Concurrence of FGFR1 mutations modulates oncogenesis in glioneuronal tumors
Bibliographic record
Abstract
FGFR1 genetic alterations are associated with brain malignancies, including FGFR1 mutations in familial and sporadic cases of low-grade glioneuronal tumors, suggesting intrinsic mechanisms of selective pressure toward FGFR1 multiple events arising in the context of a quiet genome. To decipher the molecular mechanisms triggered by multiple concurrent FGFR1 mutations, we have mapped the proximal interactome of wild-type, single- and double-mutant FGFR1 proteins through a BioID-MS approach. Our data reveal novel oncogenic functionality for the two hotspot mutations N546K and K656E, linked to evasion of lysosomal degradation. Further, we identified a modulatory tumor-suppressive role for the susceptibility variant R661P, which hampers the oncogenic potential of both hotspot N546K and K656E mutations by rescuing receptor degradation and reducing N546K affinity for the downstream effector PLCγ. Introducing the R661P missense variant was sufficient to abolish self-renewal capacity of oligodendroglioma cells and downregulate genes involved in neurodevelopment and neuro-glial cell fate decisions, both aspects overcome in the double mutants. This study sheds light on contextual oncogenic effects associated with FGFR1 alterations and their recurrence in low-mutation burden and therapy naive tumors.
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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".