Genetic susceptibility loci for breast cancer by estrogen receptor status

Abstract

Breast cancer is a heterogeneous disease, and risk factors could be differentially associated with the development of distinct tumor subtypes that manifest different biological behavior and progression. In support of this view, there is growing evidence that known breast cancer risk factors vary by hormone receptor status and perhaps other pathologic characteristics of disease. Recent work from large consortial studies has led to the discovery of novel breast cancer susceptibility loci in genic (CASP8, FGFR2, TNRC9, MAP3K1, LSP1) and nongenic regions (8q24, 2q35, 5p12) of the genome, and to the finding of substantial heterogeneity by tumor characteristics. In particular, susceptibility loci in FGFR2, TNRC9, 8q24, 2q35, and 5p12 have stronger associations for estrogen receptor-positive (ER+) disease than estrogen receptor-negative (ER -) disease. These findings suggest that common genetic variants can influence the pathologic subtype of breast cancer, and provide further support for the hypothesis that ER+ and ER(-) disease result from different etiologic pathways. Current studies had limited power to detect susceptibility loci for less common tumor subtypes, such as ER(-) disease including triple-negative and basal-like tumors. Ongoing work targeting uncommon subtypes is likely to identify additional tumor-specific susceptibility loci in the near future. Characterization of etiologic heterogeneity of breast cancer may lead to improvements in the understanding of the biological mechanisms for breast cancer, and ultimately result in improvements in prevention, early detection, and treatment.

Figure 1. Breast cancer susceptibility loci according to the approximate magnitude of their associated relative risk (per risk allele) and frequency of the risk allele

This figure shows that most low penetrance variants in susceptibly loci discovered to date fall into the lower right corner (risk allele frequencies over 20% and relative risk per risk allele under 1.3), and that common risk alleles associated with higher relative risk (upper right corners) are unlikely to exist. While additional high penetrance mutations in susceptibly loci are unlikely; moderate and low penetrance variants are likely to be discovered in the near future as the genetic coverage of whole genome scans improves for uncommon variants and the size of studies increases.

Figure 2. Mapping of disease susceptibility loci using genome wide scans

The Human Genome Project has identified over 10 million single nucleotide polymorphisms (SNPs), which are the most common form of genetic variation in the genome. GWAS take advantage of the correlation (i.e. linkage disequilibrium) between neighboring SNPs in the same chromosome (characterized by the HapMap Project) to select a subset of SNPs (called tagSNPs) that capture most common genetic variation across the genome. Genome wide SNP chips are used to genotype a large number of tagSNPs (500,000 to 1M) on DNA samples from participants in case-control studies to evaluate their association with risk of disease (see Figure 3 for a description of a multistage GWAS design). This strategy is used to map SNPs to areas of the genome likely to include disease-causing variants.

Figure 3. Multi-stage design for genome wide association studies (GWAS)

In a multi-stage design, a large number of single nucleotide polymorphisms (SNPs) selected to capture most common genetic variation across the genome (genome wide scan chip) are tested in a relatively small number of cases and controls in a “discovery study”. The SNPs showing the most significant associations with disease risk in the discovery study (e.g. P value from an association test <0.05) are re-tested in subsequent replication studies including large independent sets of cases and controls. In the example shown in the figure, SNPs with P values <0.001 in a first replication study are re-tested in a second replication study. SNPs showing strong evidence for an association with disease risk based on data from the three phases (e.g. P value from an association test <10–7) are selected as markers for chromosomal regions likely to contain disease causing variants. Very large studies and stringent statistical criteria are necessary to have sufficient power to detect associations while minimizing the probability of false positive findings. The selected markers in GWAS are further evaluated in fine mapping studies to identify causal variants, and functional studies to understand the biological mechanism of the observed associations with disease. Red and blue individuals represent cases of breast cancer and controls, respectively, being tested in different stages of the design. The green and red dots in the inverted cone represent SNPs being tested in each stage. The red dots are markers for disease susceptibly alleles.