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：
9305972
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/9305972
关键词：
Affect Algorithmic Analysis Algorithms Binding Biochemical Pathway Cancer Biology Cancer Patient Computing Methodologies Coupled DNA sequencing Data Data Set Development Disease Frequencies Gene Expression Genes 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 Protein Analysis Proteins Role Sampling Site Somatic Mutation Structural Protein Structure Tertiary Protein Structure Work base cancer cell cancer genome cancer initiation cancer type cohort genome analysis genome sequencing individual patient insight new therapeutic target novel novel therapeutics patient stratification profiles in patients 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数十种不同癌症类型的数千名患者的区域。初始分析已经表明癌症基因组可以具有许多遗传改变，但只有子集被认为对癌症的启动或进展很重要。此外，穿越患者，具有高度突变异质性，很少有基因改变在很大的情况下，许多很少发生变化的基因，其中一些是在癌细胞中功能很重要。这些因素显着复杂化了鉴定与癌症相关的基因。我们的长期目标是通过分析患有特定癌症的个体的同类基因组。关键见解我们的工作是分子相互作用和网络揭示了重要方面蛋白质功能的功能，因此提供了一个重要的背景在癌症之间观察到突变异质性。我们的具体目的是：（1）开发基于结构的方法，这些方法发现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功能。（2）开发基于网络的方法以从头发现途径由于癌症中的突变倾向于针对特定途径 - 即使其中不同的基因在不同个体中突变 - 网络中近端的基因往往在功能上相关。（3）发展代谢物使用蛋白质 - 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，因为重编形代谢越来越多被认为是癌细胞的主要适应性。通过这三个完全，紧密结合的目标 - 利用关键但经常被忽略结构和网络信息 - 我们将在分析癌症基因组的计算方法。这些分析将加深我们了解癌症生物学，并最终导致更好的患者分层，精致的预后工具和新颖的治疗学。。