982 Targeting glycans for cancer immunotherapy
Bibliographic record
Abstract
Background Despite the curative potential of checkpoint blockade immunotherapy, a majority of patients remain unresponsive to existing treatments. Glyco-immune checkpoints – interactions of cell-surface glycans with lectin, or glycan binding, immunoreceptors – have emerged as prominent mechanisms of immune evasion and therapeutic resistance in cancer.Methods Here, we describe antibody-lectin chimeras (AbLecs), a modular platform for glyco-immune checkpoint blockade. AbLecs are bispecific antibody-like molecules comprising a tumor-targeting arm as well as a lectin ‘decoy receptor’ domain that directly binds tumor glycans and blocks their ability to engage lectin receptors on immune cells ( figure 1).Results We demonstrate proof-of-concept for the AbLec platform through the development and characterization of AbLecs combining tumor-targeting antibodies with decoy receptor domains from Siglecs-7 and -9, which have been shown to mediate immune suppression in multiple cancers. We show that tumor-targeting AbLecs enhance antibody-dependent phagocytosis and cytotoxicity of cancer cells in vitro and reduced tumor burden in vivo compared to the parent monoclonal antibody. Unexpectedly, we found that the chimeric AbLec architecture results in a gain-of-function arising from their ability to block Siglec engagement at the immunological synapse. As a result, AbLecs amplify inflammatory signaling, outperforming most existing therapies and combinations in functional assays. We demonstrate that the AbLec platform can be applied to target diverse tumor-associated antigens, including HER2, CD20, and EGFR, and cancer cells with varying levels of antigen expression. Further, we designed and validated dual blockade AbLecs that simultaneously block protein-based immune checkpoints (e.g., PD-1/PD-L1, CD47/SIRPα) as well as glyco-immune checkpoints that restrain productive anticancer immune responses (e.g., Siglec-7, galectin-9). We found that glyco-immune checkpoint blockade was synergistic with blockade of these established checkpoints.Conclusions Our results indicate that AbLecs target a non-redundant axis of immune suppression in cancer and could expand the subset of patients who respond to immunotherapy. In sum, AbLecs represent a generalizable approach for glyco-immune checkpoint blockade and a new modality for cancer immunotherapy.Abstract 982 Figure 1Antibody-lectin chimeras (AbLecs) for glyco-immune checkpoint blockade. AbLecs simultaneously engage Fc receptors and block recruitment of lectin checkpoint receptors (e.g., Siglecs) to the immunological synapse. As a result, AbLecs enhanced antitumor immune responses in vitro and in vivo compared to the parent antibody
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".