Project 2: Multi-Omics of high-risk MM

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT While our own extensive studies have confirmed the significant role of the disrupted genome in Multiple Myeloma (MM), they have also re-emphasized the gaps in understanding, and the importance of immune regulation and gene-environment interaction. It is indeed likely that the evolution of MM, both before or during therapy, is the result of a complex interplay of biological perturbations driven by genetic changes and environmental influences. Our past work has also demonstrated that studying small numbers of patients at great depth can be as rewarding for scientific understanding as studies of superficial genomic events in thousands of patients. Thus we will strive to generate the first, longitudinal, translational clinical trial and comprehensive data resource of environmental genetic interactions for the highest-risk MM population. It is these patients who continue to rapidly fail highly effective therapeutics for reasons which are still completely opaque. New and bold approaches using state-of-the-art technology are required to reverse this decades-old lack of progress. Our hypothesis is that analysis of data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing MM responsiveness to therapy and subsequent outcomes. First, we will leverage a carefully studied and homogeneously treated high-risk group of “double hit” patients in a Phase 2 clinical trial with large control clinical databases and bio-repositories to derive, for each patient, a detailed map of environmental gene interactions linked to clinical outcome over time. Second, we will perform a series of complex analyses to identify MM-associated changes in and across the genome, transcriptome, epigenome, immune environment, proteome, lipidome and metabolome. Third, we will study these samples at the highest resolution technically feasible today, and seek to define gene-environment interaction changes over time that associate with response to therapy. Finally, high resolution data capturing these interaction changes and clinical response data will be linked to improve our understanding of the mechanisms underlying MM variability among patients in regards to disease outcomes. This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for MM.