Abstract: Tumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment-failure, confounds current drug screening efforts to identify effective candidate therapies for individual tumors. In the first part of the talk I will present a modeling-driven statistical framework that enables the deconvolution of tumor samples into individual subcomponents exhibiting differential drug-response, using standard bulk drug-screen measurements. In the second part of the talk I will present some efforts towards obtaining insights about tumor evolution from standard genomic data. In particular, we analyze the site frequency spectrum (SFS), a population summary statistic of genomic data, for exponentially growing tumor populations, and we demonstrate how these results can in principle be used to gain insights into tumor evolutionary parameters.
Bio: Jasmine Foo is the Northrop Professor of Mathematics and co-Director of the Therapy Modeling and Design Center at the University of Minnesota-Twin Cities. Her research group is broadly interested in mathematical biology and applied probability/statistics, with a particular focus in modeling cancer dynamics and treatment. Prof. Foo received her Ph.D. in applied mathematics and Sc.B. in mathematics and physics from Brown University. She did postdoctoral work at the Memorial Sloan Kettering Cancer Center and Dana Farber Cancer Institute, and subsequently joined the University of Minnesota in 2011, where she currently also serves as associate department head.
Meeting ID: 998 2458 4542
Passcode: 475065