Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. In this work, we used a network-based mathematical model to identify sensitivity regulators and drug combinations for the PI3Kα inhibitor alpelisib in estrogen receptor positive (ER+) PIK3CA-mutant breast cancer. The model-predicted efficacious combination of alpelisib and BH3 mimetics, for example, MCL1 inhibitors, was experimentally validated in ER+ breast cancer cell lines. Consistent with the model, FOXO3 downregulation reduced sensitivity to alpelisib, revealing a novel potential resistance mechanism. Cell line–specific sensitivity to combinations of alpelisib and BH3 mimetics depended on which BCL2 family members were highly expressed. On the basis of these results, newly developed cell line–specific network models were able to recapitulate the observed differential response to alpelisib and BH3 mimetics. This approach illustrates how network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.
Network-based mathematical models of oncogenic signaling and experimental validation of its predictions can identify resistance mechanisms for targeted therapies, as this study demonstrates for PI3Kα-specific inhibitors in breast cancer.