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（由申请人提供）：肺癌是全球癌症死亡的主要原因，在美国总体5年生存率为15%，但在早期诊断时接近50%。国家肺筛查试验（NLST）报告说，低剂量计算机断层扫描（LDCT）筛查使肺癌高危成人的肺癌死亡率降低了20%。这些引人注目的结果伴随着高的人类和社会成本，因为与筛选标准相关的极低的产量和LDCT的高假阳性率。根据吸烟史和年龄的NLST入选标准，每156例筛查中有1例肺癌。四分之一的筛查者需要昂贵的，有时是侵入性的诊断检查，但其中96.4%被证明是假阳性。一个更好的肺癌风险预测模型可以提供更好的选择标准，使LDCT在平衡获益与损害方面更有效。该研究小组开发了10种基于血液的生物标志物（蛋白质：pro-SFTPB，HE 4，IGFBP 2，LRG 1;脂质：DAS;自身抗体：LTF，ADCK 1，STK 10，TRIM 10，KM 2），并使用β-胡萝卜素和视黄醇疗效试验的诊断前血清对其进行了验证。Pro-SFTPB在泛加拿大肺癌筛查研究和医生健康研究的早期检测中得到了进一步验证，并显示出对基于流行病学数据的肺癌风险预测模型的补充。最近，发现4种循环炎症生物标志物（CRP、IL-1 RA、BCA- 1/CXCL、MDC/CCL 22）与肺癌风险独立相关。拟议的研究将把这14个生物标志物纳入PLCOm 2012，这是一个由该团队使用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。在目标1中，使用巢式病例对照研究设计，将使用从基线后6年内诊断的549名肺癌患者和1，098名匹配对照中收集的基线血清测定这14种生物标志物，然后将其纳入PLCOm 2012模型，以改善肺癌风险预测和LDCT的选择标准。在目标2中，将使用两种贝叶斯模型分析自基线起每年采集的这些受试者血清，以纳入生物标志物轨迹，从而改善肺癌的早期检测。在目标3中，将评估目标1&2中的模型的潜在临床效用。如果成功，拟议的研究将挑战流行病学建模（年龄，吸烟史等）的范式。为肺癌风险预测和早期发现的单阈值，改进LDCT筛查的选择标准，提高LDCT筛查肺癌的检出率，减少LDCT相关的危害，提高肺癌的早期发现。