May 11, 2012, Webb 1100

The talk will describe a recent mathematical/computational model for understanding aspects of cancer metastasis, initiated when primary tumor cells enter the vasculature and lymphatic system becoming circulating tumor cells (CTC's). The model is based on a Markov chain dynamical system designed so that the transition matrix has as it's steady-state a `target' vector obtained from an autopsy data set. The target vector, chosen for a given type of primary cancer (i.e. lung), contains the distribution of metastatic tumors from an ensemble population. The transition matrix can be associated with a metastatic network (directed graph) with disease progression modeled as a random walker on the network. We focus on primary lung cancer, and using the metastatic network obtained from the transition matrix, we quantify (probabilistically) the most probable disease progression pathways, along with mean first-passage times of progression. We highlight the multi-directional nature of the progression pathways that the model produces (self-seeding and re-seeding) which supports recent experimental observations carried out at Memorial Sloan-Kettering Cancer Center on the importance of primary tumor self-seeding. The work is sponsored by the National Cancer Institute under the auspices of The Scripps Research Institute Physical Sciences Oncology Center `The Physics and Mathematics of Cancer Metastasis'. If time permits, we will finish with a brief overview of related projects modeling the fluid phase of cancer.

Prof. Newton received his B.S. in Applied Math/Physics at Harvard University and his Ph.D. in Applied Mathematics from Brown University. After a post-doctoral fellowship at Stanford University, he was Assistant and Associate Professor at the University of Illinois Champaign-Urbana. He has held visiting appointments at Caltech, Brown, Hokkaido University, The Kavli Institute for Theoretical Physics at U.C. Santa Barbara, and The Scripps Research Institute. He is currently Professor of Applied Math and Engineering in the Viterbi School of Engineering at the University of Southern California.