Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer

• Published in: Tissue engineering. Part A 2024-10, Vol.30 (ja), p.605-613
• Main Authors: Guo, Jason L, Lopez, David M, Mascharak, Shamik, Foster, Deshka S, Khan, Anum, Davitt, Michael F, Nguyen, Alan T, Burcham, Austin R, Chinta, Malini S, Guardino, Nicholas J, Griffin, Michelle, Miller, Elisabeth, Januszyk, Michael, Raghavan, Shyam S, Longacre, Teri A, Delitto, Daniel J, Norton, Jeffrey A, Longaker, Michael T.
• Format: Article
• Language: English
• ISSN: 1937-3341; 1937-335X; 1937-335X
• DOI: 10.1089/ten.TEA.2024.0039

Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning (ML) to summarize a latent space across 147 local (e.g. fiber length, solidity) and global (e.g. fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Further, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive vs. outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.