Nectin4 is a novel TIGIT ligand which combines checkpoint inhibition and tumor specificity
Bibliographic record
Abstract
BACKGROUND: The use of checkpoint inhibitors has revolutionized cancer therapy. Unfortunately, these therapies often cause immune-related adverse effects, largely due to a lack of tumor specificity. METHODS: We stained human natural killer cells using fusion proteins composed of the extracellular portion of various tumor markers fused to the Fc portion of human IgG1, and identified Nectin4 as a novel TIGIT ligand. Next, we generated a novel Nectin4 blocking antibody and demonstrated its efficacy as a checkpoint inhibitor in killing assays and in vivo. RESULTS: We identify Nectin4 to be a novel ligand of TIGIT. We showed that, as opposed to all other known TIGIT ligands, which bind also additional receptors, Nectin4 interacts only with TIGIT. We show that the TIGIT-Nectin4 interaction inhibits natural killer cell activity, a critical part of the innate immune response. Finally, we developed blocking Nectin4 antibodies and demonstrated that they enhance tumor killing in vitro and in vivo. CONCLUSION: We discovered that Nectin4 is a novel ligand for TIGIT and demonstrated that specific antibodies against it enhance tumor cell killing in vitro and in vivo. Since Nectin4 is expressed almost exclusively on tumor cells, our Nectin4-blocking antibodies represent a combination of cancer specificity and immune checkpoint activity, which may prove more effective and safe for cancer immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".