Unified tumor growth mechanisms from multimodel inference and dataset integration

• Published in: PLoS computational biology 2023-07, Vol.19 (7), p.e1011215-e1011215
• Main Authors: Beik, Samantha P, Harris, Leonard A, Kochen, Michael A, Sage, Julien, Quaranta, Vito, Lopez, Carlos F
• Format: Article
• Language: English
• Subjects: Analysis; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Biological activity; Biological models (mathematics); Biology; Biology and Life Sciences; Care and treatment; Cell interactions; Datasets; Deceleration; Discovery and exploration; Growth; Heterogeneity; Humans; Hypotheses; Inference; Lung cancer; Lung Neoplasms - pathology; Mathematical models; Medicine and Health Sciences; Models, Theoretical; Outer space; Parameter estimation; Perturbation; Physical Sciences; Plastic properties; Plasticity; Probability; Regression analysis; Research and Analysis Methods; Small Cell Lung Carcinoma; Space exploration; Treatment resistance; Tumors; Variables
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011215

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.