Epithelioid Sarcoma Machine Learning Engine: $150,000 annually

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Epithelioid Sarcoma (EPS) is a very rare soft tissue of children and adults. The underlying clinical problem is that only surgery is curative: chemotherapy and radiation are ineffective. Late relapse with metastases results in a significant unmet clinical need because surgery is not possible and effective targeted therapies have not yet been developed. To meet this clinical need by developing new therapies, the biology of EPS needs to be taken into consideration: not only is INI1 (aka Smarcb1 or BAF47) absent, but cooperating mutations are also present. Effective treatment regimens will likely consist of drug combinations. To date, however, few adult cell lines are available, no pediatric cell lines, and few mouse models (thus far, 1-3 may exist, but all harbor adult EPS tumors).

EPS does not yet have a long history of international clinical data collection, with centralized imaging, histology slides or specimen collection (e.g., for future DNA and RNA studies). The purpose of this proposal is to empower a centralized resource of all available EPS data and resources so that Big Data (machine learning) can answer key questions around EPS care. Goals include Patient-oriented Registry & Biobank, Proteomics, Cutting-edge Database & Sequence Analysis Pipelines, Synchronization and Sharing, Outreach and Portability and Publication.

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