ScholarWorks@성균관대학교

초록

Resistance to anti-cancer drugs remains a major challenge in chemotherapy. Combination therapy using sensitizers has emerged as a promising strategy to restore drug sensitivity in resistant cancer cells. However, experimental screening of sensitizers is laborious and costly, highlighting the need for computational methods that enable systematic and efficient prediction. We developed SOCAR, a network-based computational framework that predicts sensitizer drugs by integrating transcriptome profiles with molecular interaction networks. SOCAR identifies resistance-associated genes and network modules, and quantifies each drug's potential to reverse these resistance mechanisms. Applied to 4,009 drugs, SOCAR accurately predicted candidate sensitizers for tamoxifen-resistant breast cancer (AUROC = 0.90). In vitro assays validated that all twelve top-ranked candidates significantly reduced cell viability (p < 0.005) when co-administered with tamoxifen. Furthermore, protein activity analyses showed that resistance-module proteins were markedly altered after acquiring resistance but were restored to normal levels following combined treatment (p < 0.05). Collectively, SOCAR provides a systems-level framework for discovering novel sensitizers and elucidating mechanisms of resistance reversal.

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