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，尽管明显有效，但众所周知，目前设计的效率低下。在R21中 应用中，提出呼出的microRNA携带的签名将有助于识别那些处于肺部恶性肿瘤最高风险的人。在以前的工作中，我们调查了非小细胞肺癌的microRNAome，与邻近的肺组织相比。最近，我们在95名个体中生成了呼出microRNA的14-miR组的定性试验数据，其中在蒸馏的4-microRNA测试组中区分病例与对照的准确度为76%（ROC AUC =0.76），并且当与简单的临床因素结合时，提高到84%（AUC=0.84），这是风险因素指数的可接受性能水平。在此期间，我们最近进一步发展了这项技术，使其更加强大和量化。 我们的假设是：呼出的microRNA特征将区分那些患有肺癌的人和那些没有的人。在一项扩大的基于爱因斯坦的病例对照研究中，我们现在希望通过以下方式增强microRNA工具的判别能力：（1）将microRNA组增加到约35-40个microRNA，部分来自癌前病变的显微解剖特征，并且还使呼出特征定量;（2）将病例对照研究扩大三倍，以允许协变量分析，通过延长我们的入组后立即随访的时间窗，改善病例和对照表型确定，沿着协变量调整;和（3）使用在前瞻性肺癌CT筛查设置中积累的嵌套病例对照设计，初步测试最佳呼出microRNA生物标志物。这三个目标将允许呼出的microRNA方法在病例对照研究中与肺癌表型的关联以及在接受CT筛查的假定高风险个体的巢式病例对照研究中的验证进行更稳健、更严格的测试。如果在这些试点中有利，我们将使用其他更广泛的资助机制将该技术嵌入前瞻性CT筛查队列。利用早期肺癌检测和预防策略的意义是巨大的。

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.