Efficacy and Safety of Atezolizumab in Chinese Patients With Advanced Thymic Carcinoma: A Multicenter, Single-Arm Phase 2 Study
Bibliographic record
Abstract
INTRODUCTION: There are limited treatment options for thymic carcinoma. This study aimed to evaluate the efficacy and safety of atezolizumab for the treatment of advanced thymic carcinoma after failure with prior systemic therapy. PATIENTS AND METHODS: Patients aged ≥ 18 years with advanced thymic carcinoma after failure with prior systemic therapy were enrolled at 10 medical centers in China. Atezolizumab 1200 mg was administered on Day 1 (baseline) every 3 weeks until unacceptable toxicity/loss of clinical benefit. The primary efficacy endpoint was the confirmed objective response rate in the full analysis set. RESULTS: Between 29 July 2020 and 28 December 2022, 34 patients enrolled (median age, 54.5 years), and 16 (47.1%) had received ≥ 2 prior lines of therapy. At the cut-off date (July 16, 2023), the median follow-up duration was 19.1 months. The confirmed objective response rate (ORR) was 14.7% (95% confidence interval, 5.0%-31.1%); disease control rate, 58.8%; median progression-free survival (PFS), 3.2 months; 24-month OS rate, 63.2%; Better ORR and PFS were observed in PD-L1-positive patients versus PD-L1-negative patients (40.0% vs. 0% and 7.4 vs. 1.5 months, respectively). No correlation was observed with TMB. The incidence of adverse events (AEs) was 94.1%; serious AEs, 26.5%; immune-related AEs, 29.4%; and AEs leading to drug withdrawal, 2.9%. CONCLUSIONS: In patients with advanced thymic carcinoma who had failed prior systemic therapy, atezolizumab was well-tolerated with a manageable toxicity profile and showed encouraging the antitumor activity, especially in patients with PD-L1-positive tumors.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".