Immune-related adverse events as potential surrogates of immune checkpoint inhibitors’ efficacy: a systematic review and meta-analysis of randomized studies
Bibliographic record
Abstract
Background: Immune-related adverse events (irAEs) are frequently reported during immune checkpoint inhibitor (ICI) therapy and are associated with long-term outcomes.It is unknown if the irAE occurrence is a valid surrogate of ICIs' efficacy.Methods: We identified articles reporting the results of randomized trials of experimental ICI therapy in solid tumors with a systematic search.The control arms could be placebo, cytotoxic/targeted therapy, or ICI therapy.We extracted the hazard ratios for overall survival (OS) with the number of OS events per arm and the number and percentages of overall and specific irAEs of grade 1-2 and grade 3-4 per arm.We estimated the treatment effect on the potential surrogate outcome with the odds ratio of the irAE rate between the experimental and the control arm.The statistical analysis consisted of weighted linear regression on a logarithmic scale between treatment effects on irAE rate and treatment effects on OS.Results: Sixty-two randomized trials were included for a total of 79 contrasts and 42 247 patients.The analyses found no significant association between the treatment effects for overall grade 1-2 or grade 3-4 irAE rates or specific (skin, gastrointestinal, endocrine) irAE rates.In the non-small-cell lung cancer (NSCLC) trial subset, we observed a negative association between treatment effects on overall grade 1-2 irAEs and treatment effects on OS in studies with patients selected for programmed death-ligand 1 expression (R 2 ¼ 0.55; 95% confidence interval 0.20-0.95;R ¼ À0.69).In the melanoma trial subset, a negative association was shown between treatment effects on gastrointestinal grade 3-4 irAEs and treatment effects on OS in trials without an ICI-based control arm (R 2 ¼ 0.77; 95% confidence interval 0.24-0.99;R ¼ À0.89).Conclusions: We found low-strength correlations between the ICI therapy effects on overall or specific irAE rates and the treatment effects on OS in several cancer types.
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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 | 0.029 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".