Making Sense Of Cancer Dat Implications For Personalized Therapy
Bulent Arman Aksoy
Overview: My research interests lie at the intersection of computational and experimental work with a focus on Systems Biology, Genomics and Cancer Biology. I am specifically interested in interpreting cancer data to discover therapeutic opportunities and novel biological mechanisms that can be experimentally investigated. My research plans include (i) developing computational methods to analyze cancer data for generating biological hypotheses (dry laboratory); (ii) using cell lines as models, testing these hypotheses in vitro with the help of basic biological techniques, such as genome editing and high-throughput profiling (wet laboratory).
Motivation: In the very near future, all cancer patients coming into the clinics will have their genomic material profiled, and we will need computational approaches that can make sense out of these data sets to enable more effective cancer therapies based on a patient’s genomic profiling results. Drawing on what we learned from large-scale projects like The Cancer Genome Atlas
(TCGA), new clinical trials that recruit patients based on their genomic profiles for specific targeted therapies are already being established; but, there are still two major challenges in translating what we learn from these data sets into the clinic: (i) a majority of the alterations that we see in patients are not immediately actionable; (ii) targeted…