From Organoids to Algorithms: Translational Platforms for Precision Oncology in Solid Tumors

The complexity of solid tumors such as pancreatic ductal adenocarcinoma (PDAC) and bladder cancer demands integrative approaches that combine biological fidelity with computational power. Here we outline a multi-modal translational framework that leverages patient-derived organoids, spatial transcriptomics, microfluidic platforms, and artificial intelligence to accelerate personalized cancer therapy development. Feasibility trials in PDAC have demonstrated the successful acquisition of high-quality biopsies for downstream applications, including organoid generation and spatial profiling. These models recapitulate key histopathological and molecular features, enabling functional drug screening and predictive modeling. AI-based classifiers trained on transcriptomic data further stratify tumors by therapeutic response, even in cases where organoid derivation is not feasible. Complementing this, the iBloC (immune Bladder-on-Chip) platform introduces a microfluidic system tailored for bladder cancer, simulating tumor-immune interactions under physiologically relevant conditions. This chip-based model supports dynamic drug testing and real-time molecular analysis, bridging the gap between preclinical research and clinical application. Together, these platforms represent a scalable and clinically relevant pipeline for precision oncology, integrating experimental and computational tools to guide individualized treatment strategies.