Interaction-based computational methods for analyzing cancer genomes

项目摘要 最近的癌症基因组测序工作已经确定了完整的蛋白质编码 为数以千计的患者覆盖数十种不同的癌症类型。初步分析 揭示了癌症基因组可以有许多基因改变，但只有一种 亚群被认为对癌症的启动或进展很重要。更远的地方，横跨 患者，有高度的突变异质性，很少有基因改变 在很高比例的病例中，以及许多不经常改变的基因，其中一些是 在癌细胞中具有重要的功能。这些因素极大地使努力复杂化 识别癌症相关基因。我们的长期目标是通过以下方式识别癌症相关基因 分析特定癌症患者群体的基因组。关键洞察力 我们工作的基础是分子相互作用和网络揭示了重要的方面 从而提供了一个重要的背景，通过它来解决 在癌症中观察到突变的异质性。我们的具体目标是：(一) 开发基于结构的方法来发现富含在体细胞突变中的蛋白质 它们的相互作用界面，因为这些位点的突变可能会影响蛋白质 功能正常。(2)开发基于网络的从头发现路径的方法 在患者样本中发生突变，因为癌症中的突变往往针对特定的 途径-即使其中不同的基因在不同的个体中发生突变-以及 网络中邻近的基因往往是功能相关的。(3)开发代谢物- 使用蛋白质-小分子网络以发现突变的中心方法 改变细胞代谢的蛋白质，因为重新编程的新陈代谢越来越多 被认为是癌细胞的一种主要适应。通过追求这三个 互补和紧密结合的目标--利用关键但经常被忽视的目标 结构和网络信息-我们将极大地推动 癌症基因组分析的计算方法。这些分析将深化我们的 对癌症生物学的理解，并最终将导致更好的患者分层， 精致的预测工具和新的治疗方法。 。

Project Summary Recent cancer genome sequencing efforts have determined the complete protein coding regions for thousands of patients across tens of different cancer types. Initial analyses have revealed that cancer genomes can have numerous genetic alterations, but only a subset are thought to be important for cancer initiation or progression. Further, across patients, there is a high degree of mutational heterogeneity with very few genes altered in a high fraction of cases, and many infrequently altered genes, some of which are functionally important in cancer cells. These factors significantly complicate efforts to identify cancer-related genes. Our long-term goal is to identify cancer-related genes by analyzing the genomes of cohorts of individuals with a particular cancer. The key insight underlying our work is that molecular interactions and networks reveal important aspects of protein functioning, and thus provide an important context by which to tackle the mutational heterogeneity observed across cancers. Our specific aims are: (1) To develop structure-based methods that uncover proteins enriched in somatic mutations in their interaction interfaces, as mutations in these sites are likely to affect protein functioning. (2) To develop network-based methods for de novo discovery of pathways that are mutated across patient samples, as mutations in cancers tend to target specific pathways—even if different genes within them are mutated in different individuals—and genes proximal in networks tend to be functionally related. (3) To develop metabolite- centric methods that use protein-small molecule networks in order to uncover mutated proteins that alter cellular metabolism, as reprogrammed metabolism is increasingly being recognized as a major adaptation of cancer cells. By pursuing these three complementary and tightly coupled aims—which exploit critical but often overlooked structural and network information—we will vastly advance the state-of-the-art in computational methods for analyzing cancer genomes. These analyses will deepen our understanding of cancer biology, and will ultimately lead to better patient stratification, refined prognostic tools, and novel therapeutics. .