Abstract 5402: Metabolic shift towards the <i>de novo</i> serine pathway in non-transformed breast cells drives epigenetic plasticity, oxidative DNA damage, and pro-tumorigenic cChanges associated with aging
Bibliographic record
Abstract
Abstract Introduction: A lipid metabolism gene signature is enriched in breast tissue at risk for estrogen receptor negative (ERneg) breast cancer (BC). Fatty acid (FA) exposure alters histone methylation, gene expression and increases metabolic flux through serine, one-carbon, glycine (SOG) and methionine pathways. We hypothesize that FA exposure induces a metabolic shift towards the SOG, increasing S-adenosylmethionine (SAM), altering histone methylation, gene expression, and promoting ERneg BC. Methods: Proteomics, metabolomics, Reactive Oxygen Species (ROS) measurement, comet assay and H3K4me3 CUT&RUN were performed in MCF-10A cells exposed to octanoic acid (OA). Single-cell RNA-seq (scRNAseq) was performed in breast tissue derived microstructures exposed to OA. Intracellular communication was analyzed using CellChat, and metabolic flux with Compass. Results: OA increased SAM, glutathione (GSH) and 2-hydroxyglutarate (2-HG); blocking the serine pathway (SSP) prevented these increases. ScRNAseq revealed that OA increased expression of the SSP transcription factor ATF3 and genes PHGDH and PSAT1 in epithelial and stromal compartments. Metabolic flux analysis revealed a significant increase in flux through SSP in Basal BSL1, Luminal Progenitor LP3, and Hormone Sensing HS1 cells after OA exposure. Differential proteomics reveals PHGDH overexpression and downregulation of proteins involved in extracellular matrix (ECM)-receptor interaction and focal adhesion post-OA exposure, along with significant increase in mitochondrial and nuclear ROS (p < 0.01). OA exposure also induced DNA damage, likely due to elevated nuclear ROS. OA increased GSH metabolism and ROS detoxification in BSL1. CUT&RUN identified 661 peaks significantly enriched upon OA (FDR < 0.01) in regulatory regions of OA-induced genes involved in neural pathways and BC, including MDK, NGF, and NGFR. CellChat predicted a decrease in ECM-cell interactions, a reduction in cell-cell adhesions, and an increase of secreted signaling upon OA exposure. The strongest secreted signals in OA were AREG (linked to proliferation, growth, and invasiveness), GDF15 (involved in EMT, invasion, and aging), and MDK (linked to neurogenesis, and aging). Conclusions: We demonstrate an FA-induced shift towards the SOG and methionine pathways that promotes epigenetic plasticity, regulates ROS, and supports the survival of cells with 'inappropriate' phenotypes. These accumulate DNA damage, leading to age-related changes in the mammary gland (elevated ROS, disrupted junctions, altered ECM interactions, and increased MDK/GDF15 expression), all supporting carcinogenesis. Our findings also provide a metabolic explanation for the elevation of PHGDH in 70% of ERneg BCs, despite gene amplification in only 6%, and point to preventive strategies targeting the SSP. Citation Format: Mariana Bustamante Eduardo, Gannon Cottone, Curtis McCloskey, Flavio Palma, Shiyu Liu, Maria Paula Zappia, Abul B.M.M.K. Islam, Elizaveta Benevolenskaya, Maxim Frolov, Jason Locasale, Marcelo Bonini, Rama Khokha, Navdeep Chandel, Seema A. Khan, Susan E. Clare. Metabolic shift towards the de novo serine pathway in non-transformed breast cells drives epigenetic plasticity, oxidative DNA damage, and pro-tumorigenic cChanges associated with aging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5402.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".