Genetic Polymorphisms in the Long Noncoding RNA MIR2052HG Offer a Pharmacogenomic Basis for the Response of Breast Cancer Patients to Aromatase Inhibitor Therapy
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".