Integrated Analysis of High Throughput Cancer Genomic Data

描述（由申请人提供）：该项目的主要目标是开发一种新的，综合的方法来分析高通量癌症基因组数据。我们计划开发新的变量选择方法，用于1）类别发现，即我们提出确定指定癌症的亚组以更好地理解潜在的癌症生物学，以及2）预测性基因特征，即我们提出确定预测患者临床表型的基因子集，包括生存和对治疗的反应。 具体来说，我们将开发一种新的方法，在聚类变量的选择。聚类在基因组癌症数据的分析中起着关键作用。例如，基于基因表达谱，可以在一组组织样品中发现重要的聚类区别，这可以反映疾病的类别、突变状态或对给定疗法的不同反应。其次，我们将开发一种新的惩罚似然回归变量选择方法，该方法利用组信息选择具有相同生物学途径的相关基因组。开发的方法将有助于识别重要的基因特征，这些基因特征可能会在任何健康研究中导致更有效的个性化治疗，其中生存时间或对治疗的反应是感兴趣的。 公共卫生相关性：我们的项目旨在开发一类新的变量选择方法，用于分析高通量癌症基因组数据。与现有方法相比，所提出的方法将导致更强大的分类发现方法，用于识别癌症亚型，并更准确地预测患者的生存和对治疗的反应。开发的方法将有助于识别重要的基因特征，这些基因特征可能会在任何健康研究中导致更有效的个性化治疗，其中生存时间或对治疗的反应是感兴趣的。

DESCRIPTION (provided by applicant): The primary goal of this project is to develop a novel, integrated approach for the analysis of high-throughput cancer genomic data. We plan to develop new variable selection methods for 1) class discovery, that is we propose to determine subgroups of the specified cancer to better understand the underlying cancer biology and 2) predictive gene signatures, that is we propose to determine a subset of genes which are predictive for patients' clinical phenotypes, including survival and response to therapy. Specifically, we will develop a new method for variable selection in clustering. Clustering plays a critical role in the analysis of genomic cancer data. For example, based on the gene expression profiles, important cluster distinctions can be found among a set of tissue samples, which may reflect categories of diseases, mutation status, or different responses to a given therapy. Second, we will develop a new penalized-likelihood method for variable selection in regression which utilizes group information to select groups of correlated genes that share the same biological pathway. The developed methodology will be useful for identifying important gene signatures that may lead to more effective personalized treatment in any health studies where survival time or response to therapy is of interest. PUBLIC HEALTH RELEVANCE: Our project aims to develop a new class of variable selection methods for analyzing high-throughput cancer genomic data. Compared to existing methods, the proposed methods will lead to more powerful methods of class discovery for identifying cancer sub-types and more accurate prediction of patients' survival and response to therapy. The developed methodology will be useful for identifying important gene signatures that may lead to more effective personalized treatment in any health studies where survival time or response to therapy is of interest.