The ITPRIPL1- CD3ε axis: a novel immune checkpoint controlling T cells activation
Bibliographic record
Abstract
Abstract The immune system is critical to fighting infections and disease. The molecular recognition of harmful entities takes place when antigen-presenting cells (APC) harboring major histocompatibility complex (MHC) molecules bound to peptides derived from harmful antigens (ligand) dock on specific T cell receptor (TCR)-CD3 complex (receptor) at the surface of CD8+ T cells. The discovery of a general immune checkpoint mechanism to avoid the harmful impact of T cell hyperactivation provoked a paradigm shift. The clinical relevance of this mechanism is highlighted by the fact that PD-1 and PD-L1 inhibitors are very effective at boosting immune reactions. Still, immune evasion frequently happens. The observation that some PD-1/PD-L1 negative tumors have a poor immune response opens the door to identifying a novel immune checkpoint mechanism. Here, we discovered that ITPRIPL1, a gene with unknown function, impairs T cell activation. Surprisingly, we found that CD3ε is the direct receptor of ITPRIPL1. This novel immune checkpoint was validated as a drug target using ITPRIPL1 KO mice and monoclonal antibodies. Thus, targeting the ITPRIPL1-CD3e axis, especially in PD-1 - PDL-1 negative patients, is a promising therapeutic strategy to reduce immune evasion.
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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) | 0.002 | 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".