Abstract 3339: Evaluation of GATA6 and KRT17 immunostaining for subtype classification in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with poor survival outcomes. Transcriptomic profiling has identified distinct PDAC subtypes associated with survival, highlighting their clinical relevance. However, reliance on RNA sequencing for subtype classification is not feasible in routine clinical practice, underscoring the need for surrogate biomarkers that are both effective and practical for predicting patient outcomes. This study evaluated GATA6 and KRT17 as multiplex immunohistochemistry (IHC) biomarkers to develop a prognostic model for PDAC subtype classification. Using a tissue microarray (TMA) comprising 130 patient-derived xenografts (PDXs), we quantified cell IHC positivity and stratified samples into subtypes based on GATA6 and KRT17 expression. Whole-slide staining of representative patient tumors validated the consistency of subtype classifications between PDXs and corresponding patient tumors. Patient TMAs (224 cores from 82 tumors) were subsequently analyzed to refine the classification system, incorporating GATA6 and KRT17 positive cell percentages, staining intensity (H-scores), and clinical variables into a comprehensive prognostic model. Initial classification based on PDX models demonstrated non-significant distinct survival trends (p=0.14), likely due to sample size limitations. However, a refined model applied to patient TMAs that incorporated additional parameters effectively stratified patients into prognostic groups with significantly different overall survival (p=0.042). These findings highlight the utility of GATA6 and KRT17 as surrogate classifiers for PDAC subtype classification and prognostication, offering a practical alternative to transcriptomic sequencing in HR-proficient PDAC. Citation Format: Ruomeng Fang, Joan Miguel Romero, Yifan Wang, Alicia Gomez Mendez, Adeline Cuggia, George Zogopoulos. Evaluation of GATA6 and KRT17 immunostaining for subtype classification in 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 3339.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".