Increased lung/liver metastasis of human breast cancer cells in NOD.SCIDIL2rγ-/- mice after treatment with soluble CD200Fc (TUM7P.922)
Bibliographic record
Abstract
Abstract CD200 overexpression increases metastasis of tumor cells to draining lymph nodes. Increased levels of a soluble form of CD200, sCD200, occur in serum of ~50% breast cancer patients, and an immunoadhesin, linking CD200 to an immunoglobulin Fc region (CD200Fc) increases metastasis in experimental models. We investigated whether CD200 levels were increased in human breast cancer cells in mice, and if overexpression of CD200 increased metastasis. NOD.SCIDIL2rγ-/- mice received 5x105 cells of different human breast cancer cell lines (MCF7; HTB19; MDA-MB-231) injected into the mammary fat pad. Groups of mice received ongoing infusion of human CD200Fc (20μg/mouse at 72hr intervals for 4 weeks). Tumors were resected at ~0.7cm3, and snap frozen for PCR analysis. Mice were monitored post resection, and sacrificed 2-3 months later. Lung and liver metastatic nodules were enumerated, and tissue snap frozen for PCR analysis. All tumors growing in NOD.SCIDIL2rγ-/- mice expressed CD200, though parent (in vitro) cell lines were uniformly negative. CD200Fc increased lung and liver metastasis of all breast cancer lines tested. Tumor metastasis was associated with changes in gene expression in tumor tissue, with significant alterations in genes encoding matrix metalloproteinases, and transcription factors modulating inflammation. Further changes in gene expression were attributed to the effect of CD200Fc infusion. Our data suggests increased soluble CD200 promotes human breast cancer metastasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".