C1 XAGE1 LOC728253 FAIM3 GBP4 EFNA1 FOXP1 DYRK2 SORBS1 TRPS1 AFF3 BCL6B PPP1R15A ISG20 COL6A3 ICA1 SPTA1 HS.400256 TNFAIP3 GPR160 HIST2H4A SHISA2 DNAJB9 CD69 LDB2 TACC2 TSC22D3 ARRDC5 LOC90925 GIMAP4 GAB1 CSAG1 PP14571 DPEP1 RGS1 RASD1 LOC100128888 C10ORF10

68 Table A3. Genes most differentially expressed in pre-B and non-pre B ALL ( > 2 fold change) from the St. Jude GEP data set. Log2 transformed gene expression values were averaged for each group and then subtracted (pre-B – pro-B). FC=fold change,

High in pre-B ALL Low in pre-B ALL FC Gene ID FC (log2) Gene ID FC (log2) Gene ID (log2) Gene ID FC (log2) NME3 1.00 RAG2 1.17 GNG11 -3.48 TMEM156 -1.66 ARNTL2 1.00 TGFBR2 1.17 CYTL1 -3.15 P2RY14 -1.66 COCH 1.01 LRRC15 1.18 STK32B -2.95 PIEZO1 -1.64 ZAP70 1.01 C11orf49 1.18 SOCS2 -2.92 GPR56 -1.63 DACT1 1.01 BCAT1 1.18 POU4F1 -2.82 IFIT1 -1.62 LIPC 1.02 MERTK 1.18 IFI44L -2.72 ECM1 -1.61 ADAM23 1.02 HIP1R 1.18 TSPAN7 -2.71 H2BFS -1.60 GALNT14 1.02 IGLL3P 1.18 ITGA6 -2.44 FSCN1 -1.60 PRL 1.02 MT1X 1.18 MRC1 -2.43 TNFRSF21 -1.59 TSSC1 1.02 LGALS1 1.19 NPR1 -2.42 DNTT -1.59 TCL6 1.03 KIAA0922 1.19 CYB5R2 -2.37 RAG1 -1.59 FAIM 1.03 BLK 1.19 WDFY3 -2.36 SLC2A5 -1.59 ADK 1.03 MTX2 1.20 EGFL7 -2.26 EFNB1 -1.56 ITM2C 1.03 EPHA3 1.20 EFNA1 -2.23 CD69 -1.55 DPY19L2P2 1.04 TACC2 1.22 H1F0 -2.22 NPDC1 -1.53 DYNC2LI1 1.04 GP5 1.24 EPHA7 -2.13 CTGF -1.52 DOCK10 1.04 ODZ4 1.24 AIF1 -2.09 MAN1A1 -1.52 ROBO1 1.05 CTNNBL1 1.24 CD99 -1.99 ITPR1 -1.51 PARP1 1.05 PLS3 1.25 ST3GAL6 -1.98 MX1 -1.50 ACCN1 1.06 MAPKBP1 1.26 ELK3 -1.97 SCN3A -1.49 TUBB2A 1.08 LINC00094 1.26 BMP2 -1.92 PLXND1 -1.49 DSTN 1.09 PSEN2 1.26 LGMN -1.90 TERF2 -1.48 PSAT1 1.10 PBK 1.26 CD34 -1.89 GIMAP4 -1.47 QRSL1 1.10 EPB41L2 1.26 PDE4B -1.86 KIAA0226L -1.46 KIF21B 1.10 SIT1 1.27 ALOX5 -1.81 BANK1 -1.46 IGHM 1.10 SEMA4C 1.27 PLAG1 -1.80 FKBP5 -1.45 ATP1A3 1.11 PHACTR1 1.28 SERINC5 -1.79 SEPP1 -1.45 PKIG 1.11 UGT8 1.29 SH3BP5 -1.78 CSF3R -1.45 CTSC 1.11 BACH2 1.29 USP18 -1.78 MME -1.42 CD38 1.11 VASH2 1.29 LTB -1.77 NR3C2 -1.42 PPM1E 1.12 NAV2 1.29 SMAD1 -1.76 C1orf38 -1.40 WASF1 1.13 SORBS1 1.31 LST1 -1.72 LPAR6 -1.39 CSGALNACT VIPR2 1.13 FAM3C 1.31 KCNK12 -1.70 1 -1.39 ALDH1A1 1.14 CRIP1 1.32 TIE1 -1.70 SLC35E3 -1.39

