SOCAR: Network-Based Computational Framework to Overcome Acquired Tamoxifen Resistance of MCF7 Cells
Abstract:
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.
Insights
This study introduces SOCAR, a computational tool to predict sensitizer drugs for overcoming anti-cancer drug resistance. SOCAR successfully identified effective sensitizers for tamoxifen-resistant breast cancer, validated through in vitro experiments.
Area of Science:
- Computational biology
- Pharmacology
- Oncology
Background:
- Drug resistance is a significant hurdle in cancer chemotherapy.
- Combination therapy with sensitizers offers a promising approach to overcome resistance.
- Experimental screening for sensitizers is resource-intensive, necessitating computational solutions.
Purpose of the Study:
- To develop a computational framework, SOCAR, for predicting sensitizer drugs.
- To identify novel sensitizers for drug-resistant cancers.
- To elucidate mechanisms of resistance reversal.
Main Methods:
- Integrated transcriptome profiles with molecular interaction networks.
- Developed a network-based computational framework (SOCAR).
- Quantified drug potential to reverse resistance mechanisms.
Main Results:
- SOCAR accurately predicted sensitizers for tamoxifen-resistant breast cancer (AUROC = 0.90).
- In vitro validation confirmed significant reduction in cell viability for top candidates.
- Protein activity analysis demonstrated restoration of resistance-module proteins by combined treatment.
Conclusions:
- SOCAR provides an efficient systems-level framework for discovering novel sensitizer drugs.
- The developed method aids in understanding and reversing cancer drug resistance mechanisms.
- This approach can accelerate the identification of combination therapies for resistant cancers.
