Abstract 1193: Digital quantification of GATA6 expression from immunohistochemistry is associated with overall survival after surgery for pancreatic cancer in the ESPAC trials
Bibliographic record
Abstract
Abstract Introduction: GATA6, a transcription factor involved in pancreaticogenesis, correlates with major transcriptomic subtypes of pancreatic cancer, and early evidence suggests a potential role as a prognostic biomarker. In this study, we tested whether semi-automated digital quantification of GATA6 immunohistochemistry (IHC) was associated with overall survival (OS) after surgical resection of pancreatic cancer in randomized trials of adjuvant chemotherapy. Materials and Methods: Tissue microarrays of pancreatic adenocarcinoma specimens were stained for GATA6 expression using IHC. Specimens came from patients in the ESPAC-3 and ESPAC-4 randomized phase III trials comparing adjuvant chemotherapy regimens. Two pathologists blinded to clinical data independently assessed GATA6 protein expression in cores using a customized computer algorithm from the open-source digital pathology software QuPath. Briefly, the digital assistant provided GATA6 IHC semiquantitative scores (0-4) for individual cells, which were made available to the pathologist for review. This semi-automated digital quantification of GATA6 expression was compared to manual quantification as previously reported (Grant et al., AACR Pancreatic Cancer, 2020). Concordance between pathologists across tissue cores was calculated using a weighted kappa statistic for scores 0 through 4, and an unweighted kappa statistic for high (3-4) and low (0-2) expression levels. Discrepancies were resolved through consensus. Median OS was estimated in GATA6 high and low subgroups using Kaplan-Meier curves. OS was compared using Cox proportional hazard regressions adjusting for adjuvant chemotherapy treatment, lymph node status, and post-operative CA-19-9. Results: GATA6 expression was quantified in 230 patients using the semi-automated digital and manual approaches. Inter-observer concordance markedly improved with digital assistance compared to the manual approach (weighted kappa statistic across all scores 0.69 versus 0.41, unweighted kappa statistic comparing high and low expression 0.71 versus 0.25). GATA6 expression measured using the semi-automated digital approach was significantly associated with OS: median OS was 25.7 months in GATA6 low cancers versus 42.4 months in GATA6 high cancers, with an adjusted hazard ratio of 1.58 (95% confidence interval 1.10-2.28, P=0.014). Conclusion: GATA6 IHC is a prognostic biomarker for pancreatic cancers treated with surgical resection and adjuvant chemotherapy that can be consistently and reliably quantified using digital assistance. Citation Format: Robert C. Grant, Kai Duan, Richard Jackson, William Greenhalf, Eithne Costello-Goldring, Paula Ghaneh, Christopher Halloran, Daniel Palmer, Thilo Hackert, Markus Buchler, Shawn Hutchison, Stephanie Ramotar, Anna Dodd, Julie Wilson, Faiyaz Notta, Grainne O'Kane, Jennifer Knox, John Neoptolemos, Steven Gallinger, Sandra Fischer. Digital quantification of GATA6 expression from immunohistochemistry is associated with overall survival after surgery for pancreatic cancer in the ESPAC trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1193.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".