Abstract PO-040: The role of mTOR in epigenetic regulation in cancer
Bibliographic record
Abstract
Abstract Changes in gene expression, including those caused by epigenetic dysregulation, represent a hallmark of cancer. The mechanistic/mammalian Target of Rapamycin (mTOR) is a serine/threonine kinase that coordinates nutrient availability to the regulation of cell growth and metabolism, which is frequently perturbed in cancer. mTOR exists in two structurally and functionally divergent complexes, mTOR complex 1 and 2 (mTORC1 and 2). mTORC1 regulates many metabolic pathways, including the serine-glycine-one carbon network (SGOC), a pathway modulating the production of S-adenosyl methionine (SAM) and α-ketoglutarate (α-KG). The latter two metabolites constitute essential co-substrates of histone methyltransferases and demethylases, respectively. However, how mTOR signaling controls epigenetic dynamics remains largely unknown. To address this important gap in knowledge, we treated two breast cancer cell lines (MCF7 and T47D) with allosteric (rapamycin) and active-site (INK128) mTOR inhibitors to study how mTOR may influence global histone methylation. We found that mTOR inhibitors significantly increased histone 3 lysine 9 tri-methylation (H3K9me3) and histone 3 lysine 27 tri-methylation (H3K27me3) but not histone 3 lysine 4 tri-methylation (H3K4me3) suggesting this effect was selective. Metabolomic tools were employed to decipher changes of epigenetic metabolites α-KG, SAM, S-Adenosyl homocysteine (SAH), methionine, and serine upon mTOR inhibition. These findings provide bases for my future work which will focus on establishing the role of alterations in mTOR signaling on epigenetic programs which drive tumorigenesis. A better understanding of this network may help identify new therapeutic targets to improve current cancer treatments. Citation Format: HaEun Kim, David Papadopoli, Ivan Topisirovic. The role of mTOR in epigenetic regulation in cancer [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-040.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".