69 ZNF167 1.14 RGS2 1.32 IRAK3 -1.69 BAALC -1.38 TUBB6 1.14 CLEC11A 1.32 STAG3 -1.68 MN1 -1.38 PPPDE1 1.15 TMSB15A 1.32 TBC1D9 -1.67 KCNA5 -1.37 CD96 1.16 SYT1 1.33 SMAGP -1.66 FHIT -1.36 GATM 1.16 CLEC2D 1.35 NPY -1.66 SLC27A3 -1.35 PCDH9 1.36 FGF9 2.08 MYO1B -1.31 ALOX5AP -1.10 RORB 1.36 IGFBP7 2.13 PROM1 -1.31 HIST1H2BH -1.09 SPAG6 1.38 FAT1 2.17 XBP1 -1.31 RNASET2 -1.09 CD72 1.39 RHOBTB1 2.18 PECAM1 -1.30 HHEX -1.08 SPIB 1.39 TRIB2 2.20 IPCEF1 -1.29 HIST1H2AG -1.07 CCDC69 1.39 SLC27A2 2.34 IFI44 -1.28 ARMCX2 -1.07 KCNJ12 1.40 BIK 2.57 FAM49A -1.27 FOS -1.07 MYBPH 1.42 PRKCZ 2.61 GIMAP6 -1.26 PTPLA -1.06 ODC1 1.43 ADARB1 2.72 CD1C -1.26 SH3TC1 -1.06 ARL4C 1.43 NYNRIN 2.91 B4GALT6 -1.25 ETS2 -1.06 RNF144A 1.46 PBX1 3.30 ISG20 -1.24 DSE -1.06 MAGEF1 1.46 C20orf103 3.65 TNFRSF1A -1.24 KCTD12 -1.05 ELOVL2 1.47 FHOD3 1.96 CCND2 -1.24 GSN -1.05 RASAL1 1.47 WNT16 1.97 C15orf5 -1.23 SERPINB6 -1.04 AEBP1 1.49 IL12RB2 2.01 SEMA6A -1.23 YES1 -1.04 POLM 1.49 LRMP 2.05 CXCR7 -1.22 DAB2 -1.03 ITGA8 1.50 CCDC165 2.05 TCEAL4 -1.22 PLSCR1 -1.03 KCNMB3 1.52 ROR1 2.07 PRKCH -1.21 SYNGR1 -1.03 TMEM121 1.58 CDKN1A -1.21 SESN1 -1.02 QPRT 1.58 DRAM1 -1.20 CLEC2B -1.02 SLAMF1 1.61 ENOSF1 -1.20 NEDD9 -1.01 KCNA3 1.62 PCTP -1.20 GPR171 -1.01 NFATC4 1.62 RECK -1.19 HIST1H2BK -1.11 UCK2 1.72 DHRS3 -1.18 AKR1C3 -1.11 SYNPO 1.73 IL3RA -1.16 CD97 -1.11 GLDC 1.76 RRAS2 -1.16 PEG10 -1.10 SLC2A6 1.76 C13orf15 -1.16 PSD3 -1.10 IGLL1 1.76 FHL1 -1.15 STAP1 -1.10 CCDC81 1.76 FUCA1 -1.15 NRGN 1.77 MYRIP -1.14 AOX1 1.80 FCGRT -1.14 MAP3K1 1.85 FLT3 -1.13 LAMA5 1.92 NFE2 -1.12 SH3BP4 1.94 GNA15 -1.12 NID2 1.96 IFI27 -1.12

70 Table A4. Genes most differentially expressed (>2 absolute fold change) in RCH-ACV and SMS-SB cells after Igµ KO. Expression values are log2-transformed.

