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 SG membership superseded other prognostic factors (Rai Staging, IGHV Status) and were prognostic for response to modern (BTK inhibition) and older CLL therapies.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.