Ontology type: schema:ScholarlyArticle
2018-09
AUTHORSDarren K. Patten, Giacomo Corleone, Balázs Győrffy, Ylenia Perone, Neil Slaven, Iros Barozzi, Edina Erdős, Alina Saiakhova, Kate Goddard, Andrea Vingiani, Sami Shousha, Lőrinc Sándor Pongor, Dimitri J. Hadjiminas, Gaia Schiavon, Peter Barry, Carlo Palmieri, Raul C. Coombes, Peter Scacheri, Giancarlo Pruneri, Luca Magnani
ABSTRACTThe degree of intrinsic and interpatient phenotypic heterogeneity and its role in tumor evolution is poorly understood. Phenotypic drifts can be transmitted via inheritable transcriptional programs. Cell-type specific transcription is maintained through the activation of epigenetically defined regulatory regions including promoters and enhancers. Here we have annotated the epigenome of 47 primary and metastatic estrogen-receptor (ERα)-positive breast cancer clinical specimens and inferred phenotypic heterogeneity from the regulatory landscape, identifying key regulatory elements commonly shared across patients. Shared regions contain a unique set of regulatory information including the motif for transcription factor YY1. We identify YY1 as a critical determinant of ERα transcriptional activity promoting tumor growth in most luminal patients. YY1 also contributes to the expression of genes mediating resistance to endocrine treatment. Finally, we used H3K27ac levels at active enhancer elements as a surrogate of intra-tumor phenotypic heterogeneity to track the expansion and contraction of phenotypic subpopulations throughout breast cancer progression. By tracking the clonality of SLC9A3R1-positive cells, a bona fide YY1-ERα-regulated gene, we show that endocrine therapies select for phenotypic clones under-represented at diagnosis. Collectively, our data show that epigenetic mechanisms significantly contribute to phenotypic heterogeneity and evolution in systemically treated breast cancer patients. More... »
PAGES1469-1480
http://scigraph.springernature.com/pub.10.1038/s41591-018-0091-x
DOIhttp://dx.doi.org/10.1038/s41591-018-0091-x
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1105714178
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30038216
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