Circulating microRNA-based screening tool for breast cancer

Abstract

// Pierre Frères 1, 2, * , Stéphane Wenric 2, * , Meriem Boukerroucha 2 , Corinne Fasquelle 2 , Jérôme Thiry 2 , Nicolas Bovy 3 , Ingrid Struman 3 , Pierre Geurts 4 , Joëlle Collignon 1 , Hélène Schroeder 1 , Frédéric Kridelka 5 , Eric Lifrange 6 , Véronique Jossa 7 , Vincent Bours 2, * , Claire Josse 2, * , Guy Jerusalem 1 1 University Hospital (CHU), Department of Medical Oncology, Liège, Belgium 2 University of Liège, GIGA-Research, Laboratory of Human Genetics, Liège, Belgium 3 University of Liège, GIGA-Research, Laboratory of Molecular Angiogenesis, Liège, Belgium 4 University of Liège, GIGA-Research, Department of EE and CS, Liège, Belgium 5 University Hospital (CHU), Department of Gynecology, Liège, Belgium 6 University Hospital (CHU), Department of Senology, Liège, Belgium 7 Clinique Saint-Vincent (CHC), Department of Pathology, Liège, Belgium * These authors contributed equally to this work Correspondence to: Guy Jerusalem, e-mail: g.jerusalem@chu.ulg.ac.be Keywords: breast cancer, circulating microRNAs, biomarkers, minimally invasive screening Received: June 24, 2015      Accepted: December 05, 2015      Published: December 29, 2015 ABSTRACT Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis. A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors. A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group. Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer.