Immunotherapeutic approaches in triple-negative breast cancer: latest research and clinical prospects
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC), as defined by the absence of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 expression, is a challenging disease with the poorest prognosis of all breast cancer subtypes. Importantly, there are currently no known molecular targets for this subgroup of patients. Recent advances in genomics and gene expression profiling have shed new light on the molecule heterogeneity of TNBC. We present an overview of the scientific evidence suggesting that clinical outcome in TNBC is affected by tumor-infiltrating immune cells. We also describe tumor-associated antigens recently identified in TNBC. Finally, we review the current literature on promising immunotherapies for TNBC, including tumor vaccine approaches, immune-checkpoint inhibitors, antagonists of immunosuppressive molecules and adoptive cell therapies. It is our contention that selected patients with TNBC with lymphocytic tumor infiltrates at diagnosis may benefit from immune-based therapies and that these immunotherapies will be most beneficial in combination with cytotoxic drugs that potentiate adaptive anti-tumor immunity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".