Integrating Biomarkers into Lung Cancer Risk Profiling

将生物标志物整合到肺癌风险分析中

• 批准号: 10716718
• 负责人: Mattias Alexander Johansson
• 金额: $ 69.77万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10716718
• 关键词: Address; Age; Bioinformatics; Biological Markers; Biometry; Blood specimen; Cancer Detection; Cancer Etiology; Cessation of life; Communities; Complement; Data; Diagnosis; Diagnostic; Early Diagnosis; Eligibility Determination; Etiology; Exclusion; Family history of; General Population; Genotype; Harm Reduction; Healthcare Systems; Individual; Kentucky; Lung; Malignant neoplasm of lung; Measurement; Measures; Modality; Modeling; Occupational Exposure; Participant; Patients; Performance; Persons; Prospective cohort; Proteins; Proteomics; Public Health; Recording of previous events; Research; Risk; Risk Assessment; Risk Factors; Risk Marker; Sampling; Smoke; Smoking; Smoking History; Smoking Status; Symptoms; Technology; Test Result; Testing; Time; Training; Translating; Translational Research; United States Preventative Services Task Force; Update; Validation; Work; biomarker panel; biomarker performance; blood-based biomarker; cancer biomarkers; cancer genetics; cancer risk; case control; clinical implementation; cohort; computed tomography screening; curative treatments; early detection biomarkers; high risk; improved; innovation; low dose computed tomography; lung basal segment; lung cancer screening; mortality; never smoker; never smoking; novel; novel marker; polygenic risk score; predictive modeling; predictive tools; prevent; programs; protein biomarkers; psychosocial; recruit; risk prediction model; screening; screening program; smoking exposure

Project 2: Integrating Biomarkers into Lung Cancer Risk Profiling Summary Screening by low-dose computed tomography (LDCT) has revolutionized early detection of lung cancer, the leading cause of cancer death. However, current eligibility criteria require participants to have a history of heavy smoking exposure. People with many years of cessation are excluded, as are those who never smoked. Currently, 30-50% of lung cancers occur in individuals who are not eligible for screening, and with decreasing smoking rates, these numbers will increase. The overarching aim of Project 2 is to address this challenge by leveraging complementary information from blood-based biomarkers to identify individuals who have high lung cancer risk despite not meeting screening eligibility criteria. Our project is a natural extension of the initial INTEGRAL program where we developed the novel INTEGRAL protein biomarker panel which measures absolute concentrations of key risk biomarkers. In INTEGRAL-AT Project 2, we aim to move the INTEGRAL panel towards clinical implementation, and also update the panel with risk markers of lung cancer in never smokers. We will first establish a biomarker- informed risk prediction model for smoking-related lung cancer. This will involve using the INTEGRAL panel protein measurements on 1,700 lung cancer cases and 2,900 cohort representatives from the Lung Cancer Cohort Consortium (LC3) to develop and independently validate a model that estimates the 3-year absolute risk of lung cancer. Second, we will evaluate the acceptability of applying such a biomarker-based eligibility criterion to screening participants. This will involve recruiting 1,000 individuals who are ‘nearly eligible’ by US Preventive Services Task Force criteria within the St Elizabeth community based LDCT screening program. Finally, we will carry out a de-novo proteomics discovery analysis on 616 never-smoking cases and 616 matched controls from LC3 to identify novel protein markers for lung cancer in never smokers and update the INTEGRAL panel to include these markers. Our long-term vision is that by using personalized information from a risk-informative and readily implementable multiplex protein-panel, lung cancer screening can be more precisely targeted to people at high risk of lung cancer. This would allow a higher fraction of lung cancer cases to be detected early without subjecting more individuals to LDCT screening, thus optimizing the benefit-to-harm ratio. We also envisage that this panel will be highly useful outside the screening context to work up never-smoking patients with a family history of lung cancer, with occupational exposures, or who present with symptoms.

项目 2：将生物标志物整合到肺癌风险分析中 概括 低剂量计算机断层扫描 (LDCT) 筛查彻底改变了肺癌的早期检测， 癌症死亡的主要原因。然而，目前的资格标准要求参与者有以下历史： 大量吸烟暴露。戒烟多年的人被排除在外，那些从未戒烟的人也被排除在外。 熏制。目前，30-50% 的肺癌发生在不符合筛查条件且患有肺癌的个体中。 吸烟率下降，这些数字将会增加。项目 2 的总体目标是解决这个问题 通过利用基于血液的生物标记物的补充信息来识别个体来应对挑战 尽管不符合筛查资格标准，但肺癌风险较高的人。 我们的项目是最初的 INTEGRAL 计划的自然延伸，我们在该计划中开发了新颖的 INTEGRAL 蛋白质生物标志物组可测量关键风险生物标志物的绝对浓度。在 INTEGRAL-AT项目2，我们的目标是将INTEGRAL小组推向临床实施，并且 更新专家组从不吸烟者的肺癌风险标志物。我们将首先建立一个生物标志物—— 吸烟相关肺癌的知情风险预测模型。这将涉及使用 INTEGRAL 面板 对 1,700 例肺癌病例和 2,900 名肺癌队列代表进行蛋白质测量 Cohort Consortium (LC3) 开发并独立验证估计 3 年绝对值的模​​型 肺癌的风险。其次，我们将评估应用这种基于生物标记的资格的可接受性 筛选参与者的标准。这将涉及招募 1,000 名“几乎符合资格”的人员 美国预防服务工作组基于圣伊丽莎白社区 LDCT 筛查的标准 程序。最后，我们将对 616 个从不吸烟的病例进行从头蛋白质组学发现分析 和 616 个来自 LC3 的匹配对照，以鉴定从不吸烟者和 更新 INTEGRAL 面板以包含这些标记。 我们的长期愿景是通过使用来自风险信息丰富且易于使用的个性化信息 可实施的多重蛋白质组合，可以更精确地针对以下人群进行肺癌筛查 肺癌的高风险。这将使更多的肺癌病例能够及早发现，而无需 让更多的人接受 LDCT 筛查，从而优化利害比。我们还设想 该小组在筛查环境之外非常有用，可以对不吸烟的患者进行检查 有肺癌家族史、有职业暴露或出现症状的人。