Lung Cancer Risk Prediction in the Pittsburgh Lung Screening Study (PLuSS) Cohort

UPCI肺癌孢子中的这个新项目3建立在临床和基于人群的资源基础上 开发和实质性的研究成果，从以前的孢子资助期间。项目3将继续 三个翻译目标是完善和验证肺癌风险预测模型和诊断血清 生物标志物组。在具体目标1中，项目3将从单核苷酸构建遗传风险指数 多态性（SNP）基因型信息，测量该指数与肺癌的相关性（Aim 1A），以及 评估该指数对我们发布的肺癌预测模型的贡献， 人口统计学、吸烟暴露和临床变量（目标1B）。在具体目标2中，项目3将 评估一个有前途的肺癌诊断规则模型的肺癌区分性能 根据10种生物标志物蛋白（Aim 2A）的血清浓度构建，并验证其在 受试者在筛选计算机断层扫描（CT）时有中度怀疑肺结节（目的 2B）和诊断性能独立于人口统计学、吸烟、临床和遗传变量（Aim 2C）。这些目标使用匹兹堡肺筛查研究中出现的共享肺癌病例（n=180） （PLuSS），一个来自临床来源的可比肺癌病例系列（n=548）和对照组[n=993 (Aim 2A）或n=482（Aim 2B）]。与生物统计学和 Bioinformatics Core将基于这些广泛的数据集开发改进的模型。了总结 translational aim使用ACRIN/NLST和ACOSOG Z4031队列的独立比较组 验证这些预测和诊断策略（目标3）。完成目标1将产生肺癌 风险预测模型，能够选择个体进行初级化学预防或增强筛查。 目标2将产生一种肺癌诊断测试，以提高临床背景下的早期肺癌检测。 CT检测结节高危受试者的主要化学预防和管理试验。最后， 目的3将评估两个大型肺癌预测和诊断模型的性能， 以及类似的病例对照队列，用于独立验证和完善最终模型。

This new Project 3 in the UPCI Lung Cancer SPORE builds on both clinical and population based resource development and substantive research findings from the prior SPORE funding periods. Project 3 will pursue three translational aims to refine and validate a lung cancer risk prediction model and a diagnostic serum biomarker panel. In Specific Aim 1, Project 3 will construct a genetic risk index from single nucleotide polymorphism (SNP) genotype information, measure the index's association with lung cancer (Aim 1 A), and evaluate the index's contribution to our published lung cancer prediction model that incorporates demographic, cigarette smoking exposure, and clinical variables (Aim 1B). In Specific Aim 2, Project 3 will evaluate the lung cancer discrimination performance of a promising lung cancer diagnostic rule model constructed from the serum concentrations of 10 biomarker proteins (Aim 2A) and verify its performance in subjects with intermediate suspicion pulmonary nodules on screening computerized tomography (CT) (Aim 2B) and diagnostic performance independent of demographic, smoking, clinical, and genetic variables (Aim 2C). These aims use shared lung cancer cases (n=180) emerging from the Pittsburgh Lung Screening Study (PLuSS), a comparable lung cancer case series (n=548) derived from clinical sources, and controls [n=993 (Aim 2A) or n=482 (Aim 2B)] sampled from the PLuSS cohort. Collaboration with the Biostatistics and Bioinformatics Core will develop improved models based on these expansive datasets. A concluding translational aim uses independent comparison groups from the ACRIN/NLST and ACOSOG Z4031 cohorts to validate these prediction and diagnostic strategies (Aim 3). Completion of Aim 1 will produce a lung cancer risk prediction model that capably selects individuals for primary chemoprevention or enhanced screening. Aim 2 will yield a lung cancer diagnostic test to improve early lung cancer detection in the contexts of clinical trials of primary chemoprevention and management of high-risk subjects with CT-detected nodules. Lastly, Aim 3 will evaluate the performance of both the lung cancer prediction and diagnostic models in two large and similar case-control cohorts for independent validation and refinement of the final models.