期刊
CANCER RESEARCH
卷 76, 期 23, 页码 7012-7023出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-16-1371
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资金
- Grants-in-Aid for Scientific Research [16H01569] Funding Source: KAKEN
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 similar to 40% or similar to 63% decrease, respectively, in the hazard of breast cancer recurrence relative to homozygous wild-type individuals. Functional genomic studies in lymphoblastoid cell lines and ER alpha-positive breast cancer cell lines showed that expression from MIR2052HG and the ESR1 gene encoding estrogen receptor-alpha (ER alpha) was induced by estrogen and AI in a SNP-dependent manner. Variant SNP genotypes exhibited increased ER alpha 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 ER alpha. RNAi-mediated silencing of MIR2052HG in breast cancer cell lines decreased ER alpha expression, cell proliferation, and anchor-age-independent colony formation. Mechanistic investigations revealed that MIR2052HG sustained ER alpha levels both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated, proteasome-dependent degradation of ER alpha. 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. (C) 2016 AACR.
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