Targeting B7‐1 in immunotherapy
Bibliographic record
Abstract
Modulation of T-cell immune functions by blocking key immune checkpoint protein interactions using monoclonal antibodies (mAbs) has been an innovative immunotherapeutic strategy. T-cells are regulated by different checkpoint proteins at the immunological synapse including the B7 ligands (B7-1 or CD80 and B7-2 or CD86), which is discussed in this review. These ligands are typically expressed on antigen presenting cells and interact with CD28 and cytotoxic T lymphocyte antigen-4 (CTLA-4) receptors on T-cells. Their interactions with CD28 trigger a costimulatory signal that potentiates T-cell activation, function and survival in response to cognate antigen. In addition, their interactions with CTLA-4 can also inhibit certain effector T-cell responses, particularly in response to sustained antigen stimulation. Through these mechanisms, the balance between T-cell activation and suppression is maintained, preventing the occurrence of immunopathology. Given their crucial roles in immune regulation, targeting B7 ligands has been an attractive strategy in cancer and autoimmunity. This review presents an overview of the essential roles of B7-1, highlighting the therapeutic benefits of modulating this protein in immunotherapy, and reviewing earlier and state-of-the-art efforts in developing anti-B7-1 inhibitors. Finally, we discuss the challenges facing the design of selective B7-1 inhibitors and present our perspectives for future developments.
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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) | 0.001 | 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) | 0.004 | 0.003 |
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".