Pezuk Julia Alejandra, Miller Thiago Luiz Araujo, Bevilacqua José Luiz Barbosa, de Barros Alfredo Carlos Simões Dornellas, de Andrade Felipe Eduardo Martins, E Macedo Luiza Freire de Andrade, Aguilar Vera, Claro Amanda Natasha Menardo, Camargo Anamaria Aranha, Galante Pedro Alexandre Favoretto, Reis Luiz F L
Hospital Sírio-Libanês, São Paulo, Brazil.
Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, Brazil.
Oncotarget. 2017 Sep 11;8(48):83940-83948. doi: 10.18632/oncotarget.20806. eCollection 2017 Oct 13.
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.
乳腺钼靶检查中BI-RADS分类为4类表示乳腺病变可疑，需要进行核心活检以明确诊断，且有大约三分之一的恶性可能性。人血浆中含有许多循环微RNA，其循环水平的变化与包括癌症在内的多种病理状态相关。在此，我们提出一种新方法来识别乳腺钼靶检查为BI-RADS 4类的女性中的恶性乳腺病变。首先，我们使用miRNome芯片和定量逆转录聚合酶链反应（qRT-PCR）来确定与对照组（BI-RADS 1或2类）相比，乳腺肿瘤女性（BI-RADS 5或6类）血样中差异表达的循环微RNA。接下来，我们使用qRT-PCR对乳腺钼靶检查为BI-RADS 4类的患者中这种循环微RNA的水平进行定量。最后，我们开发了一种机器学习方法（人工神经网络-ANN），该方法接收循环微RNA水平，并自动将BI-RADS 4类乳腺病变分类为恶性或良性。我们确定了一组最少的三种循环微RNA（miR-15a、miR-101和miR-144），其在乳腺癌患者中的水平发生了改变。对60例经活检证实为BI-RADS 4类病变的患者的血浆中这三种微RNA进行了定量。最后，我们构建了一个非常有效的人工神经网络，它能够以大约92.5%的准确率、95%的特异性和88%的敏感性将BI-RADS 4类病变正确分类为恶性或良性。我们认为，我们使用循环微RNA和机器学习方法对BI-RADS 4类乳腺病变进行分类的策略，对于患有BI-RADS 4类病变的女性来说，是一种非侵入性、无压力且有价值的辅助核心活检的方法。
