MPTH-25AN 18-GENE EXPRESSION SIGNATURE PREDICTS RECURRENCE-FREE SURVIVAL IN MENINGIOMA
Bibliographic record
Abstract
Meningioma is the most common primary brain tumor and carries a substantial risk of local recurrence. While WHO grade correlates with recurrence to a degree, there is substantial within-grade variation of recurrence risk and current risk stratification does not accurately predict which patients are likely to benefit from adjuvant radiation therapy. We hypothesized that recurrent tumors have unique gene expression profiles (GEP) that could be used to better stratify patients for radiation therapy. We optimized a recurrence predictor using a training/validation approach and a support vector machine classification method with radial-basis smoothing kernel. Three publicly available Affymetrix gene expression datasets (GSE9438, GSE16581, GSE43290) combining 127 newly diagnosed meningioma samples served as the training set. Unsupervised variable selection was used to identify an 18-gene GEP model (18-GEP) that separated recurrences with a negligible root mean square error of 0.17. The characteristics of the training dataset were as follows: WHO grade [I-92(73%), II-32(25%), III-2(2%)]; median follow-up = 5.53 years (range:0.05-25.42); recurrences = 18. This model was tested on 62 cases from our institution [validation dataset (VD)] with similar demographics, but enriched for cases with either long clinical follow-up or known recurrence. When applied to the VD, the 18-GEP separated recurrences with a misclassification error rate of 0.25 (log-rank p = 0.0003). 18-GEP was significantly predictive of tumor recurrence, independently [p = 0.0007, HR = 7.91, 95%CI = 2.35-35.78)] and was predictive after adjustment for WHO grade, mitotic index, and Simpson grade [p = 0.047, HR = 4.73, 95%CI = (1.02-26.55)]. The expression signature included genes encoding proteins involved in normal embryonic development, cell proliferation, tumor growth and invasion (FGF9, SEMA3C, EDNRA), angiogenesis (angiopoietin-2), cell cycle regulation (CDKN1A), membrane signaling (tetraspanin-7, caveolin-2), WNT-pathway inhibitors (DKK3), complement system (C1QA) and neurotransmitter regulation (SLC1A3, Secretogranin-II). In conclusion, our gene expression classifier accurately stratifies patients with meningioma by recurrence risk and has the potential to guide therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".