Unified Tumor Growth Mechanisms from Multimodel Inference and Dataset Integration

Systems approaches to elucidate biological processes that impact human health leverage mathematical models encoding mechanistic hypotheses suitable for experimental validation. However, building a single model fit to one dataset may miss alternate equally valid mathematical formulations, and available data may not be sufficient to fully elucidate mechanisms underlying system behavior. Here, we overcome these limitations via a Bayesian multimodel inference (Bayesian-MMI) approach, which estimates how multiple mechanistic hypotheses explain experimental datasets, concurrently quantifying how each dataset informs each hypothesis. We apply this approach to unanswered questions about heterogeneity, lineage plasticity, and cell-cell interaction dynamics in small cell lung cancer (SCLC) tumor growth. Through available dataset integration, we find that Bayesian-MMI predictions support tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. These results highlight that given available data, any SCLC cellular subtype can contribute to tumor repopulation post-treatment, suggesting a mechanistic interpretation for tumor recalcitrance.