Combining mechanistic quantitative systems pharmacology modeling and patient-derived organoid testing in MET-aberrant non-small cell lung cancer for high-throughput combination efficacy analysis and personalized treatment design

Non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping mutations or MET overexpression/amplification typically exhibits highly proliferative and invasive phenotypes and is a significant threat to human health. Although tyrosine kinase inhibitors targeting MET have been approved for clinical use over the past decade, treatment for MET-aberrant patients still face large unmet needs with issues such as limited response duration and drug resistance, low response rates and need for effective combination therapies, differential treatment response in patient subgroups, as well as clinical dose optimization and possibility of personalized medicine. To address these challenges, we developed a quantitative systems pharmacology (QSP) model that mechanistically recapitulated the complex regulation within the MET signaling network and integrated multiscale preclinical-clinical datasets for a total of 16 candidate drugs to drive translational drug research. This comprehensive QSP model framework, upon rigorous stepwise calibration and validation, has enabled high-throughput clinical efficacy analysis of different emerging combination therapies across varying dose ranges, offering crucial insights for drug development and dose optimization in MET-aberrant patients. We further integrated cancer patient-derived organoid (PDO) data on drug sensitivity into the QSP framework and explored the translational utility of this hybrid drug analysis paradigm towards the design of optimal personalized treatment regimens for 5 NSCLC patients harboring MET amplification. To our knowledge, our work is the first multiscale QSP investigation of MET dysregulation for translational cancer drug research, and by integrating QSP model analyses with PDO data it has opened up a new route to facilitate future cancer personalized medicine.