Statistical Strategies for Establishing Etiologic Heterogeneity of Tumors

建立肿瘤病因异质性的统计策略

• 批准号: 8509633
• 负责人: Colin B Begg
• 金额: $ 35.67万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-12 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8509633
• 关键词: Affect; Anatomic Sites; Attention; Behavior; Biologic Characteristic; Biological; Cancer Etiology; Case-Control Studies; Characteristics; Classification; Clinical; Cohort Studies; Computer software; Data; Data Set; Epidemiologic Studies; Etiology; Excision; Genetic; Goals; Heterogeneity; Knowledge; Lead; Link; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of pancreas; Malignant neoplasm of prostate; Maps; Measures; Medical; Methods; Molecular; Nature; Odds Ratio; Organ; Outcome; Patients; Population; Primary Neoplasm; Research; Research Design; Research Personnel; Risk; Risk Factors; Speed; Techniques; base; cancer risk; cancer type; design; improved; tool; tumor

DESCRIPTION (provided by applicant): The fundamental premise of this proposal is that cancer types based on anatomic site may contain sub-types that are etiologically distinct. Indeed a lot of evidence for this has emerged in recent years. The goal of the proposal is to develop a strategy for optimally identifying such etiologically distinct tumor sub-types, and to develop the statistical techniques needed to accomplish this. In addition to clarifying cancer etiology, such an approach offers the promise of a more powerful strategy for detecting new risk factors, by focusing studies to discover these new risk factors on the sub-types that possess distinct etiology. Our research plan is motivated by a crucial new result regarding the occurrence of double primary malignancies. We show that the odds ratio linking tumor sub-types of pairs of independently occurring cancers is directly related to the underlying population risk heterogeneity of the sub-types. Consequently data from studies of double primaries can be used to determine optimal tumor sub-classification from an etiologic perspective. In this proposal we build upon this result to develop multivariate clustering techniques that optimize the etiologic heterogeneity of the resulting clusters (Aim 1). We will develop analogous techniques for creating sub-types that maximize the degree of etiologic heterogeneity on the basis of known risk factors for use in settings where data on multiple primary cancers are unavailable or unobtainable (Aim 2). We will determine the implications of the use of sub-typing as a strategy for detecting new risk factors from the perspective of statistical power (Aim 3). Finally, we will develop freely-available software to allow other investigators easy access to the methods that we develop (Aim 4). The research will lead ultimately to a conceptual framework for investigating etiologic heterogeneity, and a suite of statistical tools for conducting the dat analyses.

描述（由申请人提供）：本提案的基本前提是基于解剖部位的癌症类型可能包含病因学上不同的亚型。事实上，近年来已经出现了许多证据。该提案的目标是开发一种策略，用于最佳地识别这种病因学上不同的肿瘤亚型，并开发实现这一目标所需的统计技术。除了澄清癌症病因外，这种方法还提供了一种更强大的策略来检测新的风险因素，通过将研究重点放在具有不同病因的亚型上来发现这些新的风险因素。我们的研究计划的动机是一个重要的新结果，关于双原发性恶性肿瘤的发生。我们发现，独立发生的癌症对的肿瘤亚型的比值比与潜在人群直接相关 子类型的风险异质性。因此，从病因学的角度来看，来自双原发灶研究的数据可用于确定最佳肿瘤亚分类。在这个建议中，我们建立在这一结果的基础上，开发多变量聚类技术，优化所产生的集群的病因异质性（目标1）。我们将开发类似的技术来创建亚型，在已知风险因素的基础上最大限度地提高病因异质性的程度，用于无法获得或无法获得多种原发性癌症数据的环境中（目标2）。我们将从统计功效的角度（目标3）确定使用亚型作为检测新风险因素的策略的意义。最后，我们将开发免费软件，让其他调查人员轻松访问 我们开发的方法（目标4）。这项研究将最终导致一个调查病因异质性的概念框架，以及一套进行数据分析的统计工具。