Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities

Refining the genetic risk of breast cancer with rare haplotypes and pattern mining

Hundreds of common variants have been found to confer small but significant differences in breast cancer risk, supporting the widely accepted polygenic model of inherited predisposition. Using a novel closed-pattern mining algorithm, we provide evidence that rare haplotypes may refine the association of breast cancer risk with common germline alleles. Our method, called Chromosome Overlap, consists in iteratively pairing chromosomes from affected individuals and looking for noncontiguous patterns of shared alleles. We applied Chromosome Overlap to haplotypes of genotyped SNPs from female breast cancer cases from the UK Biobank at four loci containing common breast cancer-risk SNPs. We found two rare (frequency <0.1%) haplotypes bearing a GWAS hit at 11q13 (hazard ratio = 4.21 and 16.7) which replicated in an independent, European ancestry population at P < 0.05, and another at 22q12 (frequency <0.2%, hazard ratio = 2.58) which expanded the risk pool to noncarriers of a GWAS hit. These results suggest that rare haplotypes (or mutations) may underlie the "synthetic association" of breast cancer risk with at least some common variants.

Genetic Association Analysis Implicates Six MicroRNA-Related SNPs With Increased Risk of Breast Cancer in Australian Caucasian Women

INTRODUCTION

Breast cancer (BC), a heterogeneous disease, features microRNA-related single nucleotide polymorphisms (miRSNPs) as underlying factors of BC development, thus providing targets for novel diagnostic and therapeutic strategies. This study investigated miRSNPs in BC susceptibility in Australian Caucasian women.

PATIENTS AND METHODS

The study population included patients 33 to 80 years of age stratified by molecular subtypes of breast tumors (luminal A, 47.59%), stage (stage I, 36.96%), tumor-type (ductal, 44.95%), grading (intermediate, 35.52%), size (10.1-25 mm, 31.14%), and lymph node (sentinel negative, 38.93%). Sixty-five miRSNPs underwent allelic analysis in two independent case-control cohorts (GU-CCQ-BB, 377 cases and 521 controls; GRC-BC, 267 cases and 201 controls) using a MassARRAY platform. GU-CCQ-BB, GRC-BC, and the combined populations (BC-CA) (644 cases and 722 controls) underwent independent statistical analysis.

RESULTS

In the GU-CCQ-BB population, miRSNPs TET2-rs7670522, ESR1-rs2046210, FGFR2-rs1219648, MIR210-rs1062099, HIF1A-rs2057482, and CASC16-rs4784227 were found to be associated with increased BC risk (P ≤ .05). Only ESR1-rs2046210 was also significantly associated (P ≤ .05) when replicated in the GRC-BC and BC-CA populations. No significant association was correlated with BC-clinical features (pathological types and ER/PR/HER2 status); however, MIR210-rs1062099 was found to be significantly associated (P ≤ .05) with age (>50 years) in the GU-CCQ-BB cohort.

CONCLUSION

This is the first study to demonstrate the association of MIR210-rs1062099 and TET2-rs7670522 with increased BC risk. Additionally, four previously reported SNPs (ESR1-rs2046210, FGFR2-rs1219648, HIF1A-rs2057482, and CASC16-rs4784227) were confirmed as BC risk variants. Replication and functional studies in larger Caucasian cohorts are necessary to elucidate the role of these miRSNPS in the development of BC.

Supervariants identification for breast cancer

In genome-wide association studies, signals associated with rare variants and interactions between genes are hard to detect even when the sample size is in tens of thousands. To overcome these problems, we examine the concept of supervariant. Like the classic concept of the gene, a supervariant is a combination of alleles in multiple loci, but the contributing loci can be anywhere in the genome. We hypothesize that supervariants are easy to detect and the aggregated signals are more stable in their associations with the disease than that from a single nucleoid polymorphism. Using the UK Biobank databases, we develop a ranking and aggregation method for identifying supervariants. Specifically, we examine 9,377 breast cancer cases with 46,861 controls matched by sex and age. In our simulations, the use of supervariants outperforms single-nucleotide polymorphism-based association method in detecting rare variants and signals with interactive structure. In real data analysis, we identify supervariants on Chromosomes 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 16, and 22 which cover previously reported loci that have associations with breast or other cancers, and several novel loci on Chromosomes 2, 5, 9, and 12. These findings demonstrate the validity of supervariants and its potential of discovering replicable and novel results for complex disease.

Novel applications of next-generation sequencing in breast cancer research

With the rapid development of medicine, the studies of genes have become increasingly concerned by more people and being the contend of a great of researches. The next generation sequencing with its own advantages has been widely used in gene research nowadays. It has almost replaced the traditional sequencing methods (such as Sanger sequencing method), and played an important role in a variety of complex disease researches, including breast cancer. The next generation sequencing technology has the advantages of high speed, high throughput and high accuracy. It has been widely used in various cancers (such as prostate cancer, lung cancer, pancreatic cancer, liver cancer, etc.), especially in breast cancer. Moreover, the use of the next generation sequencing technology to make DNA sequence analysis and risk prediction has made a great contribution to the research of breast cancer. We will focus on the application of whole genome sequencing, exon sequencing and targeted gene sequencing in breast cancer gene research.