Abstract LB-019: FGFR1 abnormalities in seizure-associated familial and sporadic dysembryoplastic neuroepithelial tumors
Bibliographic record
Abstract
Abstract Background: Dysembryoplastic neuroepithelial tumors (DNETs) are benign developmental brain tumors associated with intractable, drug-resistant epilepsy. Distinguishing this entity from other low-grade ganglioneuronal 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. Experimental procedures: We collected a family with three individuals with seizures and multinodular DNETs together with 100 sporadic tumors from 96 persons referred to us as DNETs. Whole-exome sequencing was performed on 46 tumours and targeted sequencing for hotspot FGFR1 mutations and BRAFp.V600E was used on the remaining samples. Blind neuropathology review and molecular characterization were performed. 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 of novel FGFR1p.R661P variant. Functional impact of this and other FGFR1 mutations were assessed using Flow Cytometry and β-galactosidase staining in HEK293 cells. Results: We identified a novel germline FGFR1 mutation, p.R661P, in a father and his two children with DNETs. Somatic activating FGFR1 mutations (p.N546K or p.K656E) were observed in cis in the tumors with the germline mutation. Pathology review distinguished DNETs (WHO grade I) (45%) from DNET-like tumors (55%). FGFR1 alterations, mainly intragenic tyrosine kinase FGFR1 duplication and multiple mutants in cis, characterized DNETs (25/43;58.1%) whereas FGFR1 mutations (10/53;19%) (p < 0.0001) and hotspot BRAFp.V600E (12/53;22.6%) (p < 0.001) were identified in DNET-like tumors. Phospho-ERK overexpression in FGFR1p.R661P and p.N546K mutant cells support enhanced MAPK/ERK activation in this condition. Conclusions: This study identifies constitutional and somatic FGFR1 alterations and hotspot BRAFV600E as key events in DNETs and DNET-like tumors respectively. The final common effect of these alterations appears to be a balanced level of signalling that results in benign rather than malignant tumors. The integrated pathology and molecular characterization performed here reveals the key role of the MAP-Kinase pathway in these epileptogenic low-grade glioneuronal tumors, pointing the way towards existing targeted therapies. Citation Format: Barbara Rivera, Tenzin Gayden, Jian Zhang, Javad Nadaf, Talia Boshari, Damien Faury, Michele Zeinieh, Romeo Blanc, David Burk, Somayyeh Fahiminiya, Eric Bareke, Ulrich Schueller, Camelia M. Monoranu, Ronald Sträter, Kornelius Kerl, Thomas Niederstadt, Gerhard Kurlemann, Benjamin Ellezam, Zuzanna Michalak, Maria Thom, Paul Lockhart, Richard Leventer, Milou Ohm, Duncan McGregor, David Jones, Jason Karamchandani, Celia Greenwood, Albert Berghuis, Susanne Bens, Reiner Siebert, Magdalena Zakrzewska, Pawel Liberski, Krzysztof Zakrzewski, Sanjay Sisodiya, Werner Paulus, Steffen Albrecht, Martin Hasselblatt, Nada Jabado, William D. Foulkes, Jacek Majewski. FGFR1 abnormalities in seizure-associated familial and sporadic dysembryoplastic neuroepithelial tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-019.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".