MODELS FOR THE MOLECULAR CLASSIFICATION OF TUMORS

肿瘤分子分类模型

• 批准号: 6254815
• 负责人: J SUNIL RAO
• 金额: $ 13.35万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-06-01 至 2006-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6254815
• 关键词: colon neoplasms; gene expression; genetic markers; human data; liver neoplasms; mathematical model; metastasis; model design /development; neoplasm /cancer classification /staging; neoplasm /cancer genetics; transforming growth factors

DESCRIPTION: (provided by Applicant) This proposal will develop a series of statistical predictive models to test the hypothesis that differences in patterns of gene expression determine the differing biologic behaviors between colon cancers that are curable with primary surgical therapy, and those that ultimately metastasize to the liver and kill. To generate data to test this hypothesis, Dr. Sanford Markowitz and his colleagues in the cancer genetics program at the Case Western Reserve University-NCI designated Comprehensive Cancer Center have established a series of data archives. The primary archive contains metastatic and non-metastatic colon cancers, anatomical staging information, clinical follow-up information and gene expressions collected on Affymetrix human 40K GeneChips using Eos Biotechnology Inc. expression algorithms. Further, an independent validation archive of 350 colon cancers containing anatomical staging information and clinical follow-up data. In addition promising candidate genes can be assayed for expression from colon cancers in this archive. The goal is to develop predictive models that will identify genes making up a so-called "metastatic signature" for colon cancer. In order to accomplish this, Dr. J. Sunil Rao of the Department of Epidemiology and Biostatistics at CWRU has established a mentoring relationship with Dr. Markowitz in which Dr. Rao will use some recent predictive data mining tools with modifications necessary for modeling of this data. Specifically, Dr. Rao will develop methods that: 1) incorporate measurement error and differential variability for gene expressions; 2) aggregate tree-based classifiers from resampled data (bagging) for accurate predictions; 3) refine bagging using a within-cluster type of bagging for further increased prediction accuracy; 4) cluster observations (and genes) based on estimating a finite mixture of Gamma distributions using approximate Bayesian computing and Dirichlet process priors; 5) formally deal with tuning parameters implicit in the tree-building process; and lastly, 6) collate results into a high-dimensional visual graphic known as the CAT scan for extracting the nature of the hypothesized metastatic signature. A full theoretical and empirical evaluation of all algorithms using simulations and the two colon cancer archives will be made.

产品说明： （申请人提供）本提案将开发一系列 统计预测模型来检验假设， 基因表达的模式决定了不同的生物学行为之间 结肠癌可以通过初次手术治疗治愈， 最终转移到肝脏致死 为了生成数据来测试这个 桑福德·马科维茨博士和他的癌症遗传学同事们 凯斯西储大学-NCI指定的综合课程 癌症中心建立了一系列的数据档案。 主存档 包括转移性和非转移性结肠癌，解剖分期 收集的信息、临床随访信息和基因表达 使用Eos Biotechnology Inc. 表达 算法 此外，350例结肠癌的独立验证档案 包含解剖学分期信息和临床随访数据。 在 另外，可以测定有希望的候选基因在结肠中的表达 癌症在这个档案 其目标是开发预测模型，以识别构成基因的基因。 所谓的结肠癌“转移标志”。 为了实现 流行病学和生物统计学系的J. Sunil Rao博士， CWRU与Markowitz博士建立了指导关系， Rao将使用一些经过修改的最新预测数据挖掘工具 这是对这些数据进行建模所必需的。 具体来说，拉奥博士将开发 方法： 1)结合测量误差和差分可变性 对于基因表达; 2）从重新采样的数据聚合基于树的分类器 （bagging）进行准确的预测; 3）使用集群内的 装袋类型，以进一步提高预测精度; 4)集群 基于估计Gamma的有限混合的观测（和基因） 分布使用近似贝叶斯计算和狄利克雷过程 先验知识; 5）形式化地处理树构建中隐含的调优参数 过程;最后， 6)整理结果， 称为CAT扫描的图形，用于提取假设的性质 转移信号 全面的理论和实证评估 将使用模拟和两个结肠癌档案的算法。