Ongoing Collaboration Leveraging the Multiple Myeloma Research Foundation CoMMpass Study Reveals Drivers of High-Risk Disease and of Durable Response
CAMBRIDGE, Mass. – December 6, 2016 – GNS Healthcare (GNS), a leading precision medicine company that applies causal machine learning technology to massive and diverse data streams to better match drugs and other health interventions to individual patients, announced the latest findings from its ongoing, multi-year collaboration with The Multiple Myeloma Research Foundation (the MMRF) leveraging the MMRF’s landmark CoMMpass Study™ (NCT0145429) and revolutionary GNS causal machine learning and simulation platform REFS™ (Reverse Engineering and Forward Simulation) to speed the discovery of innovative treatments for patients with multiple myeloma.
Several drivers of clinical outcomes in multiple myeloma disease and their associated molecular pathways, including some that are novel, were revealed by GNS at the American Society of Hematology (ASH) 58th Annual Meeting & Exposition in the session, “Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response.” The discoveries, from the largest and most comprehensive computer models ever built of molecular and clinical interactions in multiple myeloma disease, expand the existing understanding of key subpopulations of multiple myeloma patients. They explain the underlying causal mechanisms driving progression in patients with the high-risk form of the disease and identify a previously unknown molecular pathway driving the probability of a durable response to treatment.
“The MMRF’s collaboration with GNS is bearing fruit and helping us fulfill our commitment to making rapid and meaningful progress toward a cure for multiple myeloma,” said Daniel Auclair, PhD, Senior Vice President of Research at the MMRF. “The discoveries being announced today greatly expand our ability to identify treatment subgroups and high-risk subgroups. These are significant steps forward in the understanding of multiple myeloma.”
The specific discoveries include the identification of the genes CDK1, PKMY1, MELK, and NEK2 as the top drivers of high-risk disease. Well known to researchers focused on multiple myeloma and other forms of cancer, these genes represent a pathway that contains known drug targets, suggesting a validation strategy and the potential to employ drugs in combination. Among these, the MELK inhibitor OTSSP167 has recently been found to provide a synergistic effect with other drugs for the treatment of multiple myeloma. In addition, the models identified several novel pathways driving durable response, including a pathway of ribosomal genes (RPL6, RPL23, RPL12), a pathway of translation elongation factor EEF1A1 and associated pseudogenes, and a pathway of regulatory noncoding genes MIR1302-9, RP11-946L20.4, RP11-346D14.1, and RP11-506N2.1. Little is known about the connection of these genes to multiple myeloma; however, a pathway that is central to ribosomal biogenesis, ubiquitin-proteasome, is a major drug target in multiple myeloma.
These discoveries deliver value for a range of healthcare stakeholders. They may improve patient stratification and the overall efficiency of clinical trials, accelerating the ability of pharmaceutical companies to bring new and effective treatments to patients; lead to more personalized treatment protocols, enhancing the ability of health insurers and providers to offer the most beneficial treatment to individual patients; result in new treatment strategies that employ existing pharmaceuticals in combinations; and may represent targets for drug discovery and development. Together, these capabilities improve the ability to prevent progression of disease and address the continued unmet treatment needs of patients with multiple myeloma.
The causal models are the product of large-scale, multi-modal patient data from the MMRF CoMMpass Study and the revolutionary GNS causal machine learning and simulation platform REFS. Results reflect an analysis by REFS of the CoMMpass Interim Analysis 9 (IA9) dataset, which is composed of extensive clinical and genomic data, including RNAseq measurements, somatic copy numbers, single nucleotide variants, and structural variants for a population of more than 600 enrolled patients. GNS leveraged REFS to reverse-engineer the molecular pathways that affect treatment outcomes in the CoMMpass population and to assess the significance of these pathways in treatment response CoMMpass follows 1,000 newly diagnosed patients with active multiple myeloma for eight years. Its objective is to map to clinical parameters each of these patients’ myeloma cells genomic profiles, generated from specimens collected at first presentation and at progression events, to develop a more complete understanding of patient responses to treatments.
“The findings announced today underscore the power of causal models to discover drug targets and pathways from the nearly infinite number of possibilities,” said Iya Khalil, PhD, Chief Commercial Officer, Executive Vice President and Co-Founder of GNS. “In combination with the MMRF’s CoMMpass data, one of the richest datasets of its kind, we are not only accelerating the discovery and development of new therapeutics, but also helping patients and their providers with decisions about the optimal use of existing and future therapies.”
GNS and the MMRF have made a number of significant discoveries since the organizations began collaborating approximately two years ago, in the fall of 2014. The 2016 ASH Annual Meeting & Exposition presentation marks the second time in as many years that this effort combining data from the MMRF CoMMpass Sudy with causal machine learning and simulation technology has been featured at ASH. At the 2015 event, GNS and the MMRF revealed the discovery of novel drivers of clinical outcomes.
About GNS Healthcare
GNS Healthcare solves healthcare’s matching problem for leading health plans, biopharma companies, and health systems. We transform massive and diverse data streams to precisely match therapeutics, procedures, and care management interventions to individuals, improving health outcomes and saving billions of dollars. Our causal learning and simulation platform, REFS, accelerates the discovery of what works for whom and why.