Clinical Significance of Molecular Biomarkers in Glioblastoma
Bibliographic record
Abstract
AIM: To review the impact of molecular biomarkers on response to therapy and survival in patients with primary glioblastoma (GBM). MATERIALS & METHODS: Tissue specimens were analyzed for p53 mutations, EGFR amplification, loss of PTEN and p16, and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. Demographic and clinical data were gathered from medical records. RESULTS: Clinical and pathological data of 125 patients were collected and analysed. MGMT promoter methylation was associated with improved median overall survival (OS) (61 vs. 42 weeks, p = 0.01) and was an important prognosticator independent of age at diagnosis, extent of resection and post-operative ECOG performance status (HR 2.04, 95% CI 1.11-3.75). Among patients with MGMT promoter methylation, survival was significantly improved with chemoradiotherapy (CRT) over radiotherapy (RT) alone (71 vs. 14 weeks, p < 0.01). Furthermore, amongst those treated with temozolomide (TMZ) based CRT, the presence of EGFR amplification, maintenance of PTEN and wild-type p53 and p16 were each associated with trends towards improved survival. CONCLUSION: MGMT promoter methylation is a strong, independent prognostic factor for OS in GBM. EGFR amplification, maintenance of PTEN, wild-type p53 and p16 all appear to be associated with improved survival in patients treated with CRT. However, the prognostic value of these biomarkers could not be ascertained and larger prospective studies are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".