A narrative review of chemotherapy in advanced triple negative breast cancer
Bibliographic record
Abstract
Triple-negative breast cancers (TNBCs) are a heterogeneous group of aggressive tumors with high relapse rates and propensity to develop visceral or brain metastasis, representing 15% of the breast carcinomas. Their overall survival (OS) has remained static over the past 20 years. Cytotoxic chemotherapy (CT) was the only treatment option for all stages of TNBC until the first targeted therapy Olaparib was approved in patients with germline BRCA-mutation. This unsystematic narrative review is aimed at presenting an overview of the use of CT in advanced/metastatic triple-negative breast cancer (mTNBC) in these times when the personalized medicine era is slowly reaching TNBC with new therapeutic options. The information used to write it was collected from the published literature, treatment guidelines and hand searches of retrieved literature references. Standard anthracycline-based CT is the treatment of choice as first-line for metastatic breast cancer patients not previously treated with anthracyclines. First-line single-agent taxane is offered to patients who have received prior adjuvant anthracyclines or presented anthracycline failure, or as the second line in patients who have received prior anthracyclines in the metastatic setting. TNBC tumors that carry the germline BRCA1/2 mutations can benefit from the targeted use of platinum. Other drugs as eribulin, capecitabine, platinum, and gemcitabine, that have proven efficacy as single-agents or in combination as further lines, but the sequencing is not established. Combination chemotherapy can be considered when the patient presents a severe organ dysfunction aiming to achieve disease stabilization. CT remains the cornerstone treatment for mTNBC which not express targetable receptors or defective molecular pathways, and as a counterpart for targeted or immune therapies; given the limited access to these last in most countries, CT will continue in the landscape for much longer.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".