Abstract PO-002: Epigenetic regulation of metabolic pathways in response of breast cancer cells to stilbenoids
Bibliographic record
Abstract
Abstract Epigenetics is the study of heritable gene expression changes that are not due to a change in the DNA sequence. Due to the inherent reversibility of epigenetic states, epigenetic modifications, particularly DNA methylation, have attracted a significant amount of attention for the prevention and treatment of cancer. Aberrations in DNA methylation occur during cancer initiation, promotion and progression. Recent evidence suggests that these aberrations may contribute to metabolic disturbances in cancer. Although limited, there are pieces of evidence demonstrating that dietary bioactive compounds, including polyphenols from the stilbenoid subclass, such as resveratrol and pterostilbene, can modulate gene expression by targeting DNA methylation. Our recent investigation into DNA methylation patterns using Illumina 450K BeadChip microarray demonstrates that loci-specific increases and decreases in DNA methylation occur in breast cancer cells in response to resveratrol. We have identified 4,183 differentially methylated CpG sites in MCF10CA1h and 6,347 differentially methylated CpG sites in MCF10CA1a breast cancer cells upon 9-day treatment with 15µM resveratrol as compared with control cells (0.05≤differential methylation≥-0.05, nominal p<0.05, limma t-test). Interestingly, the majority of differentially methylated CpGs showed increases in DNA methylation levels after resveratrol treatment and fell into a category of regulation of oncogenic cellular signal transduction pathways. On the other hand, CpG loci hypomethylated upon resveratrol corresponded to numerous genes with tumor suppressor functions. In the present study, we extended our investigation into genes associated with metabolic pathways whose DNA methylation status changes in response of MCF10CA1a breast cancer cells to 9-day treatment with 15µM resveratrol. We found 583 CpG sites corresponding to 365 genes linked to metabolic processes. Among those genes, peroxisome proliferator-activated receptor PPARγC1A, PPARγC1B and PPARδ were hypermethylated whereas PPARα was hypomethylated. PPARs are nuclear receptors that regulate transcription of multiple genes, including targets associated with metabolic processes such as glucose homeostasis, fatty acid oxidation and synthesis. Interestingly, PPARs were shown to recruit ten-eleven translocation (TET) enzymes to specific DNA regions in order to facilitate decrease in DNA methylation and activation of transcription. This constitutes additional layer of complexity and may contribute to epigenetic effects of stilbenoids. Our results provide novel evidence on the mechanistic link between metabolism, epigenetics and stilbenoids which opens the door to further investigation on mechanistic players that could contribute to the development of epigenetic-targeting anti-cancer strategies. This study was supported by the University of British Columbia VP Academic (#10R76632) Award, CFI John R. Evans Leadership Fund and BC Knowledge Development Fund granted to B.S. Citation Format: Barbara Stefanska, Cayla Boycott, Tony Yang. Epigenetic regulation of metabolic pathways in response of breast cancer cells to stilbenoids [abstract]. In: Abstracts: AACR Special Virtual Conference on Epigenetics and Metabolism; October 15-16, 2020; 2020 Oct 15-16. Philadelphia (PA): AACR; Cancer Res 2020;80(23 Suppl):Abstract nr PO-002.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".