Cancer is the result of an orchestrated set of genomic alterations that conspire to drive uncontrolled cell growth. Dissecting temporal and spatial association of these alterations into varying time points will provide milestones into initiation, progression, metastasis and/or resistance to certain therapeutic regimens. Elucidating these milestones could provide invaluable biomarkers in the context of different stages for treatments and would help to tailor personalized therapies based on genomic information. We will develop a model to infer a possible ordering of alterations from longitudinal, multi-region, transcriptomic and single cell data by first discerning the totality of significant alterations and most likely contributors to progression using a rigorous genomic approach. Next we will infer potential evolutionary moves of each patient using sequential samples with a statistical approach rooted in evolutionary biology. Third we will construct an evolutionary network for the cohort of patients that will delineate and order significant routes of genomic alteration.
Understanding how brain tumors evolve and drive uncontrolled cell growth in Glioblastoma may lead to better prognosis and therapy for individuals suffering from this deadly disease. A key to understanding the paths of progression are to develop computational and experimental methods to dissect clonal heterogeneity and statistically model evolutionary routes. This proposal?s aim is to describe a computational framework to integrate diverse genomic data and provide an algorithmic approach to tumor evolution so that we can delineate most likely routes of genomic alterations in Glioblastoma.
