Abstract 4596: Spatial transcriptomics reveals subtype heterogeneity within the tumor epithelial compartment of pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Molecular subtyping of pancreatic ductal adenocarcinoma (PDAC) has converged on basal-like and classical subtypes with the former being associated with worse prognosis. Sequencing-based tools have emerged to bring PDAC subtyping closer to incorporation into clinical practice. We sought to test the utility of PDAC subtyping using a cohort of patient tumor samples in a clinical setting. For 29 patients with resectable PDAC, two formalin-fixed paraffin-embedded (FFPE) blocks were sequenced from each patient tumor sample (total n=58 blocks) using Nanostring nCounter. 18/58 (31%) of tumors had inconsistent PurIST-based basal-like or classical labels between the two blocks. Two tumors were selected for digital spatial transcriptomics based on having either consistent or inconsistent subtype labelling. Regions of interest (ROI) were selected to capture a diverse range of cellular compartments within the tumor microenvironment including tumor, stroma, immune cells, tumor buds and neurons. ROIs were sequenced across two and three FFPE blocks from the subtype consistent and inconsistent tumors, respectively. Gene expression was segmented by PanCK positive (epithelial) and negative (non-epithelial) signal. Across all ROIs (n=35), PanCK+ segments showed higher expression of basal-like (p=1.9e-7) and classical (p=4.2e-9) genes compared to PanCK- segments. For the subtype-consistent (classical) tumor, all ROIs (n=13) across two blocks showed predominate classical gene expression in the PanCK+ segment. In the subtype-inconsistent tumor that originally had blocks labelled PurIST classical and intermediate, ROIs (n=22) taken across three blocks showed 50, 60 and 83% of ROIs having higher basal-like expression, while remaining ROIs had predominant classical gene expression, and log2 fold change basal-like vs. classical (median) gene expression ranged from -2.4 (T-cells) to 1.3 (tumor and stroma) across ROIs. Overall, the classical gene signature was positively correlated with GATA6 expression (rho=0.40, p=3.2e-4) across all ROIs and PanCK segmentations. While these data support the notion of subtyping expression being specific to the tumor epithelial component, nearly one-third of patient samples had different subtype labels depending on which area of the tumor epithelium was captured. We demonstrate the ability for spatial transcriptomics to resolve subtype heterogeneity and the contributing cellular compartments, providing potential to better understand how aggressive disease develops and could be treated. Citation Format: James Topham, Jenny Chu, Maya Kevorkova, Steve Kalloger, Joanna Karasinska, Andrew Metcalfe, Hassan Ali, Dongxia Gao, Christine Chow, Jonathan Loree, Daniel Renouf, David Schaeffer. Spatial transcriptomics reveals subtype heterogeneity within the tumor epithelial compartment of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4596.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".