The Mathematical Neuro-Oncology Research Lab Presents:
Monday, July 28th, 2014
1:30 – 2pm
Arkes Pavilion
676 N. Saint Clair St. Suite 1300
JASMINE FOO, Ph.D.
Assistant Professor of Mathematics
The University of Minnesota
Title: Hitchhiking Index: Identifying driver and passenger mutations in cancer
Bio: Jasmine Foo is an McKnight Land Grant assistant professor at the University of Minnesota math department. She completed her PhD in Applied Math at Brown University in 2008, and carried out postdoctoral work at Harvard University/Dana Farber Cancer Center and the Memorial Sloan Kettering Cancer Center. Her research involves using stochastic processes to formulate and analyze mathematical theories of cancer evolution.
Abstract: The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutantoncoproteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such “driver’ mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models. Here we propose a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set. Our methodology is based upon a evolutionary population-dynamics model of mutation accumulation and selection in the tissue prior to cancer initiation as well as during tumorigenesis.
Monday, July 28th, 2014
2:30 – 3pm
Arkes Pavilion
676 N. Saint Clair St. Suite 1300
KEVIN LEDER, Ph.D.
Assistant Professor of Industrial and Systems Engineering The University of Minnesota
Title: Mathematical Modeling and OptimalFractionationated Irradiation for Proneural Glioblastomas
Bio: Dr. Leder is an assistant professor in Industrial and Systems Engineering at University of Minnesota.
He is interested in stochastic process models of cancer evolution, and the use of these models to investigate important biological questions regarding the initiation, progression and treatment of cancer. In addition he is interested in the study of rare events in stochastic systems. Previously, he was a postdoc at Dana Farber Cancer Institute and the Department of Industrial Engineering and Operations Research at Columbia, and received his PhD in 2008 from the Department of Applied Mathematics at Brown University. As an undergraduate, he attended the University of Colorado at Boulder and majored in Applied Math.
Abstract: Glioblastomas (GBM) are the most common and malignant primary tumors of the brain and are commonly treated with radiation therapy. Despite modest advances in chemotherapy and radiation, survival has changed very little over the last 50 years. Radiation therapy is one of the pillars of adjuvant therapy for GBM but despite treatment, recurrence inevitably occurs. Here we develop a mathematical model for the tumor response to radiation that takes into account the plasticity of the hierarchical structure of the tumor population. Based on this mathematical model we develop an optimized radiation delivery schedule. This strategy was validated to be superior in mice and nearly doubled the efficacy of each Gray of radiation administered. Time permitting I will also discuss recent extensions of this work that consider the impact of including normal tissue toxicity constraints. This is based on joint work with Hamidreza Badri, Ken Pitter, Eric Holland, and Franziska Michor.