Metabolomic Profiling Reveals Cellular Reprogramming of B-Cell Lymphoma by a Lysine Deacetylase Inhibitor through the Choline Pathway
Bibliographic record
Abstract
Despite the proven clinical antineoplastic activity of histone deacetylase inhibitors (HDACI), their effect has been reported to be lower than expected in B-cell lymphomas. Traditionally considered as "epigenetic drugs", HDACI modify the acetylation status of an extensive proteome, acting as general lysine deacetylase inhibitors (KDACI), and thus potentially impacting various branches of cellular metabolism. Here, we demonstrate through metabolomic profiling of patient plasma and cell lines that the KDACI panobinostat alters lipid metabolism and downstream survival signaling in diffuse large B-cell lymphomas (DLBCL). Specifically, panobinostat induces metabolic adaptations resulting in newly acquired dependency on the choline pathway and activation of PI3K signaling. This metabolic reprogramming decreased the antineoplastic effect of panobinostat. Conversely, inhibition of these metabolic adaptations resulted in superior anti-lymphoma effect as demonstrated by the combination of panobinostat with a choline pathway inhibitor. In conclusion, our study demonstrates the power of metabolomics in identifying unknown effects of KDACI, and emphasizes the need for a better understanding of these drugs in order to achieve successful clinical implementation.
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.
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".