bioRxivCancer BiologyPreprint
ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation
Z. Li, Y. Wu, A. Bahreini, N. M. Priedigkeit, K. Ding, C. A. Sartorius, L. Miller, M. Rosenzweig, N. Wagle, J. K. Richer, W. J. Muller, L. Buluwela, S. Ali, Y. Fang, L. Zhu, G. C. Tseng, J. Gertz, J. M. Atkinson, A. V. LEE, S. Oesterreich
Abstract
Estrogen receptor alpha (ER/ESR1) is mutated in 30-40% of endocrine resistant ER-positive (ER+) breast cancer. ESR1 mutations cause ligand-independent growth and increased metastasis in vivo and in vitro. Despite the distinct clinical features and changes in therapeutic response associated with ESR1 mutations, there are no data about their potential role in intrinsic subtype switching. Applying four luminal and basal gene set pairs, ESR1 mutant cell models and clinical samples showed a significant enrichment of basal subtype markers. Among them, the six basal cytokeratins (BCKs) were the most enriched genes. Induction of BCKs was independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated topological associated domain at the KRT14/16/17 genomic region. Unexpectedly, high BCK expression in ER+ primary breast cancer is associated with good prognosis, and these tumors show enriched activation of a number of immune pathways, a distinctive feature shared with ESR1 mutant tumors. S100A8 and S100A9 were among the most highly induced immune mediators shared between high-BCKs ER+ and ESR1 mutant tumors, and single-cell RNA-seq analysis inferred their involvement in paracrine crosstalk between epithelial and stromal cells. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features with induction of basal cytokeratins via epigenetic mechanisms in rare subpopulation of cells. This is associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities in ESR1 mutant tumors.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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