Inching towards precision medicine for multiple myeloma with causal network models.

person Jun Zhu Mount Sinai School of Medicine Tisch Cancer Institute, New York, NY info_outline Jun Zhu, Yu Liu, Li Wang, Haocheng Yu, Seungyeul Yoo, Eunjee Lee, Eric E. Schadt

Authors person Jun Zhu Mount Sinai School of Medicine Tisch Cancer Institute, New York, NY info_outline Jun Zhu, Yu Liu, Li Wang, Haocheng Yu, Seungyeul Yoo, Eunjee Lee, Eric E. Schadt Organizations Mount Sinai School of Medicine Tisch Cancer Institute, New York, NY, Sema4 Genomics, A Mount Sinai Venture, Stamford, CT, Icahn School of Medicine at Mount Sinai, New York, NY Abstract Disclosures Research Funding U.S. National Institutes of Health Sema4 Genomics and Icahn School of Medicine at Mount Sinai Background: Multiple myeloma (MM) is the 2 nd most prevalent blood cancer. For each molecular subtype of MM defined by molecular profiles, mechanisms underlying prognosis and drug response are largely unknown. Elucidating the mechanisms underlying differences in prognosis and drug response will enable a more personalized, precision medicine (PM) approach to treating patients. Methods: Big multiomics data are now widely available and contain the ingredients necessary to build causal models to uncover mechanisms of prognosis and drug response. However, data from resources like the MM Research Consortium (MMRC) dataset may contain errors (eg, sample mislabeling) that diminish modeling accuracy. We developed a probabilistic data matching method ( pro MODMatcher) to correct such errors and applied it to the MMRC data. We identified labeling errors in 10 patients, including swaps in RNA and CNV profiles. Given the corrected MMRC data, we characterized recurrent genomic aberrations in MM. We found > 8000 genes significantly associated (FDR < 0.01) with CNVs spanning the gene locations. To distinguish expression correlations driven by causal biological relationships from those driven by coincident CNV influence, we constructed a causal MM network based on the MMRC dataset. Results: Overlaps among multiomic prognostic or drug response biomarkers are sparse. To identify common mechanisms of different biomarkers, we projected them onto our MM network to define the causal context in which they occur. Prognostic biomarkers were enriched in subnetworks associated with mitotic regulation and lipid metabolism, and predicted by our network to be regulated by ZWINT , BUB1B , DTL , TPX2 and NOP16 . Proteasome inhibitor and immunomodulating drug responses were mediated by amino acid biosynthesis and cell surface protein complex subnetworks, respectively, with MTHFD2 and TMC8 inferred as the master regulators of these subnetworks, respectively. Regulators include genes (eg, BUB1B and MTHFD2 ) known to associate with tumor growth and drug response and novel therapeutic control points. Conclusions: Causal models can elucidate the molecular mechanisms underlying prognosis and drug response, enabling the design of more personalized MM treatments.