AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

Abstract

AbstractCancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties, and represent a potentially effective therapeutic target towards long-term remission by means of differentiation induction. By leveraging an Artificial Intelligence (AI) approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the LINCS database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of 5 out of 6 tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron, and A-443654 offer a potential avenue for targeted therapies against breast CSCs.