Project 2: Multi-Omics of High-Risk Multiple Myeloma
Targeting gene-environment interactions
While our extensive studies confirmed the significant role of the disrupted genome in multiple myeloma, they also reemphasized gaps in understanding and the importance of immune regulation and gene-environment interactions.
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 demonstrated that studying small numbers of people at great depth can be as rewarding for scientific understanding as studying thousands of people for superficial genomic events.
Thus, we are striving to generate the first longitudinal, translational clinical trial and comprehensive data resource of gene-environment interactions for the highest-risk multiple myeloma population. For reasons that are still completely opaque, otherwise highly effective therapies fail for people in this multiple myeloma category.
Reversing this decades-old lack of progress requires new and bold approaches using state-of-the-art technology.
Hypothesis
Our hypothesis is that analyzing data capturing gene-environment interactions at high resolution will reveal insights into biological pathways influencing responsiveness to therapy and subsequent outcomes.
First, we're 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. This work will result in a detailed map of gene-environment interactions linked to clinical outcomes over time for each person.
Second, we're 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're studying these samples at the highest resolution technically feasible and seeking to define gene-environment interaction changes over time that associate with response to therapy.
Finally, we're linking high-resolution data capturing these interaction changes and clinical response data to improve understanding of the mechanisms underlying variability in outcomes.
Individualized approach
This comprehensive resource will enable a more individualized approach to clinical surveillance and therapy for people with multiple myeloma.
Project investigators
Project lead (basic): Esteban Braggio, Ph.D.
Project co-lead (basic): Yan W. Asmann, Ph.D.
Project co-lead (clinical): Shaji Kumar, M.D.
Project co-lead (clinical): Keith Stewart, M.B., Ch.B.
