Current Treatment and Future Trends of Immunotherapy in BreastCancer
Bibliographic record
Abstract
Immunotherapy continues to redefine the solid tumor treatment landscape, with inhibitors of the PD-L1/PD-1 immune checkpoint having the most widespread impact. As the most common cancer diagnosed worldwide, there is significant interest in the development of immunotherapy for the treatment of breast cancer in both the early and metastatic settings. Recently reported results of several clinical trials have identified potential roles for immunotherapy agents alone or in combination with standard treatment for early and metastatic disease. While trials to date have been promising, immunotherapy has only been shown to benefit a select group of patients with breast cancer, defined by tumor subtype, PD-L1 expression, and line of therapy. With over 250 trials ongoing, emerging data will enable the further refinement of breast cancer immunotherapy strategies. The integration of multiple putative biomarkers and consideration of dynamic markers of early response or resistance may inform optimal patient selection for immunotherapy investigation and integration into clinical practice. This review will summarize the current evidence for immune-checkpoint blockade (ICB) in the treatment of early and metastatic breast cancer, highlighting current and potential future biomarkers of therapeutic response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".