Sialyltransferase inhibition leads to inhibition of tumor cell interactions with E-selectin, VCAM1, and MADCAM1, and improves survival in a human multiple myeloma mouse model
Bibliographic record
Abstract
Aberrant glycosylation resulting from altered expression of sialyltransferases, such as ST3 β-galactoside α2-3-sialyltransferase 6, plays an important role in disease progression in multiple myeloma (MM). Hypersialylation can lead to increased immune evasion, drug resistance, tumor invasiveness, and disseminated disease. In this study, we explore the in vitro and in vivo effects of global sialyltransferase inhibition on myeloma cells using the pan-sialyltransferase inhibitor 3Fax-Neu5Ac delivered as a per-acetylated methyl ester pro-drug. Specifically, we show in vivo that 3Fax-Neu5Ac improves survival by enhancing bortezomib sensitivity in an aggressive mouse model of MM. However, 3Fax-Neu5Ac treatment of MM cells in vitro did not reverse bortezomib resistance conferred by bone marrow (BM) stromal cells. Instead, 3Fax-Neu5Ac significantly reduced interactions of myeloma cells with E-selectin, MADCAM1 and VCAM1, suggesting that reduced sialylation impairs extravasation and retention of myeloma cells in the BM. Finally, we showed that 3Fax-Neu5Ac alters the post-translational modification of the α4 integrin, which may explain the reduced affinity of α4β1/α4β7 integrins for their counter-receptors. We propose that inhibiting sialylation may represent a valuable strategy to restrict myeloma cells from entering the protective BM microenvironment, a niche in which they are normally protected from chemotherapeutic agents such as bortezomib. Thus, our work demonstrates that targeting sialylation to increase the ratio of circulating to BM-resident MM cells represents a new avenue that could increase the efficacy of other anti-myeloma therapies and holds great promise for future clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".