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

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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Journal
PLoS ONE
Published
2026-09-16
DOI
https://doi.org/10.1371/journal.pone.0339543
Primary Topic
Bioinformatics and Genomic Networks
Type
article
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article

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

Weifan Zheng, Xiaojia Ji, Maria S. Dixon, Kevin P. Williams et al.
PLoS ONE
Bioinformatics and Genomic Networks
article

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

Weifan Zheng, Xiaojia Ji, Maria S. Dixon, Kevin P. Williams, J. E. Scott, Michael Tarpley, Esraa A. Salim
article en

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.

PLoS ONEVol. 21(9)
North Carolina Central University (US)
Good health and well-being
Openalex Percentile: Top 18%
Bioinformatics and Genomic Networks
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