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

项目概要/摘要： 计算机断层扫描（CT）是目前检测临床前肺癌最敏感的测试， 通常表现为不确定的肺结节（IPN）。CT早期发现的成功是密封的 通过国家肺筛查试验，其中肺癌死亡率相对于胸部肿瘤死亡率降低了20%。 放射线照相术。然而，在NLST中，筛查阳性率为24%，96%的阳性筛查是假的 肯定的：第虽然这些百分比已被新的筛查指南部分削弱，但这是不言自明的。 LDCT筛查对于检测肺癌是高度敏感的，但非特异性的，并且并非没有危害。 此外，更多的人，包括吸烟者和非吸烟者，在筛查之外被诊断出， 一个不确定的结节的发现。迫切需要更好地区分个体的方法 患有良性疾病的患者，应接受侵入性诊断测试。液体活检已经进入了 癌症词典作为血液或其他容易获得的生物样本中的肿瘤生物标志物的参考， 反映癌症的存在和生物学。这种竞争前的合作汇集了学术和 整个癌症谱的工业合作伙伴，以推进液体活检技术的早期检测， 作为临床工具。我们的合作伙伴关系融合了肺癌生物学家、临床医生和 生物统计学家与行业工程师，共同研究一种新型液体活检技术“EFIRM-液体活检 (eLB)“已经显示出在EGFR突变患者中检测循环DNA突变的高灵敏度， 肺癌为了解决早期肺癌检测，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种生物标志物 一组miRNA，以补充和加强基于血液的肺癌分子信号。我们的行业 合作伙伴将把这些单独的检测转换成一个单一的阵列，同时保持高灵敏度和特异性。 与我们的临床科学家一起，这个综合平台将在筛查患者中得到验证-或者顺便说一下- 在诊断上最具挑战性的尺寸范围内检测到肺结节。我们的整体研究 使用eLB-肺癌检测面板（eLB-LCDP）的血液生物标志物将告知 准确和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，标准操作程序和注释的临床材料，我们将比较eLB- LCDP与其他肺癌相关液体活检平台将在该联盟和相关NCI联盟中开发 以确定最高性能的生物标志物和平台，这些生物标志物和平台应该单独或 与包括作为患者管理的一部分而获取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 在近真实的中，受试者工作特征曲线（AUC）下的分类性能面积> 0.85- 时间临床实践

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.