Rassenti, Laura Z.

Abstract BACKGROUND Whilst the broad clonal architecture of naturally progressing chronic lymphocytic leukemia (CLL) has been described, a comprehensive picture of how chemotherapy and targeted agents reshape that landscape is lacking. Here we integrate clone-specific growth kinetics with mutational profiles, transcriptomic subtypes, and CpG-methylation-based epigenetic classes to capture the multidimensional evolutionary responses of CLL under native conditions and during treatment. METHODS We previously reported a systematic whole-exome sequencing (WES) analysis of 417 leukemia–germline pairs from 169 treatment-naïve patients ≥ 65 y (Karisani et al., Blood 2024). The treatment arm comprised 83 patients who received ibrutinib and 39 who received chlorambucil in the RESONATE-2 trial (NCT01722487). Forty-seven age-matched “watch-and-wait” patients from the CLL Research Consortium served as untreated controls. Peripheral-blood samples were collected at baseline and again ~300 d and ~600 d after therapy initiation (or diagnosis in the watch-and-wait cohort), enabling longitudinal clonal tracking. WES data were processed with PhylogicNDT to reconstruct clonal architectures, define phylogenies, and identify significant clonal shifts. MutSig tools identified mutations enriched in expanding or contracting clones. Pretreatment RNA-sequencing profiles were grouped into transcriptional subtypes by consensus clustering based on the Louvain algorithm. Differentially methylated regions plus epitypes were called from reduced representation bisulfite sequencing data. RESULTS Across the 169 patients with WES, we resolved 579 subclonal clusters. Significant clonal shifts (defined as a distribution shift of cancer cell fraction in a subclone of >95% between two timepoints) were uncommon during watch-and-wait (36%) but markedly higher after therapy — chlorambucil 67% (q = 0.01), and ibrutinib 77% (q < 10-4). In ibrutinib-treated CLL, regressing subclones were enriched for KRAS (q<10-3) and SF3B1 mutations (q<10-3), whereas expanding subclones preferentially harboured mutations in BIRC3 (q<10-4), NOTCH1 (q <10-3), TP53 (q=0.02), and POT1 (q=0.098). In chlorambucil-treated patients, no specific mutations were found to be enriched in expanding or regressing subclones. Using the recently defined CLL expression clusters (ECs) in patients treated with chemoimmunotherapy as defined by Knisbacher et al. (Nat Genet 2022), we were unable to identify any relationship between ECs and outcomes. We therefore sought to identify de novo ECs with prognostic relevance in the context of BTK-inhibition (BTKi-ECs). We identified a transcriptionally defined patient subgroup (C4, n=20) associated with inferior progression-free survival (median PFS = 63 months, log-rank q=0.05), independent of known prognostic clinical and molecular features, including epitypes, in multivariate analysis (Cox HR=3.7, 95% CI: 1.6-8.6, q=0.02). Gene-set enrichment in this cluster highlighted TNF-ɑ signalling via NF-κB (REL, TNFAIP3, NFKB2) and G2M checkpoint pathways (SMC4, EZH2). We also identified a distinct transcriptionally defined patient subgroup (C3, n=18) that was enriched for patients harboring at least one contracting subclone and no expanding subclones (q=0.005). This cluster showed a relative depletion of mutations within the chromatin modification pathway (6% vs 50%, q<0.01). Gene-set enrichment in this cluster highlighted a downregulation of genes in the TNF-ɑ signalling via NF-κB pathway (SOCS3, ICOSLG, NFKB2). CONCLUSION BTK inhibition and chemotherapy drive distinct evolutionary trajectories in CLL. Multi-omics profiling reveals mutation-defined subclones with divergent sensitivity to targeted therapy and delineates two informative expression states: a high-risk NF-κB/inflammatory cluster (C4; poor PFS) and a cluster enriched with contracting subclones with depleted TNF-ɑ signalling via NF-κB (C3). Integrating clonal growth kinetics with genomic, transcriptomic, and methylation data can potentially identify patients likely to progress on BTK inhibitors and identify actionable pathways for adaptive or combination therapy.

