Abstract 1279: Investigating the impact of PGC-1α-coupled metabolic reprogramming on breast cancer metastasis
Bibliographic record
Abstract
Abstract Peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α) is a transcriptional coactivator known to play a role in regulating cellular metabolism, contributing to pathways such as mitochondrial respiration, glutaminolysis, and lipogenesis. PGC-1α has been shown to promote cancer metastasis in mouse models of breast cancer while increasing both the global bioenergetic capacity and metabolic flexibility of breast cancer cells. However, the molecular mechanisms through which PGC-1α contributes to metabolic reprogramming to support breast cancer metastasis remain unknown. To address this, we have generated the first transgenic mouse model of breast cancer lacking PGC-1α expression specifically in the mammary epithelium (PGC-1α null), allowing us to examine how PGC-1α loss impacts breast cancer initiation, progression, and metastasis. While no significant differences were observed in tumour onset or growth between wild-type and PGC-1α null mice, lung metastatic burden was decreased in PGC-1α null mice. Ion pairing liquid chromatography-mass spectrometry was employed to measure steady state metabolite levels in mammary tumours collected from wild-type and PGC-1α null mice. By integrating our metabolomics data with RNA sequencing data from wild-type and PGC-1α null tumours, we identified potential deficits in glycolysis and aspartate metabolism in the context of mammary epithelial PGC-1α loss. Interestingly, RNA sequencing further revealed the downregulation of several genes involved in extracellular matrix remodelling in PGC-1α null tumours. Through immunohistochemistry, we further observed lower levels of α smooth muscle actin, a marker of cancer-associated fibroblasts, in PGC-1α null tumours compared with wild-type tumours. Overall, PGC-1α knockout impedes metastasis to the lung in mouse models of breast cancer. Moreover, our transgenic mouse model suggests possible cell intrinsic and extrinsic roles for PGC-1α expression, influencing breast cancer cell metabolism as well as fibroblast activation and extracellular matrix remodelling in the tumour microenvironment. By developing cell lines modeling PGC-1α overexpression or knockdown in luminal B, HER2+, and triple negative breast cancer, we will validate the findings from our transgenic mouse model and further elucidate how PGC-1α reprograms metabolism in breast cancer, how PGC-1α expression influences the tumour microenvironment, and how these mechanisms contribute to cancer cell invasion, anoikis-resistance, and metastasis. Citation Format: Emma Ciccolini, Valérie Sabourin, David Patten, Young K. Im, Steven Hébert, William J. Muller, Claudia Kleinman, Julie St-Pierre, Josie Ursini-Siegel. Investigating the impact of PGC-1α-coupled metabolic reprogramming on breast cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1279.
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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) | 0.003 | 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".