Advanced Bioinformatic Analysis & Machine Learning Tools on Plasma Proteomics with Professor Yuval Shaked, co-founder and chief scientific advisor at OncoHost. 

Clinical Study Investigates The Potential Of Host Response Characterization For Predicting Outcomes In Immunotherapy-Treated Melanoma Patients

OncoHost, global leader in host response profiling for improved personalized cancer therapy, announced today that its abstract on the use of advanced bioinformatic analysis and machine learning tools on plasma proteomics in order to predict outcomes of immunotherapy-treated melanoma patients was accepted for the American Society of Clinical Oncology (ASCO) 2020 Virtual Poster Discussion Session. The study is in collaboration with the Technion Institute of Technology, the University of Connecticut and Yale University School of Medicine and Cancer Center.

“As immunotherapy becomes increasingly common as the standard of care for advanced melanoma, the unfortunate reality is that while the treatment is effective, only a small portion of patients benefit from this treatment modality,” said Professor Yuval Shaked, co-founder and chief scientific advisor at OncoHost, and professor of Cell Biology and Cancer Science at Technion – Israel Institute of Technology. “Our study demonstrates that mechanisms of resistance to immunotherapy are directly correlated with host response to treatment. By analyzing host response profile and deriver proteins contributing to resistance, oncologists may be able to harness host response to better predict clinical outcomes and suggest optimal combination treatment with immunotherapy. This represents a significant step forward for precision oncology and personalized cancer care.”

The abstract titled “A proteomic biomarker discovery platform for predicting clinical benefit of immunotherapy in advanced melanoma” (Abstract 10037) revealed that the advanced bioinformatic analysis and host response identification, characterization and analysis of proteomic data can help profile host response protein signature to predict patient outcomes. The study, conducted by use of OncoHost’s machine learning algorithm, identified a 10-protein signature based on plasma samples collected pre- (T0) and early-on (T1) treatment from a cohort of advanced melanoma patients.

ASCO’s annual meeting is taking place from May 29-June 2 online due to COVID-19.

About OncoHost

OncoHost combines life-science research and advanced machine learning technology to develop personalized strategies to maximize the success of cancer therapy. Utilizing proprietary proteomic analysis, the company aims to understand patients’ unique response to therapy and overcome one of the major obstacles in clinical oncology today – resistance to therapy. OncoHost’s Host Response Profiling platform (PROphet) analyzes proteomic changes in blood samples to monitor the dynamics of biological processes induced by the patient (i.e., the host) in response to a given cancer therapy. This proteomic profile is highly predictive of individual patient outcome, thus enabling personalized treatment planning. PROphet also identifies potential drug targets, advancing the development of novel therapeutic strategies as well as rationally based combination therapies.

For more information, visit http://www.oncohost.com.

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This episode is sponsored by the University of California Irvine, UCI, Master of Science in Pharmacology, learn more: https://sites.uci.edu/mspharmacology/ 

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