Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition

P4HA2: A link between tumor-intrinsic hypoxia, partial EMT and collective migration

Epithelial-to-mesenchymal transition (EMT), a well-established phenomenon studied across pan-cancer types, has long been known to be a major player in driving tumor invasion and metastasis. Recent studies have highlighted the importance of partial EMT phenotypes in metastasis. Initially thought as a transitional state between epithelial and mesenchymal phenotypic states, partial EMT state is now widely recognized as a key driver of intra-tumoral heterogeneity and phenotypic plasticity, further accelerating tumor metastasis and therapeutic resistance. However, how tumor microenvironment regulates partial EMT phenotypes remains unclear. We have developed unique size-controlled three-dimensional microtumor models that recapitulate tumor-intrinsic hypoxia and the emergence of collectively migrating cells. In this study, we further interrogate these microtumor models to understand how tumor-intrinsic hypoxia regulates partial EMT and collective migration in hypoxic large microtumors fabricated from T47D breast cancer cells. We compared global gene expression profiles of hypoxic, migratory microtumors to that of non-hypoxic, non-migratory microtumors at early and late time-points. Using our microtumor models, we identified unique gene signatures for tumor-intrinsic hypoxia (early versus late), partial EMT and migration (pre-migratory versus migratory phenotype). Through differential gene expression analysis between the microtumor models with an overlap of hypoxia, partial EMT and migration signatures, we identified prolyl 4-hydroxylase subunit 2 (P4HA2), a hypoxia responsive gene, as a central regulator common to hypoxia, partial EMT and collective migration. Further, the inhibition of P4HA2 significantly blocked collective migration in hypoxic microtumors. Thus, using the integrated computational-experimental analysis, we identify the key role of P4HA2 in tumor-intrinsic hypoxia-driven partial EMT and collective migration.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.