BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort
Bibliographic record
Abstract
PURPOSE: Glioblastoma, the most common malignant brain tumor, was associated with a median survival of <1 year in the pre-temozolomide (TMZ) era. Despite advances in molecular and genetic profiling studies identifying several predictive biomarkers, none has been translated into routine clinical use. Our aim was to investigate the prognostic significance of a panel of diverse cellular molecular markers of tumor formation and growth in an annotated glioblastoma tissue microarray (TMA). METHODS AND MATERIALS: A TMA composed of archived glioblastoma tumors from patients treated with surgery, radiation, and non-TMZ chemother-apy, was provided by RTOG. RAD51, BRCA-1, phosphatase and tensin homolog tumor suppressor gene (PTEN), and miRNA-210 expression levels were assessed using quantitative in situ hybridization and automated quantitative protein analysis. The objectives of this analysis were to determine the association of each biomarker with overall survival (OS), using the Cox proportional hazard model. Event-time distributions were estimated using the Kaplan-Meier method and compared by the log-rank test. RESULTS: A cohort of 66 patients was included in this study. Among the 4 biomarkers assessed, only BRCA1 expression had a statistically significant correlation with survival. From univariate analysis, patients with low BRCA1 protein expression showed a favorable outcome for OS (p = 0.04; hazard ratio = 0.56) in comparison with high expressors, with a median survival time of 18.9 versus 4.8 months. CONCLUSIONS: BRCA1 protein expression was an important survival predictor in our cohort of glioblastoma patients. This result may imply that low BRCA1 in the tumor and the consequent low level of DNA repair cause vulnerability of the cancer cells to treatment.
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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 | 0.000 | 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) | 0.001 | 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".