Systematic growth factor profiling platform for 3D tumor models reveals estradiol-responsive cellular mechanisms of immunotherapy resistance

Current organoid culture systems face critical limitations: standardized growth factor formulations fail to capture patient-specific signaling requirements, while single-cell-type approaches overlook tumor-stromal interactions essential for understanding immunotherapy resistance. To address these challenges, we developed an automated biofabrication platform that systematically integrates patient-derived three-dimensional (3D) cultures with comprehensive growth factor profiling across 128 combinations. Through rigorous optimization of Matrigel concentration and gelation kinetics, we established standardized conditions achieving uniform signal distribution and quantitative reproducibility. Screening of 23 ovarian cancer patient samples identified universal growth factor combinations that consistently promoted robust cell growth while preserving parental tumor characteristics. Integration of growth factor response profiles with multi-scale genomic analysis revealed two estradiol-responsive cellular populations coordinating immunosuppression: a malignant cell fraction (MAL.PDCD5) that suppresses immune infiltration and a cancer-associated fibroblast fraction (FB.TNFSF10) that promotes immune exclusion through enhanced TGF-<i>β</i>signaling. Spatial transcriptomic validation demonstrated striking mutual exclusivity between FB.TNFSF10 cells and T/NK cells in native tissue architecture. Most significantly, FB.TNFSF10 abundance emerged as a robust predictor of immune checkpoint inhibitor therapy resistance across multiple cancer cohorts, independent of conventional biomarkers. This biofabrication platform provides a scalable, reproducible framework with broad applicability beyond oncology. The systematic optimization methodology is readily adaptable to other tissue types, disease models, and high-throughput drug screening applications, representing a significant advancement in functional tissue engineering for precision medicine.