Anti‐O6‐Methylguanine‐Methyltransferase (MGMT) Immunohistochemistry in Glioblastoma Multiforme: Observer Variability and Lack of Association with Patient Survival Impede Its Use as Clinical Biomarker*
Bibliographic record
Abstract
Silencing of O6-methylguanine-DNA methyltransferase (MGMT) protein expression because of MGMT gene promoter hypermethylation is considered to be associated with postoperative chemoradiotherapy benefits in glioblastoma multiforme (GBM) patients. The objective of this study was to clarify the usability of MGMT immunohistochemistry (IHC) as a clinical biomarker. We immunostained a tissue microarray containing biopsy samples of 164 GBM patients from the European Organization for Research and Treatment of Cancer and the National Cancer Institute of Canada (EORTC/NCIC) trial 26981/22981 using two commercial anti-MGMT antibodies (clones MT3.1 and MT23.2). Immunostaining results were semiquantitatively evaluated by four observers from three neuropathological laboratories using a predefined algorithm. We analyzed (i) inter- and intraobserver agreement on MGMT expression (kappa statistics); (ii) correlation of MGMT expression with MGMT promoter methylation status (kappa statistics); and (iii) correlation of MGMT expression with patient outcome (log-rank test). Interobserver agreement on MGMT expression varied from slight to almost perfect, whereas intraobserver agreement ranged from substantial to almost perfect. MGMT expression showed poor to moderate correlation with MGMT promoter methylation status. We found no significant association of MGMT expression with patient outcome. In our hands, observer variability as well as lack of association with the MGMT promoter methylation status and patient survival impeded the use of anti-MGMT immunohistochemistry as a clinical biomarker for routine diagnostic purposes.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".