Cracking the code: predicting tumor microenvironment enabled chemoresistance with machine learning in the human tumoroid models.

ABSTRACT: 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 23 distinct cellular configurations of tumoroids comprised of OVCAR3 HGSC cells, mesenchymal stem cells, HUVEC endothelial cells, and U937 monocytes to an assortment of five therapeutic ag

SUBMITTER: Bregenzer ME 

PROVIDER: S-EPMC12364706 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature