Efficient microarray meta analysis and cancer biomarker selection

高效的微阵列荟萃分析和癌症生物标志物选择

• 批准号: 7685435
• 负责人: Shuangge Ma
• 金额: $ 7.91万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7685435
• 关键词: Biological Markers; Cations; Clinical; Computational algorithm; Computers; DNA Microarray Chip; Data; Development; Elements; Family; Gene Expression; Genes; Genetic; Genome; Genomics; Goals; Hand; Individual; Influentials; Investigation; Lead; Malignant Neoplasms; Meta-Analysis; Methodology; Methods; Microarray Analysis; Modeling; Outcome; Outcome Measure; Performance; Property; Sample Size; Survival Analysis; Techniques; Tissues; anticancer research; base; cancer classification; cancer microarray; cancer type; novel; predictive modeling; research study; simulation; tool; user-friendly

DESCRIPTION (provided by applicant): An important goal in cancer research is to identify genomic biomarkers that can be used to obtain a better understanding of the genetic basis of cancers, and construct models that can be used to predict cancer occurrence and progression. Many studies have used microarrays to identify genes that have altered expression levels in various cancer tissues. Meta analysis makes it possible to (1) effectively combine experiments with different microarray platforms and/or other setup; (2) lead to more reliable and consistent gene identification results across studies and more satisfactory predictions; and (3) identify genes that are commonly activated in different types of cancer. The proposed study is the first to investigate novel regularized methods for microarray meta analysis where cancer clinical outcomes are measured along with gene expressions in multiple independent experiments. The proposed approaches can (1) effectively combine data from different platforms/ experimental setup; (2) carry out efficient biomarker selection and predictive model building simultaneously; and (3) identify influential genes that are important across different experiments, while allowing for experiment-specific predictive models. The specific aims of this study include: (1) Develop MTGDR (Meta Threshold Gradient Directed Regularization) method for regularized microarray meta analysis. (2) Develop penalized group-bridge method for regularized microarray meta analysis. (3) Apply the proposed general methodologies to cancer classification and survival analysis with microarray data. Develop user-friendly R packages implementing the proposed approaches and make them publicly available. We will consider cancer microarray meta analysis where individual experiments can have categorical clinical outcomes and right censored survival outcomes. Analysis of practical cancer studies and extensive simulations will be conducted to assess performance of proposed approaches and compare with alternatives. In this application, we emphasize not only development of new general methodologies, but also their computer implementation, applications and empirical performances.

描述（由申请人提供）： 癌症研究的一个重要目标是鉴定可用于更好地理解癌症遗传基础的基因组生物标志物，并构建可用于预测癌症发生和进展的模型。许多研究使用微阵列来鉴定各种癌症组织中表达水平改变的基因。元分析使（1）有效地将实验与不同的微阵列平台和/或其他设置相结合； （2）导致整个研究中更可靠和一致的基因鉴定结果和更令人满意的预测； （3）确定通常在不同类型的癌症中激活的基因。 拟议的研究是第一个研究用于微阵列元分析的新型正规化方法，其中在多个独立实验中测量了癌症临床结果以及基因表达。提出的方法可以（1）有效地结合了来自不同平台/实验设置的数据； （2）同时进行有效的生物标志物选择和预测模型； （3）确定在不同实验中很重要的有影响力的基因，同时允许实验特定的预测模型。 这项研究的具体目的包括：（1）开发MTGDR（元阈值梯度定向正则化）方法进行正则化微阵列元分析。 （2）开发用于正则微阵列元分析的惩罚组桥方法。 （3）将拟议的一般方法应用于微阵列数据中癌症分类和生存分析。开发实施拟议方法的用户友好的R软件包，并使它们公开可用。 我们将考虑癌症微阵列元分析，其中各个实验可以具有分类的临床结果和正确审查的生存结果。将对实践癌症研究和广泛的模拟进行分析，以评估所提出的方法的性能并与替代方法进行比较。在此应用程序中，我们不仅强调了新的一般方法的开发，还强调了他们的计算机实施，应用程序和经验性能。