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<0.0001) and CHAD (p=0.0017) genes were found to have a statistically significant difference in expression compared to normal tissue. While no significant expression difference was detected in FOXC1 (p=0.3914), CXCL13 (p=0.2138), LAMP3 (p=0.2576), FGFR2 (p=0.077) and DHRS2 (p=0.715) genes. As a result, it was found that 68.5% of the patients showed a single subtype-specific expression profile (10 BLIA, 3 BLIS, 1 MES, and 12 LAR subtypes), and 31.5% of the patients showed an expression profile of more than one subtype. A random forest model was chosen among the trained models, and in the first stage, 81% accuracy was obtained in the subtyping of TNBC and 83% in the classification of US images. It sheds light on the heterogeneous structure and the role of molecular subtyping in the tumorigenesis of TNBC patients and may contribute to routine clinical practice and the regulation of targeted therapy protocols.