C-Terminal Truncation and Fusion Partner Determine Oncogenicity of FGFR3
Bibliographic record
Abstract
Genomic alterations affecting components of the fibroblast growth factor (FGF) signaling axis can trigger aberrant pathway activation and tumor development. Genomic truncation of the FGF receptor 2 (FGFR2) exon 18 (E18) disrupts the FGFR2 carboxy (C)-terminal tail, acting as a potent driver alteration across multiple tumor types. In this study, we analyzed human oncogenomic datasets to reveal that E18 truncations are similarly prevalent in FGFR3, an FGFR2 paralog. FGFR3 E18 truncations primarily occur due to rearrangements (RE) that involve transforming acidic coiled-coil-containing protein 3 (TACC3), resulting in FGFR3ΔE18-TACC3 gene fusions. In contrast to E18-truncated FGFR2, functional in vitro and in vivo examination of Fgfr3 variants demonstrated that the truncation of Fgfr3 E18 is insufficient to promote oncogenic activity in cell lines or in the lungs and mammary glands of mice. Only the combination of an Fgfr3 E18 truncation with a RE partner gene that encodes a receptor-dimerizing domain resulted in the development of tumors, which were sensitive to FGFR inhibition. Overall, these findings suggest that patients with cancers that are positive for rearranged FGFR3, resulting in E18 truncation and a fusion to dimerizing partners, should be considered for FGFR-targeted therapies. SIGNIFICANCE: FGFR3, unlike its paralog FGFR2, requires both a C-terminal truncation and fusion to a partner gene that retains the expression of a dimerizing domain to effectively drive oncogenic signaling and tumorigenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".