The FGFR4-G388R Single-Nucleotide Polymorphism Alters Pancreatic Neuroendocrine Tumor Progression and Response to mTOR Inhibition Therapy
Bibliographic record
Abstract
Pancreatic neuroendocrine tumors (pNET), also known as islet cell tumors, exhibit a wide range of biologic behaviors ranging from long dormancy to rapid progression. Currently, there are few molecular biomarkers that can be used to predict recurrence/metastasis or response to therapy. This study examined the predictive and prognostic value of a single nucleotide polymorphism substituting an arginine (R) for glycine (G) in codon 388 of the FGFR4 transmembrane domain. We established the FGFR4 genotype of 71 patients with pNETs and correlated genotype with biologic behavior. We created an in vivo model of pNET with BON1 cells and transfected them with either FGFR4-G388 or FGFR4-R388 to determine the mechanism of action and to examine response to the mTOR inhibitor everolimus. We then validated the predictive results of experimental studies in a group of patients treated with everolimus. FGFR4-R388 is associated with more aggressive clinical behavior in patients with pNETs with a statistically significant higher risk of advanced tumor stage and liver metastasis. Using an orthotopic mouse xenograft model, we show that FGFR4-R388 promotes tumor progression by increasing intraperitoneal spread and metastatic growth within the liver. Unlike FGFR4-G388, FGFR4-R388 BON1 tumors exhibited diminished responsiveness to everolimus. Concordantly, there was a statistically significant reduction in response to everolimus in patients with FGFR4-R388. Our findings highlight the importance of the FGFR4 allele in pNET progression and identify a predictive marker of potential therapeutic importance in this disease.
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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 | 0.000 |
| 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".