Elias, KevinFendler, WojciechStawiski, KonradFiascone, StephenVitonis, Allison FBerkowitz, RossFrendl, GyorgyKonstantinopoulos, PanagiotisCrum, ChristopherKedzierska, MagdalenaCramer, DanielChowdhury, Dipanjan2017-12-062017Elias, K. M., W. Fendler, K. Stawiski, S. J. Fiascone, A. F. Vitonis, R. S. Berkowitz, G. Frendl, et al. 2017. “Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer.” eLife 6 (1): e28932. doi:10.7554/eLife.28932. http://dx.doi.org/10.7554/eLife.28932.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34493259Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81–0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3–97.6%) and negative predictive value of 78.6% (95% CI: 64.2–88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.en-USmiRNAovarian cancerserumnext generation sequencingneural networkmachine learningHumanJournal Article2017-12-0610.7554/eLife.28932