Reverse Phase Protein Array Profiling Identifies Recurrent Protein Expression Patterns of DNA Damage-Related Proteins across Acute and Chronic Leukemia: Samples from Adults and the Children's Oncology Group.

DNA damage response (DNADR) recognition and repair (DDR) pathways affect carcinogenesis and therapy responsiveness in cancers, including leukemia. We measured protein expression levels of 16 DNADR and DDR proteins using the Reverse Phase Protein Array methodology in acute myeloid (AML) (n = 1310), T-cell acute lymphoblastic leukemia (T-ALL) (n = 361) and chronic lymphocytic leukemia (CLL) (n = 795) cases. Clustering analysis identified five protein expression clusters; three were unique compared to normal CD34+ cells. Individual protein expression differed by disease for 14/16 proteins, with five highest in CLL and nine in T-ALL, and by age in T-ALL and AML (six and eleven proteins, respectively), but not CLL (n = 0). Most (96%) of the CLL cases clustered in one cluster; the other 4% were characterized by higher frequencies of deletion 13q and 17p, and fared poorly (p < 0.001). T-ALL predominated in C1 and AML in C5, but both occurred in all four acute-dominated clusters. Protein clusters showed similar implications for survival and remission duration in pediatric and adult T-ALL and AML populations, with C5 doing best in all. In summary, DNADR and DDR protein expression was abnormal in leukemia and formed recurrent clusters that were shared across the leukemias with shared prognostic implications across diseases, and individual proteins showed age- and disease-related differences.

Prognostication of DNA Damage Response Protein Expression Patterns in Chronic Lymphocytic Leukemia.

Proteomic DNA Damage Repair (DDR) expression patterns in Chronic Lymphocytic Leukemia were characterized by quantifying and clustering 24 total and phosphorylated DDR proteins. Overall, three protein expression patterns (C1-C3) were identified and were associated as an independent predictor of distinct patient overall survival outcomes. Patients within clusters C1 and C2 had poorer survival outcomes and responses to fludarabine, cyclophosphamide, and rituxan chemotherapy compared to patients within cluster C3. However, DDR protein expression patterns were not prognostic in more modern therapies with BCL2 inhibitors or a BTK/PI3K inhibitor. Individually, nine of the DDR proteins were prognostic for predicting overall survival and/or time to first treatment. When looking for other proteins that may be associated with or influenced by DDR expression patterns, our differential expression analysis found that cell cycle and adhesion proteins were lower in clusters compared to normal CD19 controls. In addition, cluster C3 had a lower expression of MAPK proteins compared to the poor prognostic patient clusters thus implying a potential regulatory connection between adhesion, cell cycle, MAPK, and DDR signaling in CLL. Thus, assessing the proteomic expression of DNA damage proteins in CLL provided novel insights for deciphering influences on patient outcomes and expanded our understanding of the potential complexities and effects of DDR cell signaling.

Protein profiling by reverse phase protein array (RPPA) in classical hairy cell leukemia (HCL) and HCL-variant.

Classical hairy cell leukemia (HCL-c) and HCL variant (HCL-v) are recognized as separate entities with HCL-v having significantly shorter overall survival. Proteomic studies, shown to be prognostic in various forms of leukemia, have not been performed in HCL. We performed reverse phase protein array-based protein profiling with 384 antibodies in HCL-c (n = 12), HCL-v (n = 4), and normal B-cells (n = 5) samples. While HCL could be distinguished from normal based on unsupervised hierarchical clustering, overlap in protein expression patterns was seen between HCL-c and HCL-v, with ∼10% of the proteins being differentially expressed, suggesting potential therapeutic targets.

Multivariant Transcriptome Analysis Identifies Modules and Hub Genes Associated with Poor Outcomes in Newly Diagnosed Multiple Myeloma Patients.

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.

Proteomic profiling based classification of CLL provides prognostication for modern therapy and identifies novel therapeutic targets.

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.

RPPA-based proteomics recognizes distinct epigenetic signatures in chronic lymphocytic leukemia with clinical consequences.

