RT Journal Article
SR Electronic
T1 Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome
JF Gut
JO Gut
FD BMJ Publishing Group Ltd and British Society of Gastroenterology
SP 317
OP 328
DO 10.1136/gutjnl-2019-318217
VO 69
IS 2
A1 N Kalimuthu, Sangeetha
A1 Wilson, Gavin W
A1 Grant, Robert C
A1 Seto, Matthew
A1 O’Kane, Grainne
A1 Vajpeyi, Rajkumar
A1 Notta, Faiyaz
A1 Gallinger, Steven
A1 Chetty, Runjan
YR 2020
UL http://gut.bmj.com/content/69/2/317.abstract
AB Introduction 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.Design 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.Results 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.Conclusion 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.