Trial reportJAMA oncology2017
Molecular-Based Recursive Partitioning Analysis Model for Glioblastoma in the Temozolomide Era: A Correlative Analysis Based on NRG Oncology RTOG 0525.
Trial report in JAMA oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00304031 (Phase III Trial Comparing Conventional Adjuvant Temozolomide With Dose-Intensive Temozolomide in Patients With Newly Diagnosed Glioblastoma), which is not on this map. Cited by 63 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Phase III Trial Comparing Conventional Adjuvant Temozolomide With Dose-Intensive Temozolomide in Patients With Newly Diagnosed Glioblastoma
Who cites it
63 citing papers in PubMed, 8 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Moderately hypofractionated versus conventionally fractionated radiation therapy with temozolomide for young and fit patients with glioblastoma: an institutional experience and meta-analysis of literature.Journal of neuro-oncology · 2022Pooled it
- Prognostic value of programmed death ligand 1 (PD-L1) in glioblastoma: a systematic review, meta-analysis and validation based on dataset.Bioengineered · 2021Pooled it
- Overexpression of c-Met is Associated with Poor Prognosis in Glioblastoma Multiforme: A Systematic Review and Meta-Analyses.Asian Pacific journal of cancer prevention : APJCP · 2021Pooled it
- MGMT promoter methylation testing to predict overall survival in people with glioblastoma treated with temozolomide: a comprehensive meta-analysis based on a Cochrane Systematic Review.Neuro-oncology · 2021Pooled it
- Prognostic value of test(s) for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation for predicting overall survival in people with glioblastoma treated with temozolomide.The Cochrane database of systematic reviews · 2021Pooled it
- Hypofractionated versus standard radiation therapy in combination with temozolomide for glioblastoma in the elderly: a meta-analysis.Journal of neuro-oncology · 2019Pooled it
- Clinically Relevant Imaging Features forAJNR. American journal of neuroradiology · 2018Pooled it
- Prognostic role of survivin in patients with glioma.Medicine · 2018Pooled it
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Timing of adjuvant chemoradiotherapy after resection and survival in glioblastoma: a single-centre cohort analysis.Journal of neuro-oncology · 2026Article
- Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.JNCI cancer spectrum · 2026Article
- Serum p-Cresol and 7-HOCA Levels and Fatty Acid and Purine Metabolism Are Associated with Survival, Progression, and Molecular Classification in GB-Serum Proteome and Metabolome Analysis Pre vs. Post Up-Front Chemoirradiation.Current oncology (Toronto, Ont.) · 2025Article
- Effects of temozolomide dosing on outcome in glioblastoma.Journal of neuro-oncology · 2025Article
- EGFR pathway alterations correlate with rapid early progression in glioblastoma.Journal of neuro-oncology · 2025Article
- Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Tumor cell-derived spermidine promotes a protumorigenic immune microenvironment in glioblastoma via CD8+ T cell inhibition.The Journal of clinical investigation · 2024Article
- Non-Immune-Mediated, p27-Associated, Growth Inhibition of Glioblastoma by Class-II-Transactivator (CIITA).Cells · 2024Article
- Incorporating Supramaximal Resection into Survival Stratification of IDH-wildtype Glioblastoma: A Refined Multi-institutional Recursive Partitioning Analysis.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model.Journal of neuro-oncology · 2024Article
- EMP3 as a prognostic biomarker correlates with EMT in GBM.BMC cancer · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors at 19 institutions in 5 countries.
Funding
Abstract
importanceThere is a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temozolomide era.
objectiveTo refine the existing clinically based recursive partitioning analysis (RPA) model by incorporating molecular variables. DESIGN, SETTING, AND
participantsNRG Oncology RTOG 0525 specimens (n = 452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semiquantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multimarker Cox regression analyses. To reclassify the prognostic risk groups, significant protein biomarkers on single-marker analysis were incorporated into an RPA model consisting of the same clinical variables (age, Karnofsky Performance Status, extent of resection, and neurologic function) as the existing RTOG RPA. The new RPA model (NRG-GBM-RPA) was confirmed using traditional immunohistochemistry in an independent data set (n = 176). MAIN OUTCOMES AND MEASURES: Overall survival (OS).
resultsIn 452 specimens, MGMT (hazard ratio [HR], 1.81; 95% CI, 1.37-2.39; P < .001), survivin (HR, 1.36; 95% CI, 1.04-1.76; P = .02), c-Met (HR, 1.53; 95% CI, 1.06-2.23; P = .02), pmTOR (HR, 0.76; 95% CI, 0.60-0.97; P = .03), and Ki-67 (HR, 1.40; 95% CI, 1.10-1.78; P = .007) protein levels were found to be significant on single-marker multivariate analysis of OS. To refine the existing RPA, significant protein biomarkers together with clinical variables (age, Karnofsky Performance Status, extent of resection, and neurological function) were incorporated into a new model. Of 166 patients used for the new NRG-GBM-RPA model, 97 (58.4%) were male (mean [SD] age, 55.7 [12.0] years). Higher MGMT protein level was significantly associated with decreased MGMT promoter methylation and vice versa (1425.1 for methylated vs 1828.0 for unmethylated; P < .001). Furthermore, MGMT protein expression (HR, 1.84; 95% CI, 1.38-2.43; P < .001) had greater prognostic value for OS compared with MGMT promoter methylation (HR, 1.77; 95% CI, 1.28-2.44; P < .001). The refined NRG-GBM-RPA consisting of MGMT protein, c-Met protein, and age revealed greater separation of OS prognostic classes compared with the existing clinically based RPA model and MGMT promoter methylation in NRG Oncology RTOG 0525. The prognostic significance of the NRG-GBM-RPA was subsequently confirmed in an independent data set (n = 176). CONCLUSIONS AND RELEVANCE: This new NRG-GBM-RPA model improves outcome stratification over both the current RTOG RPA model and MGMT promoter methylation, respectively, for patients with GBM treated with radiation and temozolomide and was biologically validated in an independent data set. The revised RPA has the potential to contribute to improving the accurate assessment of prognostic groups in patients with GBM treated with radiation and temozolomide and to influence clinical decision making.
trial registrationclinicaltrials.gov Identifier: NCT00304031.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.