LG-26GERMLINE AND SOMATIC FGFR1 ABNORMALITIES IN DYSEMBRYOPLASTIC NEUROEPITHELIAL TUMORS
Bibliographic record
Abstract
Dysembryoplastic neuroepithelial tumors (DNETs) are benign brain tumors associated with intractable, drug-resistant epilepsy. Distinguishing DNETs from other low-grade glioneuronal tumors is challenging for neuro-pathologists. We set out to identify the genetic causes of DNETs and to clarify the molecular mechanisms underlying this condition. We studied a family with multinodular DNETs together with 100 sporadic tumors referred to us as DNETs. Whole-exome sequencing was performed on 46 tumors and targeted sequencing for hotspot FGFR1 mutations and BRAFp.V600E was used on the remaining samples. Blind neuropathology review, FISH, copy number variation assays and Sanger sequencing were used to validate the findings. Supporting evidence for functional defects was obtained by in silico modelling, Flow Cytometry and -galactosidase staining. We identified a novel germline FGFR1 mutation (p.R661P) and somatic activating FGFR1 mutations (p.N546K or p.K656E) in a father and his two children with DNETs. Pathology review distinguished DNETs (WHO grade I) (45%) from non-DNETs (55%). FGFR1 alterations, mainly intragenic tyrosine kinase duplication and multiple mutants in cis, characterized DNETs (58.1%) whereas FGFR1 mutations (19%) (p= 3.698e-05) and hotspot BRAFp.V600E (22.6%) (p = 0.00046) were identified in case were DNET diagnosis was not confirmed. Phospho-ERK overexpression in FGFR1p.R661P and p.N546K cells support enhanced MAPK/ERK activation in this condition. This study identifies constitutional and somatic FGFR1 alterations and hotspot BRAF-V600E as key events in DNETs and non-DNET tumors respectively. The integrated pathology and molecular characterization performed reveals the key role of the MAP-Kinase pathway in these seizure-prone tumours, pointing the way towards existing targeted therapies.
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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 | 0.001 | 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) | 0.002 | 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".