Applications of Extracellular Vesicles in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive and refractory subtype of breast cancer, often occurring in younger patients with poor clinical prognosis. Given the current lack of specific targets for effective intervention, the development of better treatment strategies remains an unmet medical need. Over the last decade, the field of extracellular vesicles (EVs) has grown tremendously, offering immense potential for clinical diagnosis/prognosis and therapeutic applications. While TNBC-EVs have been shown to play an important role in tumorigenesis, chemoresistance and metastasis, they could be repurposed as potential biomarkers for TNBC diagnosis and prognosis. Furthermore, EVs from various cell types can be utilized as nanoscale drug delivery systems (NDDS) for TNBC treatment. Remarkably, EVs generated from specific immune cell subsets have been shown to delay solid tumour growth and reduce tumour burden, suggesting a new immunotherapy approach for TNBC. Intrinsically, EVs can cross the blood-brain barrier (BBB), which holds great potential to treat the brain metastases diagnosed in one third of TNBC patients that remains a substantial clinical challenge. In this review, we present the most recent applications of EVs in TNBC as diagnostic/prognostic biomarkers, nanoscale drug delivery systems and immunotherapeutic agents, as well as discuss the associated challenges and future directions of EVs in cancer immunotherapy.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".