Dysregulated lipid metabolism blunts the sensitivity of cancer cells to EZH2 inhibitor
Bibliographic record
Abstract
BACKGROUND: Sensitivity has been a key issue for Enhancer of zeste homolog 2 (EZH2) inhibitors in cancer therapy. The EZH2 inhibitor EPZ-6438 was first approved by the US Food and Drug Administration (FDA) in 2020. However, its inadequate anti-cancer activity in solid tumors limits its clinical application. In this study, we utilized the multiple cancer cell lines, which are less sensitive to the EZH2 inhibitor GSK126, combining animal model and clinical data to investigate the underlying mechanism. METHODS: IncuCyte S3 was used to explore the difference in the responsiveness of hematological tumor cells and solid tumor cells to GSK126. Transcriptome and metabolome of B16F10 cells after GSK126 treatment were analyzed and the distinct changes in the metabolic profile were revealed. Real-time quantitative PCR and western blot experiments were used to further verify the multi-omics data. ChIP-qPCR was performed to detected H3K27me3 enrichment of target genes. Finally, the anti-tumor effects of combining GSK126 and lipid metabolism drugs were observed with IncuCyte S3 platform, CCK-8 and animal model respectively. FINDINGS: We found that although the proliferative phenotype did not show strong difference upon treatment with GSK126, the transcriptome and metabolome changed profoundly. GSK126 treatment led to broad shifts in glucose, amino acid, and lipid metabolism. Lipid synthesis was strengthened manifested by the increasing abundance of unsaturated fatty acids. SCD1 and ELOVL2 were regulated by H3K27me3 at gene regulatory region, and upregulated by EZH2 knockdown and inhibitors. SCD1 knockdown increased cellular sensitivity to GSK126. Based on the findings above, the application of the combination with SCD1 inhibitor significantly attenuated the proliferation of cancer and increased the sensitivity to GSK126 by suppressing desaturation of fatty acids. INTERPRETATION: Dysregulated lipid metabolism can blunt the sensitivity of cancer cells to GSK126. These characteristics shed light on the novel combination therapy strategies to combat tumor resistance. FUNDING: National Natural Science Foundation of China (No. 81672091, No.91749107 and No. 81972966).
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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 | 0.001 | 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) | 0.000 | 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".