Re-Constructing and Exploiting Transcriptional Regulatory Networks in Multiple Myeloma Drug Resistance

• Published in: Blood 2019-11, Vol.134 (Supplement_1), p.5544-5544
• Main Authors: Renatino-Canevarolo, Rafael, Sudalagunta, Praneeth Reddy, Coelho Siqueira Silva, Maria D, Meads, Mark B., Tungesvik, Alexandre, De Avila, Gabriel, Raghunandan Reddy, Alugubelli, Nong, Minerva, Teer, Jamie, Welsh, Eric A., Lwin, Tint, Kulkarni, Amit, Dai, Hongyue, Dalton, William S., Shain, Kenneth H., Siqueira Silva, Ariosto
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2019-128442

Problem: Multiple myeloma (MM) is a treatable yet incurable hematologic cancer that lacks predictive biomarkers. Approach: Here we apply a systems biology approach to determine patient-specific mechanisms, as well as signatures of drug resistance in MM. To achieve this goal, we have combined ex vivo drug sensitivity data from 307 MM fresh primary samples tested with 162 drugs and combinations, with paired molecular data (RNAseq and mutational profiling) from a larger overlapping cohort of 606 MM samples from Moffitt's Multiple Myeloma Working Group (MMWG) repository in collaboration with M2Gen/Oncology Research Information Exchange Network (ORIEN). With the purpose of decoupling biological function from intracellular control mechanisms, we have re-constructed a MM-specific transcriptional regulatory network composed of clusters of co-expressing genes. We demonstrate how this gene cluster network regulates biology, and how different biological functions (e.g. Proteasome, Ribosome, Oxidative Phosphorylation) share common regulatory circuits. We have used gene set enrichment analysis (GSEA) to identify gene clusters with transcriptional profiles, and investigated mutations associated with drug resistance. Results: As a preliminary validation of this approach, we have confirmed established mechanisms of resistance (MOR) to targeted therapies, as well as proposed novel MOR to clinically relevant and experimental drugs in MM, as well as putative synergistic drug combinations. In addition, we have identified a list of low frequency mutations (