ArticlePloS one2026
From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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- Update ofFrom2025
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7 authors.
Funding
Abstract
Drug repurposing can accelerate the identification of novel therapeutic candidates for rare cancers such as inflammatory breast cancer (IBC), an aggressive type with limited therapeutic options. Here, we report an experimental validation study of compounds previously identified through two computational approaches: Literature Wide Association Studies (LWAS) and Gene Reversal Rate (GRR). Candidate compounds were tested 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 SUM149 models, we identified novel synergistic compound pairs targeting crosstalk between IGF-1R, EGFR and PI3K/Akt/mTOR pathways, with high synergy scores across multiple reference models. Using these combinations, western blott analysis revealed significant suppression in the phosphorylation of key signaling proteins and downstream effectors, while wound healing assays showed reduced cell migration with some combination treatments, 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) and identified several clinically relevant genes whose expression was significantly altered. Together, these findings validate our computational predictions and identify candidate combination strategies that may help address therapeutic resistance in IBC. This integrated computational-experimental approach establishes a pipeline for systematic drug repurposing and highlights novel therapeutic combinations for further investigation.
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