BACKGROUND Our knowledge of how the genomic landscape of chronic lymphocytic leukemia (CLL) is impacted by the selective pressure imposed by chemotherapy and targeted agents remains incomplete. We hypothesize that the extent of clonal evolution, baseline transcriptional state, and the overall and clone-specific growth kinetics of CLL will vary under native conditions or following exposure to chemotherapy and targeted therapy. METHODS We report the systematic genomic analysis of leukemia-normal whole-exome sequencing (WES) of 420 samples collected from 170 patients who were >65 years of age with treatment-naïve CLL. The treatment-cohort consisted of 83 patients who received ibrutinib (420mg daily) and 40 patients who received chlorambucil (0.5 mg/kg up to maximum 0.8 mg/kg, on days 1 and 15 of a 28-day cycle, up to 12 cycles) as part of the RESONATE-2 trial (NCT01722487). The ‘watch and wait’ (WW) cohort comprised 47 age-matched patients whose samples originated from the CLL Research Consortium and were untreated for their CLL during sampling. Sampling occurred at baseline and at day 300, and/or day 600, following initiation of therapy or following a diagnosis of CLL for those in the WW cohort. The WW cohort had more cases with a mutated IGHV (72% vs 40%), del17p (14% vs 0%), and del13q (72% vs 45%) when compared to the treatment cohort. Baseline CLL RNA-seq data were available for 150 patients. We used PhylogicNDT to reconstruct the clonal architecture from WES. By determining the fraction of cancer cells with each mutation (cancer cell fraction [CCF]) and their multiplicities (number of DNA molecules carrying a mutation per cancer cell), subclones were identified through clustering of CCFs of mutational events across samples from each patient. This allowed us to estimate the phylogenetic relationships and significant clonal shifts (defined as a distribution shift of CCF in a subclone of >95% between two timepoints, corresponding to a median ΔCCF of 18%) for each patient's CLL and to relate these shifts to somatic and clinical features. RESULTS In total, we identified 791 subclonal clusters across the three patient cohorts (ibrutinib = 406, chlorambucil = 189, WW = 196). We observed higher rates of significant clonal shifts between any two time points in patients exposed to chlorambucil (68%, q=0.008) and ibrutinib (77%, q<0.0001) when compared to patients on WW (36%). There was no significant difference in the number of patients exhibiting a clonal shift between those receiving ibrutinib and chlorambucil (q=0.3). More patients treated with ibrutinib (54%, q=0.0002) and chlorambucil (50%, q=0.004) harbored a clone that reduced in size than those on WW (17%). More patients treated with ibrutinib (71%, q<0.0002) and chlorambucil (55%, q=0.12) possessed at least one clone that increased in size during therapy than patients receiving WW (34%). In ibrutinib-treated patients, SF3B1 mutations (q=0.13, 14% vs 5%) and XPO1 mutations (q=0.13, 5% vs 0.3%) were enriched in subclones that significantly reduced over the complete time period. We also observed more BIRC3 mutations in subclones that had a significant shift in CCF when compared to subclones that remained stable over time (q=0.2, 5% vs 2%). Consistent with the overall study, with 8 years of follow-up in this patient subset, long-term progression-free survival (PFS) was significantly higher in the patients treated with ibrutinib when compared to chlorambucil (P<0.0001). This PFS benefit was also seen regardless of subclonal growth patterns. Using baseline RNA-seq data, we identified 7 of the 8 recently defined CLL expression clusters (EC), that were identified in the context of chemoimmunotherapy for CLL (Knisbacher et al. Nat Gen 2022), and could recapitulate the relationship to outcomes in individuals treated with chlorambucil. However, despite the larger cohort, no significant relationship between EC clusters and PFS were observed in ibrutinib-treated patients, suggesting that a distinct predictive baseline transcriptional signature may be present in ibrutinib-treated patients. CONCLUSION Our analyses highlight the need to interrogate the molecular features of CLL in relation to the impact of therapeutic selective pressure imposed by BTK inhibition, which appear distinct from exposure to chemotherapy. Ongoing analyses aim to identify relationships between patterns of growth and subclonal growth rates molecular and clinical features.

