Lymphoma cell surface expression of CD200 modulates anti‐tumor immunity
Bibliographic record
Abstract
CD200 is a transmembrane protein broadly expressed on a variety of cell types and delivers immunoregulatory signals through binding to receptor (CD200Rs) expressed on monocyte/myeloid cells and T lymphocytes. We showed in earlier reports that infusion of a soluble form of CD200, CD200Fc, into EL4 thymoma‐bearing C57/B6 mice enhanced tumor progression (Clin. Exp. Immunol. 2001). More recently, independent groups have reported CD200 overexpression associated with multiple myeloma, AML, and CLL. In this study we investigated the in vitro effect of blockade of CD200 expression, using both anti‐CD200 mAbs and CD200‐specific siRNAs, on CTL induction in human PBL using a CD200+ lymphoma cell line (ly5). We found that blockade of CD200 on ly5 cells significantly enhanced anti‐tumor CTL responses in vitro. Production of the inflammatory cytokines TNFa and IFNg were similarly enhanced. Cell depletion studies supported an important role for antigen presenting cells in CD200 regulation of anti‐tumor responses. We also developed a CD200 ELISA assay to measure serum CD200 levels in healthy donor controls and cancer patients, and in preliminary analyses found elevated levels of sCD200 in the plasma of patients with CLL. Our data provide novel insights into the mechanisms of CD200 mediated suppression of tumor immunity, the understanding of which may open new avenues to development of different cancer therapies.
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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".