179P Developing the AI program for TNBC subtyping

Abstract

Triple-negative breast cancer (TNBC) is a heterogeneous disease with poor prognosis and can be classified into different molecular subtypes. There is a need to develop a computer-aided diagnosis system with artificial intelligence (AI) techniques to increase the accuracy of evaluating suspicious breast lesions and identifying subtypes. A multi-gene analysis panel consisting of 14 genes and 2 reference genes was designed from the TCGA, and cBioportal datasets, which were determined in accordance with in silico analyses in 488 TNBC patients (TCGA:123, cBioportal:365). In the AI part of the study, a convolutional neural network model with 1,838,915 parameters was trained to classify Ultrasound (US) images as “normal, benign, malignant.” In the model, the images of the patients were trained with 1000 epochs. Expression levels of related genes in paraffin-embedded tumors and normal tissues of 38 patients with TNBC were investigated by the RT-PCR method. When the gene expression differences of the tumor and normal tissues of the patients were compared, AR (p=0.013), ER (p=0.025), PGR (p=0.007), FOXA1 (p=0.0154), CXCL11 (p=0.0037), IDO1 (p=0.0005), ADH1B (p=0.018), ADIPOQ (p