Her2neu Amplification Associates with Co-deletion 1p/14q in Recurrent Meningiomas
Bibliographic record
Abstract
BACKGROUND: The current methods to predict recurrence and aggressive behaviour of meningiomas rely mainly on histological grading, histological subtype, proliferative index, as well as brain invasion. In many instances, histological grade alone fails to predict recurrence in the grade I and grade II meningiomas. Deletions of 1p and 14q have previously been reported to correlate with poor prognosis in terms of either recurrence or higher histological grades. The Her2neu (ErbB2) amplification has been shown to be a useful predictor of aggressive behaviour in breast and ovarian tumours, but its significance in meningioma is so far uncertain. METHOD: In order to determine the cytogenetic differences between 22 recurrent and 25 non-recurrent meningiomas of all grades, we used fluorescent in situ hybridization (FISH) DNA probes for 1p36, 14q11.2 and 17q11.2-12 (Her2neu) on formalin fixed paraffin embedded (FFPE) tissue from the Brain Tumour Tissue Bank (BTTB), London Health Science Center (LHSC). RESULTS: We showed a positive association for meningioma recurrence correlated with 1p36 deletion plus or minus 14q 11.2 deletions in all grades of meningiomas. The Her2neu amplification was strongly associated with 1p/14q co-deletion in cases of recurrent meningiomas, especially the higher grade tumours. CONCLUSION: These cytogenetic markers can be applied in addition to histological grading for predicting the risk of recurrence and biological behaviour.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".