The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular “disease-free months” and “disease reoccurrence”. We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.

Protein expression for 384 total and post-translationally modified proteins was assessed in 871 CLL and MSBL patients and was integrated with clinical data to identify strategies for improving diagnostics and therapy, making this the largest CLL proteomics study to date. Proteomics identified six recurrent signatures that were highly prognostic of survival and time to first or second treatment at three levels: individual proteins, when grouped into 40 functionally related groups (PFGs), and systemically in signatures (SGs). A novel SG characterized by hairy cell leukemia like proteomics but poor therapy response was discovered. SG membership superseded other prognostic factors (Rai Staging, IGHV Status) and were prognostic for response to modern (BTK inhibition) and older CLL therapies. SGs and PFGs membership provided novel drug targets and defined optimal candidates for Watch and Wait vs. early intervention. Collectively proteomics demonstrates promise for improving classification, therapeutic strategy selection, and identifying novel therapeutic targets.

Summary We aimed to identify triple-negative breast cancer (TNBC) drivers that regulate survival time as predictive signatures that improve TNBC prognostication. Breast cancer (BrCa) transcriptomic tumor biopsies were analyzed, identifying network communities enriched with TNBC-specific differentially expressed genes (DEGs) and correlated strongly to TNBC status. Two anticorrelated modules correlated strongly to TNBC subtype and survival. Querying module-specific hubs and DEGs revealed transcriptional changes associated with high survival. Transcripts were nominated as biomarkers and tested as combinatoric ratios using receiver operator characteristic (ROC) analysis to assess survival prediction. ROC test rounds integrated genes with established interactions to hubs and DEGs of key modules, improving prediction. Finally, we tested whether integration of literature-derived genes for implicated hallmark cancer processes could improve prediction of survival. Complementary coexpression, differential expression, genetic interaction, and survival stratification integrated by ROC optimization uncovered a panel of “linchpin survival genes” predictive of patient survival, representing gene interactions in hallmark cancer processes.

The molecular mechanisms underlying chemoresistance in some newly diagnosed multiple myeloma (MM) patients receiving standard therapies (lenalidomide, bortezomib, and dexamethasone) are poorly understood. Identifying clinically relevant gene networks associated with death due to MM may uncover novel mechanisms, drug targets, and prognostic biomarkers to improve the treatment of the disease. This study used data from the MMRF CoMMpass RNA-seq dataset (N = 270) for weighted gene co-expression network analysis (WGCNA), which identified 21 modules of co-expressed genes. Genes differentially expressed in patients with poor outcomes were assessed using two independent sample t-tests (dead and alive MM patients). The clinical performance of biomarker candidates was evaluated using overall survival via a log-rank Kaplan–Meier and ROC test. Four distinct modules (M10, M13, M15, and M20) were significantly correlated with MM vital status and differentially expressed between the dead (poor outcomes) and the alive MM patients within two years. The biological functions of modules positively correlated with death (M10, M13, and M20) were G-protein coupled receptor protein, cell–cell adhesion, cell cycle regulation genes, and cellular membrane fusion genes. In contrast, a negatively correlated module to MM mortality (M15) was the regulation of B-cell activation and lymphocyte differentiation. MM biomarkers CTAG2, MAGEA6, CCND2, NEK2, and E2F2 were co-expressed in positively correlated modules to MM vital status, which was associated with MM’s lower overall survival.

