Developing Patient-Derived 3D-Bioprinting models of pancreatic cancer
Hang Sun, Yan Wang, Minghao Sun, Xindi Ke, Changcan Li, Bao Jin, Mingchang Pang, Yanan Wang, Shangze Jiang, Liwei Du, Shunda Du, Shouxian Zhong, Haitao Zhao, Yuan Pang, Yongliang Sun, Zhiying Yang, Huayu Yang, Yilei Mao
Abstract
• Novel preclinical pancreatic cancer (PC) models were established via 3D bioprinting (3DP). • PC 3DP model maintains high-fidelity histological and genomic characteristics. • Significant heterogeneity was observed in PC 3DP models chemotherapy drug testing in vitro . • A correlation exists between PC 3DP model drug sensitivity and actual clinical outcomes. • The PC 3DP model serves as a valuable preclinical tool for personalized treatment strategies in PC. Pancreatic cancer (PC) remains a challenging malignancy, and adjuvant chemotherapy is critical in improving patient survival post-surgery. However, the intrinsic heterogeneity of PC necessitates personalized treatment strategies, highlighting the need for reliable preclinical models. This study aimed to develop novel patient-derived preclinical PC models using three-dimensional bioprinting (3DP) technology. Patient-derived PC models were established using 3DP technology. Genomic and histological analyses were performed to characterize these models and compare them with corresponding patient tissues. Chemotherapeutic drug sensitivity tests were conducted on the PC 3DP models, and correlations with clinical outcomes were analyzed. The study successfully established PC 3DP models with a modeling success rate of 86.96%. These models preserved genomic and histological features consistent with patient tissues. Drug sensitivity testing revealed significant heterogeneity among PC 3DP models, mirroring clinical variability, and potential correlations with clinical outcomes. The PC 3DP models demonstrated their utility as reliable preclinical tools, retaining key genomic and histological characteristics. Importantly, drug sensitivity profiles in these models showed potential correlations with clinical outcomes, indicating their promise in customizing treatment strategies and predicting patient prognoses. Further validation with larger patient cohorts is warranted to confirm their potential clinical utility.