David Walter

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 patients with NSCLC adenocarcinoma makes experimentally determining treatment combinations time-consuming and expensive. In this study, 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 more than 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 radiosensitizing 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 The Cancer Genome Atlas (TCGA) data. These results show 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 patients with NSCLC. Significance:A computational framework that simulates thousands of personalized treatment strategies offers a scalable, cost-effective way to tailor therapies and improve outcomes for patients with genetically diverse NSCLC.

In silico network simulation results, detailing perturbation effects and the resulting biological behaviors (proliferation and apoptosis), including the bounds and reported mid-point

Predicted additivity of in silico combination targeted therapies on cancer cell lines, with clustering based on their impact on additivity

JSON file containing the in silico NSCLC model (this file can be imported into the BMA tool available at https://biomodelanalyzer.org/)

Results of a CRISPR knockout screen for focused genes (MYC, 53BP1, MET, PI3K, ATM) in various cancer and healthy cell lines, with the corresponding log2 fold change (Log2FC), p-values, and false discovery rates (FDR) for each cell line and experiment

Summary of mutations in the modeled cell lines

Heatmap showing effect of drug combinations on selected NSCLC cell lines

In silico predictions stratify radiation-treated patients but not treatments that were not treated with radiation

Concordance of simulated results for drug combinations with Nair and colleagues(2022)

Concordance of simulated results with published results for individual drugs