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、集成的方法来分析高通量的癌症基因组数据。我们计划为1)类发现开发新的变量选择方法，即我们建议确定特定癌症的亚组以更好地了解潜在的癌症生物学和2)预测性基因签名，即我们建议确定对患者的临床表型(包括生存和治疗反应)具有预测性的基因子集。具体地说，我们将开发一种新的方法来选择聚类中的变量。聚类在基因组癌症数据分析中起着至关重要的作用。例如，基于无性系的基因表达，可以在一组组织样本中发现重要的簇差异，这可能反映疾病的类别、突变状态或对给定治疗的不同反应。其次，我们将开发一种新的惩罚似然方法来选择回归中的变量，该方法利用群体信息来选择共享相同生物路径的相关基因组。开发的方法将有助于识别重要的基因特征，这些基因特征可能导致在任何关注生存时间或治疗反应的健康研究中进行更具选择性的个性化治疗。

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 proles, important cluster distinctions can be found among a set of tissue samples, which may reect 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 selective personalized treatment in any health studies where survival time or response to therapy is of interest.