Understanding Long Tail Driver Mutations in Cancer

项目总结/文摘

PROJECT SUMMARY/ABSTRACT The transition to genomically driven oncology has begun, catalyzed in part by efforts to rationally design effective therapies targeting the specific molecular aberrations on which individual tumors depend. This has led, inexorably, to the prospective clinical sequencing of patients with active disease to guide their cancer care. Nevertheless, a fundamental gap remains. The shift toward larger panel and whole exome sequencing has led to the identification of increasing numbers of somatic mutations in even presumed actionable cancer genes, the vast majority of which are in the so-called long right tail and lack biological or clinical validation. This significantly impairs our ability to use findings generated by prospective profiling to guide patient care. We have recently shown that such long-tail driver mutations can be the genetic basis of extraordinary responses to systemic cancer therapy. We went on to show that a systematic survey utilizing population-scale cancer genome data coupled to computational methodologies reveals similar long-tail drivers of both biological and therapeutic significance. These findings underscore the importance of long-tail driver mutations in cancer, but without a systematic approach for rapidly prioritizing and functionally and clinically validating these somatic mutations, the gap in our understanding of the clinically actionable genome will widen. We propose to overcome this urgent clinical challenge by establishing a robust and sophisticated framework for elucidating novel driver mutations in the long tail. We will first establish a comprehensive computational framework that identifies and prioritizes long-tail driver mutations that leverages not only population-scale data but integrates orthogonal measures of selection. We will then apply these methods to a cohort of greater than 50,000 prospectively sequenced active cancer patients at our Center, all possessing detailed clinical, outcome, and treatment response data, results from which can lead to the enrollment of patients on genotype-directed clinical trials. Finally, we will perform functional studies of novel long-tail driver mutations revealed by these analyses in genes for which there is an open basket study at our institution, thereby establishing a co-clinical framework by which laboratory functional validation can be paired with patient treatment response. Together, these studies seek to establish a computational-experimental framework for identifying functional mutations in the long tail that expand the treatment options for molecularly defined populations of cancer patients.