A multi-institutional phase 1 clinical trial exploring upfront multimodal standard of care and combined immunotherapies for newly diagnosed glioblastoma
Bibliographic record
Abstract
BACKGROUND: For newly diagnosed glioblastoma (GBM), a combination of upfront surgical immunotherapy with aglatimagene besadenovec (CAN-2409), followed by chemoradiation and then adjuvant nivolumab has not been tested. The aim of this study was to test the safety of this regimen and determine metrics of immune activation that may correlate with clinical outcomes. METHODS: Forty-one patients with suspected newly diagnosed GBM by imaging were enrolled in this multi-institutional, open-label, phase 1b clinical trial before surgical resection. Frozen section confirmation of high-grade glioma was required for administration of CAN-2409. This was then followed with chemoradiation and adjuvant nivolumab. Tumor and blood were assayed for genetic and immune markers before and during treatment. RESULTS: The regimen was well tolerated and generated measurable immune activation. Factors linked to survival were identified, such as baseline mutated gene pairs (eg, MED15/HRC), tumor immune cell composition, and changes in systemic cytokine, immune cells, and T-cell diversity. The most significant serial systemic immune changes were observed in a long-term survivor subset of patients with gross total resection (GTR)/methylated methylguanine methyltransferase (MGMT) promoter tumors. Median overall survival (mOS) in these patients was 30.6 months, while it was less for patients with unmethylated or subtotal resections. CONCLUSIONS: These findings suggest the opportunity for patient stratification and the potential for more durable antitumor immune responses in future clinical trials of this multimodal standard of care and combined immunotherapy regimen. ClinicalTrials.gov identifier: NCT03576612.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".