AI in Precision Oncology: Personalized Treatment Ranking Based on the Totality of Molecular Info...

Presented By: Istvan Petak, MD, PhD
Speaker Biography: Dr. Istvan Petak is an international expert in precision oncology and molecular pharmacology of targeted therapies with 25+ years of experience. He is the founder and CEO of Genomate Health, Inc. Cambridge, MA, founder and co-founder of Oncompass Medicine, Budapest, HU, and adjunct professor at the Department of Pharmaceutical Sciences at the University of Illinois at Chicago (UIC) and Szechenyi University, Gyo-, He started his molecular pathology and signal transduction therapy career at Semmelweis University and St. Jude Children's Research Hospital (Memphis, TN). His main achievements are exploring the role of apoptotic pathways in individual drug sensitivity, pioneering molecular companion diagnostics of the EGFR gene in 2003, and implementing NGS (next-generation sequencing) in precision oncology in 2008. He is the lead inventor of a novel computational method, published in 2021, for standardized, personalized treatment recommendations that has won numerous recognitions from organizations, including DIGITALEUROPE and ASCO.
Webinar: AI in Precision Oncology: Personalized Treatment Ranking Based on the Totality of Molecular Information
Webinar Abstract: The personalized treatment of each cancer patient with targeted therapies selected based on our understanding of the molecular biology of cancer has been the long-standing goal of precision oncology. The Human Project completed 20 years ago provided the blueprint for the following cancer genome projects. Today, we can identify the genetic (driver) alteration in 95% of cancer cases based on whole genome sequencing (WGS), Pan-Cancer Analysis of Whole Genomes (PCAWG) Nature 578, 82-93 (2020). According to this study, each cancer harbors an average combination of four-five genetic driver alterations out of thousands of possible mutations of hundreds of cancer driver genes. The early successes of personalizing treatment decisions based on matching one genetic alteration as a predictive biomarker to one targeted therapy are not reproducible in most patients. To solve this problem, we have developed a computational (AI) method to rank treatment options based on the totality of molecular information available for each case instead of providing information about the actionability of genetic alterations one by one.
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