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: 8741740
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8741740
• 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

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高度异质的分子改变景观。这些改变中的一小部分是因果性的“驱动”事件，其赋予肿瘤肿瘤特性，例如不适当的生长和增殖;然而，这些改变中的大多数被认为是在肿瘤进展过程中偶然在肿瘤细胞中积累的“乘客”事件。区分驾驶员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。目前区分驾驶员和乘客的努力依赖于肿瘤群体中事件的统计过度表现或其对蛋白质活性的预测影响。然而，现在人们充分认识到，癌症不是一种单一突变的疾病，也不是基因的疾病，而是在分子网络和途径中共同工作的基因组的疾病。细胞行为由细胞内生物分子之间相互作用的复杂网络引起，使得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。在这项资助中，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）研究已知驱动突变相对于其它人类遗传变异的生物网络效应，2）发展关于驱动突变赋予肿瘤细胞肿瘤行为的机制的假说，3）比较肿瘤群体中改变的网络结构的模式，4）评估在生物网络内搭配的突变的组合效应，以及5）预测所选个体肿瘤基因组中的驱动突变和扰动途径的集合。我将扩展模型，包括分子事件重叠的功能性非蛋白质编码元件，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。该项目将为肿瘤的驱动因素格局提供更全面的视角，并为癌症研究界提供一套计算工具，用于对癌症分子畸变和靶向干预的后果进行建模。