HMGA2 Expression Predicts Subtype, Survival, and Treatment Outcome in Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to establish HMGA2 as a marker of basal-like disease in pancreatic ductal adenocarcinoma (PDAC) and explore its use as a biomarker for prognosis and treatment resistance. EXPERIMENTAL DESIGN: We identified high-mobility group A2 (HMGA2) protein expression in basal PDAC cells in a single-cell RNA sequencing (RNA-seq) atlas of 172 patient samples. We then analyzed HMGA2 expression, along with expression of the classic marker GATA-binding factor 6 (GATA6), in a cohort of 580 PDAC samples with multiplex IHC. We further supplemented these data with an additional 30 diverse patient samples and multiple independent single-cell RNA-seq databases. RESULTS: We found that expression of HMGA2, but not previously described basal markers cytokeratins 5 or 17, predicted overall survival in our cohort. Combining HMGA2 and GATA6 statuses allowed for the identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, low tumor-infiltrating CD8+ T cells, increased FAP+ fibroblasts, and poorer response to gemcitabine-based chemotherapies (n = 94, median survival = 11.2 months after surgery) and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T-cell infiltrate, decreased FAP+ fibroblasts, and improved survival with gemcitabine-based chemotherapy (n = 198, median survival = 21.7 months after surgery). HMGA2 was also prognostic for overall survival in RNA-seq from an independent cohort. CONCLUSIONS: IHC stratification of primary tumors by HMGA2 and GATA6 statuses in pancreatic cancer is associated with differential outcomes, survival following chemotherapy, and tumor microenvironments. As a nuclear marker for basal disease, HMGA2 complements GATA6 to identify disease subtypes in PDAC.
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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 | 0.000 | 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".