Abstract: Many functional consequences of mutations on tumor phenotypes in chronic lymphocytic leukemia (CLL) are unknown. This may be in part due to a scarcity of information on the proteome of CLL. We profiled the proteome of 117 CLL patient samples with data-independent acquisition mass spectrometry and integrated the results with genomic, transcriptomic, ex vivo drug response, and clinical outcome data. We found trisomy 12, IGHV mutational status, mutated SF3B1, trisomy 19, del(17)(p13), del(11)(q22.3), mutated DDX3X and MED12 to influence protein expression (false discovery rate [FDR] = 5%). Trisomy 12 and IGHV status were the major determinants of protein expression variation in CLL as shown by principal-component analysis (1055 and 542 differentially expressed proteins, FDR = 5%). Gene set enrichment analyses of CLL with trisomy 12 implicated B-cell receptor (BCR)/phosphatidylinositol 3-kinase (PI3K)/AKT signaling as a tumor driver. These findings were supported by analyses of protein abundance buffering and protein complex formation, which identified limited protein abundance buffering and an upregulated protein complex involved in BCR, AKT, MAPK, and PI3K signaling in trisomy 12 CLL. A survey of proteins associated with trisomy 12/IGHV-independent drug response linked STAT2 protein expression with response to kinase inhibitors, including Bruton tyrosine kinase and mitogen-activated protein kinase kinase (MEK) inhibitors. STAT2 was upregulated in unmutated IGHV CLL and trisomy 12 CLL and required for chemokine/cytokine signaling (interferon response). This study highlights the importance of protein abundance data as a nonredundant layer of information in tumor biology and provides a protein expression reference map for CLL.

Abstract: B-PLL is defined by the presence of prolymphocytes in peripheral blood exceeding 55% of lymphoid cells. The diagnosis, mainly based on clinical and morphological data, can be difficult because of overlap with other B-cell malignancies. Because of the rarity of the disease, only case reports and small series describe its cytogenetic features. Few prognostic markers have been identified in this aggressive leukemia usually resistant to standard chemo-immuno therapy. We report here the cytogenetic and molecular findings in a large series of B-PLL. We also studied the in vitro response to novel targeted drugs on primary B-PLL cells. The study included 34 cases with a diagnosis of B-PLL validated by morphological review performed by three independent expert cytologists. The diagnosis of mantle cell lymphoma was excluded by karyotype (K) and FISH using CCND1, CCND2 and CCND3 probes. Median age at diagnosis was 72 years [46-88]. K was complex (≥3 abnormalities) in 73%, and highly complex (HCK≥5) in 45%. Combining K and FISH data, the most frequent chromosomal aberrations were: translocation targeting the MYC gene [t(MYC)] (21/34, 62%), 17p deletion including TP53 gene (13/34, 38%), trisomy 18/18q (10/33, 30%), 13q14 deletion (10/34, 29%), trisomy 3 (8/33, 24%), trisomy 12 (8/34, 24%) and 8p deletion (7/31, 23%). Whole-Exome Sequencing analysis of paired tumor-control DNA was performed in 16 patients. The most frequently mutated genes were TP53(6/16, 38%), associated with del17p in all, MYD88 (n=4), BCOR (n=4), MYC (n=3), SF3B1 (n=3), FAT1 (n=3), SETD2 (n=2), CHD2 (n=2), CXCR4 (n=2), BCLAF1 (n=2) and NFASC (n=2). Distribution of the chromosomal aberrations is shown in Fig 1. The main group of patients (21/34, 62%) had a t(MYC) that was associated with a higher % of prolymphocytes (86 vs 76, p=0.03), CD38 expression (90% vs 15%,p 〈 0.001), and a lower K complexity (HCK≥5 : 20% vs 85%, p=0.0004). Mutations in MYC and in genes involved in RNA metabolism and chromatin remodeling were almost exclusively observed with t(MYC). Principal component analysis of gene expression data in 12 cases analyzed by RNA-Seq showed that the 7 patients with t(MYC) clustered together. These results suggest that t(MYC) form a homogeneous subgroup of B-PLL. A second group with MYC gain (5/34, 15%), was associated with HCK≥5 (100% vs 36%, p=0,01) and trisomy 3 (80% vs 14%, p=0,008). Altogether, 26/34 patients (76%) had a MYC activation, translocation or gain, that were mutually exclusive. The median overall survival (OS) for the entire cohort was 126 months with a median follow-up time of 47 months [ 0.2-141]. We found MYC activation (translocation or gain) to be associated with a shorter OS (p=0.03). Regarding MYC and del17p, we identified 3 distinct cytogenetic prognostic groups, with significant differences in OS (p=0.0006) (Fig 2). The patients without MYC activation had the lower risk (n=8, median not reached). Patients with a MYC activation without del17p had an intermediate risk (n=18, 125 months). The highest risk group corresponded to patients with both MYC and TP53 aberrations (n=7, 11 months). We performed drug response profiling on primary B-PLL cells using the ATP-based CellTiter Glo kit (Promega) (n=5). We observed that after 48h of exposure to increased doses, response was heterogeneous, with a majority of samples resistant to fludarabine (n=3), ibrutinib (n=3), idelalisib (n=4), venetoclax (n=3) and OTX015 (n=4). Annexin/PI assays using flow cytometry showed that the induced cell death could be increased by combination of ibrutinib or venetoclax with OTX015 or JQ1, two BET protein inhibitors that target MYC signaling (n=1/2). In summary, B-PLL have complex and highly complex K, a high frequency of MYC activation by translocation or gain, frequent 17p deletion, and frequent mutations in MYC, TP53, BCOR, and MYD88 genes. We identified 3 prognostic subgroups according to MYC and 17p status. Patients with MYC activation + 17p deletion have the shorter OS, and should be considered as a high-risk "double-hit" subgroup. Our results show that cytogenetic analysis is a useful diagnostic tool in B-PLL that improves prognostic stratification. We recommend to perform K and FISH (MYC and TP53) analyses systematically when a B-PLL is suspected. Our in vitro data suggest that drugs targeting the BCR and BCL2 in combination with MYC inhibition may be a therapeutic option in some patients. Disclosures Baseggio: Takeda Oncology: Honoraria.

Abstract: Oncogenic MYC activation promotes proliferation in Burkitt lymphoma, but also induces cell-cycle arrest and apoptosis mediated by p53, a tumor suppressor that is mutated in 40% of Burkitt lymphoma cases. To identify molecular dependencies in Burkitt lymphoma, we performed RNAi-based, loss-of-function screening in eight Burkitt lymphoma cell lines and integrated non-Burkitt lymphoma RNAi screens and genetic data. We identified 76 genes essential to Burkitt lymphoma, including genes associated with hematopoietic cell differentiation (FLI1, BCL11A) or B-cell development and activation (PAX5, CDKN1B, JAK2, CARD11) and found a number of context-specific dependencies including oncogene addiction in cell lines with TCF3/ID3 or MYD88 mutation. The strongest genotype–phenotype association was seen for TP53. MDM4, a negative regulator of TP53, was essential in TP53 wild-type (TP53wt) Burkitt lymphoma cell lines. MDM4 knockdown activated p53, induced cell-cycle arrest, and decreased tumor growth in a xenograft model in a p53-dependent manner. Small molecule inhibition of the MDM4–p53 interaction was effective only in TP53wt Burkitt lymphoma cell lines. Moreover, primary TP53wt Burkitt lymphoma samples frequently acquired gains of chromosome 1q, which includes the MDM4 locus, and showed elevated MDM4 mRNA levels. 1q gain was associated with TP53wt across 789 cancer cell lines and MDM4 was essential in the TP53wt-context in 216 cell lines representing 19 cancer entities from the Achilles Project. Our findings highlight the critical role of p53 as a tumor suppressor in Burkitt lymphoma and identify MDM4 as a functional target of 1q gain in a wide range of cancers that is therapeutically targetable. Significance: Targeting MDM4 to alleviate degradation of p53 can be exploited therapeutically across Burkitt lymphoma and other cancers with wild-type p53 harboring 1q gain, the most frequent copy number alteration in cancer.

