Mutant GNAS drives a pyloric metaplasia with tumor suppressive glycans in intraductal papillary mucinous neoplasia
Bibliographic record
Abstract
BACKGROUND & AIMS: Intraductal Papillary Mucinous Neoplasms (IPMNs) are cystic lesions and bona fide precursors for pancreatic ductal adenocarcinoma (PDAC). Recent studies have shown that pancreatic precancer is characterized by a transcriptomic program similar to gastric metaplasia. The aims of this study were to assay IPMN for pyloric markers, to identify molecular drivers, and to determine a functional role for this program in the pancreas. METHODS: Pyloric marker expression was evaluated by RNA-seq and multiplex immunostaining in patient samples. Cell lines and organoids expressing KrasG12D +/- GNASR201C underwent RNA sequencing. A PyScenic-based regulon analysis was performed to identify molecular drivers, and candidates were evaluated by RNA-seq, immunostaining, and small interfering RNA knockdown. Glycosylation profiling was performed to identify GNASR201C-driven changes. Glycan abundance was evaluated in patient samples. RESULTS: Pyloric markers were identified in human IPMN. GNASR201C drove expression of this program as well as an indolent phenotype characterized by distinct glycosyltransferase changes. Glycan profiling identified an increase in LacdiNAcs and loss of pro-tumorigenic Lewis antigens. Knockdown of transcription factors Spdef or Creb3l1 or chitinase treatment reduced LacdiNAc deposition and reversed the indolent phenotype. LacdiNAc and 3-sulfoLeA/C abundance discriminated low from high grade patient IPMN. CONCLUSION: GNASR201C drives an indolent phenotype in IPMN by amplifying a differentiated, pyloric phenotype through SPDEF/CREB3L1 which is characterized by distinct glycans. Acting as a glycan rheostat, mutant GNAS elevates LacdiNAcs at the expense of pro-tumorigenic acidic Lewis epitopes, inhibiting cancer cell invasion and disease progression. LacdiNAc and 3-Sulfo-LeA/C are mutually exclusive and may serve as markers of disease progression.
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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) | 0.002 | 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".