Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.

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 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. Furthermore, 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 versus 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.

Desmoplastic stromal signatures predict patient outcomes in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related death. Hallmarks include desmoplasia with variable extracellular matrix (ECM) architecture and a complex microenvironment with spatially defined tumor, stromal, and immune populations. Nevertheless, the role of desmoplastic spatial organization in patient/tumor variability remains underexplored, which we elucidate using two technologies. First, we quantify ECM patterning in 437 patients, revealing architectures associated with disease-free and overall survival. Second, we spatially profile the cellular milieu of 78 specimens using codetection by indexing, identifying an axis of pro-inflammatory cell interactions predictive of poorer outcomes. We discover that clinical characteristics, including neoadjuvant chemotherapy status, tumor stage, and ECM architecture, correlate with differential stromal-immune organization, including fibroblast subtypes with distinct niches. Lastly, we define unified signatures that predict survival with areas under the receiver operating characteristic curve (AUCs) of 0.872-0.903, differentiating survivorship by 655 days. Overall, our findings establish matrix ultrastructural and cellular organizations of fibrosis linked to poorer outcomes.

Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin.

Cancer-associated fibroblasts (CAFs) are integral to the solid tumor microenvironment. CAFs were once thought to be a relatively uniform population of matrix-producing cells, but single-cell RNA sequencing has revealed diverse CAF phenotypes. Here, we further probed CAF heterogeneity with a comprehensive multiomics approach. Using paired, same-cell chromatin accessibility and transcriptome analysis, we provided an integrated analysis of CAF subpopulations over a complex spatial transcriptomic and proteomic landscape to identify three superclusters: steady state-like (SSL), mechanoresponsive (MR), and immunomodulatory (IM) CAFs. These superclusters are recapitulated across multiple tissue types and species. Selective disruption of underlying mechanical force or immune checkpoint inhibition therapy results in shifts in CAF subpopulation distributions and affected tumor growth. As such, the balance among CAF superclusters may have considerable translational implications. Collectively, this research expands our understanding of CAF biology, identifying regulatory pathways in CAF differentiation and elucidating therapeutic targets in a species- and tumor-agnostic manner.