Systems Drug Discovery and Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Carcinogenic Mechanism and Deep Learning Method

Abstract

In this study, in terms of systems biology approaches and deep learning method, we proposed a series of strategies for systems medicine design toward TNBC and non-TNBC. For systems biology approach, we constructed candidate genome-wide genetic and epigenetic network (GWGEN) by big data mining technique and identified real GWGEN of TNBC and non-TNBC by corresponding microarray data via system identification and model order selection methods. Core GWGEN of TNBC and non-TNBC were constructed from their corresponding GWGENs and then denoted by KEGG pathways to obtain core signaling pathways of TNBC and non-TNBC, which were compared to find essential carcinogenic biomarkers to bring about multiple cellular dysfunctions including cell proliferation, autophagy, immune response, cell differentiation, apoptosis, metastasis, angiogenesis, and epithelial-mesenchymal transition (EMT). With the help of the drug-target interaction (DTI) model based on deep neural network trained through feature vectors of drug-target databases, we could select candidate drugs for these drug targets. These candidate drugs were still filtered for the toxicity by LD50 and for regulation ability by connectively Map (CMap) as potential drugs, and then these potential drugs are combined as potential multiple-molecule drugs, i.e., resveratrol, sirolimus, prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, verapamil for non-TNBC.