Downregulated RCH-ACV SMS-SB ILMN_Gene Igµ KO1 Igµ KO2 Igµ KO1 Igµ KO2 Average

PRICKLE1 -2.4682 -2.4682 -1.3646 -2.1662 -2.1168 ALOX5AP -1.3968 -1.1217 -1.9901 -2.5903 -1.7747 SMAGP -1.4951 -0.9479 -1.8569 -2.7940 -1.7735 ITGB7 -1.6519 -2.2220 -1.4468 -1.6591 -1.7449 IL4I1 -0.8086 -1.8951 -1.7411 -1.7411 -1.5465 RASAL3 -1.2238 -1.8041 -1.4139 -1.6062 -1.5120 ELOVL6 -1.2493 -2.0233 -0.9935 -1.3509 -1.4043 CSPG4 -0.7698 -0.7698 -1.5534 -2.4805 -1.3934 PTPN6 -1.0967 -1.1581 -1.2496 -2.0237 -1.3820 TNF -1.2640 -1.4302 -1.2595 -1.5729 -1.3817 TLR10 -1.0476 -1.4359 -1.3949 -1.6438 -1.3806 PPP1R16B -1.0634 -1.3642 -1.3249 -1.7220 -1.3686 ITGB2 -1.3442 -1.4571 -1.0296 -1.6161 -1.3618 VGF -0.6741 -1.1776 -1.4916 -2.0603 -1.3509 CSF1R -0.8774 -1.2049 -1.6533 -1.6533 -1.3473 C19ORF51 -2.1405 -1.0472 -0.7541 -1.3866 -1.3321 TMC6 -1.2948 -1.4101 -0.9049 -1.6617 -1.3179 PLEK -1.6054 -1.5277 -0.9854 -1.0650 -1.2959 EYA4 -1.3918 -1.8898 -0.9366 -0.9366 -1.2887 TIMP1 -1.2227 -0.9938 -1.4437 -1.4777 -1.2845 KCNJ16 -1.0740 -1.0290 -1.3597 -1.6563 -1.2798 RASAL3 -0.7041 -1.9176 -1.0542 -1.4309 -1.2767 SLC39A14 -1.0532 -1.7159 -0.8814 -1.3625 -1.2533 LIMS2 -1.4033 -1.1976 -0.9415 -1.2957 -1.2095 EHD4 -0.8046 -2.0628 -0.9233 -1.0343 -1.2062 SLC17A9 -1.1642 -1.1371 -0.9180 -1.4298 -1.1623 NPW -1.5808 -1.3462 -0.6691 -0.9895 -1.1464 ELOVL6 -1.1441 -1.4841 -1.0100 -0.9357 -1.1435 POLR3G -0.9626 -1.5587 -0.8686 -1.1653 -1.1388 RINL -0.8970 -0.7945 -1.3339 -1.5001 -1.1314 STEAP3 -1.4163 -1.2707 -0.7765 -1.0552 -1.1297 SLC7A2 -1.0939 -1.5471 -0.8642 -0.9942 -1.1249 TRIB1 -0.8931 -1.6978 -0.8961 -0.8961 -1.0958 HBEGF -0.6716 -0.8192 -1.3500 -1.5420 -1.0957 COTL1 -1.2271 -1.2348 -1.2081 -0.6986 -1.0922 SMAGP -0.9926 -0.9888 -0.8123 -1.5110 -1.0762 CST7 -1.4671 -1.0464 -0.8858 -0.8858 -1.0713

71 SLAMF1 -0.8998 -0.9857 -1.1254 -1.2657 -1.0691 LOC644590 -0.9093 -0.9919 -1.3325 -0.9913 -1.0562 SLC29A1 -0.9383 -0.6162 -1.2662 -1.3966 -1.0543 RRP12 -1.2584 -1.4194 -0.7135 -0.8161 -1.0519 STOM -1.1731 -1.2855 -0.6963 -1.0091 -1.0410 HS.10862 -1.1196 -1.4995 -0.7061 -0.8325 -1.0394 IFI30 -1.2246 -0.7233 -1.1039 -1.0944 -1.0365 MYBBP1A -1.1876 -1.2488 -1.0343 -0.6683 -1.0348 DHCR7 -0.7411 -1.1430 -1.1139 -1.1176 -1.0289 LY9 -0.8167 -0.8167 -0.9188 -1.5057 -1.0145 DYNC1I1 -0.7656 -0.7656 -1.2669 -1.2394 -1.0094

