Efficacy and safety of avelumab treatment in patients with metastatic Merkel cell carcinoma: experience from a global expanded access program
Bibliographic record
Abstract
BACKGROUND: Avelumab, a human anti-programmed death-ligand 1 immunoglobulin G1 monoclonal antibody, showed favorable efficacy and safety in patients with metastatic Merkel cell carcinoma (mMCC) in the phase II JAVELIN Merkel 200 trial, leading to approval in multiple countries. We describe real-world experience with avelumab in patients with mMCC from an expanded access program. METHODS: Eligible patients had mMCC and progressive disease during or after chemotherapy or were ineligible for chemotherapy or clinical trial participation. Patients received an initial 3-month supply of avelumab (administered as 10 mg/kg intravenously every 2 weeks until progressive disease or unacceptable toxicity); resupply was allowed following complete response, partial response, stable disease, or clinical benefit per physician assessment. RESULTS: Between December 15, 2015, and March 4, 2019, 558 of 620 requests from 38 countries were medically approved, and 494 patients received avelumab. Among 240 evaluable patients, the objective response rate was 46.7% (complete response in 22.9%, including 3 of 16 potentially immunocompromised patients), and the disease control rate was 71.2%. The median duration of treatment in evaluable patients with response was 7.9 months (range, 1.0-41.7) overall and 5.2 months (range, 3.0-13.9) in immunocompromised patients. No new safety signals were identified. The expanded access program closed for new requests on December 31, 2018, as required after regulatory approval; benefitting patients continued to receive avelumab. CONCLUSIONS: The avelumab expanded access program for patients with mMCC demonstrated efficacy and safety in a real-world setting, consistent with the results from JAVELIN Merkel 200, and provided a treatment for patients with limited options.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".