Bioengineered 3D models of human pancreatic cancer recapitulate in vivo tumour biology

Personalized cancer medicine currently lacks custom platforms that mimic the microenvironment of human tissues. Here, the authors show how self-assembled patient-derived models of pancreatic cancer recapitulate key biological features of the original tumours such as matrix composition and stemness. Patient-derived in vivo models of human cancer have become a reality, yet their turnaround time is inadequate for clinical applications. Therefore, tailored ex vivo models that faithfully recapitulate in vivo tumour biology are urgently needed. These may especially benefit the management of pancreatic ductal adenocarcinoma (PDAC), where therapy failure has been ascribed to its high cancer stem cell (CSC) content and high density of stromal cells and extracellular matrix (ECM). To date, these features are only partially reproduced ex vivo using organoid and sphere cultures. We have now developed a more comprehensive and highly tuneable ex vivo model of PDAC based on the 3D co-assembly of peptide amphiphiles (PAs) with custom ECM components (PA-ECM). These cultures maintain patient-specific transcriptional profiles and exhibit CSC functionality, including strong in vivo tumourigenicity. User-defined modification of the system enables control over niche-dependent phenotypes such as epithelial-to-mesenchymal transition and matrix deposition. Indeed, proteomic analysis of these cultures reveals improved matrisome recapitulation compared to organoids. Most importantly, patient-specific in vivo drug responses are better reproduced in self-assembled cultures than in other models. These findings support the use of tuneable self-assembling platforms in cancer research and pave the way for future precision medicine approaches.

Automated summary

Personalized cancer medicine lacks custom platforms that mimic the microenvironment of human tissues. The authors developed a highly tuneable ex vivo model of pancreatic cancer based on self-assembly of patient-derived models, showing better reproduction of in vivo drug responses. These findings support the use of tuneable self-assembling platforms in cancer research, paving the way for future precision medicine approaches.