Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome
Bibliographic record
Abstract
INTRODUCTION: Transcriptional analyses have identified several distinct molecular subtypes in pancreatic ductal adenocarcinoma (PDAC) that have prognostic and potential therapeutic significance. However, to date, an indepth, clinicomorphological correlation of these molecular subtypes has not been performed. We sought to identify specific morphological patterns to compare with known molecular subtypes, interrogate their biological significance, and furthermore reappraise the current grading system in PDAC. DESIGN: We first assessed 86 primary, chemotherapy-naive PDAC resection specimens with matched RNA-Seq data for specific, reproducible morphological patterns. Differential expression was applied to the gene expression data using the morphological features. We next compared the differentially expressed gene signatures with previously published molecular subtypes. Overall survival (OS) was correlated with the morphological and molecular subtypes. RESULTS: We identified four morphological patterns that segregated into two components ('gland forming' and 'non-gland forming') based on the presence/absence of well-formed glands. A morphological cut-off (≥40% 'non-gland forming') was established using RNA-Seq data, which identified two groups (A and B) with gene signatures that correlated with known molecular subtypes. There was a significant difference in OS between the groups. The morphological groups remained significantly prognostic within cancers that were moderately differentiated and classified as 'classical' using RNA-Seq. CONCLUSION: Our study has demonstrated that PDACs can be morphologically classified into distinct and biologically relevant categories which predict known molecular subtypes. These results provide the basis for an improved taxonomy of PDAC, which may lend itself to future treatment strategies and the development of deep learning models.
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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 | 0.001 |
| 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 | 0.001 | 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".