Understanding Long Tail Driver Mutations in Cancer

项目总结/摘要 向基因组驱动的肿瘤学的过渡已经开始，部分原因是合理设计 针对个体肿瘤所依赖的特定分子畸变的有效疗法。这 无情地导致了对活动性疾病患者的前瞻性临床测序，以指导他们的癌症护理。 然而，一个根本性的差距仍然存在。向更大的面板和整个外显子组测序的转变导致了 到甚至在假定的可作用的癌症基因中鉴定越来越多的体细胞突变， 其中绝大多数是在所谓的长右尾，缺乏生物学或临床验证。这 显著削弱了我们使用前瞻性分析所产生的发现来指导患者护理的能力。我们有 最近表明，这种长尾驱动突变可能是对 全身癌症治疗。我们继续表明，一项利用人口规模癌症的系统调查 结合计算方法的基因组数据揭示了生物学和生物学的类似长尾驱动因素， 治疗意义这些发现强调了长尾驱动突变在癌症中的重要性， 如果没有一种系统的方法来快速优先化和功能性和临床验证这些体细胞 突变，我们对临床可操作基因组的理解的差距将扩大。我们建议 通过建立一个强大而复杂的框架来阐明 长尾中的新驱动突变。我们将首先建立一个全面的计算框架， 识别并优先考虑长尾驱动突变，不仅利用人口规模的数据， 选择的正交措施。然后，我们将这些方法应用于超过50，000人的队列 前瞻性测序的活动性癌症患者在我们的中心，所有拥有详细的临床，结果， 治疗反应数据，其结果可导致患者入组基因型导向的临床试验 审判最后，我们将对这些分析揭示的新型长尾驱动突变进行功能研究 在我们机构进行的开放篮子研究中， 实验室功能验证可与患者治疗反应配对。所有这些 研究试图建立一个计算-实验框架，用于识别基因组中的功能突变。 长尾，扩大了分子定义的癌症患者群体的治疗选择。

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.