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.
|Effective start/end date||12/8/14 → 11/30/17|
- National Cancer Institute: $181,613.00
- National Cancer Institute: $107,593.00
- National Cancer Institute: $110,342.00
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