Abstract B111: HMGA2 and protein leucine methylation drive pancreatic cancer lineage plasticity
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is classified into two distinct subtypes, classical and basal. Basal PDACs (∼25% of PDACs) have the worst overall survival and are the only class to act as an independent poor prognostic factor. Therefore, there is an urgent need to understand basal PDAC development, identify biomarkers and find its unique vulnerabilities. We found high expression of the chromatin architectural protein, HMGA2 in the basal subtype of pancreatic cancer. This was across patient-derived cell lines, organoids and in RNA-seq and scRNA-seq patient datasets representing hundreds of patients. We also validated by multiplex immunohistochemistry (mIHC) in >500 diverse patient tissue microarray. We introduce a novel genetically engineered mouse model (GEMM) of basal pancreatic cancer. We find that in this model and in human orthotopic xenografts, HMGA2 is required for cancer growth and sufficient to drive a basal subtype. While HMGA2 is a chromatin factor, we have unexpectedly discovered that it plays an essential role in mRNA translation. Our data demonstrate that HMGA2 controls protein synthesis dynamics through reduced methylation of the protein phosphatase PP2A and subsequent activation of S6 Kinase, a key node of translation regulation. HMGA2 accomplishes this through inhibiting the expression of leucine carboxyl methyltransferase 1 (LCMT1). Importantly, loss of LCMT1 in the classical subtype is sufficient to increase mRNA translation and cause the acquisition of basal features. Thus, we describe a new mechanism by which lineage plasticity is regulated in pancreatic cancer. Most importantly, we demonstrate that high levels of HMGA2 in the basal subtype, and high levels of mRNA translation, render basal pancreatic cancer uniquely susceptible to inhibitors of protein synthesis. As such, we describe a new biomarker and subtype-specific treatment for basal pancreatic cancer. Citation Format: Sita Kugel, Stephanie Dobersch, Naomi Yamamoto, Aidan Schutter, Sarah M. Cavender, Tess M. Robertson, Gun Ho Jang, Annie N. Samraj, Faiyax Notta, Robert N. Eisenman, Andrew C. Hsieh. HMGA2 and protein leucine methylation drive pancreatic cancer lineage plasticity [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 B111.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".