A narrative review of immune checkpoint inhibitors in early stage triple negative breast cancer
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is an aggressive disease characterized by heterogeneous molecular and immunological characteristics that portends worse overall survival compared to other breast cancer subtypes. Until now, chemotherapy has remained the cornerstone of TNBC treatment despite recent efforts to explore new molecularly targeted therapeutic targets and personalized treatments. Given TNBC has a more immunogenic tumor microenvironment than other breast cancer subtypes, there is hope that immunotherapy will emerge as a new pillar of treatment in TNBC. Based on the IMPASSION130 and KEYNOTE-355 studies, the combination of nab-paclitaxel plus atezolizumab, and chemotherapy plus pembrolizumab, respectively, have been approved by the Food and Drug Administration for locally recurrent, unresectable, or metastatic TNBC in the first line setting. Several studies have now been published demonstrating programmed cell death-1 protein (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors given alongside neoadjuvant taxane and anthracycline-based chemotherapy with or without a platinum agent significantly improves pathologic complete response rate. The choice of chemotherapy given in cooperation with PD-1/PD-L1 checkpoint inhibitor seems to determine the amount benefit derived from immunotherapy. However, longer term follow-up is required to ascertain whether immunotherapy, specifically PD-1/PD-L1 blockade, will improve event-free survival and overall survival. Future studies are underway investigating the role of immunotherapy in the adjuvant setting and in patients with residual disease after neoadjuvant therapy. Other unanswered questions remain including the total duration of immunotherapy, and which patient population would benefit from these expensive and sometimes toxic therapies. This narrative review aims to provide insight on the current landscape of immune checkpoint inhibitors in early TNBC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".