Updates on the Importance of CD200:CD200R Checkpoint Blockade in Solid Tumors and B cell Malignancies
Bibliographic record
Abstract
The last several years have seen the introduction into clinical medicine of a family of reagents directed towards so-called "checkpoint inhibitors", which act at gateways in a developing immune response to regulate unwanted and/or harmful selfdirected activation responses.The molecules involved at such gateways generally belong to an extended immunoglobulin supergene family, and contribute inhibitory signals to dampen over-exuberant responses.They include, but are not limited to, molecules of the CD28/cytotoxic T-lymphocyte antigen-4 (CTLA-4):B7.1/B7.2receptor/ligand family; PD-1 and PDL-1; CD200 and CD200R; TIGIT and VISTA and their respective ligands (VSIG-3/IGSF11, Nectin), all of which are presumed to play a physiological role in maintaining natural self-tolerance.In the field of cancer immunotherapy, where the ultimate clinical goal is to improve immuno-targeting of cancer cells, triggering these checkpoint inhibitory signaling pathways, has the potential to thwart effective tumor immunity.This in turn has led to the characterization and application of multiple reagents, including antibodies and other designed inhibitory molecules, which can act as checkpoint blockade agents.Such reagents have had a dramatic effect on human cancer treatment, with marked success for anti-CTLA-4 and PD-1 in particular in clinical trials.This review elaborates on the promise on other more under-appreciated target molecules for checkpoint blockade in human B cell malignancies and solid tumors, particularly CD200:CD200R, and describes both the background, and newer studies, which highlight the potential importance of targeting the CD200:CD200R dyad in cancer immunobiology/therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".