Cracking the Code: Predicting Tumor Microenvironment Enabled Chemoresistance with Machine Learning in the Human Tumoroid Models

High-grade serous tubo-ovarian cancer (HGSC) is marked by substantial inter- and intra-tumor heterogeneity. The tumor microenvironments (TME) of HGSC show pronounced variability in cellular make-up across metastatic sites, which is linked to poorer patient outcomes. The influence of cellular composition on therapy sensitivity, including chemotherapy and targeted treatments, has not been thoroughly investigated. In this study, we examined the premise that the variations in cellular composition can forecast drug efficacy. Using a high-throughput 3D in vitro tumoroid model, we assessed the drug responses of twenty-three distinct cellular configurations to an assortment of five therapeutic agents, including carboplatin and paclitaxel. By amalgamating our experimental findings with random forest machine learning algorithms, we assessed the influence of TME cellular composition on treatment reactions. Our findings reveal notable disparities in drug responses correlated with tumoroid composition, underscoring the significance of cellular diversity within the TME as a predictor of therapeutic outcomes. However, our work also emphasizes the complex nature of cell composition's influence on drug response. This research establishes a foundation for employing human tumoroids with varied cellular composition as a method to delve into the roles of stromal, immune, and other TME cell types in enhancing cancer cell susceptibility to various treatments. Additionally, these tumoroids can serve as a platform to explore pivotal cellular interactions within the TME that contribute to chemoresistance and cancer recurrence.