Integrated Analysis of High Throughput Cancer Genomic Data

项目摘要和相关性 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的癌症 基因组数据我们计划开发新的变量选择方法用于1）类发现，即我们建议确定 特定癌症的亚组以更好地理解潜在的癌症生物学和2）预测性基因特征， 也就是说，我们建议确定一个基因子集，这些基因可以预测患者的临床表型，包括生存率 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。集群在以下方面发挥着关键作用： 基因组癌症数据的分析。例如，基于基因表达谱，重要的聚类区别 可以在一组组织样本中找到，这可能反映疾病的类别，突变状态，或不同的 对特定治疗的反应。其次，我们将开发一种新的惩罚似然变量选择方法， 回归，其利用组信息来选择共享相同生物途径的相关基因组。 开发的方法将有助于识别重要的基因签名，可能会导致更有效的 在任何关注生存时间或对治疗的反应的健康研究中进行个性化治疗。

Project Summary and Relevance 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.