Reshaping the tumor microenvironment by degrading glycoimmune checkpoints Siglec-7 and -9
Bibliographic record
Abstract
Cancer treatment has been rapidly transformed by the development of immune checkpoint inhibitors targeting CTLA-4 and PD-1/PD-L1. However, many patients fail to respond, especially those with an immunosuppressive tumor microenvironment (TME), suggesting the existence of additional immune checkpoints that act through orthogonal mechanisms. Sialic acid-binding immunoglobulin-like lectin (Siglec)-7 and -9 are newly designated glycoimmune checkpoints that are abundantly expressed by tumor-infiltrating myeloid cells. We discovered that T cells express only basal levels of Siglec transcripts; instead, they acquire Siglec-7 and -9 from interacting myeloid cells in the TME via trogocytosis, which impairs their activation and effector function. Mechanistically, Siglec-7 and -9 suppress T cell activity by dephosphorylating T cell receptor (TCR)-related signaling cascades. Using sulfur fluoride exchange (SuFEx) click chemistry, we developed a ligand that binds to Siglec-7 and -9 with high-affinity and exclusive specificity. Using this ligand, we constructed a Siglec-7/9 degrader that targets membrane Siglec-7 and -9 to the lysosome for degradation. Administration of this degrader induced efficient Siglec degradation in both T cells and myeloid cells in the TME. We found that Siglec-7/9 degradation has a negligible effect on macrophage phagocytosis, but significantly enhances T cell anti-tumor immunity. The degrader, particularly when combined with anti-CTLA-4, enhanced macrophage antigen presentation, reshaped the TME, and resulted in long-lasting T cell memory and excellent tumor control in multiple murine tumor models. These findings underscore the need to consider exogenous checkpoints acquired by T cells in the TME when selecting specific checkpoint blockade therapy to enhance T cell immunity.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".