Abstract Using the polymerase chain reaction we examined for specific Ig kappa-L chain V region gene (V kappa gene) rearrangement in small lymphocytic non-Hodgkin's lymphomas that express Ig bearing a major kappa-L chain associated cross-reactive Id, designated 17.109. Previously, we identified the 17.109-cross-reactive Id in chronic lymphocytic leukemia as a serologic marker for expression of a highly conserved V kappa gene, designated Humkv325. Using sense-strand oligonucleotides specific for the 5'-end of this Vkappa gene and antisense oligonucleotide specific for a Jkappa region consensus sequence, we could amplify specifically Humkv325 when juxtaposed with Jkappa through Ig gene rearrangement. This allowed us to amplify rearranged Vkappa genes from DNA isolated from minute amounts of lymphoma biopsy material for molecular analyses. Our studies demonstrate that 17.109-reactive SL NHL, with or without associated CLL, rearrange, and presumably express, Humkv325 without substantial somatic diversification. Our data suggest that malignant B cells in SL NHL, in contrast to NHL of follicular center cell origin, may express immunoglobulin variable region genes with little or no somatic hypermutation.

Abstract Richter syndrome (RS), an aggressive lymphoma that develops in patients with chronic lymphocytic leukemia (CLL), is a striking example of histologic transformation. While recent therapeutic advances have transformed the treatment landscape of CLL and lymphoma, RS remains associated with dismal overall survival. Despite an advanced genomic and molecular characterization of CLL over the past decade, the current understanding of the genetic factors driving evolution of CLL to RS is limited. To decipher the genetics underlying this transformation, we have performed an integrative analysis of exome, genome and transcriptome data generated from matched RS and CLL samples from a discovery cohort of 53 patients with newly diagnosed RS of DLBCL histology. Through computational deconvolution of CLL and RS clones, we constructed phylogenetic relationships and traced evolution of CLL to RS, confirming both clonal related (87%) and unrelated cases (13%). In addition to identifying recognized RS-risk genetic lesions, we discovered novel RS-specific alterations, including 5 putative somatic driver genes (IRF2BP2, SRSF1, B2M, DNMT3A and EZH2), frequent copy number alterations beyond del(9p21)(CDKN2A/B), (including amp(7q21.2) (CDK6), amp(9p24) (PDL1/L2), and amp(1q23)(MCL1)), and recurrent whole genome duplication and chromothripsis. Integration of exome and genome sequencing data led to the identification of distinct molecular subtypes of RS with prognostic importance. To confirm these molecular subtypes, a validation cohort of 47 RS cases has been assembled with paired exome and transcriptome data. To further investigate the stepwise clonal evolution of CLL to RS, we performed single-cell RNA-sequencing on biopsy samples obtained at diagnosis from 5 individuals with clonally related transformation. Using a novel tool, CNVSingle, we inferred allele specific single-cell copy number alterations that enabled identification of the single-cell clusters representing distinct CLL and RS genetic subclones as well as intermediate, or transitional, evolutionary states. RS cells displayed gene expression enriched in pathways of MYC targets and cell cycle, in line with similar analysis on bulk transcriptomes. Finally, by ultra-low pass (ULP)-WGS sequencing of plasma samples from RS patients, we demonstrate detection of RS tumor DNA in plasma months prior to initial clinical diagnosis (n=3 of 6) or post-allogeneic stem cell transplant relapse (n=2 of 2). cfDNA is thus a promising tool for early detection of emerging RS and RS relapse as well as for non-invasive detection surrounding diagnosis. Altogether, our study defines RS-specific alterations and provides a molecular definition of RS, identifies distinct genetic subtypes of RS with prognostic significance, traces the evolutionary path to RS and suggests future strategies for improved detection. Citation Format: Erin M Parry, Ignaty Leshchiner, Romain Guieze, Connor Johnson, Eugen Tausch, Sameer A Parikh, Camilla K Lemvigh, Conor Messer, Filippo Utro, Chaya Levovitz, Kahn Rhrissorrakrai, Matthew S Davids, Julien Broseus, Shuqiang Li, Ziao Lin, Binyamin A Knisbacher, Christof Schneider, Laura Z Rassenti, Thomas J Kipps, Nitin Jain, William Wierda, Florence Cymbalista, Neil E Kay, Kenneth J Livak, Brian P Danysh, Chip Stewart, Donna Neuberg, Jennifer R Brown, Laxmi Paridi, Stephan Stilgenbauer, Gaddy Getz, Catherine Wu. Evolutionary history of transformation from chronic lymphocytic leukemia to Richter syndrome [abstract]. In: Proceedings of the Third AACR International Meeting: Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2022 Jun 23-26; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2022;3(5_Suppl):Abstract nr A13.

