Randomized Phase II Study of Durvalumab with or without Tremelimumab in Patients with Metastatic Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: PD-L1 is overexpressed by dendritic cells in patients with metastatic castration-resistant prostate cancer (mCRPC) progressing on androgen receptor pathway inhibitors. We tested whether checkpoint blockade could enhance antitumor activity in mCRPC. PATIENTS AND METHODS: In a multicenter open-label noncomparative randomized phase II study, patients with mCRPC treated with ≤1 prior cytotoxic chemotherapy, with measurable disease and progression on abiraterone and/or enzalutamide, were randomized to durvalumab 1,500 mg intravenously every 4 weeks ±4 doses of tremelimumab 75 mg intravenously. The primary endpoint was objective response (OR) by iRECIST using a Simon two-stage design. Correlative testing included PD-L1/cluster designation 8 IHC on baseline tumor biopsies and deep targeted sequencing of plasma cell-free DNA. RESULTS: Fifty-two patients were enrolled. Median age was 70 years (range, 50-83 years), and 52% had prior taxane therapy for mCRPC. In stage I, 13 patients were randomized to durvalumab with no OR observed. Durvalumab + tremelimumab advanced to stage II with 39 patients enrolled (receiving a median three cycles, range 1-53). Durvalumab + tremelimumab-related adverse events were mainly ≤ grade 2 but led to discontinuation in seven patients. There were seven ORs [19.4% (95% confidence interval: 8.2%-36.0%); intention to treat 17.9% (95% confidence interval: 7.5%-33.5%)]. Five responding tumors were PD-L1-positive and two exhibited DNA damage repair defects. Responses were observed without high tumor mutational burden or other genomic indices of immunotherapy sensitivity. CONCLUSIONS: Durvalumab + tremelimumab is active in mCRPC, but patient selection remains a challenge. Further studies to develop predictive biomarkers are warranted.
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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) | 0.002 | 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) | 0.006 | 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".