Molecular Pathways: Targeting CD96 and TIGIT for Cancer Immunotherapy
Bibliographic record
Abstract
Abstract The receptors CD96 and TIGIT are expressed on the surface of T and natural killer (NK) cells, and recent studies suggest both play important inhibitory roles in immune function. CD96 has been shown to modulate immune cell activity in mice, with Cd96−/− mice displaying hypersensitive NK-cell responses to immune challenge and significant tumor resistance. TIGIT overexpression has been shown to reduce NK-cell–mediated cytotoxicity. TIGIT is also upregulated on T cells during cancer and chronic viral infection, with expression associated with effector T-cell exhaustion and increased regulatory T-cell suppression. The counterbalance between the putative inhibitory CD96 and TIGIT receptors and the activating receptor, CD226, offers unique strategies for immuno-oncology drug development. Blocking CD96 or TIGIT with mAbs has been shown to improve tumor control in mice, in particular when used in combination with PD-1/PD-L1 blockade. These results have highlighted these pathways as promising new targets for immune modulation. This review will examine the rationale behind targeting CD96 and TIGIT, and discuss the potential approaches in translating these preclinical findings into novel clinical agents. Clin Cancer Res; 22(21); 5183–8. ©2016 AACR.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".