The Herbert and Florence Irving Institute for Cancer Dynamics hosts a seminar series on the topic of mathematical sciences underpinning cancer research. The monthly seminars take place on the third Thursday of the month, 4:00-5:00 PM EST. The presentations are held via Zoom and are open to the Columbia community and to researchers outside Columbia University.
On Thursday, March 17th (4:00-5:00 PM, EST), IICD welcomes Pablo G. Cámara, PhD, Assistant Professor of Genetics, Department of Genetics, Perelman School of Medicine, University of Pennsylvania.
Title: Estimating Dynamic Cell Abundances from Gene Expression Data of Bulk Tissues.
Abstract: Tumor heterogeneity is an outstanding roadblock in the development of effective therapies for the treatment of cancer. The cell composition of a tumor at any time point is the result of multiple dynamic cellular processes, including tumor-derived cell differentiation processes, paracrine communication between tumor cells and the microenvironment, and evolution from selective pressure and clonal competition. The application of concepts from metric geometry to the study of tumor configuration spaces provides a natural mathematical framework for making inferences about these continuous and dynamic cellular processes. In this talk, I will present a geometric approach for estimating dynamic cell abundances from gene expression data of bulk tissues using single-cell RNA-seq datasets as a reference. I will then illustrate the utility of this analytic approach with its application to the study of the drivers of mesenchymal transformation in pediatric ependymoma, a rare but devastating type of glioma in children.
Bio: Pablo G. Cámara, Ph.D. is an Assistant Professor of Genetics at the University of Pennsylvania, Perelman School of Medicine, and a member of the Penn Institute for Biomedical Informatics and the Center for Artificial Intelligence and Data Science for Integrated Diagnostics. He received a Ph.D. in Theoretical Physics from Universidad Autonoma de Madrid and performed research in this field for several years. Fascinated by the interesting and fundamental open questions in biology, in 2014 he shifted his research focus into quantitative biology. His current research utilizes principles of mathematics to address some of the problems that the complexity and scale of biological data pose to traditional computational methods. Drawing ideas from topology, geometry, statistics, physics, and computer science, and making use of high-throughput single-cell technologies and large-scale population studies, his group aims to achieve a more complete understanding of the cellular composition and signaling networks of tumors in relation to their genetics.