A deletion of FGFR2 creating a chimeric IIIb/IIIc exon in a child with Apert syndrome
Bibliographic record
Abstract
BACKGROUND: Signalling by fibroblast growth factor receptor type 2 (FGFR2) normally involves a tissue-specific alternative splice choice between two exons (IIIb and IIIc), which generates two receptor isoforms (FGFR2b and FGFR2c respectively) with differing repertoires of FGF-binding specificity. Here we describe a unique chimeric IIIb/c exon in a patient with Apert syndrome, generated by a non-allelic homologous recombination event. CASE PRESENTATION: We present a child with Apert syndrome in whom routine genetic testing had excluded the FGFR2 missense mutations commonly associated with this disorder. The patient was found to harbour a heterozygous 1372 bp deletion between FGFR2 exons IIIb and IIIc, apparently originating from recombination between 13 bp of identical DNA sequence present in both exons. The rearrangement was not present in the unaffected parents. CONCLUSIONS: Based on the known pathogenesis of Apert syndrome, the chimeric FGFR2 protein is predicted to act in a dominant gain-of-function manner. This is likely to result from its expression in mesenchymal tissues, where retention of most of the residues essential for FGFR2b binding activity would result in autocrine activation. This report adds to the repertoire of rare cases of Apert syndrome for which a pathogenesis based on atypical FGFR2 rearrangements can be demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".