SHEPHERD Therapeutics Presents First Public Data at 2021 ESMO Congress Highlighting Ability of DELVE Computational Approach to Optimize Therapeutic Selection for Cancer Patients
Excerpt from the Press Release:
BOSTON, Sept. 16, 2021 (GLOBE NEWSWIRE) — SHEPHERD Therapeutics, a company dedicated to catalyzing lifesaving treatments for cancer patients, is pleased to present an e-poster of results from a computational approach to optimizing therapeutic selection for cancer patients using the Company’s proprietary precision-oncology DELVE platform, at the European Society for Medical Oncology (ESMO) Virtual Congress 2021. In this first public presentation of a DELVE analysis, DELVE was able to predict drug efficacy on cancer models and correctly select indications for existing therapies, supporting its utility in predicting new indications for cancer therapies.
While significant progress has been made in developing new therapies for cancer patients, many patients lack treatment options that result in positive outcomes, particularly for rare cancers. Existing patient-therapy matching algorithms frequently rely on mutations and well-studied targets for which only a limited number of FDA-approved therapies exist. For drug developers, point-of-care clinicians, patients and families, this gap equates to a dearth of options, or a process of trial and error informed by little data.
In contrast, DELVE uses computational and mathematical tools informed by transcriptomic data (from the complete set of RNA transcripts that are produced by the genome) to match therapies with the models, cancers, and specific patients that will be most impacted by drug treatment, regardless of mutational status. DELVE leverages bioinformatics, chemoinformatics, proprietary algorithms, deep learning neural networks, random tree forests, and other tools to generate transcriptomic-level drug response-resistance signatures. The platform integrates over 75,000 patient samples representing 161 cancers, as well as healthy tissues, and was deployed to predict drug response and resistance across thousands of in vivo, ex vivo, and in vitro cancer models.
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