The availability of targeted therapy and improved molecular characterization of Chronic Lymphocytic Leukemia (CLL) require a re-evaluation of treatment paradigms. As CLL heterogeneity is dependent on molecular and environmental factors, there is a need to create a new classification based on the integration of several factors. Here, we accomplish this goal by identifying CLL signatures using Reverse Phase Protein Array. Protein expression for 384 total and post translationally modified proteins was assessed in 871 CLL and Mature Small B Cell Leukemia (MSBL: HCL, HCLV, LGL-T, MCL, MZL, PLL, Richter's, T-Cell PLL) patients and was integrated with clinical data to identify strategies for improving diagnostics and therapy, making this the largest CLL proteomics study to date. Proteins were categorized into 40 protein functional groups (PFGs) based on literature and intra-dataset protein correlations and patients clustered based on PFG expression patterns into 6 recurrent protein expression signatures (PES) (Figure 1A). Individual protein expression (58/384 proteins), PFG expression (32/40) and overall PES were all highly prognostic of survival (OS) and time to first or second treatment (TTFT, TTST) (Figures 1B-C). The adhesion, apoptosis-occurring, apoptosis-regulating, heat shock, histone1 (marks), histone 2 (modifiers) and the STP-regulation PFGs were prognostic for all 3 outcome measures. Notably SG-A contained most of the MSBL and 15/16 cases of hairy cell leukemia, but the CLL cases within this SG fared very poorly. For OS, groups A and C had markedly inferior survival (P<0.0001) (10.3 and 20.3 median years) relative to the other 4 groups, which were statistically similar to each other. First treatment occurred sooner for Groups A and C (5.8 and 5.23 median years). Additionally, the TTST was also inferior for Group A (median 3.5 years). There were significant differences in age, hemoglobin, platelets, % BM and PB lymphocytes and β2M between the SG, but not for race (p = 0.84) or gender (p = 0.72). , Historically adverse cytogenetic aberrations del 11q and del17p events (23% overall) were less common in SG A, B, D and E (15, 14, 16, 17%) and overrepresented in SG C (32%), while historically favorable 13q changes were seen across all groups as was Trisomy 12 (14% overall), although SGs A and E were enriched (25%, 22%) while SG-F was low (5%) for Trisomy 12. SG membership superseded other traditional prognostic factors (Rai Staging, IGHV Status) and were prognostic for modern (BTK inhibition) and older CLL therapies. SGs A and C responded poorly to chemotherapy regimens compared to the other groups, whereas all groups responded well to BTK inhibitors except for SG-A. SGs and PFGs membership provided novel drug targets (see our other abstracts on TP53BP1 and ASNS) and defined optimal candidates for Watch and Wait (WaW) vs. early intervention. A model based on the accumulation of irregularities in ANXA1, TFRC, and SMAD2.p245 expression, optimally predicted TTFT overall and in early stage CLL patients. Patients with < 1 negative level of either of the 3 proteins, have a median TTFT of 14.59 years, whereas patients having 2-3 have a median of 5-6.27 years (P<0.0001). CHEK1.pS345, GAB2, IGFBP2, S100A4, WEE1.pS642, and ZAP70 were universally overexpressed by all SGs, suggesting them as ideal targets for inhibition. Collectively proteomics demonstrates promise for improving classification, therapy strategy determination, and identifying novel therapeutic targets. Figure 1 Figure 1. Disclosures Ferrajoli: Janssen: Other: Advisory Board ; AstraZeneca: Other: Advisory Board, Research Funding; BeiGene: Other: Advisory Board, Research Funding. Thompson: Genentech: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Amgen: Other: Institution: Honoraria, Research Grant/Funding; Adaptive Biotechnologies: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding, Expert Testimony; Pharmacyclics: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Janssen: Consultancy, Honoraria; Gilead: Other: Institution: Advisory/Consultancy, Honoraria; AbbVie: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding. Burger: Pharmacyclics LLC: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; Beigene: Research Funding, Speakers Bureau; TG Therapeutics: Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; Gilead: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau; Novartis: Other: Travel/Accommodations/Expenses, Speakers Bureau; AstraZeneca: Consultancy; Janssen: Consultancy, Other: Travel/Accommodations/Expenses, Speakers Bureau. Wierda: Xencor: Research Funding; Karyopharm: Research Funding; Gilead Sciences: Research Funding; Acerta Pharma Inc.: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; AstraZeneca: Research Funding; Juno Therapeutics: Research Funding; KITE Pharma: Research Funding; Sunesis: Research Funding; Miragen: Research Funding; Oncternal Therapeutics, Inc.: Research Funding; Cyclacel: Research Funding; Loxo Oncology, Inc.: Research Funding; Janssen: Research Funding; Genentech: Research Funding; GSK/Novartis: Research Funding; Genzyme Corporation: Consultancy; AbbVie: Research Funding.