Cancer cells and viruses share common glycoepitopes: exciting opportunities toward combined treatments
Bibliographic record
Abstract
Aberrant glycosylation patterns of glycoproteins and glycolipids have long been recognized as one the major hallmarks of cancer cells that has led to numerous glycoconjugate vaccine attempts. These abnormal glycosylation profiles mostly originate from the lack of key glycosyltransferases activities, mutations, over expressions, or modifications of the requisite chaperone for functional folding. Due to their relative structural simplicity, O-linked glycans of the altered mucin family of glycoproteins have been particularly attractive in the design of tumor associated carbohydrate-based vaccines. Several such glycoconjugate vaccine formulations have generated potent monoclonal anti-carbohydrate antibodies useful as diagnostic and immunotherapies in the fight against cancer. Paradoxically, glycoproteins related to enveloped viruses also express analogous N- and O-linked glycosylation patterns. However, due to the fact that viruses are not equipped with the appropriate glycosyl enzyme machinery, they need to hijack that of the infected host cells. Although the resulting N-linked glycans are very similar to those of normal cells, some of their O-linked glycan patterns often share the common structural simplicity to those identified on tumor cells. Consequently, given that both cancer cells and viral glycoproteins share both common N- and O-linked glycoepitopes, glycoconjugate vaccines could be highly attractive to generate potent immune responses to target both conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".