Upregulated RCH-ACV SMS-SB ILMN_Gene Igµ KO1 IgµKO2 IgµKO1 IgµKO2 Average

FNDC5 2.2593 2.1283 2.8788 2.8818 2.5371 LOC100129878 1.5796 2.0268 2.4302 2.5279 2.1411 LOC648868 1.2585 1.7784 2.7918 2.5315 2.0901 LOC100132810 1.5332 1.7873 2.2954 2.4221 2.0095 C10ORF10 2.3614 2.1426 1.6997 1.7668 1.9926 MGC24125 1.5327 1.6009 1.9174 2.5291 1.8950 HPS4 1.4577 1.9099 1.7760 2.1046 1.8120 CCM2 1.6189 2.3456 1.3698 1.6741 1.7521 LOC728175 1.2248 1.5160 1.7613 2.2877 1.6974 NAV2 0.8713 1.1514 2.3397 2.3993 1.6904 GZMK 1.1728 1.3650 2.0699 2.0610 1.6672 TNFRSF17 0.8987 1.5593 2.0726 2.0427 1.6433 SELL 1.1759 1.8934 1.5754 1.8812 1.6315 LOC100128888 1.8935 1.5313 1.4021 1.6428 1.6175 BACH2 1.1386 1.4068 1.7984 2.1126 1.6141 BACH2 1.7277 1.1783 1.7757 1.7720 1.6134 GAB1 2.2639 1.6955 1.0751 1.2335 1.5670 DTX1 1.5038 1.3609 1.6820 1.6977 1.5611 ARPP-21 0.8353 0.9694 2.1523 2.1593 1.5291 CTGF 1.6035 1.3915 1.5649 1.5392 1.5248 YPEL3 1.2547 1.9619 1.5088 1.3507 1.5190 SPTA1 1.3026 1.1083 1.6575 1.8664 1.4837 ARPP-21 1.2536 0.8265 1.8098 2.0331 1.4808 BACH2 1.2948 1.1759 1.6165 1.7140 1.4503 SH2D4B 1.4844 1.0232 1.5360 1.7226 1.4416 MPP1 1.1293 1.2130 1.5143 1.7414 1.3995 SOCS2 1.1617 1.3497 1.4170 1.5970 1.3814

