Understanding Long Tail Driver Mutations in Cancer

项目摘要/摘要 向基因驱动的肿瘤学的过渡已经开始，这在一定程度上是由于合理设计 针对个别肿瘤所依赖的特定分子畸变的有效治疗。这有 无情地引导了活动期疾病患者的预期临床测序，以指导他们的癌症护理。 尽管如此，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 到鉴定越来越多的体细胞突变，即使是推定的可操作的癌症基因， 其中绝大多数是所谓的长右尾，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产生的结果来指导患者护理的能力。我们有 最近的研究表明，这种长尾驱动基因突变可以成为对 系统性癌症治疗。我们继续展示了一项利用人口规模的癌症进行的系统调查 与计算方法相结合的基因组数据揭示了类似的生物和生物长尾驱动因素 治疗意义。这些发现强调了长尾驱动基因突变在癌症中的重要性，但 没有一种系统的方法来快速确定优先顺序并在功能和临床上验证这些躯体 突变，我们对临床上可操作的基因组的理解的差距将会扩大。我们建议 克服这一紧迫的临床挑战，建立一个强大而复杂的框架来阐明 新奇的长尾驱动突变。我们将首先建立一个全面的计算框架， 识别并确定长尾驱动因素变化的优先级，这些变化不仅利用了人口规模的数据，而且还集成了 选择的正交性度量。然后，我们将把这些方法应用于超过50,000人的队列 对我们中心的活动性癌症患者进行前瞻性测序，所有患者都有详细的临床、转归和 治疗反应数据，结果可能导致患者登记参加以基因为导向的临床试验 审判。最后，我们将对这些分析揭示的新的长尾驱动基因突变进行功能研究 在我们机构有一个开放的篮子研究的基因中，从而建立了一个共同临床框架 通过它，实验室功能验证可以与患者的治疗反应配对。加在一起，这些 研究试图建立一种计算-实验框架，以确定细胞功能突变。 为分子定义的癌症患者群体扩大治疗选择的长尾。

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.