Efficacy and safety profiles of programmed cell death-1/programmed cell death ligand-1 inhibitors in the treatment of triple-negative breast cancer: A comprehensive systematic review
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is associated with worse prognosis, with limited treatment regiments available and higher mortality rate. Immune checkpoint inhibitors targeting programmed cell death-1 (PD-1) or programmed cell death-ligand 1 (PD-L1) showed great potentials in treating malignancies and may serve as potential therapies for TNBC. This systematic review aims to evaluate the efficacy and safety profiles of PD-1/PD-L1 inhibitors in the treatment of TNBC. Literature search was performed via PubMed, EBSCOhost, Scopus, and CENTRAL databases, selecting studies which evaluated the use of anti-PD-1/PDL1 for TNBC from inception until February 2019. Risk of bias was assessed by the Newcastle-Ottawa Scale (NOS). Overall, 7 studies evaluating outcomes of 1395 patients with TNBC were included in this systematic review. Anti-PD-1/PD-L1 showed significant antitumor effect, proven by their promising response (objective response rate (ORR), 18.5-39.4%) and survival rates (median overall survival (OS), 9.2-21.3 months). Moreover, anti- PD-1/PD-L1 yielded better outcomes when given as first-line therapy, and overexpression of PD-L1 in tumors showed better therapeutic effects. On the other hands, safety profiles were similar across agents and generally acceptable, with grade ≥3 treatment- related adverse effects (AEs) ranging from 9.5% to 15.6% and no new AEs were experienced by TNBC patients. Most grade ≥3 AEs are immune-mediated, which are manifested as neutropenia, fatigue, peripheral neuropathy, and anemia. PD-1/PD-L1 inhibitors showed promising efficacy and tolerable AEs, and thus may benefit TNBC patients. Further studies of randomized controlled trials with larger populations are needed to better confirm the potential of these agents.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".