72 DPEP1 1.9022 2.0094 0.7531 0.8462 1.3777 LOC645993 1.2254 1.7737 1.2709 1.2263 1.3741 BTNL3 1.2815 1.6512 1.2827 1.2447 1.3650 MXD3 1.1608 1.6635 1.3024 1.2955 1.3556 CLEC2D 1.0370 1.0688 1.6579 1.6322 1.3490 GBP4 1.6559 1.3792 1.1601 1.1589 1.3386 ARPP-21 1.2087 0.6234 1.6283 1.8807 1.3353 LOC100129878 1.5281 2.0248 0.7177 1.0470 1.3294 RCAN1 1.4686 1.9379 0.7175 1.1809 1.3262 LOC399804 0.7415 0.9880 1.7927 1.7343 1.3141 ARPP-21 0.7990 0.8684 1.8735 1.7054 1.3116 NYNRIN 0.9031 1.0356 1.5551 1.7193 1.3033 HIP1R 1.1500 1.7627 1.0630 1.2102 1.2965 S1PR4 0.9341 1.1220 1.4721 1.5792 1.2768 HS.434957 1.4684 0.9833 1.1566 1.3502 1.2396 MYLK 1.2675 1.0441 1.1053 1.5371 1.2385 RCAN1 1.3266 1.2087 1.0881 1.3130 1.2341 GAB1 1.4392 1.7006 0.6152 1.1628 1.2295 C20ORF103 1.2471 1.6403 0.9310 1.0388 1.2143 LRRC26 1.0806 1.4062 1.1859 1.1795 1.2130 RASD1 0.9218 1.1061 1.3005 1.4842 1.2032 CMTM8 0.9765 1.2378 1.2015 1.3321 1.1870 RASAL1 1.1037 1.0351 1.2502 1.3162 1.1763 YPEL5 1.4437 1.2492 0.8362 1.1190 1.1620 CXCR4 0.9661 1.3879 0.8356 1.3936 1.1458 LOC90925 1.2295 1.5326 0.8226 0.9699 1.1387 LOC389816 0.9506 1.3491 1.1107 1.1020 1.1281 SHROOM3 0.9526 0.7612 1.3478 1.4462 1.1270 RAG1 1.2766 1.2470 1.0561 0.9267 1.1266 LOC100130503 1.1988 0.6834 1.2245 1.3940 1.1252 MME 1.5357 1.2703 0.7432 0.9207 1.1175 KLHL24 1.1397 1.1342 1.1570 1.0190 1.1125 TMEM71 0.6785 1.0861 1.3109 1.3562 1.1079 LOC100130123 1.2800 0.6672 1.0794 1.3391 1.0914 ZFP36L2 0.7961 1.0503 1.0072 1.4731 1.0817 AKAP12 1.1064 0.6663 0.9463 1.5907 1.0774 GPR137C 1.4334 0.9614 0.6372 1.2535 1.0714 YPEL1 0.9821 0.8289 1.1424 1.3110 1.0661 TXNIP 1.4691 1.0400 0.8289 0.9222 1.0651 RAB37 0.8603 1.3944 0.6852 1.3159 1.0639 CXCR4 0.9756 1.1435 0.9389 1.1976 1.0639

73 SOCS2 0.9893 0.9816 1.0863 1.1501 1.0518 PIM1 1.2959 0.9938 1.0427 0.8622 1.0486 LOC100130476 0.6085 1.3336 1.0580 1.1258 1.0315 LOC100130503 0.7344 0.6212 1.0273 1.7402 1.0308 AXUD1 1.3704 0.6926 0.9538 1.0922 1.0272 LOC730101 1.1103 0.8570 1.0736 1.0222 1.0157 CLEC2D 0.8102 1.0686 0.8777 1.2784 1.0087 ADCY9 0.6181 0.6417 1.3321 1.4296 1.0054

74 -Curriculum Vitae- Stefan Koehrer, M.D.

First name: Stefan Surname: Koehrer Date of birth: 14/10/1986 Address: CCRI - Children’s Cancer Research Institute Wilhelm-Exnergasse 10/11 Zimmermannplatz 10, 1090 Wien, 1090 Wien (work) (private) Telephone: +431404700 (work) +436643857802 (private) Email: [email protected] (work) [email protected] (private)

