Phase II trial of concurrent sunitinib, temozolomide, and radiotherapy with adjuvant temozolomide for newly diagnosed MGMT unmethylated glioblastoma
Bibliographic record
Abstract
Abstract Background The overall prognosis of glioblastoma (GBM) remains dismal, particularly for patients with unmethylated O6-methylguanine-DNA-methyltransferase (MGMT) promoter. In this phase II trial, we tested the combination of the antiangiogenic agent sunitinib with radiotherapy and temozolomide (TMZ) for newly diagnosed unmethylated MGMT GBM patients. Methods We enrolled 37 patients with unmethylated MGMT promoter GBM, age 18–70, and KPS ≥70. Patients received 12.5 mg of daily sunitinib for 7 days, followed by concurrent chemoradiation plus 12.5 mg sunitinib, then adjuvant TMZ. The primary endpoint was progression-free survival (PFS), and secondary endpoints were overall survival (OS), safety, and neutrophil-to-lymphocyte ratio (NLR) biomarker. Results At a median follow-up time of 15.3 months (range: 3.1–71.3 months), the median PFS was 7.15 months (95% CI: 5.4–10.5) and the 6-month PFS was 54.0%. Median OS was 15.0 months (95% CI: 13.8–19.4) and 2-year OS rate was 17.1%. Patients receiving >3 cycles of adjuvant TMZ, undergoing surgery at progression, and presenting a post-concurrent NLR ≤6 experienced a significant improved OS with hazard ratios of 0.197 (P = .001), 0.46 (P = .049), and 0.38 (P = .021), respectively, on multivariable analysis. Age >65 years predicted for worse OS with hazard ratio of 3.92 (P = .037). Grade ≥3 thrombocytopenia occurred in 22.9%, grade ≥3 neutropenia in 20%, and grade ≥3 thromboembolic events in 14.3% of patients. There were no grade 5 events. Conclusion Our findings suggest a potential benefit of combining sunitinib with chemoradiation in newly diagnosed GBM patients with unmethylated MGMT status and provide a strong rationale to test this combination in future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".