Abstract Richter’s syndrome (RS) arising from chronic lymphocytic leukemia (CLL) is a striking example of an aggressive malignant histology that emerges from indolent cancer. RS is a major barrier to disease control in CLL and is associated with poor clinical outcomes and limited survival. The genetic basis of RS is poorly understood, and its relationship to the antecedent CLL remains incompletely characterized. To study RS, we performed whole-exome sequencing (WES) on samples collected from 52 patients with RS of diffuse large B cell lymphoma (DLBCL) histology. For this genomic characterization, samples from 42 patients were analyzed as ‘trios’ (matched germline, CLL and RS tissue DNA) and those from 10 as ‘duos’ (matched CLL and RS DNA). Beyond addressing contamination of CLL DNA in the germline sample, we developed methods for discriminating between the RS and CLL clones which often coexist in the same samples. The discovery cohort revealed that RS and CLL were clonally-related in 45/52 (87%) cases based on WES analysis, with a distinct RS clone emerging from a CLL subclone. The remaining 13% were determined to be clonally unrelated. RS clones presented ~3x higher rates of additional mutations than the ancestral CLL clones from which they developed. We identified novel RS somatic driver mutations (in IRF2BP2, SRSF1, B2M, DNMT3A and others), a high rate of copy number variations with recurrent deletions (e.g., del(17p) [TP53], del(13q14.3), del(7q36), and del(15q13.11) [MGA], del(9p21.3) [CDKN2A/B], del(16q12.2)), focal amplifications (amp(7q21.2) [CDK6], amp(8q24.2) [RECQL4, MYC], amp(13q31.2) [ERCC5], and frequent whole genome duplication. To further investigate RS and CLL clonal evolution, we performed single-cell RNA-sequencing on biopsies at the time of RS diagnosis in 5 individuals with clonally related transformation. Using our novel tool, CNVSingle, we inferred allele specific single-cell copy number alterations, yielding cluster-specific copy number profiles that matched the WES results of individual subclones of the RS and CLL populations. This enabled mapping genetic clones to specific expression patterns. Finally, we devised and tested a methodology that uses cfDNA for early detection of emerging Richter’s disease and have successfully identified Richter‘s tumor DNA in the blood several months prior to the clinical diagnosis. Our study thus defines drivers, distinct molecular subtypes and evolutionary path to RS and suggests strategies for its improved detection. Citation Format: Erin M. Parry, Ignaty Leshchiner, Romain Guièze, Connor Johnson, Eugen Tausch, Sameer Parikh, Camilla Lemvigh, Conor Messer, Daniel Rosebrock, Filippo Utro, Chaya Levovitz, Kahn Rhrissorrakrai, Matthew Davids, Raquel A. Jacobs, Kara Slowik, Julien Broseus, Shanye Yin, Shuqiang Li, Geoff Fell, Ziao Lin, Binyamin A. Knisbacher, Neil Ruthen, Dimitri Livitz, Christof Schneider, Jialin Ma, Julian Hess, Laura Z. Rassenti, Thomas J. Kipps, Nitin Jain, William Wierda, Florence Cymbalista, Neil E. Kay, Kenneth J. Livak, Brian P. Danysh, Chip Stewart, Donna Neuberg, Jennifer R. Brown, Laxmi Parida, Stephan Stilgenbauer, Gad Getz, Catherine J. Wu. Evolutionary history of transformation from chronic lymphocytic leukemia to Richter’s syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4007.

