Abstract PO-051: Metabolic alterations reveal epigenetic vulnerabilities in breast cancer
Bibliographic record
Abstract
Abstract Tumor progression and therapeutic resistance in cancer are characterized by changes in cell identity, which is regulated in part by the epigenetic landscape of the cells. Alterations in metabolic profiles can contribute to changes in cell fate during tumor progression through remodelling of chromatin landscapes. We show that chemoresistance in triple-negative breast cancer is characterized by altered cellular metabolism, impacting the levels of metabolites required for DNA and histone methylation. Using models of chemotherapy resistance breast cancer, we have identified metabolic and epigenetic dependencies specific to drug-resistant breast cancer cells. We show that the remodelling of epigenetic states upon chemotherapy-induced metabolic stress can contribute to drug resistance in breast cancer cells by enabling the maintenance of transposable element repression and preventing the induction of cytotoxic stress-induced viral mimicry. While these metabolic and epigenetic changes contribute to the enhanced tumorigenic fitness of chemo resistant breast cancer, they reveal epigenetic vulnerabilities that can be therapeutically targeted in drug-resistant tumors. Citation Format: Genevieve Deblois. Metabolic alterations reveal epigenetic vulnerabilities in breast 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-051.
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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 | 0.001 | 0.001 |
| 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) | 0.020 | 0.004 |
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".