The Lipid Metabolism as Target and Modulator of BOLD‐100 Anticancer Activity: Crosstalk with Histone Acetylation
Bibliographic record
Abstract
The leading first-in-class ruthenium-complex BOLD-100 currently undergoes clinical phase-II anticancer evaluation. Recently, BOLD-100 is identified as anti-Warburg compound. The present study shows that also deregulated lipid metabolism parameters characterize acquired BOLD-100-resistant colon and pancreatic carcinoma cells. Acute BOLD-100 treatment reduces lipid droplet contents of BOLD-100-sensitive but not -resistant cells. Despite enhanced glycolysis fueling lipid accumulation, BOLD-100-resistant cells reveal diminished lactate secretion based on monocarboxylate transporter 1 (MCT1) loss mediated by a frame-shift mutation in the MCT1 chaperone basigin. Glycolysis and lipid catabolism converge in the production of protein/histone acetylation substrate acetyl-coenzymeA (CoA). Mass spectrometric and nuclear magnetic resonance analyses uncover spontaneous cell-free BOLD-100-CoA adduct formation suggesting acetyl-CoA depletion as mechanism bridging BOLD-100-induced lipid metabolism alterations and histone acetylation-mediated gene expression deregulation. Indeed, BOLD-100 treatment decreases histone acetylation selectively in sensitive cells. Pharmacological targeting confirms histone de-acetylation as central mode-of-action of BOLD-100 and metabolic programs stabilizing histone acetylation as relevant Achilles' heel of acquired BOLD-100-resistant cell and xenograft models. Accordingly, histone gene expression changes also predict intrinsic BOLD-100 responsiveness. Summarizing, BOLD-100 is identified as epigenetically active substance acting via targeting several onco-metabolic pathways. Identification of the lipid metabolism as driver of acquired BOLD-100 resistance opens novel strategies to tackle therapy failure.
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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 | 0.001 |
| 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 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".