Targeting FGFR4 abrogates HNF1A-driven metastasis in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: We previously identified an oncogenic role for the transcription factor HNF1A in pancreatic ductal adenocarcinoma (PDAC). However, the role of HNF1A in the metastatic progression of PDAC remains unknown and targeting modalities for HNF1A-dependent phenotypes have yet to be identified. METHODS: Transwell chambers were used to assess the effects of HNF1A and FGFR4 modulation on the migration and invasion of ATCC and patient-derived PDAC cells in vitro. An intrasplenic injection xenograft model was used to evaluate the impact of HNF1A knockdown and overexpression on metastatic tumor burden. Single-cell RNA sequencing (scRNA-seq), tissue microarray (TMA) data, and UMAP spatial profiling were used to identify FGFR4 as an HNF1A target gene upregulated in metastatic cells. RNAi and two FGFR4 inhibiting modalities (H3B-6527 and U3-1784) were utilized to demonstrate the efficacy of FGFR4 inhibiting agents at reducing HNF1A-driven metastasis. RESULTS: Knockdown of HNF1A significantly decreases and HNF1A overexpression significantly increases PDAC cell migration and invasion. In vivo studies show that HNF1A knockdown significantly abrogates metastasis, while overexpression significantly promotes metastasis. scRNA-seq shows that FGFR4 is upregulated in metastatic PDAC cells and staining for HNF1A and FGFR4 in a PDAC TMA reveals significant correlation between HNF1A and FGFR4 in PDAC patients. Further, knockdown and inhibition of FGFR4 significantly decreases HNF1A-mediated cell migration and invasion, and blocks HNF1A-driven metastasis in vivo. CONCLUSIONS: These findings demonstrate that HNF1A drives PDAC metastasis via upregulation of FGFR4, and FGFR4 inhibition is a potential mechanism to target metastasis in PDAC patients.
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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 | 0.001 |
| 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".