Mice with highly metastatic breast cancer transfer express exosomes in serum which increase metastatic capacity of a poorly metastatic tumor
Bibliographic record
Abstract
Abstract Altered interaction between CD200 and CD200R, represents an example of “checkpoint blockade” disrupting an effective, tumor-directed, host response in murine breast cancer cells. In CD200R1KO mice, long-term cure of EMT6 breast cancer, including metastatic spread to lung and liver, was achieved in BALB/c mice. The reverse was observed with 4THM tumors, an aggressive, inflammatory breast cancer, with increased tumor metastasis in CD200R1KO. To explore possible explanations for this difference we measured the frequency of circulating tumor cells (CTCs) in peripheral blood of tumor bearers, as well as in lung/liver and draining lymph nodes, and in addition infused exosomes from non-tumor controls, or tumor bearers, with/without additional infusions of anti-cytokine antibodies into tumor bearing mice. The measured frequency of circulating tumor cells (CTCs) in peripheral blood was equivalent in the two models in both WT and CD200R1KO mice. Increased metastasis in EMT6 tumor bearers was seen following adoptive transfer of serum, or serum-derived exosomes, from 4THM tumor bearers, an effect which was attenuated by anti-IL-6, and anti-IL-17, but not anti-TNFa, antibody. Anti-IL-6 also attenuated enhanced migration of EMT6 cells in vitro induced by 4THM serum or exosomes, or recombinant IL-6. Exosome cytokine proteomic profiles responses in 4THM and EMT6 tumor bearer mice were regulated by CD200:CD200R interactions, with attenuation of both IL-6 and IL-17 in 4THM CD200tg mice, and enhanced levels in 4THM CD200R1KO mice. We hypothesize that these cytokines act on the microenvironment at sites within the host, and/or directly on tumor cells themselves, to increase 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".