Unified tumor growth mechanisms from multimodel inference and dataset integration
Fig 5
Likely model topologies vary across datasets; transition rates vary according to subtype presence in similar ways.
(A) Hypothesis assessment of model topologies, per dataset. Probability indicates the result of Bayes theorem using equivalent prior probabilities per topology (e.g., 9% probability that one of the topologies in the x-axis best represents a dataset) and Bayesian evidence values (marginal likelihoods) summed per topology. Model topologies represented by images and corresponding numbers along the x-axis. Posterior probability based on marginal likelihoods of all candidate models that include A as an initiating subtype. (B) Division and phenotypic transition parameters for TKO, RPM, and SCLC-A cell line datasets, comparing between higher-probability topologies (A) and four-subtype topology per dataset. Red arrowheads indicate higher A-to-A2 transition rate in 3-subtype TKO topology (A, A2, Y) compared to A-to-Y and A2-to-Y. Teal arrowheads indicate higher A-to-N transition rate in 4-subtype RPM topology compared to A-to-Y and N-to-Y. TKO, p53fl/fl;Rbfl/fl;p130fl/fl tumors [28]; RPM, Rb1fl/fl;Trp53fl/fl;Lox-Stop-Lox[LSL]-MycT58A tumors [36]; SCLC-A cell lines, a subset of SCLC cell lines from the CCLE [54] that we previously assigned as representative of tumors made up largely of the SCLC-A subtype [33]. (*) indicates significance between samples from BMA parameter distributions at family-wise error rate (FWER) = 0.01, averaged over ten sampling iterations using one-way ANOVA plus Tukey HSD.