CRISPR activation screens map the genomic landscape of cancer glycome remodeling
Bibliographic record
Abstract
Abstract Many cancer types upregulate expression of sialic acid-containing glycans. These oligosaccharides subsequently engage inhibitory Siglec receptors on immune cells, allowing cancer cells to evade immune surveillance. The genetic mechanisms by which this glycome remodeling occurs remain poorly defined. Understanding the ways that cancer cells change their cell surface glycosylation is critical for identification of biomarkers and targets for glycan-directed immunotherapy. In this study, we performed multiple gain-of-function CRISPR activation (CRISPRa) screens to broadly define genetic pathways that regulate expression of Siglec-binding glycans. We show that Siglec ligand expression is largely controlled through genetic competition between genes that catalyze α2-3 sialylation and GlcNAcylation of galactose residues. Perturbation of enzyme expression at this key biosynthetic node provides multiple “paths” by which cancers can acquire elevated expression of Siglec ligands. We further show that cancer glycome remodeling is aided by overexpression of novel “professional ligands” that facilitate Siglec-glycan binding. Notably, we also find that expression of the CD24 gene is genetically dispensable for cell-surface binding of the inhibitory receptor Siglec-10. Finally, by integrating our functional genetic model with clinical tumor genomic data, we identify the sulfotransferase enzyme GAL3ST4 as a potential novel driver of immune evasion in glioma cells. Taken together, this study provides a first-in-class genomic atlas to aid understanding of cancer-associated glycosylation and identifies immediately actionable targets for cancer immunotherapy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".