Exhaled microRNAs leveraged for lung cancer risk assessment

描述(由申请人提供)：通过低剂量CT扫描进行早期肺癌筛查，虽然表面上看是有效的，但目前的设计是出了名的低效。在此R21中 应用方面，有人建议呼出的microRNA携带一个签名，这将有助于识别那些肺部恶性肿瘤风险最高的人。在之前的工作中，我们调查了非小细胞肺癌的MicroRNAome，并与邻近的肺组织进行了比较。最近，我们为95名个体的14-miR呼气microRNA小组生成了定性的试点数据，其中在蒸馏的4-microRNA测试小组中，区分病例和对照的准确率为76%(ROC-AUC=0.76)，再加上简单的临床因素，准确率提高到84%(AUC=0.84)，这是风险因素指数的可接受表现水平。在此期间，我们最近进一步发展了这项技术，使其更加稳健和量化。我们的假设是：呼出的microRNA信号将区分那些患有肺癌的人和那些没有患肺癌的人。在一项扩大的以爱因斯坦为基础的病例对照研究中，我们现在希望通过以下方式来增强microRNA工具的判别能力：(1)将microRNA面板增加到~35-40个microRNA，部分来自癌前病变的显微解剖特征，并使呼出的特征成为定量的；(2)将病例对照研究扩大三倍，以允许协变量分析，改善病例和对照表型确定，通过延长我们的登记后立即跟踪的窗口，以及协变量调整；以及(3)试点测试最好的呼出microRNA生物标记物使用嵌套的病例对照设计，这是在预期的肺癌CT筛查环境中积累的。这三个目标将使呼出的microRNA方法在病例对照研究中与肺癌表型的相关性以及在接受CT筛查的假定高危个体的嵌套病例对照研究中得到更有力、更严格的测试。如果对这些试验有利，我们将使用其他更广泛的资助机制，将这项技术嵌入到未来的CT筛查队列中。这对利用肺癌早期检测和预防策略的影响是巨大的。

DESCRIPTION (provided by applicant): Early lung cancer screening by low-dose CT scanning, though apparently effective, is notoriously inefficient as currently devised. In this R21 application, it is proposed that exhaled microRNAs carry a signature that will be useful in identifying those at highest risk for lung malignancy. In previous work, we surveyed the microRNAome of non-small cell lung cancers, compared to adjacent lung tissue. Recently, we generated qualitative pilot data for a 14-miR panel of exhaled microRNAs in 95 individuals, where discrimination of cases from controls in the distilled 4-microRNA test panel entailed an accuracy of 76% (ROC-AUC=0.76), and when coupled with simple clinical factors, improved to 84% (AUC=0.84), an acceptable performance level for risk factor indices. In the interim, we have recently evolved the technology further to be more robust and quantitative. Our hypothesis is: An exhaled microRNA signature will distinguish those individuals that harbor a lung cancer from those who do not. In a broadened Einstein-based case-control study, we now wish to augment discriminant capacity of the microRNA tools by: (1) increasing the microRNA panel to ~35-40 microRNAs, in part from premalignant lesion micro dissected signatures, and also render the exhaled signature to be quantitative; (2) expand the case- control study three-fold to allow covariate analysis, improve case and control phenotype ascertainment, by lengthening our window of immediate post-enrollment follow-up, along with covariate adjustment; and (3) pilot test the best exhaled microRNA biomarkers using a nested case-control design that is accruing in a prospective lung cancer CT screening setting. These three aims will allow a more robust, rigorous testing of the exhaled microRNA approach for both association with the lung cancer phenotype in a case-control study, and validation in a nested case-control study of putative high risk individuals undergoing CT screening. If favorable in these pilots, we would then embed the technology in prospective CT screening cohorts, using other more expansive funding mechanisms. The implications for leveraging early lung cancer detection and prevention strategies are substantial.