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Gut. 2019 Jun 14. pii: gutjnl-2019-318217. doi: 10.1136/gutjnl-2019-318217. [Epub ahead of print]

Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome.

Author information

Anatomical Pathology, Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada.
Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Latner Thoracic Surgery Laboratory, Division of Thoracic Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada.
Department of Medical Oncology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada.
PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Division of Research, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada.
Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Hepatobiliary/Pancreatic Surgical Oncology Program, University Health Network, Toronto, Ontario, Canada.
Contributed equally



Transcriptional analyses have identified several distinct molecular subtypes in pancreatic ductal adenocarcinoma (PDAC) that have prognostic and potential therapeutic significance. However, to date, an indepth, clinicomorphological correlation of these molecular subtypes has not been performed. We sought to identify specific morphological patterns to compare with known molecular subtypes, interrogate their biological significance, and furthermore reappraise the current grading system in PDAC.


We first assessed 86 primary, chemotherapy-naive PDAC resection specimens with matched RNA-Seq data for specific, reproducible morphological patterns. Differential expression was applied to the gene expression data using the morphological features. We next compared the differentially expressed gene signatures with previously published molecular subtypes. Overall survival (OS) was correlated with the morphological and molecular subtypes.


We identified four morphological patterns that segregated into two components ('gland forming' and 'non-gland forming') based on the presence/absence of well-formed glands. A morphological cut-off (≥40% 'non-gland forming') was established using RNA-Seq data, which identified two groups (A and B) with gene signatures that correlated with known molecular subtypes. There was a significant difference in OS between the groups. The morphological groups remained significantly prognostic within cancers that were moderately differentiated and classified as 'classical' using RNA-Seq.


Our study has demonstrated that PDACs can be morphologically classified into distinct and biologically relevant categories which predict known molecular subtypes. These results provide the basis for an improved taxonomy of PDAC, which may lend itself to future treatment strategies and the development of deep learning models.


basal subtype; classical subtype; deep learning; pancreatic ductal adenocarcinoma; pattern-based morphological classification system


Conflict of interest statement

Competing interests: None declared.

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