The chronic lymphocytic leukemia (CLL) armamentarium has evolved significantly, with novel therapies that inhibit Bruton Tyrosine Kinase, PI3K delta and/or the BCL2 protein improving outcomes. Still, the clinical course of CLL patients is highly variable and most previously recognized prognostic features lack the capacity to predict response to modern treatments indicating the need for new prognostic markers. In this study, we identified four epigenetically distinct proteomic signatures of a large cohort of CLL and related diseases derived samples (n = 871) using reverse phase protein array technology. These signatures are associated with clinical features including age, cytogenetic abnormalities [trisomy 12, del(13q) and del(17p)], immunoglobulin heavy-chain locus (IGHV) mutational load, ZAP-70 status, Binet and Rai staging as well as with the outcome measures of time to treatment and overall survival. Protein signature membership was identified as predictive marker for overall survival regardless of other clinical features. Among the analyzed epigenetic proteins, EZH2, HDAC6, and loss of H3K27me3 levels were the most independently associated with poor survival. These findings demonstrate that proteomic based epigenetic biomarkers can be used to better classify CLL patients and provide therapeutic guidance.

Identification of Gene Co-Expression Networks Associated with Consensus Molecular Subtype-1 of Colorectal Cancer.

Colorectal cancer (CRC) is driven in part by dysregulated Wnt, Ras-Raf-MAPK, TGF-ß, and PI3K-Akt signaling. The progression of CRC is also promoted by molecular alterations and heterogeneous-yet interconnected-gene mutations, chromosomal instability, transcriptomic subtypes, and immune signatures. Genomic alterations of CRC progression lead to changes in RNA expression, which support CRC metastasis. An RNA-based classification system used for CRC, known as consensus molecular subtyping (CMS), has four classes. CMS1 has the lowest survival after relapse of the four CRC CMS phenotypes. Here, we identify gene signatures and associated coding mRNAs that are co-expressed during CMS1 CRC progression. Using RNA-seq data from CRC primary tumor samples, acquired from The Cancer Genome Atlas (TCGA), we identified co-expression gene networks significantly correlated with CMS1 CRC progression. CXCL13, CXCR5, IL10, PIK3R5, PIK3AP1, CCL19, and other co-expressed genes were identified to be positively correlated with CMS1. The co-expressed eigengene networks for CMS1 were significantly and positively correlated with the TNF, WNT, and ERK1 and ERK2 signaling pathways, which together promote cell proliferation and survival. This network was also aligned with biological characteristics of CMS1 CRC, being positively correlated to right-sided tumors, microsatellite instability, chemokine-mediated signaling pathways, and immune responses. CMS1 also differentially expressed genes involved in PI3K-Akt signaling. Our findings reveal CRC gene networks related to oncogenic signaling cascades, cell activation, and positive regulation of immune responses distinguishing CMS1 from other CRC subtypes.

Multivariate transcriptome analysis identifies networks and key drivers of chronic lymphocytic leukemia relapse risk and patient survival.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5+ CD19+ B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk. METHODS: Networks were investigated by using a novel weighted gene network co-expression analysis method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from CLL patients (N = 203). Gene Ontology analysis was used to identify biological functions overrepresented in each module. Differential Expression of modules and individual genes was assessed using an ANOVA (Binet Stage A and B relapsed patients) or T-test (SF3B1 mutations). The clinical relevance of biomarker candidates was evaluated using log-rank Kaplan Meier (survival and relapse interval) and ROC tests. RESULTS: Eight distinct modules (M2, M3, M4, M7, M9, M10, M11, M13) were significantly correlated with relapse and differentially expressed between relapsed and non-relapsed Binet Stage A CLL patients. The biological functions of modules positively correlated with relapse were carbohydrate and mRNA metabolism, whereas negatively correlated modules to relapse were protein translation associated. Additionally, M1, M3, M7, and M13 modules negatively correlated with overall survival. CLL biomarkers BTK, BCL2, and TP53 were co-expressed, while unmutated IGHV biomarker ZAP70 and cell survival-associated NOTCH1 were co-expressed in modules positively correlated with relapse and negatively correlated with survival days. CONCLUSIONS: This study provides novel insights into CLL relapse biology and pathways associated with known and novel biomarkers for relapse and overall survival. The modules associated with relapse and overall survival represented both known and novel pathways associated with CLL pathogenesis and can be a resource for the CLL research community. The hub genes of these modules, e.g., ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1, and URAHP, can be studied further as new therapeutic targets or clinical markers to predict CLL patient outcomes.

A network approach reveals driver genes associated with survival of patients with triple-negative breast cancer.

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.