Integrating Biomarkers into Lung Cancer Risk Profiling

项目2：将生物标志物整合到肺癌风险分析中 摘要 低剂量计算机断层扫描(LDCT)的筛查已经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具有以下历史 大量吸烟。戒烟多年的人被排除在外，那些从不戒烟的人也是如此 抽了烟。目前，30%-50%的肺癌发生在没有资格进行筛查的个人中，并且 吸烟率下降，这些数字将会增加。项目2的总体目标就是解决这个问题 通过利用基于血液的生物标记物的补充信息来识别个人面临的挑战 尽管不符合筛查资格标准，但肺癌风险很高。 我们的项目是最初的积分计划的自然延伸，我们在那里开发了小说 测量关键风险生物标志物绝对浓度的整体蛋白质生物标志物面板。在……里面 积分-在项目2，我们的目标是将积分小组推向临床实施，并且 用从不吸烟的人患肺癌的风险标记物更新小组。我们将首先建立一个生物标记物- 吸烟相关肺癌的知情风险预测模型。这将涉及使用整体式面板 1700例肺癌患者和2900例肺癌队列代表的蛋白质测定 队列联盟(LC3)开发并独立验证一个模型，该模型估计3年绝对 患肺癌的风险。其次，我们将评估应用这种基于生物标记物的资格的可接受性 筛选参赛者的标准。这将涉及到招募1,000人，这些人在 美国预防服务工作组圣伊丽莎白社区内基于LDCT筛查的标准 程序。最后，我们将对616例不吸烟病例进行从头蛋白质组学发现分析。 和616名来自LC3的对照组进行匹配，以确定从不吸烟和不吸烟的肺癌的新蛋白标记物 更新整体面板以包括这些标记。 我们的长期愿景是通过使用来自风险的个性化信息-信息量大且容易 可实施的多重蛋白质组，肺癌筛查可以更精确地针对以下人群 患肺癌的风险很高。这将使更高比例的肺癌病例在早期被发现而不是 让更多的人接受LDCT筛查，从而优化益处/危害比。我们还设想 在筛查环境之外，这个小组将非常有用，以帮助从不吸烟的有 有肺癌家族史、有职业接触史或有症状者。

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.