Influence of Extracellular Vesicles on Lung Stromal Cells during Breast Cancer Metastasis
Bibliographic record
Abstract
Breast cancer is a prominent cause of cancer diagnosis and death in women globally, with over 90% of deaths being attributed to complications that arise from metastasis. One of the common locations for breast cancer metastasis is the lung, which is associated with significant morbidity and mortality. Curative treatments for metastatic breast cancer patients are not available and the molecular mechanisms that underlie lung metastasis are not fully understood. In order to better treat these patients, identifying events that occur both prior to and during metastatic spread to the lung is essential. Several studies have demonstrated that breast cancer-derived extracellular vesicles secreted from the primary breast tumor play a key role in establishing the lung pre-metastatic niche to support colonization of metastatic tumor cells. In this review, we summarize recent work supporting the influence of extracellular vesicles on stromal components of the lung to construct the pre-metastatic niche and support metastasis. Furthermore, we discuss the potential clinical applications of utilizing extracellular vesicles for diagnosis and treatment. Together, this review highlights the dynamic nature of extracellular vesicles, their roles in breast cancer metastasis to the lung, and their value as potential biomarkers and therapeutics for cancer prevention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".