Abstract BackgroundAlternative splicing (AS) is a fundamental process in eukaryotes contributing to the diversity of mRNA isoforms with variable ratios of intron/exon. SF3B1 is a pivotal protein of the spliceosome machinery. Mutations in the SF3B1 gene have prognostic value in Chronic Lymphocytic Leukemia (CLL). Our previous studies have shown that SF3B1 inhibition with macrolides induces apoptosis specifically in CLL compared with normal cells. SF3B1 inhibition is associated with a widespread increase in intron retention (IR) on most transcripts, suggesting that IR can be used as a marker of spliceosome inhibition in CLL cells. However, it is unknown how the activity of SF3B1 contributes to the spliceosome activity and the poor clinical prognosis in CLL.MethodsTo better understand this process, we performed a comprehensive analysis to quantify the abundance of individual exonic and intronic mapped reads on annotated RNA-Seq transcripts derived from the B cells of 98 CLL patients and nine healthy volunteers (Normal B cells – NBC). We calculated ratios for intron and exon abundance for each transcript and use this as a measure of intron retention (IR) and a surrogate for alternative splicing (AS).ResultsWe found that 66% of CLL B-cells transcripts had significant IR elevation compared to NBC and that transcripts with high IR were associated with low expression levels and mRNA downregulation. The IR increase in CLL B-cells was independent of prognostic factors such as IgVH or SF3B1 mutations. Transcripts with the highest IR levels belonged to biological pathways associated with gene expression and RNA splicing. Also, we observed a >2-fold increase of active pSF3B1 in CLL B-cells compared to NBC. Additionally, when the CLL-B cells were treated with macrolides (pladienolide-B), a significant decrease in pSF3B1, but not total SF3B1 protein was observed. ConclusionsOur findings suggest that IR/ARS is increased in CLL and that this process is associated with SF3B1 phosphorylation and susceptibility to SF3B1 inhibitors. These data provide additional support to the relevance of AS in carcinogenesis and evidence of pSF3B1 participation in this process.

Abstract Most human cancers converge to a deregulated methylome with reduced global levels and elevated methylation at select CpG islands. To investigate the emergence and dynamics of the cancer methylome, we characterized genome-wide DNA methylation in preneoplastic monoclonal B-cell lymphocytosis (MBL) and chronic lymphocytic leukemia (CLL), including serial samples collected across disease course. We detected the aberrant tumor-associated methylation landscape at CLL diagnosis and found no significant differentially methylated regions in the high-count MBL-to-CLL transition. Patient methylomes showed remarkable stability with natural disease and posttherapy progression. Single CLL cells were consistently aberrantly methylated, indicating a homogeneous transition to the altered epigenetic state and a distinct expression profile together with MBL cells compared with normal B cells. Our longitudinal analysis reveals the cancer methylome to emerge early, which may provide a platform for subsequent genetically driven growth dynamics, and, together with its persistent presence, suggests a central role in disease onset. Significance: DNA methylation data from a large cohort of patients with MBL and CLL show that epigenetic transformation emerges early and persists throughout disease stages with limited subsequent changes. Our results indicate an early role for this aberrant landscape in the normal-to-preneoplastic transition that may reflect a pan-cancer mechanism. See related commentary by Rossi, p. 6. This article is highlighted in the In This Issue feature, p. 1

