From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells

Abstract

Drug repurposing offers a promising approach for identifying novel treatments, especially for rare cancers like inflammatory breast cancer (IBC), an aggressive type with limited therapeutic options. Here, we present a comprehensive validation and verification study of compounds identified through two computational approaches: Literature Wide Association Studies (LWAS) and Gene Reversal Rate (GRR), using orthogonal cell viability assays in 2D models across IBC and non-IBC cell lines. In the SUM149 IBC cell line, repurposed compounds predicted from LWAS achieved a 70% success rate, with several showing nanomolar potency, while those predicted from GRR showed a 38% success rate. Through systematic combination screening in both 2D and 3D-spheroid models, we identified novel synergistic compound pairs targeting crosstalk between IGF1-R, EGFR and PI3K/Akt/mTOR pathways, with high synergy scores across multiple reference models. Using these combinations, western blotting analysis revealed significant suppression in the phosphorylation of key signaling proteins and downstream effectors, while wound healing assays demonstrated reduced cell migration for some combinations, suggesting effective pathway inhibition. To further validate these findings at the transcriptional level, RNA-Seq analysis in SUM149 cells confirmed that the GRR drug combinations significantly reversed the IBC gene expression signature (IBC-GES). These findings not only validated our computational predictions but also identified promising combination strategies that could potentially overcome drug resistance in IBC. Our integrated computational-experimental approach establishes a framework for systematic drug repurposing and highlights novel therapeutic combinations warranting further investigation.