Project 2: Multi-Omics of High-Risk Multiple Myeloma
While our extensive studies have confirmed the significant role of the disrupted genome in multiple myeloma, they have also reemphasized the gaps in understanding and the importance of immune regulation and gene-environment interaction. It's likely that the evolution of multiple myeloma both before and during therapy is the result of a complex interplay of biological perturbations driven by genetic changes and environmental influences.
Our past work also has demonstrated that studying small numbers of patients at great depth can be as rewarding for scientific understanding as studying superficial genomic events in thousands of patients. Thus, we are striving to generate the first longitudinal, translational clinical trial and comprehensive data resource of environmental genetic interactions for the highest risk multiple myeloma population. It is these patients for whom highly effective therapeutics fail for reasons that 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 for this research project is that analysis of data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing multiple myeloma responsiveness to therapy and subsequent outcomes.
First, we are leveraging 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 biorepositories to derive for each patient a detailed map of environmental gene interactions linked to clinical outcome over time.
Second, we are performing a series of complex analyses to identify multiple myeloma-associated changes in and across the genome, transcriptome, epigenome, immune environment, proteome, lipidome and metabolome.
Third, we are studying these samples at the highest resolution technically feasible today and seeking 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 are being linked to improve our understanding of the mechanisms underlying multiple myeloma variability among patients in regard to disease outcomes. This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for multiple myeloma.