CD200R1-CD200 checkpoint inhibits phagocytosis differently from SIRPα-CD47 to suppress tumor growth
Bibliographic record
Abstract
Targeting macrophage inhibitory receptors like signal regulatory protein α (SIRPα) is a promising avenue in cancer treatment. Whereas the ligand of SIRPα, CD47, is widely expressed on tumor cells, its simultaneous presence on all normal cells raises concerns about toxicity and efficacy. This study identifies CD200R1, which binds CD200 on specific tumor types and limited normal cells, as an alternative inhibitory checkpoint for phagocytosis. Blocking or removing CD200R1 from macrophages or CD200 from tumor cells increases phagocytosis and suppresses tumor growth. In humans, CD200R1 is mainly expressed in immunosuppressive macrophages and is induced by interleukin-4. Unlike SIRPα that utilizes phosphatases Src homology 2 domain phosphatase (SHP)−1 and SHP-2, CD200R1 mediates its inhibitory effect via the kinase Csk. Combined CD200R1-CD200 and SIRPα-CD47 blockade further boosts phagocytosis and reduces tumor growth of CD200-expressing tumors, compared to either blockade alone. Thus, targeting CD200R1-CD200 is a promising strategy for immune checkpoint blockade in macrophages, either alone or alongside blockade of other checkpoints. CD200R1 is a transmembrane receptor expressed on macrophages. Here the authors report that the interaction of CD200R1 with its ligand CD200, expressed by tumor cells, suppresses phagocytosis, and that targeting CD200R1-CD200 promotes macrophage-mediated anti-tumor response.
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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 | 0.001 |
| 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".