Department of Computer Science, Stanford University
Deciphering Cancer Biology using Boolean Methods
Thursday, November 19, 2015, 10:30 AM
10042 Floor Conference Room, Biomedical Science Tower 3
Abstract: Cancer is a complex disease characterized by a large number of point mutations, large structural changes and epigenetic dysregulation. Cancer genome sequencing projects such as The Cancer Genome Altas (TCGA) have profiled tens of different tumor types with hundreds of samples per tumor type. These projects have demonstrated convincingly that cancer genomes exhibit considerable heterogeneity among different individuals. New analytical techniques are needed to extract common biological principles from massive amounts of data to provide useful mechanistic insights about cancer and thereby guide effective therapy.
I will present Boolean implications, a new data mining method that can be used to mine large, heterogene- ous cancer data sets and demonstrate its application to derive new actionable hypotheses with the potential for biological discovery and targeted therapy. First, I will describe the use of Boolean implications to under- stand the role of a common mutation in leukemia in driving aberrant DNA hypermethylation, which subse- quently led to the identification of a targeted therapy for patients with the mutation. In the second half of my talk, I will describe MiSL, a new method for mining synthetic lethal partners of recurrent cancer mutations by analyzing pan-cancer primary tumor data. Initial results are promising, and indicate that MiSL can be widely applicable and can greatly accelerate target discovery for cancer-specific mutations.
Bio: Subarna Sinha is a research scientist in the Department of Computer Science at Stanford University. She received her B.S. in electronics from Indian Institute of Technology, Kharagpur and her M.S. and Ph.D. in electrical engineering from University of California at Berkeley. Following graduation, she worked in industry research labs (Intel, Synopsys) for almost a decade, where she led research in various aspects of semiconductor chip design automation. Since 2012, she focused her research efforts in computational cancer systems biology. Her current research interests are in developing algorithms for precision medicine in cancer, combinatorial algorithms for tumor genomics and logical modeling of signaling networks.