Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with a million new cases diagnosed globally each year and a 5-year survival rate of less than 20%. The lack of therapeutic options personalized to individual patients leads to high variation in survival. The combination of patient stratification with personalized treatment has the potential to improve outcomes; however, the variation in mutations found in NSCLC adenocarcinoma patients makes experimentally determining treatment combinations time-consuming and expensive. Here, we developed an interpretable mechanistic model to decipher complex signaling interplay and guide personalized therapy in NSCLC adenocarcinoma. This 'virtual tumor' model encompassed key tumor intrinsic oncogenic signaling pathways, for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Diverse genetic profiles were simulated for testing over 10,000 therapeutic strategies to identify optimal approaches to overcome resistance mechanisms specific to genetic profiles and p53 status. The virtual tumor model reproduced drug additivity screens, predicted radio-sensitizing genes validated in a CRISPR screen, and identified 53BP1 as a potential drug target that improved the therapeutic window during radiotherapy. A 19-gene signature derived from the virtual tumor framework stratified patients most likely to benefit from radiotherapy, which was validated using TCGA data. These results demonstrate the utility of virtual tumors to predict effective therapeutic combinations and present a computational resource for large-scale screening of personalized therapies to guide clinical decision-making in NSCLC patients.