Safety, Immunologic, and Clinical Activity of Durvalumab in Combination with Olaparib or Cediranib in Advanced Leiomyosarcoma: Results of the DAPPER Clinical Trial.
Bibliographic record
Abstract
PURPOSE: Non-inflamed (cold) tumors such as leiomyosarcoma do not benefit from immune checkpoint blockade (ICB) monotherapy. Combining ICB with angiogenesis or PARP inhibitors may increase tumor immunogenicity by altering the immune cell composition of the tumor microenvironment (TME). The DAPPER phase II study evaluated the safety, immunologic, and clinical activity of ICB-based combinations in pretreated patients with leiomyosarcoma. PATIENTS AND METHODS: Patients were randomized to receive durvalumab 1,500 mg IV every 4 weeks with either olaparib 300 mg twice a day orally (Arm A) or cediranib 20 mg every day orally 5 days/week (Arm B) until unacceptable toxicity or disease progression. Paired tumor biopsies, serial radiologic assessments and stool collections were performed. Primary endpoints were safety and immune cell changes in the TME. Objective responses and survival were correlated with transcriptomic, radiomic, and microbiome parameters. RESULTS: Among 30 heavily pretreated patients (15 on each arm), grade ≥ 3 toxicity occurred in 3 (20%) and 2 (13%) on Arms A and B, respectively. On Arm A, 1 patient achieved partial response (PR) with increase in CD8 T cells and macrophages in the TME during treatment, while 4 had stable disease (SD) ≥ 6 months. No patients on Arm B achieved PR or SD ≥ 6 months. Transcriptome analysis showed that baseline M1-macrophage and B-cell activity were associated with overall survival. CONCLUSIONS: Durvalumab plus olaparib increased immune cell infiltration of TME with clinical benefit in some patients with leiomyosarcoma. Baseline M1-macrophage and B-cell activity may identify patients with leiomyosarcoma with favorable outcomes on immunotherapy and should be further evaluated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".