Triple-Negative Breast Cancers: An Updated Review on Treatment Options
Bibliographic record
Abstract
Morphologic features of tumour cells have long been validated for the clinical classification of breast cancers and are regularly used as a "gold standard" to ascertain prognostic outcome in patients. Identification of molecular markers such as expression of the receptors for estrogen (er) and progesterone (pgr) and the human epidermal growth factor receptor 2 (her2) has played an important role in determining targets for the development of efficacious drugs for treatment and has also offered additional predictive value for the therapeutic assessment of patients with breast cancer. More recent technical advancements in identifying several cancer-related genes have provided further opportunities to identify specific subtypes of breast cancer. Among the subtypes, tumours with triple-negative cells are identified using specific staining procedures for basal markers such as cytokeratin 5 and 6 and the absence of er, pgr, and her2 expression. Patients with triple-negative breast cancers therefore have the disadvantage of not benefiting from currently available receptor-targeted systemic therapy. Optimal conditions for the therapeutic assessment of women with triple-negative breast tumours and for the management of their disease have yet to be validated in prospective investigations. The present review discusses the differences between triple-negative breast tumours and basal-like breast tumours and also the role of mutations in the BRCA genes. Attention is also paid to treatment options available to patients with triple-negative breast tumours.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".