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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. http://ianwoodsocial.universitypunjabi.org/2016/07/29/on-the-other-hand-some-cultures-value-authoritative-speakers-while-others-may-prefer-a-down-to-earth-low-profile-speakerLittle 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. http://www.youtube.com/embed/Pf7JuJcVzQUThey 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.
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