GATA6 Expression Distinguishes Classical and Basal-like Subtypes in Advanced Pancreatic Cancer
Bibliographic record
Abstract
Abstract Purpose: To determine the impact of basal-like and classical subtypes in advanced pancreatic ductal adenocarcinoma (PDAC) and to explore GATA6 expression as a surrogate biomarker. Experimental Design: Within the COMPASS trial, patients proceeding to chemotherapy for advanced PDAC undergo tumor biopsy for RNA-sequencing (RNA-seq). Overall response rate (ORR) and overall survival (OS) were stratified by subtypes and according to chemotherapy received. Correlation of GATA6 with the subtypes using gene expression profiling, in situ hybridization (ISH) was explored. Results: Between December 2015 and May 2019, 195 patients (95%) had enough tissue for RNA-seq; 39 (20%) were classified as basal-like and 156 (80%) as classical. RECIST response data were available for 157 patients; 29 basal-like and 128 classical where the ORR was 10% versus 33%, respectively (P = 0.02). In patients with basal-like tumors treated with modified FOLFIRINOX (n = 22), the progression rate was 60% compared with 15% in classical PDAC (P = 0.0002). Median OS in the intention-to-treat population (n = 195) was 9.3 months for classical versus 5.9 months for basal-like PDAC (HR, 0.47; 95% confidence interval, 0.32–0.69; P = 0.0001). GATA6 expression by RNA-seq highly correlated with the classifier (P < 0.001) and ISH predicted the subtypes with sensitivity of 89% and specificity of 83%. In a multivariate analysis, GATA6 expression was prognostic (P = 0.02). In exploratory analyses, basal-like tumors, could be identified by keratin 5, were more hypoxic and enriched for a T-cell–inflamed gene expression signature. Conclusions: The basal-like subtype is chemoresistant and can be distinguished from classical PDAC by GATA6 expression. See related commentary by Collisson, p. 4715
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".