Developing Graph Models and Efficient Algorithms for the Study of Cancer Disease

开发用于癌症疾病研究的图模型和高效算法

• 批准号: 8805849
• 负责人: Songjian Lu
• 金额: $ 5.59万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8805849
• 关键词: Address; Algorithm Design; Algorithms; Bioinformatics; Biological; Biological Process; Cancer Biology; Cancer Patient; Cancerous; Cataloging; Catalogs; Cell Death; Cell Proliferation; Cell physiology; Characteristics; Chromosomes; Cities; Clinical; Clinical Data; Complex; Computational Biology; Computational algorithm; Computers; Computing Methodologies; Copy Number Polymorphism; DNA Methylation; Data; Data Set; Development; Disease; Enzymes; Epigenetic Process; Family; Foundations; Gene Expression; Genes; Genetic; Genetic Transcription; Genomics; Goals; Graph; Heterogeneity; Individual; Information Networks; International; Knowledge; Lead; Malignant Neoplasms; Methods; Mining; Modeling; Modification; Mutation; Nature; Normal Cell; Ontology; Outcome; PPP3CA gene; Pathway interactions; Patients; Play; Population; Process; Proteins; Research; Resources; Role; Running; Sampling; Signal Pathway; Signal Transduction; Signal Transduction Pathway; Signaling Molecule; Signaling Protein; Single Nucleotide Polymorphism; Solutions; Somatic Mutation; Source; Stream; Structure; System; Techniques; The Cancer Genome Atlas; Time; Training; Travel; anticancer research; base; biological systems; cancer genome; cancer therapy; cancer type; career development; cell behavior; computerized tools; data mining; design; differential expression; experience; genome-wide; insight; model design; model development; neoplastic cell; novel; personalized medicine; protein expression; protein protein interaction; response; technique development; theories; tool; tumor

DESCRIPTION (provided by applicant): Cancers are driven by inheritable genetic/epigenetic changes, including somatic mutations and copy number variations (CNV). Genetic changes perturb cellular signaling pathways through the following mechanisms: 1) by changing the structures (and therefore the functions) of signaling proteins, through somatic mutations; and 2) by changing the quantity of proteins involved in signaling pathways (e.g., increasing expression of an enzyme producing a certain signaling molecule), through CNV and DNA methylation. Each signaling pathway regulates the expression of a set of genes that usually perform certain functions together, such as handling cell proliferation or death. We call this set of genes a response-module. If a pathway is perturbed, then expressions of genes regulated by the pathway will change accordingly, further altering the behavior of cells and turning normal cells into cancerous ones. One important objective of cancer disease mechanism research is to understand which genetic change is responsible for the perturbation of which signaling pathway. Currently, large-scale studies, such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have detected genetic changes (e.g., somatic mutation and CNA) and expression changes (e.g., RNA expression and protein expression) in tens of thousands of tumors. These data provide an unprecedented opportunity to study cancer disease mechanisms and to investigate the heterogeneity of common cancers. However, the scale of the data also poses significant challenges in computational methodology development, particularly because many bioinformatics problems belong to a class of problems referred to as "NP-hard problems." The PI of this transitional K99/R00 proposal has extensive experience in developing algorithms addressing this type of computational problem. The major goal of this proposal is to provide sufficient training for the PI to gain biological insight so that he can develop efficient algoriths to enhance computational cancer research. In addition to the PI receiving formal didactic and out-of-class training in cancer biology, this project will also develop specific computational algorithms and tools to study cancer disease mechanisms using the TCGA data. More specifically, the project proposes two aims. AIM 1 is to develop graph models and design efficient algorithms that are capable of revealing perturbed signaling pathways by combining multiple types of "omics" data. AIM 2 is to study the impact of pathway perturbations on cancer development and clinical outcome The specific aims proposed will address the following major issues or challenges: 1) Given a set of genes that have been differently expressed as the result of perturbations of multiple signaling pathways in tumors, how do we group them into units in such a way that the genes in each unit are likely to have been regulated by one common signal?; and 2) Given the dozens or hundreds of somatic mutations or CNAs in a tumor, how do we recognize the small portion that is likely to perturb cancer pathways, as well as trace perturbation sources, i.e., somatic mutations or CNAs in the tumor, to those cancer pathways? By applying the tools developed in this project to different types of cancer data from TCGA or other resources, we will have a better understanding of disease mechanisms for different cancers.

描述（由申请人提供）： 癌症是由可遗传的遗传/表观遗传变化驱动的，包括体细胞突变和拷贝数变异（CNV）。遗传变化通过以下机制扰乱细胞信号传导途径：1）通过体细胞突变改变信号传导蛋白的结构（并因此改变其功能）;以及2）通过改变参与信号传导途径的蛋白质的量（例如，通过CNV和DNA甲基化增加产生某种信号分子的酶的表达）。每个信号通路调节一组基因的表达，这些基因通常一起执行某些功能，例如处理细胞增殖或死亡。我们称这组基因为反应模块。如果一个通路受到干扰，那么由该通路调节的基因表达将相应地改变，进一步改变细胞的行为，并将正常细胞转化为癌细胞。 癌症发病机制研究的一个重要目标是了解哪种遗传变化导致哪种信号通路的扰动。目前，大规模的研究，如癌症基因组图谱（TCGA）和国际癌症基因组联盟（ICGC），已经检测到遗传变化（例如，体细胞突变和CNA）和表达变化（例如，RNA表达和蛋白质表达）在数万个肿瘤中。这些数据为研究癌症疾病机制和调查常见癌症的异质性提供了前所未有的机会。然而，数据的规模也对计算方法的发展提出了重大挑战，特别是因为许多生物信息学问题属于一类称为“NP难问题”的问题。“这个过渡性K99/R 00提案的PI在开发解决这类计算问题的算法方面拥有丰富的经验。该提案的主要目标是为PI提供足够的培训，以获得生物学见解，以便他能够开发有效的算法来加强计算癌症研究。 除了PI接受癌症生物学的正式教学和课外培训外，该项目还将开发特定的计算算法和工具，以使用TCGA数据研究癌症疾病机制。更具体地说，该项目提出了两个目标。目的1是开发图形模型和设计有效的算法，能够揭示扰动信号通路结合多种类型的“组学”数据。目的2是研究通路扰动对癌症发展和临床结果的影响。提出的具体目标将解决以下主要问题或挑战：1）给定一组由于肿瘤中多种信号通路的扰动而不同表达的基因，我们如何将它们分组为单位，使每个单位中的基因可能受到一个共同信号的调节？以及2）鉴于肿瘤中的数十或数百个体细胞突变或CNA，我们如何识别可能干扰癌症途径的小部分，以及追踪干扰源，即，肿瘤中的体细胞突变或CNA，对这些癌症通路有什么影响？通过将该项目开发的工具应用于来自TCGA或其他资源的不同类型的癌症数据，我们将更好地了解不同癌症的疾病机制。