Novel Fibroblast Growth Factor Receptor 3–Fatty Acid Synthase Gene Fusion in Recurrent Epithelioid Glioblastoma Linked to Aggressive Clinical Progression
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with a median overall survival (OS) of 15-18 months despite standard treatments. Approximately 8% of GBM cases exhibit genomic alterations in fibroblast growth factor receptors (FGFRs), particularly FGFR1 and FGFR3. Next-generation sequencing techniques have identified various FGFR3 fusions in GBM. This report presents a novel FGFR3 fusion with fatty acid synthase (FASN) in a 41-year-old male diagnosed with GBM. The patient presented with a persistent headache, and imaging revealed a right frontal lobe lesion. Surgical resection and subsequent histopathology confirmed GBM. Initial NGS analysis showed no mutations in the IDH1, IDH2 or H3F3 genes, but revealed a TERT promoter mutation and CDKN2A/2B and PTEN deletions. Postoperative treatment included radiotherapy and temozolomide. Despite initial management, recurrence occurred four months post-diagnosis, confirmed by MRI and histology. A second surgery identified a novel FGFR3-FASN fusion, alongside increased Ki67 expression. The recurrence was managed with regorafenib and bevacizumab, though complications like hand-foot syndrome and radiation necrosis arose. Despite initial improvement, the patient died 15 months after diagnosis. This case underscores the importance of understanding GBM's molecular landscape for effective treatment strategies. The novel FGFR3-FASN fusion suggests potential implications for GBM recurrence and lipid metabolism. Further studies are warranted to explore FGFR3-FASN's role in GBM and its therapeutic targeting.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".