Proteogenomics refines the molecular classification of chronic lymphocytic leukemia.

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.

Drug-microenvironment perturbations reveal resistance mechanisms and prognostic subgroups in CLL.

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited and systematic analyses are lacking. Using chronic lymphocytic leukaemia (CLL) as a model disease, we investigated the influence of 17 microenvironmental stimuli on 12 drugs in 192 genetically characterised patient samples. Based on microenvironmental response, we identified four subgroups with distinct clinical outcomes beyond known prognostic markers. Response to multiple microenvironmental stimuli was amplified in trisomy 12 samples. Trisomy 12 was associated with a distinct epigenetic signature. Bromodomain inhibition reversed this epigenetic profile and could be used to target microenvironmental signalling in trisomy 12 CLL. We quantified the impact of microenvironmental stimuli on drug response and their dependence on genetic alterations, identifying interleukin 4 (IL4) and Toll-like receptor (TLR) stimulation as the strongest actuators of drug resistance. IL4 and TLR signalling activity was increased in CLL-infiltrated lymph nodes compared with healthy samples. High IL4 activity correlated with faster disease progression. The publicly available dataset can facilitate the investigation of cell-extrinsic mechanisms of drug resistance and disease progression.

Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia.

Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

The protein landscape of chronic lymphocytic leukemia.

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.

Genetic characterization of B-cell prolymphocytic leukemia: a prognostic model involving MYC and TP53.

B-cell prolymphocytic leukemia (B-PLL) is a rare hematological disorder whose underlying oncogenic mechanisms are poorly understood. Our cytogenetic and molecular assessments of 34 patients with B-PLL revealed several disease-specific features and potential therapeutic targets. The karyotype was complex (≥3 abnormalities) in 73% of the patients and highly complex (≥5 abnormalities) in 45%. The most frequent chromosomal aberrations were translocations involving MYC [t(MYC)] (62%), deletion (del)17p (38%), trisomy (tri)18 (30%), del13q (29%), tri3 (24%), tri12 (24%), and del8p (23%). Twenty-six (76%) of the 34 patients exhibited an MYC aberration, resulting from mutually exclusive translocations or gains. Whole-exome sequencing revealed frequent mutations in TP53, MYD88, BCOR, MYC, SF3B1, SETD2, CHD2, CXCR4, and BCLAF1. The majority of B-PLL used the IGHV3 or IGHV4 subgroups (89%) and displayed significantly mutated IGHV genes (79%). We identified 3 distinct cytogenetic risk groups: low risk (no MYC aberration), intermediate risk (MYC aberration but no del17p), and high risk (MYC aberration and del17p) (P = .0006). In vitro drug response profiling revealed that the combination of a B-cell receptor or BCL2 inhibitor with OTX015 (a bromodomain and extra-terminal motif inhibitor targeting MYC) was associated with significantly lower viability of B-PLL cells harboring a t(MYC). We concluded that cytogenetic analysis is a useful diagnostic and prognostic tool in B-PLL. Targeting MYC may be a useful treatment option in this disease.

MDM4 Is Targeted by 1q Gain and Drives Disease in Burkitt Lymphoma.

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.

Lymphoma and Leukemia Cell Vulnerabilities and Resistance Identified by Compound Library Screens.

Response to anticancer agents is often restricted to subsets of patients. The recognition of factors underlying this heterogeneity and the identification of biomarkers associated with response to drugs would greatly improve the efficacy of drug treatment. Platforms that can comprehensively map drug response in high-throughput ex vivo provide a unique tool to identify associated biomarkers and provide hypotheses for mechanisms underlying variable response. Such screens can be performed on cell lines and short-term cultures of primary cells to take advantage of the respective models' strength, which include, e.g., the ability to silence genes in cell lines and the "indefinite" supply of primary cells where clonal selection can be avoided. Cohorts of such samples represent the natural diversity of cancers, including rarer mutations and combinatorial patterns of mutations.We here summarize a simple and scalable method for the measurement of viability after drug exposure based on ATP measurements as a surrogate for viability, which we use to measure and understand drug response in cell lines and primary cells.