Breast cancer metastasis in a human bone NOD/SCID mouse model
Bibliographic record
Abstract
A major dilemma facing patients with breast cancer is how to decide between over treating indolent tumors and failing to adequately treat aggressive, potentially lethal cancers. Determination of the metastatic potential of a patient's breast cancer would clearly help guide those treatment decisions. Breast cancer commonly spreads to bone in 70% of women with advanced disease. However, the mechanism of bone metastasis is not well understood. One possibility is that the microenvironment within bone marrow, highly rich in growth factors and cytokines, is suitable for the proliferation of breast cancer cells. In this study, we developed a method for implanting human bone in NOD/SCID mice and show that the human bone implants are viable for more than 20 weeks. This human bone NOD/SCID mouse model provides an opportunity to functionally characterize human breast cancer cell behavior in an in vivo human microenvironment. Several breast tumor cell lines have been shown to grow in the human-bone-NOD/SCID model system, however each line has a different functional profile. Here we show that cotransplantation of GFP-MDA-MB-231 breast cancer cells with morcellized human bone allows for tissue specific metastasis to an initially tumor free bone implant. Furthermore, metastasis of breast tumor cells to implanted tumor-free human bone was seen when patient bone containing a metastatic breast tumor was implanted in the host mouse. With this model, we can distinguish between primary invasive breast tumors with and without bone metastatic potential.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".