bioRxivCancer BiologyPreprint
Morphospatial profiling of cancer-associated fibroblasts reveals architectural subtypes of pancreatic ductal adenocarcinoma
Adam S. BryceiD, Silvia MartinelliiD, Leonor Schubert Santana, Leah Officer-JonesiD, Rachel L. Baird, Fiona BallantyneiD, Kai RakoviciD, Ian R. PowleyiD, Dina TataraniD, David J. MeltzeriD, Christopher Walsh, Fraser Duthie, Lucas FarndaleiD, Adalberto Claudio QuirosiD, Ke YuaniD, John P. C. Le QuesneiD, Fieke E. M. FroelingiD, Stephan B. DreyeriD, David Kuang-Fu ChangiD
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with an urgent need for biomarkers to predict prognosis and guide treatment. Understanding the complex spatial biology of pancreatic cancer-associated fibroblasts (CAFs) and the broader architecture of the PDAC tumour microenvironment is central to this challenge. Using a multi-omics approach across multiple spatial resolutions in a large human PDAC cohort, we integrate geometry and shape to define discrete morphological CAF subtypes, expanding CAF phenotyping beyond conventional proteomics. We then reveal an architectural and molecular axis of PDAC at tissue level, suggestive of epithelial-stromal co-evolution, with translational implications and prioritisation of stromal targets. Finally, we recapitulate this axis by introducing four unique, internally validated architectural subtypes of PDAC, each characterised by a common microenvironment and CAF enrichment profile. These archetypes outperform conventional pathology in prognostication, and predict response to adjuvant chemotherapy. Collectively, this study establishes a novel morphological paradigm for spatial biology, illuminates the architectural landscape of PDAC, and provides a framework for spatial biomarker discovery to close the translational gap in this devastating disease.
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 1
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