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

• 批准号: 8918351
• 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
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-24 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8918351
• 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; Genes; Genetic; Genetic Variation; Genome; Grant; Growth; Heterogeneity; Human Genetics; Human Genome; 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 RNA; Untranslated Regions; Validation; Variant; Work; anticancer research; behavior change; cancer genome; cancer genomics; cancer statistics; cancer therapy; cancer type; computerized tools; data visualization; 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

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）：对肿瘤基因组特征的大规模研究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。少数这些改变是赋予肿瘤肿瘤特性的因果“驱动”事件，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积累在肿瘤细胞中的“乘客”事件。区分司机和乘客是癌症研究的迫切需要，对于理解肿瘤的分子起源、确定药物开发的新靶点、揭示对治疗的耐药性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统计上对肿瘤群体事件的过度代表或它们对蛋白质活性的预测影响。然而，现在人们很清楚地认识到，癌症不是单一突变的疾病，也不是基因的疾病，而是在分子网络和途径中共同作用的基因群的疾病。细胞行为源于细胞内生物分子之间复杂的相互作用网络，例如驱动突变通过改变网络结构和功能赋予肿瘤细胞肿瘤行为。在这项授权中，我建议将肿瘤中检测到的分子变化建模为网络扰动，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)提出驱动突变赋予肿瘤细胞肿瘤行为的机制假设，3)比较肿瘤群体中改变的网络结构模式，4)评估生物网络中同时存在的突变的综合效应。5)在选定的个体肿瘤基因组中预测一系列驱动突变和干扰通路。我将扩展模型，包括重叠功能性非蛋白编码元件的分子事件，现在已知覆盖80%的人类基因组，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，将与已建立的合作者合作，通过实验验证网络扰动建模发现的新计算结果。该项目将为肿瘤驱动因素提供一个更全面的视角，并为癌症研究界提供一套计算工具，用于对分子畸变和癌症靶向干预的后果进行建模。