A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

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.

Assessment of PARP4 as a candidate breast cancer susceptibility gene.

PURPOSE: PARP4 has been proposed as a candidate breast cancer susceptibility gene. However, its function and involvement in breast carcinogenesis is unclear. We sought to determine the variant frequency of PARP4 in BRCA-negative women referred for genetic testing from Singapore and to perform functional analyses of PARP4. METHODS: Next-generation sequencing of PARP4 was conducted for 198 BRCA-negative cases from Singapore. Three independent case-control association analyses of PARP4 were performed for (1) our Singaporean cohort, (2) three dbGaP datasets, and (3) cases from TCGA, with controls from the Exome Aggregation Consortium (ExAC). PARP4 knockout cells were generated utilizing the CRISPR-Cas9 approach in MDA-MB-231 (breast cancer) and MCF10A (normal breast) cell lines, and colony formation, cell proliferation, and migration assays carried out. RESULTS: Candidate variants in PARP4 were identified in 5.5% (11/198) of our Singapore cohort. Case-control association studies for our cases and the dbGaP datasets showed no significant association. However, a significant association was observed for PARP4 variants when comparing 988 breast cancer cases from the TCGA provisional data and 53,105 controls from ExAC (ALL) (OR 0.249, 95% CI 0.139-0.414, P = 2.86 × 10-11). PARP4 knockout did not affect the clonogenicity, proliferation rate, and migration of normal breast cells, but appeared to decrease the proliferation rate and clonogenicity of breast cancer cells. CONCLUSIONS: Taken together, our results do not support that PARP4 functions as a cancer susceptibility gene. This study highlights the importance of performing functional analyses for candidate cancer predisposition genes.

Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese.

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.

Identification of Novel Breast Cancer Risk Loci.

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 JAK2 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 NOTCH3 SNP rs200504060 and the HIF1A SNP rs142179458 with breast cancer risk. In silico 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. Cancer Res; 77(19); 5428-37. ©2017 AACR.