Incorporating Biomarkers to Improve Lung Cancer Risk Prediction

结合生物标志物改善肺癌风险预测

• 批准号: 9020598
• 负责人: Ziding Feng
• 金额: $ 69.23万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9020598
• 关键词: Adoption; Adult; Age; All-Trans-Retinol; Autoantibodies; Bayesian Modeling; Beta Carotene; Biological Assay; Biological Markers; Body mass index; CCL22 gene; Cancer Etiology; Cancer Intervention and Surveillance Modeling Network; Cancer Model; Cancer Patient; Cessation of life; Chronic Obstructive Airway Disease; Clinical; Colorectal Cancer; Complement; Data; Diagnosis; Diagnostic; Dose; Drosophila pros protein; Early Diagnosis; Education; Epidemiology; Equilibrium; Family history of; Goals; Health; Human; IGFBP2 gene; Individual; Inflammation; Lipids; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of lung; Malignant neoplasm of prostate; Measures; Modeling; Nested Case-Control Study; Participant; Physicians; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Proteins; Proteomics; Race; Recording of previous events; Reporting; Research Design; Risk; Sampling; Screening for Ovarian Cancer; Selection Criteria; Sensitivity and Specificity; Serum; Smoker; Smoking History; Smoking Status; Staging; Survival Rate; Time; United States; Unnecessary Procedures; WFDC2 gene; Women' s Health; X-Ray Computed Tomography; anakinra; base; biomarker panel; blood-based biomarker; cancer risk; candidate marker; cohort; cost; efficacy trial; epidemiologic data; epidemiological model; high risk; improved; inflammatory marker; lung cancer screening; mortality; predictive modeling; public health relevance; screening

 DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer death worldwide, with overall 5-year survival rates in the United States of 15% but approaching 50% when diagnosed at an early stage. The National Lung Screening Trial (NLST) reported that low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in adults who were at high risk of lung cancer. These dramatic results come with high human and societal cost because of the extremely low yield associated with the screening criteria and high false positive rate by LDCT. NLST entry criteria, based on smoking history and age, yielded 1 lung cancer for every 156 screened. One quarter of those screened required expensive, sometimes invasive diagnostic work-up, yet 96.4% of them turned out to be false positives. A better lung cancer risk prediction model can provide better selection criteria and make LDCT more effective in balancing benefit versus harm. This study team has developed 10 blood-based biomarkers (protein: pro-SFTPB, HE4, IGFBP2, LRG1; lipid: DAS; autoantibody: LTF, ADCK1, STK10, TRIM10, KM2) and validated them using pre-diagnostic sera from the Beta-Carotene and Retinol Efficacy Trial. Pro-SFTPB was further validated on the Pan-Canadian Early Detection of Lung Cancer Screening Study and Physician Health Study and shown to complement lung cancer risk prediction models based on epidemiologic data. Recently, 4 circulating inflammation biomarkers (CRP, IL-1RA, BCA- 1/CXCL, MDC/CCL22) were found to be independently associated with lung cancer risk. The proposed study will incorporate these 14 biomarkers into PLCOm2012, a 6-year lung cancer risk prediction model developed and validated by this team using PLCO epidemiological data, to improve lung cancer risk prediction. In Aim 1, using a nested case-control study design these 14 biomarkers will be assayed using sera collected at baseline from 549 lung cancer patients diagnosed within 6 years after baseline and 1,098 matched controls, and then incorporated into the PLCOm2012 model to improve lung cancer risk prediction and the selection criteria for LDCT. In Aim 2, sera collected annually up to five years since baseline for these subjects will be analyzed using two Bayesian models to incorporate biomarker trajectories to improve early detection of lung cancer. In Aim 3, the models from Aim 1&2 will be evaluated for their potential clinical utilities. If successful, the proposed study will challenge the paradigm of epidemiological modeling (age, smoking history, etc.) for lung cancer risk prediction and single threshold for early detection, improve selection criteria for LDCT screening, increase yield of lung cancer by LDCT screening, reduce LDCT-associated harm, and improve early detection of lung cancer.

 描述（由适用提供）：肺癌是全世界癌症死亡的主要原因，在美国，总5年生存率为15％，但在早期被诊断时接近50％。国家肺筛查试验（NLST）报告说，低剂量计算机断层扫描（LDCT）筛查在患有肺癌高风险的成年人中，肺癌死亡率降低了20％。这些戏剧性的结果是由于与筛查标准相关的产量极低和LDCT的高误报率，因此具有很高的人力和社会成本。基于吸烟史和年龄，NLST进入标准每156次筛查每156个肺癌。被筛选的四分之一需要昂贵的，有时是侵入性的诊断性检查，但其中96.4％的人被证明是误报。更好的肺癌风险预测模型可以提供更好的选择标准，并使LDCT在平衡利益与危害方面更有效。该研究团队已经开发了10个基于血液的生物标志物（蛋白质：Pro-SFTPB，HE4，IGFBP2，LRG1; Lipid：Das; Das; Autoantibody：LTF，ADCK1，STK10，TRIM10，KM2），并使用beta-carotene effericen and otininol Effericen和otininolofecy and-necnostic sera验证了它们。在泛加拿大肺癌筛查研究和医师健康研究的泛加拿大早期检测中进一步验证了Pro-SFTPB，并证明基于流行病学数据来补充肺癌风险预测模型。最近，发现4个循环炎症生物标志物（CRP，IL-1RA，BCA-1/CXCL，MDC/CCL22）与肺癌风险独立相关。拟议的研究将将这14个生物标志物纳入PLCOM2012，这是该团队使用PLCO流行病学数据开发和验证的6年肺癌风险预测模型，以改善肺癌风险预测。在AIM 1中，使用嵌套的病例对照研究设计这14个生物标志物将使用在基线后6年内从基线和1,098匹匹配的对照组中诊断出的549例肺癌患者收集的血清，然后纳入PLCOM2012模型中，以改善肺癌风险预测和LDCT的选择标准。在AIM 2中，将使用两种贝叶斯模型分析这些受试者基线以来每年收集长达五年的血清，以融合生物标志物轨迹，以改善对肺癌的早期检测。在AIM 3中，将对AIM 1和2的模型进行评估，以实现其潜在的临床实用性。如果成功的话，拟议的研究将挑战肺癌风险预测的流行病学建模（年龄，吸烟史等）的范式，以预测肺癌的风险预测和单个阈值，改善了LDCT筛查的选择标准，通过LDCT筛查增加了肺癌的产量，减少LDCT伴随伴侣的危害，并改善了肺癌的早期检测。