Osteoactivin Promotes Breast Cancer Metastasis to Bone
Bibliographic record
Abstract
The skeleton is a preferred site of metastasis in patients with disseminated breast cancer. We have used 4T1 mouse mammary carcinoma cells, which metastasize to bone from the mammary fat pads of immunocompetent mice, to identify novel genes involved in this process. In vivo selection of parental cells resulted in the isolation of independent, aggressively bone metastatic breast cancer populations with reduced metastasis to the lung. Gene expression profiling identified osteoactivin as a candidate that is highly and selectively expressed in aggressively bone metastatic breast cancer cells. These cells displayed enhanced migratory and invasive characteristics in vitro, the latter requiring sustained osteoactivin expression. Osteoactivin depletion in these cells, by small interfering RNA, also lead to a loss of matrix metalloproteinase-3 expression, whereas forced osteoactivin expression in parental 4T1 cells was sufficient to elevate matrix metalloproteinase-3 levels, suggesting that this matrix metalloproteinase may be an important mediator of osteoactivin function. Overexpression of osteoactivin in an independent, weakly bone metastatic breast cancer cell model significantly enhanced the formation of osteolytic bone metastases in vivo. Finally, high levels of osteoactivin expression in primary human breast cancers correlate with estrogen receptor-negative status and increasing tumor grade. Thus, we have identified osteoactivin as a protein that is expressed in aggressive human breast cancers and is capable of promoting breast cancer metastasis to bone.
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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".