نبذة مختصرة : Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ER alpha + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ER alpha-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ER alpha + breast cancer models. The ER alpha + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ER alpha is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ER alpha signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ER alpha phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK. Reliable luminal estrogen receptor (ER alpha+) breast cancer models are limited. Here, the authors use patient derived breast epithelial and breast cancer explant cultures grown in several extracellular matrix scaffolds and show that ER alpha expression is regulated by matrix stiffness via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. ; Peer reviewed
Relation: We are grateful to the patients who participated in this research and made it possible, and to the surgical personnel at Helsinki University Hospital who assisted with the recruitment and collection of the sample material. We thank Leena Saikko, Tiina Raatikainen, and Maiju Merisalo-Soikkeli for their excellent technical support. We thank Vanessa Fuller for the comments on the manuscript and Juhi Somani for assistance with statistical analysis. We thank the Biomedicum Functional Genomics Unit (FuGU) for their high-quality genome profiling services, the Biomedicum Imaging Unit (BIU) for the microscopy support, and the Laboratory Animal Center (LAC) of the University of Helsinki for providing the mice used in this work. We acknowledge the provision of facilities and technical support by Aalto University at OtaNano-Nanomicroscopy Center (Aalto-NMC). This study was funded by grants from the IMI EU-EFPIA PREDECT 115188, Business Finland (Grant No: 544/31/2015 and 2489/31/2017), the Academy of Finland, Academy of Finland Centre of Excellence (HYBER 2014-2019) and iCAN Flagship, ERC-Advanced Grant (DRIVEN), the Finnish Cancer Organization, the Sigrid Juselius Foundation, a Biocentrum Helsinki collaboration grant, Finnish Cancer Institute (FCI), HiLIFE, Jane and Aatos Erkko Foundation, and RESCUER project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 847912. L.M. acknowledges The Finnish Foundation for Technology Promotion, Walter Ahlstrom Foundation and Jenny and Antti Wihuri Foundation for financial support. J. P. acknowledges the Instrumentarium Science Foundation grant of an Instrufoundation fellow.; http://hdl.handle.net/10138/338046; 000723756500005
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