Data for Precision Medicine in Cancer: Big and Deep
Precision medicine is being fueled by data that is not only big, but also deep. To tackle some of the most vexing problems in cancer, big data is being generated on deep molecular levels. For the individual tumor, measurements of the genome, epigenome, transcriptome, proteome and metabolome are being coupled with traditional clinical data. Facing us are data-driven challenges, such as: how does this multiscale data advance our knowledge of cancer biology and how can we use it to improve patient outcomes? While deep learning approaches tackle these types of questions often without domain-specific knowledge, systems biology has emerged as a field to address these questions by embracing domain-specific knowledge. The field of cancer systems biology unites the data scientist and molecular biologist toward a common goal of identifying clinically actionable insights from a better understanding of cancer biology by harnessing big and deep data across multiple scales.
This presentation will illustrate the principles of cancer systems biology with examples that aim to harness big and deep data to advance precision medicine in cancer.
As Professor of Radiology and Biomedical Data Science at Stanford University, Sylvia Plevritis leads an integrative cancer research program that bridges genomics, proteomics, imaging and population sciences. On the molecular level, her lab is both “dry” and “wet”: it develops biocomputational algorithms to infer regulatory networks underlying tumor progression and conducts bench work on tumor samples for optimizing combination therapy. On the population level, her lab develops simulation models of clinical cancer progression to estimate the effectiveness of alternative cancer control strategies on incidence and mortality rates.
Sylvia is as the Director of the Stanford Center in Cancer Systems Biology (CCSB), Director of the Stanford Cancer Systems Biology Scholars Program (CSBS), and co-Division Chief of Integrative Biomedical Imaging Informatics at Stanford (IBIIS). She has been a Principal Investigator with the NCI Cancer Intervention Surveillance Network (CISNET) for over fifteen years. She serves on the Leadership Council of the Stanford Bio-X Program and the Program Leadership of the Stanford Cancer Institute. She also serves on the Scientific Advisory Board of the National Cancer Institute (NCI).
Sylvia received her Ph.D. in Electrical Engineering and M.S. in Health Services Research from Stanford University. She has authored over 100 scientific articles. She is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and Distinguished Investigator in the Academy of Radiology Research. In 2016, she received the Inaugural Award for Basic Scientist of the Year in Stanford Radiology. She has served on numerous NIH study sections and chaired scientific programs for the several professional societies including the American Association for Cancer Research (AACR) and the s International Society for Computational Biology (ISCB) RECOMB-DREAM.