The glycosyltransferase ST3GAL4 drives immune evasion in acute myeloid leukemia by synthesizing ligands for the glyco-immune checkpoint receptor Siglec-9
Bibliographic record
Abstract
Immunotherapy has demonstrated promise as a treatment for acute myeloid leukemia (AML). However, there is still an urgent need to identify new molecules that inhibit the immune response to AML. Most prior research in this area has focused on protein-protein interaction interfaces. While carbohydrates also regulate immune recognition, the role of cell-surface glycans in driving AML immune evasion is comparatively understudied. The Siglecs, for example, are an important family of inhibitory, glycan-binding signaling receptors that have emerged as prime targets for cancer immunotherapy in recent years. In this study, we find that AML cells express ligands for the receptor Siglec-9 at high levels. Integrated CRISPR genomic screening and clinical bioinformatic analysis identified ST3GAL4 as a potential driver of Siglec-9 ligand expression in AML. Depletion of ST3GAL4 by CRISPR-Cas9 knockout (KO) dramatically reduced the expression of Siglec-9 ligands in AML cells. Mass spectrometry analysis of cell-surface glycosylation in ST3GAL4 KO cells revealed that Siglec-9 primarily binds N-linked sialoglycans on these cell types. Finally, we found that ST3GAL4 KO enhanced the sensitivity of AML cells to phagocytosis by Siglec-9-expressing macrophages. This work reveals a novel axis of immune evasion and implicates ST3GAL4 as a possible target for immunotherapy in AML.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".