A Phase Two, Single-Arm, Open-Label Study With Dostarlimab Monotherapy in Participants With Untreated Stage II/III dMMR/MSI-H Locally Advanced Rectal Cancer (AZUR-1)
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) had the second highest cancer mortality worldwide in 2020; nearly a third of CRCs were rectal cancers (RC). A recent study demonstrated that dostarlimab, an immune-checkpoint inhibitor, was highly effective in treating mismatch repair deficient (dMMR) locally advanced RC as all included patients had a clinical complete response (cCR) without radiation or chemotherapy. This study's objective is to evaluate the efficacy and safety of dostarlimab monotherapy in patients with previously untreated locally advanced dMMR RC. PATIENTS/METHODS: AZUR-1 (NCT05723562) is a multicenter, open-label, nonrandomized, single-arm phase 2 study enrolling approximately 150 patients across 10 countries. Key eligibility criteria include dMMR status or microsatellite instability-high (MSI-H) phenotype. Dostarlimab 500 mg will be administered intravenously every 3 weeks for 9 cycles. The primary endpoint is cCR by independent central review (ICR) at 12 months. Key secondary endpoints include cCR by ICR at 24 and 36 months, and 3-year event-free survival by investigator assessment. Additional secondary endpoints include organ preservation rate at 3 years and disease-specific survival and overall survival at 5 years. Efficacy and safety will be assessed in all patients who receive ≥1 dose of dostarlimab. All patients will be followed for 5 years (unless consent is withdrawn). CONCLUSIONS: AZUR-1 will evaluate the efficacy of dostarlimab immunotherapy in dMMR/MSI-H RC. Utilizing novel aspects including long follow-up of all patients and standardization of clinical response assessment, this study will provide international multicentric data to evaluate tumor response in an immunotherapy setting and new evidence on long-term outcomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".