BAYESIAN BIOMARKER MODELS FOR CANCER GENETICS STUDIES

用于癌症遗传学研究的贝叶斯生物标记模型

• 批准号: 6659844
• 负责人: Sally W Thurston
• 金额: $ 9.49万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-13 至 2005-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6659844
• 关键词: DNA damage; DNA repair; adduct; asbestos; biomarker; cancer risk; clinical research; environmental exposure; gene environment interaction; genetic markers; genetic polymorphism; genetic susceptibility; human subject; lung neoplasms; mathematical model; model design /development; neoplasm /cancer epidemiology; neoplasm /cancer genetics; smoking; statistics /biometry; tobacco abuse

DESCRIPTION (Taken from the Investigator?s Abstract) This proposal aims to develop statistical methods which incorporate biomarkers and genetic information to improve estimates of cancer risk. This methodology will be developed using data from an ongoing study of lung cancer, smoking history, asbestos exposure, DNA adducts (a biomarker of smoking exposure), and genes associated with adduct formation and DNA repair (including CYP1A1 MSP1, GSTM1 and XRCC1). Currently the study has 2800 subjects, approximately half of whom are cases. Genotyping for multiple genes is ongoing. Phenol-related adducts have been measured in nontumorous lung tissue for 69 cases and in mononuclear blood cells for 38 cases and 42 controls. Polycyclic aromatic hydrocarbon adducts have been measured in normal lung tissue for 143 cases. The Bayesian paradigm, which can incorporate information from prior studies, will be used throughout. This paradigm can easily handle missing data such as lung adduct counts which cannot be measured in controls. All models for lung cancer risk will allow for synergistic effects between smoking and asbestos exposure. The specific aims of the proposal are: (1) to develop a model for DNA adduct counts given genes and smoking, and to then use the predicted adduct counts in a logistic regression model for lung cancer risk which corrects for the measurement error bias induced by using blood adducts instead of lung adducts in controls; (2) to develop a pharmacokinetic model for the number of blood adducts over time as a function of genes and changing smoking behavior, and to use the predicted cumulative adduct burden in a model for cancer risk; (3) by combining aims 1 and 2, to develop a model of cancer risk which both takes into account the changing adduct load over time and corrects for measurement error bias; (4) to extend the models in aims 1-3 to allow for nonlinear relationships between covariates and lung cancer risk using generalized additive models; (5) using new data, to compare the performance of the models developed in aims 1-4 to model which do not use biomarkers, and to assess the sensitivity of the outputs to both model and prior specification; and, (6) to develop guidelines on optimal sample sizes, sample selection and timing schemes for biomarker measurement, for future biomarker and exposure-mediated cancer studies.

描述（摘自研究者的摘要） 该提案旨在开发包含生物标志物的统计方法 和遗传信息以改善癌症风险的估计。这种方法论 将使用正在进行的肺癌、吸烟研究的数据来开发 病史、石棉暴露、DNA 加合物（吸烟暴露的生物标志物），以及 与加合物形成和 DNA 修复相关的基因（包括 CYP1A1 MSP1、 GSTM1 和 XRCC1)。目前该研究有 2800 名受试者，其中大约一半 其中有案例。多个基因的基因分型正在进行中。苯酚相关 已在 69 例非肿瘤肺组织中检测到加合物，并在 38例病例和42例对照的单核血细胞。多环芳香族 已在 143 例正常肺组织中检测到碳氢化合物加合物。 贝叶斯范式可以整合先前研究的信息， 将在整个过程中使用。这种范例可以轻松处理丢失的数据，例如 在对照中无法测量的肺加合物计数。适用于肺的所有型号 癌症风险将导致吸烟和石棉之间产生协同效应 接触。该提案的具体目标是：（1）建立一个模型 DNA 加合物计数给定基因和吸烟，然后使用预测的 肺癌风险逻辑回归模型中的加合物计数 纠正因使用血液加合物而引起的测量误差偏差 对照中的肺加合物； (2)建立药代动力学模型 随着时间的推移，血液加合物的数量作为基因和吸烟变化的函数 行为，并在模型中使用预测的累积加合物负荷 癌症风险； (3) 结合目标1和2，建立癌症风险模型 两者都考虑了加合物负载随时间的变化并进行修正 对于测量误差偏差； (4) 扩展目标 1-3 中的模型以允许 使用协变量和肺癌风险之间的非线性关系 广义加性模型； (5) 使用新数据，比较性能 目标 1-4 中开发的模型不使用生物标志物进行建模，并且 评估输出对模型和先前规范的敏感性； (6) 制定关于最佳样本量、样本选择和 生物标志物测量、未来生物标志物和暴露介导的计时方案 癌症研究。