Triple negative breast cancer: approved treatment options and their mechanisms of action
Bibliographic record
Abstract
PURPOSE: Breast cancer, the most prevalent cancer worldwide, consists of 4 main subtypes, namely, Luminal A, Luminal B, HER2-positive, and Triple-negative breast cancer (TNBC). Triple-negative breast tumors, which do not express estrogen, progesterone, and HER2 receptors, account for approximately 15-20% of breast cancer cases. The lack of traditional receptor targets contributes to the heterogenous, aggressive, and refractory nature of these tumors, resulting in limited therapeutic strategies. METHODS: Chemotherapeutics such as taxanes and anthracyclines have been the traditional go to treatment regimens for TNBC patients. Paclitaxel, docetaxel, doxorubicin, and epirubicin have been longstanding, Food and Drug Administration (FDA)-approved therapies against TNBC. Additionally, the FDA approved PARP inhibitors such as olaparib and atezolizumab to be used in combination with chemotherapies, primarily to improve their efficiency and reduce adverse patient outcomes. The immunotherapeutic Keytruda was the latest addition to the FDA-approved list of drugs used to treat TNBC. RESULTS: The following review aims to elucidate current FDA-approved therapeutics and their mechanisms of action, shedding a light on the various strategies currently used to circumvent the treatment-resistant nature of TNBC cases. CONCLUSION: The recent approval and use of therapies such as Trodelvy, olaparib and Keytruda has its roots in the development of an understanding of signaling pathways that drive tumour growth. In the future, the emergence of novel drug delivery methods may help increase the efficiency of these therapies whiel also reducing adverse side effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".