Abstract B112: HMGA2 Predicts Treatment Outcome in Pancreatic Cancer
Bibliographic record
Abstract
Abstract The recognition of distinct transcriptional subtypes in pancreatic ductal adenocarcinoma (PDAC) has defined a group of poorly differentiated tumors with worse prognosis, but these findings have yet to reach clinical application. These tumors, termed “basal,” are unique for their loss of epithelial identity and relative chemoresistance compared to “classical” tumors. To develop a prognostic biomarker for the basal subtype, we have identified that the chromatin architectural protein HMGA2 is highly expressed in this subset of cancers. Using a tumor microarray of 580 primary biopsies from a diverse set of PDAC patients undergoing surgical resection, we performed multiplex immunohistochemistry for HMGA2, previously published markers of basal and classical disease, and immune subsets. We then associated patient outcome and known clinical data from 491 of these samples to staining patterns. We found that expression of HMGA2, but not published basal markers CK5 or CK17, predicted overall survival in our cohort. Combination of HMGA2 status with GATA6 status allowed for identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, decreased CD8+ T cells, and poorer response to gemcitabine-based chemotherapies (n=94, median survival = 11.2 months post-surgery); and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T cell infiltrate, and improved survival with gemcitabine-based chemotherapy (n=198, median survival = 21.7 months post-surgery). Importantly, these findings were also true for Black patients, who have been underrepresented in previous subtyping studies. HMGA2 was also predictive of overall survival in RNA sequencing from metastatic tumors in an independent cohort. As a positive nuclear marker for basal disease, HMGA2 complements GATA6 as a dual-indicator test for disease subtype in PDAC. We aim to introduce this novel biomarker in a prospective multi-center clinical trial to further validate its use in selecting chemotherapy regimens and across other under-represented racial groups. Citation Format: Naomi Yamamoto, Stephanie Dobersch, Ian Loveless, Annie N. Samraj, Gun Ho Jang, Miki Haraguchi, Liang-I Kang, Marianna B. Ruzinova, Kiran R. Vij, Jacqueline L. Mudd, Thomas Walsh, Rachael A. Safyan, E. Gabriela Chiorean, Sunil R. Hingorani, Nathan M. Bolton, Li Li, Ryan C. Fields, David G. DeNardo, Faiyaz Notta, Howard C. Crawford, Nina G. Steele, Sita Kugel. HMGA2 Predicts Treatment Outcome in Pancreatic Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B112.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".