Exosomes from Macrophages Exposed to Apoptotic Breast Cancer Cells Promote Breast Cancer Proliferation and Metastasis
Bibliographic record
Abstract
Exosomes have recently become the subject of increasing research interest. Interactions between tumor and host cells via exosomes play crucial roles in the initiation, progression and invasiveness of breast cancer. In our study, we used exosomes isolated from a co-culture model of THP-1-derived macrophages exposed to apoptotic MCF-7 or MDA-MB-231 breast cancer cell line cells to investigate their effects on naïve MCF-7 or MDA-MB-231 cells in vitro and in vivo. This post-chemotherapy tumor microenvironment model allowed us to explore possible mechanisms that explain increased proliferation and metastasis of breast cancer seen in some patients. Our results suggest that while exosomes derived from macrophages normally inhibit proliferation and metastasis of MCF-7 or MDA-MB-231 cells, exposure of macrophages to breast cancer cells that have experienced chemotherapy are modified them to promote these processes. Exosomes from macrophages exposed to apoptotic cancer cells have increased amounts of IL-6 that increases the phosphorylation of STAT3, which likely explains the increased transcription of STAT3 target genes such as CyclinD1, MMP2 and MMP9. These observations suggest that the inhibition of exosome secretion and STAT3 signaling pathway activation might suppress the growth and metastasis of malignant tumors, and provide new targets for therapeutic treatment of malignant tumors after chemotherapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".