The role of immunotherapy in metastatic triple negative breast cancer: a narrative review of the current clinical trials
Bibliographic record
Abstract
Triple negative breast cancer is an aggressive subtype of breast cancer that lacks expression of estrogen and progesterone receptors, and does not overexpress the human epithelial growth factor 2 receptor. Triple negative breast cancer occurs in approximately 20% of breast cancer cases, and patients are more likely to present with an advanced stage, have higher relapse rates, and have a worse prognosis than other breast cancer types. There are no targeted therapies for the majority of patients with triple negative breast cancer. The mainstay of treatment remains chemotherapy and the historical median overall survival for metastatic patients is around one year. Immunotherapy has dramatically improved outcomes in other malignancies by harnessing the body’s immune system to attack cancer cells. Triple negative breast cancer is the most immunogenic type of breast cancer, with a large genetic mutational burden and a relatively high number of immune cells found in the cancer microenvironment compared to other breast cancer subtypes. While initial studies of single agent immunotherapy in metastatic triple negative breast cancer were promising, the survival outcomes have been disappointing. Combining systemic therapy with immunotherapy has become a recent focus of clinical trials, and is now the current standard of care for treatment naive metastatic triple negative patients that are programmed death-ligand 1 positive. The efficacy of combined chemoimmunotherapy for first-line treatment has yet to be replicated, and recent clinical data questions the true benefit. This review summarizes the clinical trials of single agent and combination strategies for immunotherapy in metastatic triple negative breast cancer. We also examine the potential role for biomarkers, and future direction of treatment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".