Abstract: Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the western world and shows a very heterogeneous clinical course. While the genetic landscape of CLL has been well characterized during recent years it can only partially explain the underlying biology of this heterogeneity. Proteogenomics could offer a valuable tool to fill this gap and improve the understanding of CLL biology. Methods: Here, we performed a large proteogenomic analysis (n=263) of three clinically annotated CLL cohorts: For the discovery cohort (Germany_1: n=68) we performed in-depth HiRIEF LC-MS based proteomics (more than 9000 proteins quantified) alongside genome-, transcriptome and ex-vivo drug response-profiling with 43 clinically established drugs. The proteome of two additional validation cohorts (Germany_2: n=44, Sweden_1: n=89), were characterized by data-independent acquisition (DIA) mass spectrometry. Results: To connect the CLL genotype with the molecular phenotype, we investigated associations between recurrent genetic alterations of CLL, mRNA expression and protein abundance. We found that trisomy 12, IGHV status and SF3B1 mutations had the greatest impact on protein abundances. CLL specific recurrent chromosomal deletions and gains (trisomy 12, del17p, del13q, del11q, gain8q24) consistently impacted on gene expression and protein abundance through gene dosage effects. We explored functional consequences of these gene dosage effects and found that the additional copy of chromosome 12 increased the abundance of central B-cell receptor (BCR) protein complexes through cis- and trans-effects, which could explain the increased response of trisomy 12 patient samples to BCR inhibition. Somatic mutations of TP53, ATM and XPO1 were associated with less, but specific and biologically-relevant protein abundance changes. p53 for instance, was the most upregulated protein in TP53 mutated samples, owing to the known stabilisation of mutant p53. This effect was not detectable on transcript level. ATM and XPO1 protein abundances were significantly lower in ATM and XPO1 mutated cases, indicating loss-of-function phenotypes of these mutations. To understand global similarities and differences between CLL patients on the proteomic level, we performed unsupervised clustering and identified clinically meaningful subgroups. Unsupervised clustering of the proteomics data identified six subgroups with contrasting clinical behaviour. TP53 mutations, IGHV status, trisomy 12 and their interactions explained five subgroups. These results show that quantitative mass spectrometry-based proteomics distinguished clinically relevant subgroups of CLL. Most importantly, we identified a previously unappreciated subgroup of CLL, comprising 20% of all cases, which could be uncovered by proteomic profiling and showed no association with frequent genetic or transcriptional alterations. This new CLL subgroup was characterized by accelerated disease progression, SF3B1 mutation-independent splicing alterations, metabolomic reprogramming and increased vulnerability to inhibitors of metabolic enzymes and the proteasome. Surprisingly, major BCR signaling proteins were downregulated in this subgroup, suggesting less dependence on BCR activity. In accordance with this observation, an unsupervised analysis revealed that low levels of many BCR signaling proteins (e.g. PLCG2 and PIK3CD) were associated with short time to next treatment. The existence of this subgroup could be confirmed in the validation cohorts. Finally, we performed an unsupervised multi-omics factor analysis (MOFA) across all omics data sets in parallel. This unsupervised analysis confirmed the existence of the above identified CLL subgroups and an important role of SF3B1 mutation-independent splicing alterations in CLL. Conclusion: Our integrative multi-omics analysis provides the first comprehensive overview of the interplay between genetic variants, the transcriptome, and the proteome, along with functional consequences for drug response and clinical outcome in CLL. Importantly, we identified a new subgroup with accelerated disease progression, a distinct proteomic signature and a clinically exploitable drug sensitivity profile. Figure Disclosures Mueller-Tidow: BiolineRx: Research Funding; Daiichi Sankyo: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMBF: Research Funding; Wilhelm-Sander-Stiftung: Research Funding; Jose-Carreras-Siftung: Research Funding; Bayer AG: Research Funding; Deutsche Krebshilfe: Research Funding; Deutsche Forschungsgemeinschaft: Research Funding; Janssen-Cilag Gmbh: Membership on an entity's Board of Directors or advisory committees. Dreger:Neovii: Research Funding; Roche: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Janssen: Consultancy; Gilead: Consultancy, Speakers Bureau; AstraZeneca: Consultancy; AbbVie: Consultancy, Speakers Bureau. Stilgenbauer:Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Mundipharma: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding. Tausch:Roche: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Dietrich:Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees.

