Next generation transcriptomics-based precision oncology

About this Class

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 describe a few new computational approaches that we have developed to this end: First, SELECT and ENLIGHT, that aim to predict patient response from bulk tumor transcriptome. Second, PERCEPTION, which aim to advance precision cancer therapy from single cell tumor transcriptomics. Thirdly, DeepPT, a precision oncology expression-based approach that starts from tumor histopathological images.  Finally, as time permits, I will briefly describe the development of liquid-based transcriptomics (LBT) and discuss the challenges laying ahead. Bio Eytan Ruppin received his M.D. and Ph.D. (Computer Science) from Tel-Aviv University where he has served as a professor of Computer Science & Medicine since 1995, conducting computational multi-disciplinary research spanning a wide variety of topics, including neuroscience, evolutionary computation, natural language processing, machine learning and systems biology. He joined the University of Maryland in July 2014 as a Computer Science professor and director of its center for bioinformatics and computational biology (CBCB), before joining the NCI in January 2018, where he founded and is Chief of its Cancer Data Science department. He is a member of the editorial board of EMBO Reports and Molecular Systems Biology,  a fellow of the International Society for Computational Biology (ISCB), and is the recipient of the NCI Director award and the Delano Award for Computational Biosciences. Dr. Ruppin is also a co-founder of startup companies involved in precision medicine and cancer drug discovery.   For inquiries, please contact our presenter, Dr. Eytan Ruppin at eytan.ruppin@nih.gov.