BAYESIAN BIOMARKER MODELS FOR CANCER GENETICS STUDIES

描述（来自研究者？s摘要） 该提案旨在开发纳入生物标志物的统计方法 和遗传信息来改善癌症风险的估计。这种方法 将利用一项正在进行的肺癌、吸烟、 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CYP 1A 1 MSP 1， GSTM 1和XRCC 1）。目前这项研究有2800名受试者，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 在69例非肿瘤性肺组织和10例非肿瘤性肺组织中测定了加合物。 检测38例患者和42例对照者的外周血单个核细胞。多环芳族 本文测定了143例正常肺组织中烃加合物的含量。 贝叶斯范式，它可以结合先前研究的信息， 将在整个过程中使用。这个范例可以很容易地处理丢失的数据， 在对照组中无法测量的肺加合物计数。所有型号的肺 癌症风险将允许吸烟和石棉之间的协同效应 exposure.该提案的具体目标是：（1）制定一个模型， 给定基因和吸烟的DNA加合物计数，然后使用预测的 肺癌风险的逻辑回归模型中的加合计数， 校正了使用血液加合物引起的测量误差偏差 肺加合物的对照;（2）建立一个药代动力学模型， 随着时间的推移，血液加合物的数量随着基因和吸烟的变化而变化 行为，并在模型中使用预测的累积加合物负担， （3）结合目标1和目标2，开发癌症风险模型 其既考虑了加合物负载随时间的变化， （4）扩展目标1-3中的模型， 协变量与肺癌风险之间的非线性关系 （5）使用新的数据，比较 在目标1-4中开发的模型不使用生物标志物， 评估输出对模型和先前规格的敏感性; 及（6）就最佳样本量、样本选择及 生物标志物测量的定时方案，用于未来的生物标志物和生物标志物介导的 癌症研究。

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.