Abstract 7288: Targeted degradation of mobile immune checkpoints to advance cancer therapy
Bibliographic record
Abstract
Abstract Immune checkpoint inhibitors (ICIs) targeting T cell-intrinsic checkpoints, such as CTLA-4 and PD-1, have shown promising outcomes across diverse cancer patient populations. However, resistance to these ICIs remains prevalent, especially in patients with an immunosuppressive tumor microenvironment (TME), suggesting the existence of additional immune checkpoints that act through orthogonal mechanisms. To overcome therapeutic resistance, there is an unmet need to identify novel immune checkpoints involved in cancer progression and to develop innovative therapeutic strategies capable of reprogramming the TME by neutralizing tumor-promoting factors. Here, we identify sialic acid-binding immunoglobulin-like lectin (Siglec)-7 and -9 as mobile immune checkpoints acquired by T cells from myeloid cells via trogocytosis in the TME. These trogocytosed Siglec checkpoints suppress T cell activity by dephosphorylating T cell receptor (TCR)-related signaling pathways through engagement with tumor-associated sialoglycans. To target these mobile checkpoints, we leveraged sulfur(VI) fluoride exchange (SuFEx) click chemistry to develop a high-affinity Siglec-7/-9 ligand and converted it into a degrader for lysosomal degradation of both Siglecs. This degrader efficiently reduced Siglec levels in T cells and myeloid cells, significantly restoring T cell function and enhancing anti-tumor immunity. The degrader, particularly when combined with anti-CTLA-4, remodeled the TME and generated durable T cell memory, resulting in excellent tumor control in multiple murine models. These findings underscore the significance of degrading exogenously acquired Siglec checkpoints on T cells as a novel strategy to overcome resistance to established ICIs in cancer therapy. Citation Format: Chao Wang, Yingqin Hou, Jaroslav Zak, Kelli A. McCord, Qinheng Zheng, Shereen Chung, Ruben D. Peraza, James C. Paulson, John R. Teijaro, Xu Zhou, K Barry Sharpless, Je!rey V. Ravetch, Matthew S. Macauley, Peng Wu. Targeted degradation of mobile immune checkpoints to advance cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7288.
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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) | 0.003 | 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".