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.

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) ( = 1310), T-cell acute lymphoblastic leukemia (T-ALL) ( = 361) and chronic lymphocytic leukemia (CLL) ( = 795) cases. Clustering analysis identified five protein expression clusters; three were unique compared to normal CD34+ cells.

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 ( = 12), HCL-v ( = 4), and normal B-cells ( = 5) samples.

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.

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.

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-β, 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.

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.

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.

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.