Back

Early Epigenetic and Metabolic Responses to the Adipocyte Secretome Reveal Stress-Adaptive States in Triple-Negative Breast Cancer

Townsel, A.; Jaffe, M.; He, S.; Wu, Y.; Ingram, A.; Tipton, M.; Kemp, M. L.; Henry, C. J.; Haynes, K. A.

2026-04-08 cancer biology
10.64898/2026.04.06.716548 bioRxiv
Show abstract

Obesity is a well-established risk factor for triple-negative breast cancer (TNBC), yet how adipocyte-derived signals reprogram cancer cell metabolism and chromatin states remains poorly defined. Here, we investigate how adipocyte-driven lipogenesis reshapes metabolic-epigenetic coupling to support stress-adaptive cell states and functional changes in epithelial TNBC cells. Using an integrated multi-omic approach, we combine RNA sequencing (RNA-seq), chromatin accessibility (ATAC-seq), metabolic flux modeling, and functional metabolic assays in lipogenic BT-549 cells. Computational modeling trained on RNA-seq predicts shifts in metabolic pathway usage, including enhanced NAD-linked metabolism. RNA-seq reveals a predominance of gene activation, consistent with ATAC-seq data showing a strong bias toward increased accessibility. Regions of increased accessibility are enriched for stress-adaptive and antioxidant pathways, including superoxide dismutase 2 (SOD2) and metallothioneins (MT1F, MT1E, MT2A). Functionally, lipogenic cells exhibit increased spare respiratory capacity, altered ATP-linked respiration, elevated extracellular acidification, and reduced reactive oxygen species (ROS) accumulation, consistent with a bioenergetically flexible, stress-adaptive metabolic state. Together, these findings reveal that adipocyte-driven metabolic rewiring promotes selective chromatin opening and activation of stress-adaptive gene programs, enabling TNBC cells to buffer oxidative pressure for enhanced proliferation and survival after exposure to the adipocyte secretome.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.