Simulating therapeutic combinations for lung cancer patients with heterogeneous genetic predisposition

Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolutionized immunotherapy. However, challenges such as frequent drug resistance and low response rates highlight the need for novel therapeutic strategies. Boolean network modeling is a powerful mathematical tool to simulate complex biological processes and optimize potential treatment strategies. This study developed a Boolean network model for NSCLC patients with different mutational backgrounds and evaluated the therapeutic effects by incorporating key kinase mutation inhibitors and immunological interventions. Simulations in both the Boolean network model and another quantitative model consistently suggested that the optimal therapeutic strategy involves a combination of KRAS inhibitor and ICI for KRAS-mutant patients, which is also in line with mouse model studies and the KRYSTAL-7 phase-2 clinical trial data. It would be reasonable to expect further validations from the recently announced KRYSTAL-7 phase-3 clinical trial comparing the combined therapy over pembrolizumab monotherapy in the future. Our approach highlights the value of computational modeling to evaluate and refine therapeutic strategies for precision oncology.