Positive progress: current and evolving role of immune checkpoint inhibitors in metastatic triple-negative breast cancer
Bibliographic record
Abstract
Background: Triple-negative breast cancer (TNBC) represents an aggressive breast cancer subtype with historically poor overall outcomes, due primarily to a lack of effective targeted agents. Chemotherapy has been the primary treatment approach, although immune checkpoint inhibitors (ICIs) are currently being investigated to improve patient outcomes. This review examines the clinical implications of current evidence on the use of ICIs for the treatment of metastatic TNBC. Methods: Our systematic search identified two phase III and five phase I/II trials reporting on the efficacy of ICIs used as monotherapy or combined with chemotherapy for the treatment of metastatic TNBC. Results: The phase III IMpassion 130 trial showed a significant improvement in median progression-free survival in the intent-to-treat (net 1.7 months, p = 0.002) and PD-L1-positive populations (net 2.5 months, p < 0.001) for the addition of first-line atezolizumab versus placebo to nab-paclitaxel in metastatic TNBC. Although median overall survival was not significantly improved in patients receiving atezolizumab overall [net 2.3 months, hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.72–1.02, p = 0.078], numerical improvements in the PD-L1-positive population were compelling (net 7.0 months, HR 0.71; 95% CI 0.54–0.93). Toxicity profiles were as expected, and no new safety signals were observed. Pembrolizumab monotherapy did not significantly improve overall survival in similar patients that had received prior treatment in KEYNOTE-119. Conclusions: Atezolizumab plus nab-paclitaxel represents a potential new first-line standard of care for patients with metastatic PD-L1-positive TNBC. Other ICIs used as monotherapy, or combined with chemotherapy for advanced TNBC, as well as their use for earlier stage disease, are areas of ongoing investigation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".