Medical Education since 4/2017 Fellowship in Pediatrics St. Anna Kinderspital, Vienna, Austria 03/2016-03/2017 Fellowship in Pediatrics and Adolescent Medicine at Ulm University, Germany 10/2005-8/2011 Studies of Medicine (M.D.) at the Medical University of Vienna, Austria Research Experience Since 4/2017 Researcher at CCRI – Children’ Cancer Research Institute 12/2011-1/2016 Postgraduate, Department of Leukemia, MD Anderson Cancer Center Delineating the role of pre-BCR signaling and Syk in B-cell acute lymphoblastic leukemia 2/2010-11/2011 Research Associate, Department of Hematology, Medical University of Vienna B-cell receptor sequence analysis of immunocytoma patients with the objective to discover relationships between B-cell receptor composition and disease behavior. 7/2009-9/2009 Research Intern at MD Anderson Cancer Center Department of Leukemia Focused on Chronic Lymphocytic Leukemia and Myeloproliferative Disorders. Wrote a Case Report about CLL associated ITP and the effectiveness of the second-generation thrombopoietin-receptor agonist Eltrombopag. 8/2007-10/2010 Diploma Student, Dep. of Clin. Pharmacology, Medical University of Vienna Diploma thesis on „Combined mTORC1/mTORC2/PI3K Inhibition in Melanoma“. Oral Abstract Presentations American Society of Hematology (ASH) Annual Meeting 2014: Stefan Koehrer, Ondrej Havranek, R. Eric Davis, Felix Seyfried, Greg P. Coffey, Ekaterina Kim, Nathalie Y. Rosin, et al. Immunoglobulin Heavy Chain (IgH) Knock Out Inhibits Proliferation of Pre- BCR+ B-Cell Acute Lymphoblastic Leukemia (B-ALL) Via a FOXO1 and MYC Dependent Mechanism. American Society of Hematology (ASH) Annual Meeting 2013: Stefan Koehrer, Richard E. Davis, Greg Coffey, Ekaterina Kim, Nathalie Y. Rosin, Elisa ten Hacken, Susan O’Brien, et al. “Pre-BCR Signaling Activity Predicts Sensitivity To Syk Kinase Inhibition In B Cell Acute Lymphoblastic Leukemia (B-ALL).” Blood 122, no. 21 (December 6, 2013): 614–614.

75 Meeting Abstracts Havranek O, Koehrer S, Xu J, et al. B-Cell Receptor Signaling in Diffuse Large B-Cell Lymphoma: Tonic Alone in the Germinal Center B-Cell Subtype, Plus Self Antigen-Induced in the Activated B-Cell Subtype. Blood. 2015;126(23):464–464. Ondrej Havranek, Stefan Koehrer, Justin M Comer, Zhiqiang Wang, Jingda Xu, Dipanjan Ghosh, Nicholas Shinners, Luhong Sun, Wencai Ma, Jan A Burger and R Eric Davis. ”The B-Cell Receptor Is Required for Optimal Viability, Growth, and Chemotherapy Resistance of Diffuse Large B-Cell Lymphoma Cell Lines of the Germinal Center B-Cell Subtype.” ASH Annual Meeting Abstracts 2014. Ekaterina Kim, Stefan Koehrer, Nathalie Y. Rosin, Zhiqiang Wang, Deborah A. Thomas, Farhad Ravandi, Steven M. Kornblau, et al. “Bruton′s Tyrosine Kinase Inhibitor Ibrutinib Interferes With Constitutive and Induced Pre-B Cell Receptor Signaling In B-Cell Acute Lymphoblastic Leukemia.” Blood 122, no. 21 (December 6, 2013): 1399–1399. Rosin Nathalie Y., Ekaterina Kim, Stefan Koehrer, Zhiqiang Wang, Susan O’Brien, William G. Wierda, Deborah A. Thomas, et al. “The PI3K Delta Inhibitor Idelalisib Interferes With Pre-B Cell Receptor Signaling In Acute Lymphoblastic Leukemia (ALL): A New Therapeutic Concept.” Blood 122, no. 21 (December 6, 2013): 2632–2632. Stefan Koehrer, Greg Coffey, Ekaterina Kim, Nathalie Y Rosin, Uma Sinha, Anjali Pandey, Medhat Shehata, et al. “Efficacy of PRT060318, a Novel Highly Specific SYK Inhibitor, in Acute Lymphoblastic Leukemia (ALL).” ASH Annual Meeting Abstracts 120, no. 21 (November 16, 2012): 3532. Ekaterina Kim, Stefan Koehrer, Nathalie Y Rosin, Deborah A. Thomas, Farhad Ravandi, Steven M. Kornblau, Hagop M. Kantarjian, et al. “Activity of Bruton’s Tyrosine Kinase (BTK) Inhibitor Ibrutinib (PCI-32765) in B-Cell Acute Lymphoblastic Leukemia (B-ALL).” ASH Annual Meeting Abstracts 120, no. 21 (November 16, 2012): 2569. Nathalie Y Rosin, Stefan Koehrer, Ekaterina Kim, Susan O’Brien, William G. Wierda, Deborah A. Thomas, Zeev Estrov, Hagop M. Kantarjian, Brian J. Lannutti, and Jan A. Burger. “In Vitro Effects of PI3K{delta} Inhibitor GS-1101 (Cal-101) in Acute Lymphoblastic Leukemia (ALL).” ASH Annual Meeting Abstracts 120, no. 21 (November 16, 2012): 3534. Publications Matthew D. Blunt#, Stefan Koehrer#, et al. “The dual Syk/JAK inhibitor cerdulatinib antagonizes B-cell receptor and microenvironmental signaling in chronic lymphocytic leukemia“ Clinical Cancer Research, clincanres.1662.2016 (2016). (Epub ahead of print) #Joint first author Köhrer S, Havranek O, Seyfried F, et al. “Pre-BCR Signaling In Precursor B-Cell Acute Lymphoblastic Leukemia regulates PI3K/AKT, FOXO1, and MYC, and can be targeted by SYK inhibition” Leukemia 2016; 30(6):1246–1254. Stefan Köhrer, Jan A. Burger “B-Cell Receptor Signaling in Chronic Lymphocytic Leukemia and Other B-Cell Malignancies” Clinical Advances in Hematology & Oncology 14, Issue 1 (January 2016): 55-65. Stefan Köhrer, M. J. Keating, and W. G. Wierda “Eltrombopag, a Second-Generation Thrombopoietin Receptor Agonist, for Chronic Lymphocytic Leukemia-Associated ITP” Leukemia 24, no. 5 (May 2010): 1096–98. doi:10.1038/leu.2010.45. Kim E, Hurtz C, Köhrer S, Wang Z, Balasubramanian S, Chang BY, Müschen M, Davis RE & Burger JA (2017) “Ibrutinib inhibits pre-BCR+ B-cell acute lymphoblastic leukemia progression by targeting BTK and BLK” Blood 129: 1155–1165 Havranek O, Xu J, Köhrer S, Wang Z, et al. (2017) “Tonic B-cell receptor signaling in diffuse large B- cell lymphoma” Blood 130: 995–1006

