Bioengineered 3D models of human pancreatic cancer recapitulate in vivo tumour biology.
|
IF: 17.694
|
Cited by: 34
|

Abstract

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.

Keywords

Spatial Transcriptomics
Gene Expression
PROCEDURE

MeSH terms

Bioengineering
Carcinoma, Pancreatic Ductal
Cell Culture Techniques
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Models, Biological
Neoplastic Stem Cells
Pancreatic Neoplasms
Reproducibility of Results
Stromal Cells
Tumor Cells, Cultured

Authors

Osuna de la Peña, David
Trabulo, Sara Maria David
Collin, Estelle
Liu, Ying
Sharma, Shreya
Tatari, Marianthi
Behrens, Diana
Erkan, Mert
Lawlor, Rita T
Scarpa, Aldo
Heeschen, Christopher
Mata, Alvaro
Loessner, Daniela

Recommend literature