Development of metastases in mice induced by plasma sample collected during radiotherapy in patient with triple-negative breast cancer: Role of Rab4A
Bibliographic record
Abstract
ABSTRACT The relapse rate in early-stage triple-negative breast cancer (TNBC) is significantly higher than in other breast cancer subtypes. This study assessed the relevance to target RAB4A to prevent the development of metastases that occur after treatment. The ability of cancer cells to invade peritumoral tissue is associated with the expression of membrane-type matrix metalloproteinase-1 on their surface, which is regulated by RAB4A. When RAB4A was downregulated using shRNA in the TNBC cells D2A1 and MDA-MB-231, a significant reduction in the proteolytic activity of MT1-MMP and the invasion capacity of these TNBC cells were measured. Plasma samples from an early-stage TNBC patient, who developed metastases six months after treatment, were collected before radiotherapy and after the fourth radiation dose. Compared to the plasma collected before radiotherapy, the plasma collected during the treatment significantly enhanced the invasiveness of the TNBC cells, as assessed with Boyden chambers. The development of lung metastases was also stimulated when the D2A1 cells were preincubated with this plasma before their i.v. injection in female Balb/c mice. Importantly, these adverse effects of plasma collected during radiotherapy were significantly blocked by downregulating RAB4A. These results highlight the relevance of developing RAB4A inhibitors to prevent the development of metastases occurring after treatment in 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".