BAYESIAN BIOMARKER MODELS FOR CANCER GENETICS STUDIES

(摘自《调查者--S摘要》) 该提案旨在开发包含生物标记物的统计方法 和遗传信息，以改善对癌症风险的估计。这种方法论 将使用一项正在进行的肺癌、吸烟研究的数据进行开发 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)。目前这项研究有2800名受试者，大约一半 其中有几个是案例。多个基因的基因分型正在进行中。与苯酚有关 对69例非肿瘤性肺组织中的加合物进行了测定。 38例患者和42例正常对照的单核细胞。多环芳香族 对143例正常肺组织进行了碳氢加合物测定。 贝叶斯范式可以结合先前研究的信息， 将在整个过程中使用。此范例可以轻松处理丢失的数据，例如 不能在对照组中测量的肺内加合物计数。肺的所有模型 癌症风险将允许吸烟和石棉之间的协同效应 曝光。该提案的具体目标是：(1)开发一个模型，用于 DNA加合物计数给定的基因和吸烟，然后使用预测 肺癌风险的Logistic回归模型中的加合物计数 改用血液加合物引起的测量误差偏差 (2)建立肺加合物的药代动力学模型。 随着时间的推移血液加合物的数量作为基因和改变吸烟的函数 行为，并在模型中使用预测的累积累加负担 癌症风险；(3)结合目标1和目标2，建立癌症风险模型 它既考虑了随时间变化的加法负荷，又修正了 用于测量误差偏差；(4)扩展AIMS 1-3中的模型，以允许 协变量与肺癌风险之间的非线性关系 (5)使用新的数据，比较两种模型的性能 在目标1-4中开发的模型用于模拟不使用生物标记物的模型，以及 评估产出对模型和先前规范的敏感度； 和，(6)制定关于最佳样本量、样本选择和 生物标记物测量的计时方案、未来生物标记物的计时方案和曝光中介的计时方案 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.