Efficacy and safety of PD-1 and PD-L1 inhibitors combined with chemotherapy in randomized clinical trials among triple-negative breast cancer
Bibliographic record
Abstract
Background: The combination of immune checkpoint inhibitors (ICIs) and chemotherapy (CT) is a new strategy to explore cancer treatment in recent years, and it is also practiced in triple-negative breast cancer (TNBC). However, several published randomized controlled trials (RCTs) reported heterogeneous results. We conducted this meta-analysis to yield insights into the efficacy and safety of the combination of ICIs and CT for TNBC patients in both the adjuvant and neoadjuvant settings. Method: EMBASE, PUBMED, Cochrane, and www.clinicaltrials.gov databases were searched to determine potential eligible studies from the inception to 20 May 2022. Published RCTs on PD-1/PD-L1 ICIs combined with CT for TNBC patients were included. Result: This meta-analysis included six double-blind RCTs comprising 4,081 TNBC patients treated with PD-1 or PD-L1 ICIs plus CT or placebo plus CT. The combination strategy benefited a better pathologic complete response (pCR) by 29% (RR = 1.29; 95% CI: 1.17–1.41; I2 = 0%) and a better progression-free survival (PFS) (HR = 0.82; 95% CI: 0.74–0.90; I2 = 0%) in the neoadjuvant and the adjuvant settings, respectively, especially in PD-L1-positive population (HR = 0.71; 95% CI: 0.62–0.81; I2 = 13%). The safety profiles were generally tolerable in both settings but the combination treatment will increase the risk of severe adverse events in the adjuvant setting (RR = 1.33; 95% CI 1.08–1.62, I2 = 0%). Additionally, the combination will increase the risk of any-grade hypothyroidism, hyperthyroidism, pneumonia, and rash in the adjuvant setting, and the risk of any-grade hypothyroidism, hyperthyroidism, infusion-related reactions, and severe cutaneous reactions in the neoadjuvant setting. Conclusion: This meta-analysis demonstrated a significant pCR benefit and confirms the PFS benefit with PD-1/PD-L1 ICIs plus CT in TNBC patients with tolerable safety events in both neoadjuvant and adjuvant settings.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".