High‐resolution and quantitative spatial analysis reveal intra‐ductal phenotypic and functional diversification in pancreatic cancer
Bibliographic record
Abstract
Abstract A ‘classical’ and a ‘basal‐like’ subtype of pancreatic cancer have been reported, with differential expression of GATA6 and different dosages of mutant KRAS . We established in situ detection of KRAS point mutations and mRNA panels for the consensus subtypes aiming to project these findings to paraffin‐embedded clinical tumour samples for spatial quantitative analysis. We unveiled that, next to inter‐patient and intra‐patient inter‐ductal heterogeneity, intraductal spatial phenotypes exist with anti‐correlating expression levels of GATA6 and KRAS G12D . The basal‐like mRNA panel better captured the basal‐like cell states than widely used protein markers. The panels corroborated the co‐existence of the classical and basal‐like cell states in a single tumour duct with functional diversification, i.e. proliferation and epithelial‐to‐mesenchymal transition respectively. Mutant KRAS G12D detection ascertained an epithelial origin of vimentin‐positive cells in the tumour. Uneven spatial distribution of cancer‐associated fibroblasts could recreate similar intra‐organoid diversification. This extensive heterogeneity with functional cooperation of plastic tumour cells poses extra challenges to therapeutic approaches. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".