Circulating microRNA-based screening tool for breast cancer
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Pierre Frères1,2,*, Stéphane Wenric2,*, Meriem Boukerroucha2, Corinne Fasquelle2, Jérôme Thiry2, Nicolas Bovy3, Ingrid Struman3, Pierre Geurts4, Joëlle Collignon1, Hélène Schroeder1, Frédéric Kridelka5, Eric Lifrange6, Véronique Jossa7, Vincent Bours2,*, Claire Josse2,*, Guy Jerusalem1
1University Hospital (CHU), Department of Medical Oncology, Liège, Belgium
2University of Liège, GIGA-Research, Laboratory of Human Genetics, Liège, Belgium
3University of Liège, GIGA-Research, Laboratory of Molecular Angiogenesis, Liège, Belgium
4University of Liège, GIGA-Research, Department of EE and CS, Liège, Belgium
5University Hospital (CHU), Department of Gynecology, Liège, Belgium
6University Hospital (CHU), Department of Senology, Liège, Belgium
7Clinique Saint-Vincent (CHC), Department of Pathology, Liège, Belgium
*These authors contributed equally to this work
Guy Jerusalem, e-mail: firstname.lastname@example.org
Keywords: breast cancer, circulating microRNAs, biomarkers, minimally invasive screening
Received: June 24, 2015 Accepted: December 05, 2015 Published: December 29, 2015
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.
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