Untargeted metabolomics identifies metabolic dysregulation of sphingolipids associated with aggressive chronic lymphocytic leukaemia and poor survival
Bibliographic record
Abstract
BACKGROUND: Metabolic dependencies of chronic lymphocytic leukaemia (CLL) cells may represent new personalized treatment approaches in patients harbouring unfavourable features. METHODS: Here, we used untargeted metabolomics and lipidomics analyses to isolate metabolomic features associated with aggressive CLL and poor survival outcomes. We initially focused on profiles associated with overexpression of the adverse metabolic marker glycosyltransferase (UGT2B17) associated with poor survival and drug resistance. RESULTS: Leukaemic B-cell metabolomes indicated a significant perturbation in lipids, predominantly bio-active sphingolipids. Expression of numerous enzyme-encoding genes of sphingolipid biosynthesis pathways was significantly associated with shorter patient survival. Targeted metabolomics further exposed higher circulating levels of glucosylceramides (C16:0 GluCer) in CLL patients relative to healthy donors and an aggressive cancer biology. In multivariate analyses, C16:0 GluCer and sphinganine were independent prognostic markers and were inversely linked to treatment-free survival. These two sphingolipid species function as antagonistic mediators, with sphinganine being pro-apoptotic and GluCer being pro-proliferative, tested in leukemic B-CLL cell models. Blocking GluCer synthesis using ceramide glucosyltransferase inhibitors induced cell death and reduced the proliferative phenotype, which further sensitized a leukaemic B-cell model to the anti-leukaemics fludarabine and ibrutinib in vitro. CONCLUSIONS: Specific sphingolipids may serve as prognostic markers in CLL, and inhibiting enzymatic pathways involved in their biosynthesis has potential as a therapaeutic approach.
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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 | 0.001 | 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".