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）：针对性的癌症治疗需要清楚地了解驱动恶性细胞生长的遗传改变。癌症病因的三个特征使因果遗传改变的鉴定很复杂：1。）通常需要多次相互作用改变以引起癌症，2。）几种不同的变化可能足以产生单个癌症表型，然后3.）致癌细胞活性的致密背景和恶性细胞生长的潮流后果出现。我们建议将机器学习算法的变体应用于识别导致特定癌症或提供有关临床课程和治疗功效的预后信息的脱节性遗传改变的任务。这些分析从低级生物信息学资源中综合信息，我们已经开发出来映射染色体变化并监视转录因子活动的全局模式。基于这些基础，目前的研究开发了高级分析工具，以绘制低级基因组事件之间的组合相互作用。具体而言，这些研究试图：目标1：开发图形用户界面（GUI）软件，以支持具有有限计算背景的生物学家进行组合基因组分析。目标2：优化疾病进展和治疗反应的组合预测。 AIM 3：开发基于原始的统计模型，以识别遗传改变和转录控制信号的功能互补组。这些研究中生产的生物信息学工具将创建一个广义的分析基础设施，用于绘制癌症中的复杂病因并部署特定于患者的靶向疗法。