Radiation-induced lung metastasis development is MT1-MMP-dependent in a triple-negative breast cancer mouse model
Bibliographic record
Abstract
BACKGROUND: The prognosis of triple-negative breast cancer (TNBC) is still difficult to establish. Some TNBC benefit from radiotherapy (RT) and are cured, while in other patients metastases appear during the first 3 years after treatment. In this study, an animal model of TNBC was used to determine whether the expression of the cell membrane protease MT1-MMP in cancer cells was associated with radiation-stimulated development of lung metastases. METHODS: Using invasion chambers, irradiated fibroblasts were used as chemoattractants to assess the invasiveness of TNBC D2A1 cell lines showing downregulated expression of MT1-MMP, which were compared with D2A1-wt (wild-type) and D2A1 shMT1-mock (empty vector) cell lines. In a mouse model, a mammary gland was irradiated followed by the implantation of the downregulated MT1-MMP D2A1, D2A1-wt or D2A1 shMT1-mock cell lines. Migration of D2A1 cells in the mammary gland, number of circulating tumour cells and development of lung metastases were assessed. RESULTS: The reduction of MT1-MMP expression decreased the invasiveness of D2A1 cells and blocked the radiation enhancement of cancer cell invasion. In BALB/c mice, irradiation of the mammary gland has stimulated the invasion of cancer cells, which was associated with a higher number of circulating tumour cells and of lung metastases. These adverse effects of radiation were prevented by downregulating the MT1-MMP. CONCLUSIONS: This study shows that the MT1-MMP is necessary for the radiation enhancement of lung metastasis development, and that its expression level and/or localisation could be evaluated as a biomarker for predicting the early recurrence observed in some TNBC patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".