Detection, Characterization, and Inhibition of FGFR–TACC Fusions in IDH Wild-type Glioma
Bibliographic record
Abstract
PURPOSE: Oncogenic fusions consisting of fibroblast growth factor receptor (FGFR) and TACC are present in a subgroup of glioblastoma (GBM) and other human cancers and have been proposed as new therapeutic targets. We analyzed frequency and molecular features of FGFR-TACC fusions and explored the therapeutic efficacy of inhibiting FGFR kinase in GBM and grade II and III glioma. EXPERIMENTAL DESIGN: Overall, 795 gliomas (584 GBM, 85 grades II and III with wild-type and 126 with IDH1/2 mutation) were screened for FGFR-TACC breakpoints and associated molecular profile. We also analyzed expression of the FGFR3 and TACC3 components of the fusions. The effects of the specific FGFR inhibitor JNJ-42756493 for FGFR3-TACC3-positive glioma were determined in preclinical experiments. Two patients with advanced FGFR3-TACC3-positive GBM received JNJ-42756493 and were assessed for therapeutic response. RESULTS: Three of 85 IDH1/2 wild-type (3.5%) but none of 126 IDH1/2-mutant grade II and III gliomas harbored FGFR3-TACC3 fusions. FGFR-TACC rearrangements were present in 17 of 584 GBM (2.9%). FGFR3-TACC3 fusions were associated with strong and homogeneous FGFR3 immunostaining. They are mutually exclusive with IDH1/2 mutations and EGFR amplification, whereas they co-occur with CDK4 amplification. JNJ-42756493 inhibited growth of glioma cells harboring FGFR3-TACC3 in vitro and in vivo. The two patients with FGFR3-TACC3 rearrangements who received JNJ-42756493 manifested clinical improvement with stable disease and minor response, respectively. CONCLUSIONS: RT-PCR sequencing is a sensitive and specific method to identify FGFR-TACC-positive patients. FGFR3-TACC3 fusions are associated with uniform intratumor expression of the fusion protein. The clinical response observed in the FGFR3-TACC3-positive patients treated with an FGFR inhibitor supports clinical studies of FGFR inhibition in FGFR-TACC-positive 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 | 0.000 |
| 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 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".