Measuring plasma levels of three microRNAs can improve the accuracy for identification of malignant breast lesions in women with BI-RADS 4 mammography

Author(s):Show less -

Pezuk, Julia Alejandra ^[1] ; Araujo Miller, Thiago Luiz ^{[2, 1]} ; Barbosa Bevilacqua, Jose Luiz ^[1] ; Simoes Dornellas de Barros, Alfredo Carlos ^[1] ; Martins de Andrade, Felipe Eduardo ^[1] ; de Andrade e Macedo, Luiza Freire ^[1] ; Aguilar, Vera ^[1] ; Menardo Claro, Amanda Natasha ^{[1, 3]} ; Camargo, Anamaria Aranha ^[1] ; Favoretto Galante, Pedro Alexandre ^[1] ; Reis, Luiz F. L. ^[1]

Affiliation

^[1] Hosp Sirio Libanes, Sao Paulo - Brazil

^[2] Univ Sao Paulo, Inst Quim, Dept Bioquim, Sao Paulo - Brazil

^[3] SulAmerica, Sao Paulo - Brazil

• Document type: Journal article
• Source: ONCOTARGET; v. 8, n. 48, p. 83940-83948, OCT 13 2017.
• Web of Science Citations: 5

Abstract

A BI-RADS category of 4 from a mammogram indicates suspicious breast lesions, which require core biopsies for diagnosis and have an approximately one third chance of being malignant. Human plasma contains many circulating microRNAs, and variations in their circulating levels have been associated with pathologies, including cancer. Here, we present a novel methodology to identify malignant breast lesions in women with BI-RADS 4 mammography. First, we used the miRNome array and qRT-PCR to define circulating microRNAs that were differentially represented in blood samples from women with breast tumor (BI-RADS 5 or 6) in comparison to controls (BI-RADS 1 or 2). Next, we used qRT-PCR to quantify the level of this circulating microRNAs in patients with mammograms presenting with BI-RADS category 4. Finally, we developed a machine learning method (Artificial Neural Network-ANN) that receives circulating microRNA levels and automatically classifies BI-RADS 4 breast lesions as malignant or benign. We identified a minimum set of three circulating miRNAs (miR-15a, miR-101 and miR-144) with altered levels in patients with breast cancer. These three miRNAs were quantified in plasma from 60 patients presenting biopsy-proven BI-RADS 4 lesions. Finally, we constructed a very efficient ANN that could correctly classify BI-RADS 4 lesions as malignant or benign with approximately 92.5% accuracy, 95% specificity and 88% sensibility. We believe that our strategy of using circulating microRNA and a machine learning method to classify BI-RADS 4 breast lesions is a non-invasive, non-stressful and valuable complementary approach to core biopsy in women with BI-RADS 4 lesions. (AU)

• FAPESP's process: 13/03107-0 - Correlation between the expression profile of microRNAs found in plasma and the presence or absence of malignant breast lesions in patients with mammography BI-RADS 4
• Grantee: Luiz Fernando Lima Reis
• Support type: Regular Research Grants
• FAPESP's process: 14/14025-8 - Identification of a microRNAs expression profile in plasma of patients with BI-RADS 4 mammography radiological images capable of discriminate between presence or absence of malignant breast lesions
• Grantee: Julia Alejandra Pezuk
• Support type: Scholarships in Brazil - Post-Doctorate