Targeting sphingolipid metabolism in chronic lymphocytic leukemia
Bibliographic record
Abstract
Elevated levels of circulating C16:0 glucosylceramides (GluCer) and increased mRNA expression of UDP-glucose ceramide glycosyltransferase (UGCG), the enzyme responsible for converting ceramides (Cer) to GluCer, represent unfavorable prognostic markers in chronic lymphocytic leukemia (CLL) patients. To evaluate the therapeutic potential of inhibiting GluCer synthesis, we genetically repressed the UGCG pathway using in vitro models of leukemic B cells, in addition to UGCG pharmacological inhibition with approved drugs such as eliglustat and ibiglustat, both individually and in combination with ibrutinib, assessed in cell models and primary CLL patient cells. Cell viability, apoptosis, and proliferation were evaluated in vitro, and survival and apoptosis were examined ex vivo. UGCG inhibition efficacy was confirmed by quantifying intracellular sphingolipid levels through targeted lipidomics using mass spectrometry. Other inhibitors of sphingolipid biosynthesis pathways were similarly assessed. Blocking UGCG significantly decreased cell viability and proliferation, highlighting the oncogenic role of UGCG in CLL. The efficient inhibition of UGCG was confirmed by a significant reduction in GluCer intracellular levels. The combination of UGCG inhibitors with ibrutinib demonstrated synergistic effect. Inhibitors that target alternative pathways within sphingolipid metabolism, like sphingosine kinases inhibitor SKI-II, also demonstrated promising therapeutic effects both alone and when used in combination with ibrutinib, reinforcing the oncogenic impact of sphingolipids in CLL cells. Targeting sphingolipid metabolism, especially the UGCG pathway, represents a promising therapeutic strategy and as a combination therapy for potential treatment of CLL patients, warranting further investigation.
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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".