Epigenomic determinants of clinical outcomes in MCL patients treated on E1405

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DESCRIPTION (provided by applicant): Mantle cell lymphoma (MCL) is an aggressive form of Non-Hodgkin's lymphoma whose pathogenesis is not clearly understood. Most patients relapse and eventually die of this disease, and new treatment options for relapsed MCL are urgently needed. Data from our lab and others suggests that there are profound changes in distribution of DNA methylation across the MCL genome. The genes affected by changes in methylation involve critical intracellular processes like transcription, cell cycle and cell survival. Integratve analysis of genome-wide methylation and expression can yield biological insights and identify patient subgroups that are not apparent by gene expression alone. Moreover, methylation signatures using array-based techniques can be prognostic and predictive for patient outcomes. Measurement of DNA methylation and RNA transcript abundance by massively parallel sequencing (MPS) has a greater dynamic range, is more sensitive, and captures genomic information more completely than microarray based approaches. We hypothesize that integrative high-resolution analysis of DNA methylation and gene expression can identify the key epigenomic determinants of clinical outcomes in primary MCL. We therefore propose to use RNA-seq and HELP-tagged deep sequencing to assay genome-wide RNA expression and methylation, in pretreatment tumor samples from three cohorts of MCL patients (a) Patients treated on ECOG 1405, a multicenter Phase II clinical trial of VcR-CVAD with maintenance Rituximab for MCL (b) MCL patients treated by the University of Wisconsin Network and (c) MCL patients treated at Hackensack University Medical Center. We will identify the differentially methylated/expressed genes associated with prognosis and then build a multi-variable model predictive for clinical outcomes. Our correlative studies leverage the unique opportunity provided by these high resolution platforms and banked specimens towards (1) improving selection of MCL patients likely to benefit most from chemotherapy (2) identifying clinically relevant patient sub-groups not apparent by gene expression profiling alone and (3) understanding the biological basis for heterogeneous clinical outcomes in MCL.