Sialylation Inhibition Can Partially Revert Acquired Resistance to Enzalutamide in Prostate Cancer Cells
Bibliographic record
Abstract
Prostate cancer is a lethal solid malignancy and a leading cause of cancer-related deaths in males worldwide. Treatments, including radical prostatectomy, radiotherapy, and hormone therapy, are available and have improved patient survival; however, recurrence remains a huge clinical challenge. Enzalutamide is a second-generation androgen receptor antagonist that is used to treat castrate-resistant prostate cancer. Among patients who initially respond to enzalutamide, virtually all acquire secondary resistance, and an improved understanding of the mechanisms involved is urgently needed. Aberrant glycosylation, and, in particular, alterations to sialylated glycans, have been reported as mediators of therapy resistance in cancer, but a link between tumour-associated glycans and resistance to therapy in prostate cancer has not yet been investigated. Here, using cell line models, we show that prostate cancer cells with acquired resistance to enzalutamide therapy have an upregulation of the sialyltransferase ST6 beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) and increased levels of α2,6-sialylated N-glycans. Furthermore, using the sialyltransferase inhibitor P-SiaFNEtoc, we discover that acquired resistance to enzalutamide can be partially reversed by combining enzalutamide therapy with sialic acid blockade. Our findings identify a potential role for ST6GAL1-mediated aberrant sialylation in acquired resistance to enzalutamide therapy for prostate cancer and suggest that sialic acid blockade in combination with enzalutamide may represent a novel therapeutic approach in patients with advanced disease. Our study also highlights the potential to bridge the fields of cancer biology and glycobiology to develop novel combination therapies for prostate cancer.
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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 | 0.001 |
| 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".