bioRxivCancer Biology
C/EBPB-dependent Adaptation to Palmitic Acid Promotes Stemness in Hormone Receptor Negative Breast Cancer
X. Liu, A. Rulina, M. H. Choi, L. Pedersen, J. Lepland, S. Takle, N. Madeleine, S. Peters, C. E. Wogsland, S. M. Grondal, J. Lorens, H. Goodarzi, A. Molven, P. E. Lonning, S. Knappskog, N. Halberg
Abstract
Epidemiological studies have established a positive association between obesity and the incidence of postmenopausal breast cancer. Moreover, it is known that obesity promotes stem cell-like properties of breast cancer cells. However, the cancer cell-autonomous mechanisms underlying this correlation are not well defined. Here we demonstrate that obesity-associated tumor formation is driven by cellular adaptation rather than expansion of pre-existing clones within the cancer cell population. While there is no correlation with specific mutations, cellular adaptation to obesity is governed by palmitic acid (PA) and leads to enhanced tumor formation capacity of breast cancer cells. This process is governed epigenetically through increased chromatin occupancy of the transcription factor CCAAT/enhancer-binding protein beta (C/EBPB). Obesity-induced epigenetic activation of C/EBPB regulates cancer stem-like properties by modulating the expression of key downstream regulators including CLDN1 and LCN2. Collectively, our findings demonstrate that obesity drives cellular adaptation to PA drives tumor initiation in the obese setting through activation of a C/EBPB dependent transcriptional network. SignificanceCellular adaptation to obesity-induced palmitic acid drives tumor initiation through activation of a C/EBPB-dependent transcriptional network. This highlights a cell-autonomous mechanistic connection between obesity and postmenopausal hormone receptor negative breast cancer.
- organism: human
- assay: DNA methylation
- assay: proteomics
- research focus: chromatin & epigenetic regulation
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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