From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells
- 1Biomanufacturing Research Institute and Technology Enterprise, North Carolina Central University, Durham, North Carolina, United States of America
- 2Department of Pharmaceutical Sciences, North Carolina Central University, Durham, North Carolina, United States of America
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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BibTeX
@article{salim2026from,
title = {{From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells}},
author = {Esraa A. Salim and Xiaojia Ji and Michael Tarpley and Maria S. Dixon and Weifan Zheng and John E. Scott and Kevin P. Williams},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0339543},
url = {https://doi.org/10.1371/journal.pone.0339543},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells
AU - Esraa A. Salim
AU - Xiaojia Ji
AU - Michael Tarpley
AU - Maria S. Dixon
AU - Weifan Zheng
AU - John E. Scott
AU - Kevin P. Williams
PY - 2026
DA - 2026/09/16
JO - PLOS One
DO - 10.1371/journal.pone.0339543
UR - https://doi.org/10.1371/journal.pone.0339543
AB - 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.
SN - 1932-6203
ER -