Speaker Profile
Ph.D., Director of Precision Medicine Research, Sutter Health
Biography
Dr. Gregory Tranah is also an Adjunct Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco. He received his PhD in Ecology from University of California, Davis and was a postdoctoral fellow in Epidemiology at the Harvard School of Public Health. Dr. Tranah’s current research program is focused on identifying inherited and acquired genetic factors that impact aging and disease with the goal of revolutionizing risk assessment and identifying widely applicable genomic tests that identify persons who would benefit from specific pharmacologic and behavioral treatments to prevent disability and disease. He is the principal investigator of several NIH grants and Sutter-CPMC Foundation grants. Dr. Tranah is the Genetics Director for several large population-based epidemiologic cohorts and is actively conducting large-scale genomic analyses in collaboration with several consortia focused on aging and disease. At the CPMC Research Institute he directs the genomics core of the Personalized Medicine program which is focused on identifying genomic predictors of treatment response for several highly malignant cancers. Dr. Tranah is currently leading a research program dedicated to accelerating the translation of research discoveries related to health and aging to clinical settings across Sutter Health and is applying this experience to the development of the Sutter Health Precision Medicine Program.
Session Abstract – PMWC 2018 Silicon Valley
Session Synopsis: Health care providers increasingly require multi-omic data sets, including phenotypic data informed by genomic data. Such data needs to be obtained in an economically sustainable way and made available on an agile user-friendly platform so that these data may inform clinical care and lead to health improvements.Pharmaceutical companies (“Pharmas”) are interested in obtaining datasets containing phenotypic/clinical and genomic information generated from patient cohorts of specific disease areas. Such datasets can help Pharma researchers identify drug targets or find biomarkers, validate hypotheses related to the interaction of genomics with disease or with specific therapies, and identify candidate populations for future clinical trials. Payers are also interested in the outcomes related to new discoveries and therapies in order to reimburse for these treatments. This session will focus on how both health care provider organizations, Pharmas and Payers are working toward solving these complex and challenging problems from a technical and business model perspective.
