Untargeted metabolomics identifies metabolic dysregulation of sphingolipids associated with aggressive chronic lymphocytic leukaemia and poor survival
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it