Network approaches to identify cancer drivers from high-dimensional tumor data

从高维肿瘤数据中识别癌症驱动因素的网络方法

基本信息

批准号：
8610127
负责人：
Hannah Kathryn Carter
金额：
$ 38.75万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-24 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8610127
关键词：
Affect Algorithms Antineoplastic Agents Basic Science Behavior Biological Biological Process Cancer Center Cancer Etiology Cataloging Catalogs Cells Clinic Clinical Clinical Research Code Collaborations Communities Complex Computer software DNA Data Data Analyses Data Collection Data Set Development Disease Disease Pathway Elements Event Functional RNA Genes Genetic Genetic Variation Genome Grant Growth Heterogeneity Human Genetics Human Genome Imagery Individual Informatics Inherited Institutes Intervention Malignant Neoplasms Maps Masks Medicine Methods Methylation MicroRNAs Modeling Molecular Molecular Models Mutation Network-based Normal Cell Open Reading Frames Pathway Analysis Pathway interactions Patients Pattern Population Predisposition Property Proteins Reporting Research Research Personnel Role Software Tools Somatic Mutation Structure Techniques Therapeutic Time Translations Tumorigenicity Untranslated Regions Validation Variant Work anticancer research behavior change cancer genome cancer genomics cancer statistics cancer therapy cancer type computerized tools drug development effective therapy genome analysis molecular modeling neoplastic neoplastic cell network models novel open source promoter public health relevance resistance mechanism tool tumor tumor progression tumorigenesis

项目摘要

DESCRIPTION (provided by applicant): Large-scale efforts to characterize tumor genomes have uncovered a highly heterogeneous landscape of molecular alterations that distinguish tumor cells from normal cells. A small number of these alterations are causal 'driver' events that confer neoplastic properties to tumors, such as inappropriate growth and proliferation; however, the majority of these alterations are thought to be 'passenger' events that accumulate in tumor cells by chance over the course of tumor progression. Discriminating drivers from passengers is a pressing need in cancer research and will be critical for understanding the molecular origins of tumors, identifying novel targets for drug development, uncovering mechanisms of resistance to therapeutics, and ultimately selecting the most effective therapies for patients. Current efforts t discriminate drivers from passengers rely on statistical over-representation of events in a population of tumors or their predicted effects on protein activity. However, it is now well appreciated that cancer is not a disease of single mutations, nor of genes, but of groups of genes working together in molecular networks and pathways. Cellular behaviors result from complex networks of interactions among biological molecules within the cell, such that driver mutations confer neoplastic behaviors to tumor cells by altering network structure and function. In this grant I propose to model molecular alterations detected in tumors as network perturbations and use these models to discriminate drivers from passengers. These network models will allow us to study cancer in new ways: they will be used to 1) study the biological network effects of known driver mutations versus other human genetic variation, 2) develop hypotheses about the mechanisms by which driver mutations confer neoplastic behaviors to tumor cells, 3) compare patterns of altered network structure across tumor populations, 4) evaluate the combined effect of mutations collocated within a biological network, and 5) predict the set of driver mutations and perturbed pathways in selected individual tumor genomes. I will extend the models to include molecular events overlapping functional non-protein coding elements now known to cover 80% of the human genome, and quantify how acquired alterations observed in a tumor interact with inherited variants in the patient's genome. Finally, will work with established collaborators to experimentally validate novel computational findings uncovered by network perturbation modeling. This project will provide a more global view of the driver landscape in tumors and supply the cancer research community with a suite of computational tools for modeling the consequences of molecular aberrations and targeted interventions in cancer.

描述（由申请人提供）：表征肿瘤基因组的大规模努力已经揭示了区分肿瘤细胞与正常细胞的分子改变的高度异质性景观。其中一小部分改变是因果“驱动”事件，赋予肿瘤肿瘤特性，例如不适当的生长和增殖；然而，大多数这些改变被认为是在肿瘤进展过程中偶然在肿瘤细胞中积累的“过客”事件。区分司机和乘客是癌症研究的迫切需要，对于了解肿瘤的分子起源、确定药物开发的新靶点、揭示治疗耐药机制以及最终为患者选择最有效的治疗方法至关重要。目前区分司机和乘客的努力依赖于肿瘤群体中事件的统计过度代表性或其对蛋白质活性的预测影响。然而，现在人们普遍认识到，癌症不是单一突变或基因的疾病，而是分子网络和通路中共同作用的基因组的疾病。细胞行为是由细胞内生物分子之间复杂的相互作用网络产生的，因此驱动突变通过改变网络结构和功能而赋予肿瘤细胞肿瘤行为。在这笔拨款中，我建议将肿瘤中检测到的分子改变建模为网络扰动，并使用这些模型来区分司机和乘客。这些网络模型将使我们能够以新的方式研究癌症：它们将用于 1) 研究已知驱动突变与其他人类遗传变异的生物网络效应，2) 提出关于驱动突变赋予肿瘤行为的机制的假设肿瘤细胞，3) 比较肿瘤群体中网络结构改变的模式，4) 评估生物网络内突变的综合效应，5) 预测选定的个体肿瘤基因组中的驱动突变组和扰动途径。我将扩展模型，以包括与目前已知覆盖人类基因组 80% 的功能性非蛋白质编码元件重叠的分子事件，并量化在肿瘤中观察到的获得性改变如何与患者基因组中的遗传变异相互作用。最后，将与既定的合作者合作，通过实验验证网络扰动模型发现的新颖的计算结果。该项目将为肿瘤驱动因素提供更全面的视角，并为癌症研究界提供一套计算工具，用于模拟分子畸变的后果和癌症的针对性干预措施。