Richter's syndrome (RS) arising from chronic lymphocytic leukemia (CLL) is a striking example of an aggressive malignant histology that emerges from indolent cancer. A major barrier to disease control in CLL, RS is associated with poor clinical outcomes and limited survival. The genetic basis of RS is poorly understood and its relationship to antecedent CLL remains incompletely characterized. Notable challenges to the genomic study of RS includes those of sample acquisition, the distinction between true tumor events rather than sequence artifacts in archival fixed tissue, and the limitations of available computational techniques for deconvoluting admixtures of CLL and RS DNA within the same biopsy specimen. To address these challenges and characterize the genetic profile of RS, we performed whole-exome sequencing (WES) on samples collected from 42 patients with RS of diffuse large B cell lymphoma (DLBCL) histology. For this genomic characterization, samples from 37 patients were analyzed as 'trios' (matched germline, CLL and RS tissue DNA) and those from 5 as 'duos' (matched CLL and RS DNA). CLL diagnosis preceeded RS diagnosis by a median of 60.6 months (range 0.1-234.5). The median number of prior CLL-directed therapies was 2 (range 0-10). 8 patients had no prior CLL-directed therapy, while 9 were exposed to novel agents. The median time from most proximal CLL sampling to RS was 2.7 months (range: 82.2 months pre- to 3.8 months post RS diagnosis). Critical analytic innovations applied to this dataset included addressing contamination of CLL DNA in the germline sample (through the tool DeTIN) and generating the ability to discriminate between clones arising from RS or from CLL, even while both histologies were commonly co-existing within originating biopsies (via the tool PhylogicNDT). From this discovery cohort of 42 cases, 36 (86%) revealed RS and CLL to be clonally-related based on WES analysis, with a distinct RS clone emerging from an existing CLL subclone. Of the 6 (14%) cases determined to be clonally unrelated by WES, 4 had been previously examined by IGHV sequencing; only 1 of 4 was categorized as clonally unrelated, likely due to CLL and RS admixture. RS displayed mutational signatures reflecting aging (CpG), canonical AID, and non-canonical AID processes. Through deconvoluting clonal composition using PhylogicNDT in related sample trios (n=31), we established several notable differences compared to antecedent CLL. First, RS clones presented higher rates of additional mutations than the ancestral CLL clones from which they developed (2.47 vs. 0.86 Mut/Mb, p<0.0001). Second, the frequency of CLL-associated driver mutations in the RS clones was altered:TP53 mutation (n=21, 50%), NOTCH1 mutation (n=17, 40%) NFKBIE (n=5, 12%), EGR2 (n=6, 14%), XPO1 (n=5, 12%), and RPS15 (n=3, 7%). Third, the clonal transition to RS was marked by a diversity of additional driver mutations and particularly increased copy number variants (CNVs). RS biopsies had recurrent arm level events, including del(17p) in 44%, del(4p) in 27%, del(4q) in 27%, del(9p) in 22%, del(17q) in 22% and del(9q) in 20% (Q<0.05, GISTIC). The top focal alterations in RS biopsies, as identified by tool GISTIC included (Q value <= 0.05): amp(7q21.2) - CDK6,amp(8q24.3) - RECQL4, amp(13q31.3) - ERCC5, amp(19p13.3), amp(19q13.42), del(8p12), del(17p13.1) - TP53, del(13q14.3), del(7q35), and del(16q12.2). Finally, whole genome doubling was observed in 6 cases upon transition to RS. To further investigate RS and CLL clonal evolution at high resolution, we performed single-cell RNA-sequencing (10x Genomics), on biopsies at the time of RS diagnosis in 5 individuals with clonally related transformation. Data analysis and clustering was conducted using Seurat(v3) with doublet removal (DoubletFinder). We identified CNV in single-cell transcriptomes that matched the WES copy-number profiles of individual subclones of the RS and CLL populations and thus linked subclones identified by WES to single-cell expression clusters and states, revealing CLL, RS and intermediate evolutionary cell states. In conclusion, we identify that RS arises from CLL subclones through distinct mutational trajectories. Further molecular subclassification of RS, including genetic characterization of additional cases, and linking mutational data with clinical outcomes is ongoing and has potential to alter clinical classification