Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.
Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.
It has been estimated that >1,000 genetic loci have yet to be identified for breast cancer risk. Here we report the first study utilizing targeted next-generation sequencing to identify single-nucleotide polymorphisms (SNP) associated with breast cancer risk. Targeted sequencing of 283 genes was performed in 240 women with early-onset breast cancer (≤40 years) or a family history of breast and/or ovarian cancer. Common coding variants with minor allele frequencies (MAF) >1% that were identified were presumed initially to be SNPs, but further database inspections revealed variants had MAF of ≤1% in the general population. Through prioritization and stringent selection criteria, we selected 24 SNPs for further genotyping in 1,516 breast cancer cases and 1,189 noncancer controls. Overall, we identified the SNP rs56118985 to be significantly associated with overall breast cancer risk. Subtype analysis performed for patient subgroups defined by ER, PR, and HER2 status suggested additional associations of the SNP rs200504060 and the SNP rs142179458 with breast cancer risk. analysis indicated that coding amino acids encoded at these three SNP sites were conserved evolutionarily and associated with decreased protein stability, suggesting a likely impact on protein function. Our results offer proof of concept for identifying novel cancer risk loci from next-generation sequencing data, with iterative data analysis from targeted, whole-exome, or whole-genome sequencing a wellspring to identify new SNPs associated with cancer risk. .
<p>Supplementary data contains list of genes and SNP details</p>
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