Comparative efficacy, safety, and tolerability of immune checkpoint inhibitors (ICIs) in cancer.
Bibliographic record
Abstract
e15151 Background: Multiple ICIs have been approved, and in some diseases there is a choice of more than one ICI. The comparative safety, efficacy, and tolerability are not known. Here we report on a network meta-analysis comparing different ICIs targeting PD1 or PDL1. Methods: Randomized trials (RCTs) supporting the registration of a single agent anti-PD1 or anti-PDL1 inhibitors between 2015-2019 were identified. We extracted the hazard ratio (HR) for overall survival (OS) and calculated the odds ratio (OR) for commonly reported safety and tolerability outcomes. We then performed a network meta-analysis including only disease sites in which more than one ICI has been approved. Multiple pair-wise comparisons were then performed. When more than 2 comparisons were available for a pair of ICIs these were pooled into a single estimate. Analyses were performed in Microsoft Excel and RevMan 5.3. Results: Of 16 RCTs included, 10 in non-small-cell lung cancer, 2 in melanoma, 2 in head and neck squamous cell carcinoma and 2 in urothelial cancer. There was a total of 10673 patients in the analysis. Compared to pembrolizumab, efficacy was similar for nivolumab (HR 1.06, 95% CI 0.97-1.16) and for atezolizumab (HR 1.05, 95% CI 0.93-1.20). However, avelumab appeared inferior (HR 1.29, 95% CI 1.07-1.57). Pembrolizumab showed similar odds of serious adverse events (SAEs) as nivolumab (OR 1.12, 95% CI 0.56-2.27) and atezolizumab (OR 1.05, 95% CI 0.55-2.04). However, compared to nivolumab, atezolizumab was associated with more SAEs (OR 2.14, 95% CI 1.47-3.12). Avelumab had the lowest odds of grade 3-4 adverse events compared to pembrolizumab (OR 0.42, 95% CI 0.24-0.74), nivolumab (OR 0.38, 95% CI 0.24-0.62) and atezolizumab (OR 0.21, 95% CI 0.14-0.33). Atezolizumab was associated with more grade 3-4 adverse events than nivolumab (OR 1.84, 95% CI 1.37-2.47). The odds of treatment discontinuation without progression were similar between nivolumab and atezolizumab (OR 1.20, 95% CI 0.73-2.00), but higher with pembrolizumab compared to nivolumab (OR 1.35, 95% CI 0.83-2.17) and atezolizumab (OR 2.56, 95% CI 1.29-5.00). Pembrolizumab was associated with higher OR of immune related adverse events (IRAEs) compared to nivolumab (OR 2.12, 95% CI 1.49-3.03) and atezolizumab (OR 1.63, 95% CI 1.09-2.43), while the OR of IRAEs was almost similar between nivolumab and atezolizumab. Conclusions: Pembrolizumab, nivolumab, and atezolizumab have similar efficacy. Avelumab appears efficacious. Safety and tolerability seem better with avelumab, but worse with atezolizumab and pembrolizumab.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.035 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".