and prognostication of RS. Disclosures Guieze: abbvie: Honoraria, Other: advisory board, travel funds; janssen cilag: Honoraria, Other: advisory board, travel funds; roche: Other: travle funds; gilead: Honoraria, Other: travel funds; astrazanecka: Honoraria, Other: advisory board. Leshchiner:PACT Pharma, Inc.: Consultancy. Tausch:Roche: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Davids:Genentech: Consultancy, Research Funding; Janssen: Consultancy; Gilead Sciences: Consultancy; Novartis: Consultancy, Research Funding; MEI Pharma: Consultancy, Research Funding; Surface Oncology: Research Funding; AbbVie: Consultancy; Adaptive Biotechnologies: Consultancy; Merck: Consultancy; Pharmacyclics: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Verastem: Consultancy, Research Funding; Syros Pharmaceuticals: Consultancy; Sunesis: Consultancy; Zentalis: Consultancy; AstraZeneca: Consultancy, Research Funding; Ascentage Pharma: Consultancy, Research Funding; Eli Lilly: Consultancy; Research to Practice: Honoraria; Celgene: Consultancy; BeiGene: Consultancy; Bristol Myers Squibb: Research Funding. Elagina:AbbVie: Current Employment. Jain:TG Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Precision Bioscienes: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; Pfizer: Research Funding; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Fate Therapeutics: Research Funding; Verastem: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Cellectis: Research Funding; BeiGene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Aprea Therapeutics: Research Funding; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; ADC Therapeutics: Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Kipps:Oncternal Therapeutics, Inc.: Other: Cirmtuzumab was developed by Thomas J. Kipps in the Thomas J. Kipps laboratory and licensed by the University of California to Oncternal Therapeutics, Inc., which provided stock options and research funding to the Thomas J. Kipps laboratory, Research Funding; Celgene: Honoraria, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; VelosBio: Research Funding; Pharmacyclics/ AbbVie, Breast Cancer Research Foundation, MD Anderson Cancer Center, Oncternal Therapeutics, Inc., Specialized Center of Research (SCOR) - The Leukemia and Lymphoma Society (LLS), California Institute for Regenerative Medicine (CIRM): Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Ascerta/AstraZeneca, Celgene, Genentech/F. Hoffmann-La Roche, Gilead, Janssen, Loxo Oncology, Octernal Therapeutics, Pharmacyclics/AbbVie, TG Therapeutics, VelosBio, and Verastem: Membership on an entity's Board of Directors or advisory committees. Broséus:Novartis: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Gilead: Honoraria. Kay:MEI Pharma: Research Funding; Cytomx: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Purroy:AstraZenica: Current Employment. Utro:IBM: Current Employment. Rhrissorrakrai:IBM: Current Employment. Levovitz:IBM: Current Employment. Parikh:GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; MorphoSys: Research Funding; TG Therapeutics: Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria. Brown:Abbvie, Acerta, AstraZeneca, Beigene, Invectys, Juno/Celgene, Kite, Morphosys, Novartis, Octapharma, Pharmacyclics, Sunesis, TG Therapeutics, Verastem: Consultancy; Gilead, Loxo, Sun, Verastem: Research Funding; Janssen, Teva: Speakers Bureau. Parida:IBM: Current Employment. Neuberg:Madrigak Pharmaceuticals: Current equity holder in publicly-traded company; Pharmacyclics: Research Funding; Celgene: Research Funding. Stilgenbauer:Novartis: Consultancy, Honoraria, Other, Research Funding; Mundipharma: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding. Getz:Scorpion Therapeutics: Consultancy, Current equity holder in publicly-traded company, Other: Founder; IBM: Research Funding; Pharmacyclics: Research Funding; Broad Institute: Patents & Royalties: MuTect, ABSOLUTE, MutSig, MSMuTect, MSMutSig, POLYSOLVER and TensorQTL. Wu:Pharmacyclics: Research Funding; BionTech: Current equity holder in publicly-traded company.