Development of a multi-omic clinical decision platform to guide personalized therapy

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PROJECT SUMMARY The era of “big data” has opened the door for genomic and systems biology approaches to be applied to current challenges in life sciences and precision medicine. One critical challenge in these areas is how to prioritize research findings to validate and identify actionable insights that can translate into better outcomes for patients. In this regard, we have assembled a multidisciplinary group of scientists and physicians from academia and industry with a focus on creating discovery pipelines that combine high-throughput profiling technologies with advanced statistical and machine learning approaches to generate predictive tools that enable us to move rapidly from big data to better diagnoses and treatment. In this regard, we propose to apply these approaches to develop a computational clinical decision tool that will improve disease forecasting and treatment plans for Multiple Myeloma (MM), an incurable cancer that originates in bone marrow plasma cells and affects more than 30,000 patients a year. Though there have been some advances in the number and diversity of available therapeutic options for these patients, relapse remains inevitable, and MM ultimately remains a terminal diagnosis. The clinical assay and computational pipeline developed in this project will combine a targeted sequencing panel specific to myeloma patients and clonality estimates with RNA- sequencing and drug repurposing to expand therapeutic options for MM patients. We will develop this unique tool with the following specific aims: (1) Develop an integrated genomic clinical decision tool to guide precision treatment of MM and validate therapy recommendations using PDX profiling, and (2) Validate MM precision medicine platform in a prospective clinical trial and generate clone-specific treatment recommendations. To achieve these objectives, we will integrate a Cancer Genetic, Inc.'s FOCUS::Myeloma panel, a targeted panel designed to specifically interrogateall the genes and copy number alterations commonly altered in myeloma, and into a computational drug selection pipeline that utilizes RNA-sequencing data and drug repurposing algorithms to generate therapeutic recommendations matched to a patient's unique disease profile. These recommendations will be validated in mouse avatars of myeloma to confirm and refine drug predictions. We will implement our assay in a prospective clinical trial of 100 patients to determine if the treatment decisions generated by our pipeline achieves an improvement in standard-of-care. Finally, we will perform clonal modeling on relapsed patients to retrospectively evaluate clone-specific treatment responses. Completion of these studies will result in a clinic-ready assay and computational tool that will guide MM precision treatment decisions and inform new therapeutic strategies based on a patient's unique cancer profile. genomic clonal modeling