整合AI驱动治疗匹配的患者来源3D肿瘤模型用于靶点发现和个体化治疗

Many cancer therapies show strong preclinical activity yet fail clinically due to inadequate experimental models. Conventional 2D cultures lack physiological relevance because they cannot reproduce the complex tumor-stromal-immune interactions or the biomechanical cues that shape tumor behavior and therapeutic response. This limitation is particularly pronounced in aggressive tumors, where patient heterogeneity and dynamic microenvironmental interactions drive treatment outcomes, or in rare tumors, where data on response to available therapies is scarce. To address this translational gap, we developed two patient-derived 3D platforms: (1) 3D tumoroids generated from the dissociated tumor tissues and co-cultured with matched peripheral blood mononuclear cells (PBMC), enabling tumor-immune-stromal interactions, and (2) 3D-bioprinted constructs formed using two bioinks: one incorporating tumor and tumor-microenvironment (TME) cells, and the other containing endothelial cells and pericytes to create perfusable vascular channels flowing PBMC and drugs. We are validating the ability of these high-throughput 3D models to recapitulate patient-specific tumor biology and predict responses to chemotherapy, immunotherapy, and targeted therapies. Their predictive performance is being evaluated in an IRB-approved clinical study (SMC-9417-22) involving 80 patients across seven cancer types. To guide personalized therapy selection, we integrate standard-of-care and investigational drugs with AI-derived treatment matches generated by ENLIGHT-DP (Pangea Biomed), a deep-learning platform that infers gene expression from tumor HandE slides and combines them with proprietary predictive biomarkers to produce individualized drug-response scores. AI-prioritized treatments are reviewed with oncologists and then tested on 3D platforms. Preliminary evidence suggests a correlation between the 3D tumoroid models and clinical outcomes. Notably, in a case of mucosal melanoma, standard therapies failed both clinically and ex vivo, whereas ENLIGHT-DP screening identified regorafenib, which demonstrated potent activity in the 3D model. Compassionate-use treatment led to a durable clinical response lasting nearly 12 months. In another metastatic melanoma case harboring an ALK rearrangement (identified via Tempus sequencing and prioritized by ENLIGHT-DP), lorlatinib demonstrated significant efficacy ex vivo and produced a sustained clinical response in the patient for more than 6 months at the time of this writing, with near-complete responses of visceral and brain metastases. Together, these patient-derived 3D models, integrated with AI-based drug prioritization, provide a robust platform for functional precision oncology, enabling personalized drug screening, reducing ineffective treatments, and bridging the gap between preclinical modeling and clinical response.