The safety and efficacy of immune checkpoint blockade in children, adolescents, and young adults: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Immune checkpoint blockade (ICB) has changed the treatment landscape for many types of adult cancer. However, for children, adolescents, and young adults (CAYAs) clinical experience lags behind that of adults. Therefore, we performed a systematic review and meta-analysis to evaluate the safety and efficacy of ICB in the CAYA population. Methods: We searched PubMed, Embase, and Cochrane Library databases for clinical trials evaluating ICB therapies for cancer in CAYA patients. We pooled the incidences of treatment-related adverse events (TRAEs), objective response rates (ORRs), stable disease (SD), and their corresponding confidence intervals (CIs). For the ORR and TRAE endpoints, we performed a subgroup analysis of each drug (PD-1, PD-L1, and CTLA-4) and tumor type. Results: 15 trials were included, comprising 797 patients (median age ranging from 6.5 to 16.0 years). All-grade TRAE rate of 66 % was found (95 % CI 60–71), while the proportion of grade 3/4 TRAEs was 19 % (95 % CI 14–27). For tumor type subgroup analysis of all-grade TRAEs and grade 3/4 TRAEs, solid tumors had the highest rates, 92 % (95 % CI 41–99) and 32 % (95 % CI 11–63), respectively. Fatigue, anemia, and nausea were the most frequently reported TRAEs. The ORR was 13 % (95 % CI 5–27). In subgroup analyses, PD-1 inhibitors and Hodgkin Lymphoma had the highest ORR, with 25 % (95 % CI 8–56) and 59 % (95 % CI 23–87), respectively. SD was noted in 21 % (95 % CI 14–30) of patients. Conclusions: Overall, ICB is well tolerated in CAYA patients with different cancer types, and certain subsets of CAYA cancer are ICB-responsive.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".