Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston, United States; Harvard Medical School, Boston, United States; Surgical ICU Translational Research Center, Brigham and Women’s Hospital, Boston, United States
Harvard Medical School, Boston, United States; Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
Stephen J Fiascone
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston, United States; Harvard Medical School, Boston, United States
Allison F Vitonis
Harvard Medical School, Boston, United States; Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, United States; Department of Epidemiology, Harvard School of Public Health, Boston, United States
Ross S Berkowitz
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston, United States; Harvard Medical School, Boston, United States
Gyorgy Frendl
Harvard Medical School, Boston, United States; Surgical ICU Translational Research Center, Brigham and Women’s Hospital, Boston, United States; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, United States
Panagiotis Konstantinopoulos
Harvard Medical School, Boston, United States; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States
Christopher P Crum
Harvard Medical School, Boston, United States; Division of Women’s and Perinatal Pathology, Department of Pathology, Brigham and Women’s Hospital, Boston, United States
Magdalena Kedzierska
Department of Clinical Oncology, Medical University of Lodz, Lodz, Poland
Daniel W Cramer
Harvard Medical School, Boston, United States; Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, United States; Department of Epidemiology, Harvard School of Public Health, Boston, United States
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.
