“Next generation transcriptomics-based precision oncology”
Precision oncology has made significant advances, mainly by targeting actionable mutations and fusion events involving cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome in guiding patients’ treatment. I will first describe SELECT and ENLIGHT, which aim to predict patient response from the bulk tumor transcriptome. Second, PERCEPTION, which aims to do that from tumor single cell expression, considering tumor heterogeneity. Thirdly, I will present ENLIGHT-DeepPT, a precision oncology expression-based approach that starts from tumor histopathological images. Finally, I will discuss our ongoing work on LIQUID-BASED TRANSCRIPTOMICS (LBT), aiming to harness both bulk and single-cell white blood cells (WBC) expression, to infer the tumor immune microenvironment and predict patients’ response from the blood.
About Dr. Ruppin
Eytan Ruppin received his M.D. and Ph.D. from Tel-Aviv University where he has served as a professor of Computer Science & Medicine since 1995, conducting computational multi-disciplinary research spanning computational neuroscience, natural language processing, machine learning and systems biology. In 2014, he joined the University of Maryland as director of its center for bioinformatics and computational biology (CBCB). In 2018, he moved to the NCI where he founded and is chief of its Cancer Data Science Lab (CDSL). His research is focused on developing new computational approaches for advancing transcriptomics-based precision oncology. Eytan is a member of the editorial board of Molecular Systems Biology, a fellow of the International Society for Computational Biology (ISCB), a recipient of the NCI Director and the Delano awards and is a co-founder of a few precision medicine startup companies.