Baliga Lab researchers and collaborators found that genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma

The 75 percent failure rate of cancer therapies underscores the need for personalized treatments. No two patients are alike, therefore a sophisticated approach is necessary to accurately predict from profiling of a patient’s tumor their clinical outcome including their responsiveness to specific therapies.

ISB researchers Drs. Matt Wall, Serdar Turkarslan, Nitin Baliga, and others, along with collaborators, have developed a technology to generate a patient-specific disease-relevance network map to accurately predict risk, personalize therapies, and discover novel drug targets for solid and liquid tumors. They used this technology to discover predictive signatures to stratify patients into different risk groups to enable personalized therapy recommendations and discover novel drug targets for Multiple Myeloma. By enabling precise disease subtyping and patient stratification, this technology has the capability to streamline drug discovery and clinical trial design by rationally guiding the selection of novel targets, and accurately matching patients to drugs and drug combinations.


Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.