Prediction of breast cancer risk by low-penetrance variants including single nucleotide polymorphisms in microRNA-related genes

Introduction: In recent years, there are growing interests in adding common genetic variants to breast cancer risk prediction models and assessing their predictive performance and clinical validity. This study aims to evaluate the performance of low-penetrance variants, which has been previously identified through genome-wide association studies (GWASs), including single nucleotide polymorphisms (SNPs) in microRNA (miRNA)-related genes for predicting breast cancer risk in Korean women. Methods: The study subjects were derived from the previous GWAS conducted in the Seoul Breast Cancer Study. Phenotype data and genotyping data were available in 1,772 breast cancer patients and 10-yr age frequency matched 1,772 controls, thus a total of 3,544 subjects were included in the final analysis. Of the SNPs which were directly genotyped using Affymetrix 6.0 or imputed based on HapMap 2.0, 255 SNPs in 33 miRNA biogenesis pathway genes, 1,707 SNPs in 1,457 miRNA genes and their flanking regions, and 14 SNPs located in miRNA target site in 10 breast cancer related genes were selected. In addition, the fifty-one SNPs which were previously identified through GWAS as representing breast cancer susceptibility loci were selected and tested for their association. The association of individual SNP, genetic risk score (GRS), and other potential risk factors of breast cancer was tested by multivariate logistic regression analysis with adjustment for confounding factors. GRS was calculated by multiplying β-coefficient of each SNP by the number or risk allele, and summing the product. Two relative risks were calculated based on multivariate logistic regression with or without GRS as one of explanatory variables. Non-genetic factor model included age, menopausal status, family history of breast cancer in the first degree relatives, age at menarche, and number of children. Education and age at first full term i ivxlcdm pregnancy were also included as product terms with age category variable by 50. Individual relative risk and 5-year probability of developing breast cancer were estimated based on the model with or without GRS, respectively. The contribution of adding GRS to the breast cancer risk model constructed based only on non-genetic factors (non-genetic model) was evaluated by comparing the area under the receiver-operating-characteristic (ROC) curve (AUC) and by measuring integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Results: Of 51 SNPs located in 34 loci, 20 SNPs located in 13 loci showed significant associations with breast cancer risk (Ptrend < 0.05) and 15 SNPs were selected for calculating GRS as independent SNPs. Among miRNArelated SNPs, the most significant association was observed in SNP rs6578126 in EIF2C2 (ORper-allele = 0.66; 95% CI = 0.53-0.83; P = 3.8E-04; FDR P = 9.7E-02). Hormone receptor subtype specific associations were observed in rs4689695 in mir-4798 and rs1061657 in 3’ UTR target region of TBX3. SNP rs1061657 was associated with the decreased risk of ER-positive breast cancer with an OR of 0.79 (95% CI = 0.70-0.91; P = 6.8E-04; FDR P = 1.3E-01) but not with ER-negative breast cancer (Pheterogeneity = 1.5E-02). In addition, SNP rs1061657 in 3’ UTR target region of TBX3 was associated with the risk of PR-negative breast cancer (ORper-allele = 0.83; 95% CI = 0.730.93; P = 6.0E-03; FDR P = 9.0E-02) but not with PR-positive breast cancer, although test for heterogeneity was not significant Pheterogeneity = 1.9E-01). The GRS including 18 SNPs newly identified 3 SNPs in miRNA-related genes and 15 GWAS-SNPs replicated in study subjects was associated with breast cancer risk with an adjusted OR of 2.59 for the fifth quintile compared to the first quintile (95% CI = 2.05-3.27; Ptrend = 1.4E-20) suggesting independent association of GRS with breast cancer risk. The AUC of non-genetic model was 0.6082 (95% CI = 0.5885-0.6278). The contribution of GRS to the nongenetic model, as measured by the difference in AUC, was 0.0392 (P < ii 0.0001) resulting in AUC of 0.6473 (95% CI = 0.6282-0.6665). The estimated IDI was higher in ER-positive tumors (IDI = 0.0309; P < 0.0001) than in ERnegative tumors (IDI = 0.0228; P < 0.0001) although absolute difference in AUC between the two models was larger in ER-negative than in ER-positive tumors. Discriminatory ability of GRS measured in IDI was also better for PR-positive tumors than PR-negative tumors (IDI = 0.315; P < 0.0001 vs. IDI = 0.0243; P < 0.0001). As for relative risk estimation, approximately 18.9% of controls were reclassified into the lower risk group and 8.2% of cases were reclassified into the higher risk group after the inclusion of GRS. GRS also affected the classification of 5 year projected probabilities of developing breast cancer. Based on genetic model, 7% of controls and 10% of cases were correctly reclassified into the appropriate risk group. Conclusions: In summary, three SNPs in miRNA related-genes were found to be associated with overall breast cancer risk or the risk of specific subtype of breast cancer. GRS measuring the combined effect of 18 multiple genetic variants including 15 GWAS-SNPs provided modest but significant improvement in discrimination and in stratification. The improvement in performance from GRS was more prominent when predicting ER-positive or PR-positive breast cancer cases.