Interaction-based computational methods for analyzing cancer genomes

用于分析癌症基因组的基于交互的计算方法

基本信息

批准号：
9159560
负责人：
MONA SINGH
金额：
$ 36.11万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9159560
关键词：
Accounting Affect Algorithms Binding Biochemical Pathway Cancer Biology Cancer Patient Computing Methodologies Coupled DNA Sequence Data Data Analyses Data Set Development Disease Frequencies Gene Expression Genes Genome Goals Heterogeneity Human Individual Information Networks Internet Lead Malignant Neoplasms Medicine Metabolic Metabolism Methods Molecular Mutate Mutation Mutation Analysis Network-based Open Reading Frames Pathway interactions Patients Phenotype Play Process Proteins Role Sampling Site Somatic Mutation Structural Genes Structure Tertiary Protein Structure Work base cancer cell cancer genome cancer initiation cancer type cohort genome sequencing insight new therapeutic target novel novel therapeutics patient stratification prognostic tool protein S precursor protein function protein structure small molecule software development tumor tumor initiation tumor progression

项目摘要

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. .

项目概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涵盖数十种不同癌症类型的数千名患者。已经揭示癌症基因组可以有许多遗传改变，但只有一个此外，子集被认为对癌症的发生或进展很重要。患者中，基因很少，存在高度的突变异质性在很大一部分情况下，以及许多不常见的基因，其中一些是这些因素在癌细胞中具有重要的功能，使研究工作变得非常复杂。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。分析患有特定癌症的个体群体的基因组。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蛋白质功能，从而为解决这一问题提供了重要的背景我们的具体目标是：（1）观察到跨癌症的突变异质性。开发基于结构的方法来揭示富含体细胞突变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 (2) 开发基于网络的途径从头发现方法在患者样本中发生突变，因为癌症的突变往往针对特定的途径——即使其中的不同基因在不同个体中发生突变——并且 (3) 开发代谢物- 使用蛋白质-小分子网络来发现突变的中心方法随着重新编程的新陈代谢越来越多，改变细胞代谢的蛋白质通过追求这三种能力，被认为是癌细胞的主要适应。互补且紧密结合的目标——利用关键但经常被忽视的目标结构和网络信息——我们将极大地推进最先进的技术分析癌症基因组的计算方法将加深我们的研究。对癌症生物学的了解，最终将导致更好的患者分层，完善的预后工具和新颖的治疗方法。。