76 Shubhchintan Randhawa, Byung S. Cho, Dipanjan Ghosh, Mariela Sivina, Stefan Koehrer, et al. “Effects of pharmacological and genetic disruption of CXCR4 chemokine receptor function in B-cell acute lymphoblastic leukaemia” Br J Haematol. 2016 Aug;174(3):425-36. doi: 10.1111/bjh.14075. Werzowa, Johannes, Stefan Koehrer, Sabine Strommer, et al. “Vertical Inhibition of the mTORC1/mTORC2/PI3K Pathway Shows Synergistic Effects against Melanoma In Vitro and In Vivo.” Journal of Investigative Dermatology 131, no. 2 (February 2011): 495–503. doi:10.1038/jid.2010.327. Awards ASH Abstract Achievement Award 2014 ASH Abstract Achievement Award 2013 Merit Award from the Medical University of Vienna 2007 Laboratory Techniques Western Blot Wet and Semidry Western Blot protocols, Chemiluminescence detection Viability Assays Cell Titer Blue, XTT assay FACS Cell cycle (Ethidium bromide, EdU/PI), apoptosis (Apo2.7, PI/DiOC6), bead based cell proliferation assays, multicolor flow cytometry for cell surface and intracellular antigens Cell Culture Melanoma and leukemia cell lines, CLL primary samples Confocal Microscopy Cell surface and intracellular staining protocols DNA Techniques IgVH PCR, cloning, sequencing, CRISPR gene editing Transfection Electroporation with NEON and Amaxa transfection systems Animal Work Intraperitoneal and oral administration of substances in mice Other Skills/Interests Languages German (native speaker), English (fluent), Latin (good knowledge) Technical Microsoft Office, Adobe Creative Suite, SPSS, GraphPad Prism 6, FlowJo, basic computer programming skills Interests Sports (Tennis, Soccer, Skiing, Mountain biking), Reading, Traveling, History, Leading member of a local film group