| Literature DB >> 27758888 |
James N Ingle1, Fang Xie2, Matthew J Ellis3, Paul E Goss4, Lois E Shepherd5, Judith-Anne W Chapman5, Bingshu E Chen5, Michiaki Kubo6, Yoichi Furukawa7, Yukihide Momozawa6, Vered Stearns8, Kathleen I Pritchard9, Poulami Barman10, Erin E Carlson10, Matthew P Goetz11, Richard M Weinshilboum2, Krishna R Kalari10, Liewei Wang2.
Abstract
Genetic risks in breast cancer remain only partly understood. Here, we report the results of a genome-wide association study of germline DNA from 4,658 women, including 252 women experiencing a breast cancer recurrence, who were entered on the MA.27 adjuvant trial comparing the aromatase inhibitors (AI) anastrozole and exemestane. Single-nucleotide polymorphisms (SNP) of top significance were identified in the gene encoding MIR2052HG, a long noncoding RNA of unknown function. Heterozygous or homozygous individuals for variant alleles exhibited a ∼40% or ∼63% decrease, respectively, in the hazard of breast cancer recurrence relative to homozygous wild-type individuals. Functional genomic studies in lymphoblastoid cell lines and ERα-positive breast cancer cell lines showed that expression from MIR2052HG and the ESR1 gene encoding estrogen receptor-α (ERα) was induced by estrogen and AI in a SNP-dependent manner. Variant SNP genotypes exhibited increased ERα binding to estrogen response elements, relative to wild-type genotypes, a pattern that was reversed by AI treatment. Further, variant SNPs were associated with lower expression of MIR2052HG and ERα. RNAi-mediated silencing of MIR2052HG in breast cancer cell lines decreased ERα expression, cell proliferation, and anchorage-independent colony formation. Mechanistic investigations revealed that MIR2052HG sustained ERα levels both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated, proteasome-dependent degradation of ERα. Taken together, our results define MIR2052HS as a functionally polymorphic gene that affects risks of breast cancer recurrence in women treated with AI. More broadly, our results offer a pharmacogenomic basis to understand differences in the response of breast cancer patients to AI therapy. Cancer Res; 76(23); 7012-23. ©2016 AACR. ©2016 American Association for Cancer Research.Entities:
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Year: 2016 PMID: 27758888 PMCID: PMC5135610 DOI: 10.1158/0008-5472.CAN-16-1371
Source DB: PubMed Journal: Cancer Res ISSN: 0008-5472 Impact factor: 12.701