824 MOLECULAR UNDERPINNINGS OF NEUROENDOCRINE DIFFERENTIATION IN ESOPHAGEAL ADENOCARCINOMA AND ITS PROGNOSTIC SIGNIFICANCE
Bibliographic record
Abstract
Abstract The presence of neuroendocrine (NE) differentiation has been previously reported in both morphological subtypes, (intestinal and diffuse), of esophageal adenocarcinoma, (EAC). This is more commonly seen in the post-treatment setting and is thought to confer a more aggressive phenotype. However, the molecular underpinning and therapeutic implications of NE differentiation within EAC is poorly understood. Herein, the goal is to understand the molecular mechanisms, evolution and the prognostic significance of NE development in EAC tumours. Methods We interrogated a previously published transcriptome dataset with matched H&E slides, TCGA EAC (N = 86). Moreover, we have begun reviewing H&E slides from EAC patients from UHN (N = 50). We created a NE gene expression signature (N = 25 genes) from the literature as an initial proof of concept. We quantified the presence of a NET-like/organoid morphology in the matched H&E slides and correlated it with the average z-score of the NE gene signature calculated from the matched transcriptome data. Results NE differentiation was present in 27/86 cases with a mean of 21.01% +/− 20.9 within the tumour area. We compared the expression of our NE gene signature with the proportion of NE morphology and observed a moderate correlation between morphology with the gene expression (R^2 = 0.546, P < 0.001 ordinary least squares regression), providing validation that the organoid/NET-like morphological pattern correlates with NE differentiation. Furthermore, we have validated the presence of NE morphology in a subset of the UHN samples using electron microscopy and immunohistochemistry (chromogranin and synaptophysin). Conclusion This is a first of a kind study to profile a specific morphology with a transcriptional signature within EAC across a large cohort of patient samples. Correlation of NE-features with clinical outcome together with treatment resistant implications is currently underway.
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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".