Abstract: Introduction and Methods To investigate the mechanisms by which microenvironmental signals induce treatment resistance in chronic lymphocytic leukemia (CLL) we conducted a high-throughput perturbation assay combining pairwise 15 clinically relevant drugs with 18 stimulations mimicking the tumor microenvironment. We combined this with multi-layer omics data using whole exome sequencing, genome-wide DNA-methylation profiles and RNA sequencing to investigate molecular determinants of microenvironmental drug resistance. In parallel we assessed the in-vivo relevance of investigated pathways in 100 CLL infiltrated- and 100 healthy lymph node (LN) biopsies by staining for stimulus specific downstream pathway components using immunohistochemistry (IHC). Results We initially clustered drugs and microenvironmental stimuli separately based on the similarity of their response profiles across all CLL samples. We found drug response profiles to be correlated closely for drugs targeting the same signaling-pathway, e.g. the B-cell receptor inhibitors Ibrutinib (BTK) and PRT062607 (Syk) (Pearson correlation coefficient: r=0.78, 95% CI: 0.72 - 0.83). In contrast, responses to microenvironmental stimuli were less correlated, indicating diverse effects are caused by the tumor microenvironment (e.g. Interleukin 4 (IL-4) vs. Interleukin 2, Pearson correlation coefficient: r=0.17, 95% CI: 0.03 - 0.31). Twelve of the 18 tested stimulations enhanced survival of CLL cells, whereas two reduced the viability of CLL cells in-vitro (comparisons yielded BH adjusted t-test p-values 〈 0.0001). The strongest pro-survival effects were mediated by IL-4 and Toll-like receptor (TLR) 7/8/9 stimulation, whereas transforming growth factor β1 (TGF-β1) and Interleukin-6 reduced the viability of CLL cells in-vitro. Ten of the 18 stimulation effects were modulated by genetic alterations in CLL cells, for 6 stimuli two or more response modulating genetic alterations were identified (FDR 10%). IGHV mutation status for instance modulated the effects of TLR 7/8/9 agonists as well as Interleukine-1β, CD40 ligand and TGF-β1. We will present a detailed analysis of how microenvironmental stimuli depend on specific genetic alterations in CLL. To understand how soluble stimuli interfere with drug response we searched for specific interactions between stimulations and drugs with the following linear model: Viability ~ drug-effect + stimulation-effect + drug-effect:stimulation-effect and tested for significance of the interaction term. This approach allowed us to dissect interactions independently of the individual effects. We discovered different types of drug-effect:stimulation-effect interactions: 1) The microenvironmental stimulus had a strong pro-survival effect on the baseline viability and interfered with the response to drugs (e.g. IL-4:Ibrutinib, p-value 〈 0.0001). 2) The microenvironmental stimulus had no major effect on baseline viability but significantly interfered with response to drugs (e.g. Interferon-γ:Ibrutinib, p-value: 0.014). 3) The microenvironmental stimulus and the drug exhibited no major individual effect on CLL cell survival, but together significantly increased CLL cell viability (e.g. Interferon-γ:Ralimetinib, p-value: 〈 0.0001). We will present a detailed analysis of microenvironmental stimuli and their influence on drug response. In the final step we investigated the biological relevance of selected microenvironmental stimuli in patient LN- and bone marrow biopsies by IHC. CLL infiltrated LN exhibited a higher signaling activity of the IL-4 and TLR pathway than non-neoplastic LN (t-test, p-values 〈 0.0001). A high signaling activity of IL-4 was associated with shorter time to treatment, indicating that CLL progresses faster with strong microenvironmental support. Conclusion We present a systematic investigation on the tumor microenvironment in CLL, the underlying molecular determinants as well its interference with drug response which will serve as a scientific resource and starting point for further mechanistical investigations. Figure Disclosures Kriegsmann: Celgene: Research Funding; Bristol-Myers Squibb: Research Funding; Sanofi: Research Funding; Morphosys: Research Funding. Müller-Tidow:MSD: Membership on an entity's Board of Directors or advisory committees.

Abstract: Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Abstract: B-PLL is tightly linked to MYC aberrations (translocation or gain) and 17p (TP53) deletion. Cases of B-PLL with MYC aberration and 17p (TP53) deletion have the worst prognosis.