Combinatorial Genomics in Cancer

癌症组合基因组学

• 批准号: 7291530
• 负责人: STEVE W COLE
• 金额: $ 43.91万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-26 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7291530
• 关键词: Algorithms; Bioinformatics; Cancer Etiology; Characteristics; Chromosome abnormality; Clinical; Complex; Computer software; Disease Progression; Etiology; Event; Foundations; Genetic; Genetic Transcription; Genomics; Machine Learning; Malignant Neoplasms; Maps; Monitor; Mutation; Oncogenic; Patients; Pattern; Phenotype; Research Infrastructure; Resources; Signal Transduction; Statistical Models; Transcriptional Regulation; Treatment Efficacy; Variant; base; cancer cell; cancer therapy; cell growth; combinatorial; graphical user interface; prognostic; response; tool; transcription factor

DESCRIPTION (provided by applicant): Targeted therapy of cancer requires a clear understanding of the genetic alterations that drive malignant cell growth. Identification of causal genetic alterations is complicated by three characteristics of cancer etiology: 1.) multiple interacting alterations are often required to cause cancer, 2.) several distinct alterations may be sufficient to generate a single cancer phenotype, and 3.) oncogenic alterations appear in a dense background of normal genetic activity and spurious consequences of malignant cell growth. We propose to apply a variant of the machine learning algorithm PRIM to the task of identifying disjunctive sets of conjunctive genetic alterations that cause specific cancers or provide prognostic information about clinical course and treatment efficacy. These analyses synthesize information from low-level bioinformatics resources we have already developed to map chromosomal alterations and monitor global patterns of transcription factor activity. Based on those foundations, the present studies develop high-level analytic tools to map combinatorial interactions among low-level genomic events. Specifically, these studies seek to: Aim 1: Develop graphical user interface (GUI) software to support combinatorial genomic analyses by biologists with limited computational background. Aim 2: Optimize combinatorial prediction of disease progression and treatment response. Aim 3: Develop PRIM-based statistical models to identify functional complementation groups of genetic alterations and transcriptional control signals. The bioinformatic tools produced in these studies will create a generalized analytic infrastructure for mapping complex etiologies in cancer and deploying patient-specific targeted therapies.

描述（由申请人提供）：癌症的靶向治疗需要清楚地了解驱动恶性细胞生长的遗传改变。致病性遗传改变的鉴定因癌症病因学的三个特征而复杂化：1.）通常需要多种相互作用的改变来引起癌症，2.）几种不同的改变可能足以产生单一的癌症表型，和3.）致癌改变出现在正常遗传活性的密集背景和恶性细胞生长的虚假结果中。我们建议将机器学习算法PRIM的变体应用于识别导致特定癌症或提供有关临床过程和治疗效果的预后信息的合取遗传改变的分离集的任务。这些分析综合了我们已经开发的低水平生物信息学资源的信息，以绘制染色体变异并监测转录因子活性的全球模式。基于这些基础，本研究开发了高水平的分析工具，以映射低水平基因组事件之间的组合相互作用。具体而言，这些研究旨在：目标1：开发图形用户界面（GUI）软件，以支持有限的计算背景的生物学家的组合基因组分析。目的2：优化疾病进展和治疗反应的组合预测。目标3：建立基于PRIM的统计模型，以识别遗传改变和转录控制信号的功能互补组。这些研究中产生的生物信息学工具将创建一个通用的分析基础设施，用于绘制癌症的复杂病因并部署针对患者的靶向疗法。