Revolutionary Metabolic Simulations Identify Gene Targets to Overcome Drug Resistance in Breast Cancer

Researchers from the Korea Advanced Institute of Science and Technology (KAIST) have developed an innovative computational approach to tackle one of the major hurdles in cancer therapy: drug resistance. Using advanced metabolic network models, the team simulated the metabolism of drug-resistant breast cancer cells, leading to the identification of specific gene targets that can restore the cells' sensitivity to treatment. The study focused on cell lines resistant to common chemotherapy agents such as doxorubicin and paclitaxel. By integrating proteomic data, they built cell-specific metabolic models and performed gene knockout simulations, revealing key genes like GOT1, GPI, and SLC1A5 that, when suppressed, re-sensitize resistant cancer cells to drugs. Experimental validation confirmed that inhibiting these genes successfully restored drug responsiveness, not only in the original resistant cell lines but also across other breast cancer variants. This approach underscores the potential of metabolism-focused strategies for overcoming drug resistance and could be applied to a broad spectrum of diseases, including metabolic disorders like diabetes. Professor Yoosik Kim emphasized that this technology can predict metabolic regulation switches using minimal data, paving the way for new therapeutic targets. The study was published in Proceedings of the National Academy of Sciences and represents a promising leap forward in personalized cancer treatment strategies. Source: https://medicalxpress.com/news/2025-07-metabolic-network-simulations-reveal-potential.html