The different prognostic significance of polysialic acid and CD56 expression in tumor cells and lymphocytes identified in breast cancer
Bibliographic record
Abstract
Protein glycosylation, the attachment of carbohydrates onto proteins, is a fundamental process that alters the biological activity of proteins. Changes to glycosylation states are associated with many forms of cancer including breast cancer. Through immunohistological analysis of breast cancer patient tumors, we have discovered the expression of an atypical glycan-polysialic acid (polySia)-in breast cancer. Notably, we have identified polySia expression in not only tumor cells but also on tumor-infiltrating lymphocytes (TILs) and our study reveals ST8Sia4 as the predominant polysialyltransferase expressed. Evaluation of ST8Sia4 expression in tumor cells identified an association between high expression levels and poor patient outcomes whereas ST8Sia4 expression in infiltrating stromal cells was associated with good patient outcomes. Investigation into CD56, a protein known to be polysialylated, found CD56 and polySia expression on breast tumor cells and TILs. CD56 expression did not positively correlate with polySia expression except in patient tumors which expressed HER2. In these HER2 expressing tumors, CD56 expression was significantly associated with HER2 expression score. Evaluation of CD56 tumor cell expression identified a significant association between CD56 expression and poor patient outcomes. By contrast, CD56 expression on TILs was significantly associated with good clinical outcomes. Tumors with CD56+ TILs were also consistently polySia TIL positive. Interestingly, in tumors where TILs were CD56 low-to-negative, a polySia+ lymphocyte population was still identified and the presence of these lymphocytes was a poor prognostic indicator. Overall, this study provides the first detailed report of polySia and CD56 in breast cancer and demonstrates that the prognostic significance is dependent on the cell type expression within the tumor.
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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 | 0.001 |
| 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 | 0.000 | 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".