Aromatase Inhibitor-Associated Bone Fractures: A Case-Cohort GWAS and Functional Genomics
Bibliographic record
Abstract
Bone fractures are a major consequence of osteoporosis. There is a direct relationship between serum estrogen concentrations and osteoporosis risk. Aromatase inhibitors (AIs) greatly decrease serum estrogen levels in postmenopausal women, and increased incidence of fractures is a side effect of AI therapy. We performed a discovery case-cohort genome-wide association study (GWAS) using samples from 1071 patients, 231 cases and 840 controls, enrolled in the MA.27 breast cancer AI trial to identify genetic factors involved in AI-related fractures, followed by functional genomic validation. Association analyses identified 20 GWAS single nucleotide polymorphism (SNP) signals with P < 5E-06. After removal of signals in gene deserts and those composed entirely of imputed SNPs, we applied a functional validation "decision cascade" that resulted in validation of the CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 genes. These genes all displayed estradiol (E2)-dependent induction in human fetal osteoblasts transfected with estrogen receptor-α, and their knockdown altered the expression of known osteoporosis-related genes. These same genes also displayed SNP-dependent variation in E2 induction that paralleled the SNP-dependent induction of known osteoporosis genes, such as osteoprotegerin. In summary, our case-cohort GWAS identified SNPs in or near CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 that were associated with risk for bone fracture in estrogen receptor-positive breast cancer patients treated with AIs. These genes displayed E2-dependent induction, their knockdown altered the expression of genes related to osteoporosis, and they displayed SNP genotype-dependent variation in E2 induction. These observations may lead to the identification of novel mechanisms associated with fracture risk in postmenopausal women treated with AIs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".