Polysialic acid is upregulated on activated immune cells and negatively regulates anticancer immune activity
Bibliographic record
Abstract
Suppression of anticancer immune function is a key driver of tumorigenesis. Identifying molecular pathways that inhibit anticancer immunity is critical for developing novel immunotherapeutics. One such molecule that has recently been identified is the carbohydrate polysialic acid (polySia), whose expression is dramatically upregulated on both cancer cells and immune cells in breast cancer patient tissues. The role of polySia in the anticancer immune response, however, remains incompletely understood. In this study, we profile polySia expression on both healthy primary immune cells and on infiltrating immune cells in the tumour microenvironment (TME). These studies reveal polySia expression on multiple immune cell subsets in patient breast tumors. We find that stimulation of primary T-cells and macrophages in vitro induces a significant upregulation of polySia expression. We subsequently show that polySia is appended to a range of different carrier proteins within these immune cells. Finally, we find that selective removal of polySia can significantly potentiate killing of breast cancer cells by innate immune cells. These studies implicate polySia as a significant negative regulator of anticancer 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) | 0.001 | 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".