EFIRM-Liquid Biopsy (eLB): Ultrasensitive ctDNA and miRNA Detection for Early Assessment of Lung Cancer

项目摘要/摘要： 计算机断层扫描(CT)是目前检测临床前肺癌最敏感的检查，它 通常表现为不明肺结节(IPN)。CT早期发现的成功被封存了 通过全国肺部筛查试验，肺癌死亡率比胸部降低了20% 放射摄影术。然而，在NLST中，筛查阳性率为24%，96%的阳性筛查是假的 阳性。尽管这些百分比已经被新的筛查指南部分削弱，但这是不言而喻的。 LDCT筛查对检测肺癌高度敏感，但不具特异性，也不是没有危害。 此外，更多的人，包括吸烟者和不吸烟者，被诊断为筛查外的依据是 发现一个不确定的结节。迫切需要一些方法来更好地区分个体 良性疾病，应该接受侵入性诊断测试。液体活组织检查已经找到了进入 癌症词典是指血液中的肿瘤生物标记物或其他容易获得的生物标本 反映癌症的存在和生物学特征。这一竞争前的合作将学术和 整个癌症领域的行业合作伙伴，以推动液体活检技术的早期检测，这些技术 可作为临床工具使用。我们的合作伙伴关系包括肺癌生物学家、临床医生和 生物统计学家与行业工程师共同研究一种新的液体活检技术--EFIRM-液体活检 (ELB)已经显示出检测EGFR突变患者循环DNA突变的高灵敏度 肺癌。为了解决肺癌的早期检测问题，我们的学术研究人员将开发和验证 对肺癌中常见的10种DNA突变进行独立检测，并引入6-生物标志物 补充和加强肺癌血液分子信号的miRNA小组。我们的行业 合作伙伴将把这些单独的检测转换为单一的阵列，同时保持高灵敏度和特异性。 在我们的临床医生科学家的帮助下，这一集成平台将在接受筛查--或顺便说一句--的患者中得到验证 在最具诊断挑战性的大小范围内检测到肺结节。我们的整体研究 建议是，使用ELB-肺癌检测小组(ELB-LCDP)的血液生物标记物将告知 准确可靠地将结节分类为良性或恶性。除了贡献分子和 技术专长、标准操作程序和带注释的临床材料，我们将比较ELB- 与其他肺癌相关液体活检平台将在该NCI财团和相关NCI财团开发 确定最高性能的生物标记物和平台，应单独或 与包括作为患者管理一部分获得的临床和成像变量的模型相结合。 使用这种正交的多参数询问方法，我们假设ELB-LCDP可以实现 接收器工作特性曲线(AUC)下的分类性能区域(AUC；0.85) 临床实习的时间。

Project Summary/Abstract: Computed tomography (CT) is currently the most sensitive test for detecting preclinical lung cancer, which typically presents as an indeterminate pulmonary nodule (IPN). The success of CT for early detection was sealed by the National Lung Screening Trial, in which lung cancer mortality was reduced by 20% relative to chest radiography. However, in the NLST, the screen positivity rate was 24% and 96% of positive screens were false positive. Although these percentages have been partially attenuated by new screening guidelines, it is axiomatic that LDCT screening is highly sensitive for detecting lung cancer, but non-specific, and not without harms. Furthermore, more individuals, both smokers and non-smokers, are diagnosed outside of screening based on the finding of an indeterminate nodule. Methods are urgently needed to better differentiate between individuals with benign disease and those should undergo invasive diagnostic testing. Liquid biopsy has found its way into the cancer lexicon as a reference to tumor biomarkers within blood or other readily accessible biospecimens that reflect the presence and biology of cancer. This precompetitive collaboration brings together academic and industrial partners across the cancer spectrum to advance liquid biopsy technologies for early detection that are viable as clinical tools. Our partnership interleaves the expertise of lung cancer biologists, clinicians, and biostatisticians with industry engineers, converging on a novel liquid biopsy technology “EFIRM-Liquid biopsy (eLB)” that has already shown high sensitivity detecting circulating DNA mutations in patients with EGFR-mutant lung cancers. To address early lung cancer detection, our academic researchers will develop and validate independent assays for 10 DNA mutations commonly observed in lung cancer as well as introduce a 6-biomarker panel of miRNA to complement and strengthen the blood-based molecular signal of lung cancer. Our industry partners will convert these individual assays to a single array while preserving high sensitivity and specificity. With our clinician scientists, this integrated platform will be validated in patients with screen- or incidentally- detected lung nodules in the size ranges that are most diagnostically challenging. Our overall research proposition is that blood-based biomarkers using the eLB-Lung Cancer Detection Panel (eLB-LCDP) will inform the accurate and robust classification of nodules as benign or malignant. Beyond contributing molecular and technological expertise, standard operating procedures, and annotated clinical materials, we will compare eLB- LCDP with other lung cancer-associated liquid biopsy platforms to be developed in this and related NCI consortia to determine the highest performing biomarkers and platforms that should move to clinical translation, alone or in combination with models that include clinical and imaging variables acquired as part of patient management. Using this orthogonal, multiparametric interrogation approach, we hypothesize that the eLB-LCDP can achieve a classification performance area under the receiver operating characteristic curve (AUC) > 0.85 in near real- time clinical practice.