Stroma vs epithelium‐enhanced prognostics through histologic stratification in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
The mixture of epithelial and stromal components in pancreatic ductal adenocarcinoma (PDAC) may confound sequencing-based studies of tumor gene expression. Virtual microdissection has been suggested as a bioinformatics approach to segment the aforementioned components, and subsequent prognostic gene sets have emerged from this research. We examined the prognostic signature from the epithelial gene set of one such study using laser capture microdissected (LCM) epithelial samples. We also examined this gene set in matched stromal samples to determine whether prognostic findings were specific to the epithelium. LCM samples from 48 long-term and 48 short-term PDAC survivors were obtained. The resultant epithelial and stromal components were subjected to direct mRNA quantification using a 49 gene published PDAC classifier. Component-specific unsupervised hierarchical clustering was used to derive groups and survival differences were quantified. Immunohistochemical validation of particular genes was performed in an independent cohort. Clustering in the epithelial component yielded prognostic differences in univariable analysis (P = .02), but those differences were not significant when controlled for other clinicopathologic covariates (P = .06). Clustering in the stromal component yielded prognostic differences that persisted in the presence of other clinicopathologic covariates (P = .0005). Validation of selected genes in the epithelium (KRT6A-negative prognostic [P = .004]) and stroma (LY6D-improved prognostic [P = .01] and CTSV-negative prognostic [P = .0002]) demonstrated statistical independence in multivariable analysis. Although the genes used in this study were originally identified as being representative of the epithelial component of PDAC, their expression in the stroma appears to provide additional information that may aid in improved prognostication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".