Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
Abstract
Recent 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 31, 2017
- Source ID
- 10.7554/elife.28932
Entities
People
- Allison F. Vitonis
- Christopher P Crum
- Daniel W. Cramer
- Dipanjan Chowdhury
- Gyorgy Frendl
- Kevin M. Elias
- Konrad Stawiski
- Magdalena Kedzierska
- Panagiotis A Konstantinopoulos
- Ross S. Berkowitz
- Stephen J Fiascone
- Wojciech Fendler
Organizations
- Brigham and Women's Hospital
- Dana–Farber Cancer Institute
- Foundation for Polish Science
- Harvard Medical School
- Harvard T.H. Chan School of Public Health
- Honorable Tina Brozman Foundation
- Ian Potter Foundation
- Medical University of Łódź
- National Institutes of Health
- United States Department of Defense