Elizabeth A. Lipke

To investigate the ratiometric role of fibroblasts in prostate cancer (PCa) progression, this work establishes a matrix‐inclusive, 3D engineered prostate cancer tissue (EPCaT) model that enables direct coculture of neuroendocrine‐variant castration‐resistant (CPRC‐ne) or androgen‐dependent (ADPC) PCa cells with tumor‐supporting stromal cell types. Results show that the inclusion of fibroblasts within CRPC‐ne and ADPC EPCaTs drives PCa aggression through significant matrix remodeling and increased proliferative cell populations. Interestingly, this is observed to a much greater degree in EPCaTs formed with a small number of fibroblasts relative to the number of PCa cells. Fibroblast coculture also results in ADPC behavior more similar to the aggressive CRPC‐ne condition, suggesting fibroblasts play a role in elevating PCa disease state and may contribute to the ADPC to CRPC‐ne switch. Bulk transcriptomic analyses additionally elucidate fibroblast‐driven enrichment of hallmark gene sets associated with tumorigenic progression. Finally, the EPCaT model clinical relevancy is probed through a comparison to the Cancer Genome Atlas (TCGA) PCa patient cohort; notably, similar gene set enrichment is observed between EPCaT models and the patient primary tumor transcriptome. Taken together, study results demonstrate the potential of the EPCaT model to serve as a PCa‐mimetic tool in future therapeutic development efforts.

This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.