Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	Dr.Peng Jiang，Earl Stadtman Investigator National Cancer Institute National Institute of Health, Bethesda, MD, USA
Inviter:	张世华研究员
Title:	Big-data Approaches to Model Cancer Therapy Response and Resistance
Time & Venue:	2019.5.23 10:00 S813
Abstract:	The rapid growth of big-data resources, catalyzed by breakthroughs in genomics technologies, has resulted in a paradigm shift in cancer research. I will introduce my recent works that integrated the vast amount of public data to model the cancer therapy efficacy. To predict immunotherapy response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in immune-hot tumors and the prevention of T cell infiltration in immune-cold tumors [1]. TIDE repurposed many clinical data cohorts without immunotherapy to identify immune evasion signatures as surrogate immunotherapy biomarkers. Using pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers. TIDE also revealed new immunotherapy resistance regulators, such as SERPINB9, which hijacked the self-protection strategy of T cells for tumor immune evasion. Besides the immunotherapy focus, we also developed CARE, a computational method focused on targeted therapies, to identify synergistic drug combinations to overcome the resistance to primary treatments, using cell line compound screens [2]. In summary, my recent works demonstrated that the integration of big public data is a cost-effective approach to rediscover new therapeutic knowledge [3].