Clinical and genomic features of the morphological subtypes in advanced pancreatic cancer.

765 Background: In resected pancreatic cancer (PDA), morphology from routine histopathology slides provides a rapid inexpensive biomarker that predicts overall survival and correlates with transcriptomic subtypes. In this study, we evaluated clinical and genomic associations of morphological subtypes in resected and advanced disease and validated the consistency of subtypes in patient-derived organoids (PDO) and mouse xenografts (PDXs) during in vitro and in vivo modeling. Methods: Our cohort included PDA tumor tissues from 152 resectable (stage I/II) and 228 advanced cases (Stage III/IV). Hematoxylin and eosin-stained slides were blindly reviewed by two pathologists and classified into subtypes based on Kalimuthu (1). Morphological subtypes were correlated with clinical and genomic data from whole-genome and transcriptome sequencing. Histological preparations from PDOs and PDXs obtained from pancreatic resections and metastases were reviewed using the same criteria. Results: Morphological subtypes were significantly associated with clinical patterns. Locally advanced PDA exhibited the highest proportion of glandular tumors. Metastatic tumors were enriched for non-glandular morphologies. The non-glandular morphologies were significantly associated with lower survival rates in both resected and advanced tumors. Furthermore, morphological subtypes were significantly associated with unique genomic alterations. Compared to glandular tumors, non-glandular tumors were associated with increased KRAS copy number, KRAS imbalances, and polyploid genomes. In advanced settings, glandular and non-glandular tumors mostly exhibited classical and basal-like transcriptional subtypes, respectively. Squamous tumors had the highest mutation burden and the highest proportion of Basal A signature (2). Using differential gene expression, we identified transcriptional signatures of the morphological subtypes. PDOs and PDXs maintained the morphological subtypes, although non-glandular tumors had a lower success rate for PDO establishment. Conclusions: Morphological subtypes have distinct clinical and genomic associations across all stages of pancreatic cancer, which can be consistently modeled both in vitro and in vivo . These results demonstrate that morphological subtyping offers a rapid and biologically-relevant classification of PDA that could be used for drug development and stratification to predict therapy selection. 1. Gut; 69:317-328 (2020). 2. Nat Gen; 52:231-240 (2020).

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Journal
Journal of Clinical Oncology
Published
2025-01-27
DOI
https://doi.org/10.1200/jco.2025.43.4_suppl.765
Primary Topic
Cancer Genomics and Diagnostics
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article
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article

Clinical and genomic features of the morphological subtypes in advanced pancreatic cancer.

Yuanchang Fang, Erica S. Tsang, Steven Gallinger, Grainne M. O’Kane et al.
Journal of Clinical Oncology
Cancer Genomics and Diagnostics
article

Clinical and genomic features of the morphological subtypes in advanced pancreatic cancer.

Yuanchang Fang, Erica S. Tsang, Steven Gallinger, Grainne M. O’Kane, Zhen-Mei Liu, Anna Dodd, Amy Xian Zhang, Nhu‐An Pham, Karen Ng, Maryam Monajemzadeh, Faiyaz Notta, Jennifer J. Knox, Tom W Ouellette, Eugenia Flores‐Figueroa, Julie M. Wilson, Robert C. Grant, Nikolina Radulovich, Gun Ho Jang, Milena Gallucci, Ayah Elqaderi
article en

Abstract

765 Background: In resected pancreatic cancer (PDA), morphology from routine histopathology slides provides a rapid inexpensive biomarker that predicts overall survival and correlates with transcriptomic subtypes. In this study, we evaluated clinical and genomic associations of morphological subtypes in resected and advanced disease and validated the consistency of subtypes in patient-derived organoids (PDO) and mouse xenografts (PDXs) during in vitro and in vivo modeling. Methods: Our cohort included PDA tumor tissues from 152 resectable (stage I/II) and 228 advanced cases (Stage III/IV). Hematoxylin and eosin-stained slides were blindly reviewed by two pathologists and classified into subtypes based on Kalimuthu (1). Morphological subtypes were correlated with clinical and genomic data from whole-genome and transcriptome sequencing. Histological preparations from PDOs and PDXs obtained from pancreatic resections and metastases were reviewed using the same criteria. Results: Morphological subtypes were significantly associated with clinical patterns. Locally advanced PDA exhibited the highest proportion of glandular tumors. Metastatic tumors were enriched for non-glandular morphologies. The non-glandular morphologies were significantly associated with lower survival rates in both resected and advanced tumors. Furthermore, morphological subtypes were significantly associated with unique genomic alterations. Compared to glandular tumors, non-glandular tumors were associated with increased KRAS copy number, KRAS imbalances, and polyploid genomes. In advanced settings, glandular and non-glandular tumors mostly exhibited classical and basal-like transcriptional subtypes, respectively. Squamous tumors had the highest mutation burden and the highest proportion of Basal A signature (2). Using differential gene expression, we identified transcriptional signatures of the morphological subtypes. PDOs and PDXs maintained the morphological subtypes, although non-glandular tumors had a lower success rate for PDO establishment. Conclusions: Morphological subtypes have distinct clinical and genomic associations across all stages of pancreatic cancer, which can be consistently modeled both in vitro and in vivo . These results demonstrate that morphological subtyping offers a rapid and biologically-relevant classification of PDA that could be used for drug development and stratification to predict therapy selection. 1. Gut; 69:317-328 (2020). 2. Nat Gen; 52:231-240 (2020).

Journal of Clinical OncologyVol. 43(4_suppl)
Ontario Institute for Cancer Research (CA), University Health Network (CA), University of Toronto (CA), Princess Margaret Cancer Centre (CA)
Openalex Percentile: Top 99%
Cancer Genomics and Diagnostics
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