The Lung EArly Proteins project: A LEAP toward implementing biomarkers in lung cancer screening

摘要：肺早期蛋白(LEAP)计划 肺癌是全球最常见的癌症死亡原因。低剂量CT筛查可以减少 现吸烟者和曾经吸烟者的肺癌死亡率，但利弊平衡可能是 通过利用新工具优化决策而得到改进。我们的目标是翻译一种新的面板 蛋白质生物标志物，以优化决策(1)启动筛查和(2)活组织检查结节。 我们开发了肺癌风险和病因综合分析U19中的积分小组 项目。我们通过分析收集到的多达3个诊断前样本，确定了要包括在小组中的蛋白质 在前瞻性队列研究中，对700多对肺癌病例对照进行了诊断前数年的研究。这个 蛋白质使基于吸烟的模型的AUC提高了0.15，组织学上的改善 类型。肺早期蛋白(LEAP)项目将进行两项晚期翻译研究，以评估 在两个关键背景下实施这一小组的可能性。 目标1将确定随着时间的推移重复测量蛋白质标记物是否可以更好地预测 肺癌的发展比单项测量要好。肺癌的重复测量模型将是 使用来自546名肺癌患者和546名对照的血液样本(每名参与者最多5份)开发 前列腺癌、肺癌和卵巢癌筛查试验，并使用来自 来自胡萝卜素和视黄醇疗效试验的539例病例和539名对照。我们假设AUC 重复测量的模型将高于单次测量的模型。 目标2将确定蛋白质标记物是否可以在筛查发现的高危人群中识别癌症 肺结节。将利用血液建立基于生物标记物的结节恶性预测模型 匹兹堡肺筛查研究中685名参与者(131例癌症)的样本，然后进行验证 在意大利多中心肺检测(BIOMILD)试验和ST 伊丽莎白肺癌筛查计划。我们假设生物标记物模型的AUC将是 高于已建立的、经过验证的结节恶性预测模型(布罗克模型)。 LEAP项目将清楚地定义整体小组在临床上有用的背景。我们的 任务是通过优化经过验证和有效的筛查干预措施来降低肺癌死亡率， 使用新的工具将其好处扩大到所有高危个人，并减少筛查危害。

Summary: The Lung EArly Proteins (LEAP) Project Lung cancer is the most common cause of cancer death worldwide. Screening by low-dose CT can reduce lung cancer mortality among current and former smokers, but the balance of benefits and harms could be improved by leveraging novel tools to optimize decision making. Our objective is to translate a novel panel of protein biomarkers to optimize the decision (1) to initiate screening and (2) to biopsy a nodule. We developed the INTEGRAL panel within the Integrative Analysis of Lung Cancer Risk and Etiology U19 project. We identified proteins to include on the panel by analyzing pre-diagnostic samples collected up to 3 years before diagnosis among over 700 lung cancer case-control pairs in prospective cohort studies. The proteins improved the AUC of a smoking-based model by 0.15, with improvements across histological types. The Lung EArly Proteins (LEAP) project will carry out two late-stage translational studies to evaluate the potential to implement this panel in two key contexts. Aim 1 will determine whether repeated measurements of protein markers over time can better predict development of lung cancer than a single measurement. A repeat-measures model for lung cancer will be developed using blood samples (up to 5 per participant) from 546 lung cancer cases and 546 controls in the Prostate, Lung, and Ovarian Cancer Screening Trial, and independently validated using samples from 539 cases and 539 controls from the Carotene and Retinol Efficacy Trial. We hypothesize that the AUC of the model with repeat measurements will be higher than for a model with a single measurement. Aim 2 will determine whether the protein markers can identify cancers among high-risk screen-detected lung nodules. A biomarker-based prediction model for nodule malignancy will be developed using blood samples from 685 participants (131 cancers) in the Pittsburgh Lung Screening Study, and then validated among 830 participants (213 cancers) in the Multicentric Italian Lung Detection (bioMILD) trial and the St Elizabeth Lung Cancer Screening program. We hypothesize that the AUC of the biomarker model will be higher than for an established, validated nodule malignancy prediction model (the Brock model). The LEAP project will clearly define the contexts in which the INTEGRAL panel is clinical useful. Our mission is to reduce lung cancer mortality by optimizing a proven and effective screening intervention, using novel tools to extend its benefits to all high-risk individuals and reduce screening harms.