Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs)

Functional annotation of breast cancer risk loci: current progress and future directions

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

Common variants in breast cancer risk loci predispose to distinct tumor subtypes

BACKGROUND

Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.

METHODS

Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.

RESULTS

Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.

CONCLUSION

This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Population-Based Estimates of the Age-Specific Cumulative Risk of Breast Cancer for Pathogenic Variants in CHEK2: Findings from the Australian Breast Cancer Family Registry

Simple Summary

It is well established that women who carry pathogenic CHEK2 variants have about a 3-fold increased risk of developing breast cancer. CHEK2 is now commonly included in genetic tests for breast cancer predisposition and increasingly used to inform the clinical management of women who are identified to carry pathogenic variants. Important information for counselling these women includes knowing how breast cancer risk, due to having a pathogenic variant in CHEK2, changes over a woman’s lifetime. This information is currently not well established. By conducting a population-based case-control-family study of pathogenic CHEK2 variants we aimed to provide this information and estimated the penetrance (age-specific cumulative risk) of breast cancer to be 18% (95% CI 11–30%) to age 60 years and 33% (95% CI 21–48%) to age 80 years. These findings provide new and important information for the clinical management of breast cancer risk for women carrying pathogenic variants in CHEK2.

Abstract

Case-control studies of breast cancer have consistently shown that pathogenic variants in CHEK2 are associated with about a 3-fold increased risk of breast cancer. Information about the recurrent protein-truncating variant CHEK2 c.1100delC dominates this estimate. There have been no formal estimates of age-specific cumulative risk of breast cancer for all CHEK2 pathogenic (including likely pathogenic) variants combined. We conducted a population-based case-control-family study of pathogenic CHEK2 variants (26 families, 1071 relatives) and estimated the age-specific cumulative risk of breast cancer using segregation analysis. The estimated hazard ratio for carriers of pathogenic CHEK2 variants (combined) was 4.9 (95% CI 2.5–9.5) relative to non-carriers. The HR for carriers of the CHEK2 c.1100delC variant was estimated to be 3.5 (95% CI 1.02–11.6) and the HR for carriers of all other CHEK2 variants combined was estimated to be 5.7 (95% CI 2.5–12.9). The age-specific cumulative risk of breast cancer was estimated to be 18% (95% CI 11–30%) and 33% (95% CI 21–48%) to age 60 and 80 years, respectively. These findings provide important information for the clinical management of breast cancer risk for women carrying pathogenic variants in CHEK2.

Impact of pre- and post-variant filtration strategies on imputation

Quality control (QC) methods for genome-wide association studies and fine mapping are commonly used for imputation, however they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1089 NCBI recorded individuals for additional validation. Without QC-based variant pre-filtration, we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) < 0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). Thus, to maintain confidence and enough SNVs, we propose here a two-step filtering procedure which allows less stringent filtering prior to imputation and post-imputation in order to increase the number of very rare and rare variants compared to conservative filtration methods.

A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers

Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.

Transcriptome-wide association study of breast cancer risk by estrogen-receptor status

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.

Fine-mapping of a novel premenopausal breast cancer susceptibility locus at Chr4q31.22 in Caucasian women and validation in African and Chinese women

We previously identified a novel breast cancer susceptibility variant on chromosome 4q31.22 locus (rs1429142) conferring risk among women of European ancestry. Here, we report replication of findings, validation of the variant in diverse populations and fine-mapping of the associated locus in Caucasian population. The SNP rs1429142 (C/T, minor allele frequency 18%) showed association for the overall breast cancer risk in Stages 1-4 (n = 4,331 cases/4271 controls; p = 4.35 × 10-8 ; odds ratio, ORC-allele ,1.25), and an elevated risk among premenopausal women (n = 1,503 cases/4271 controls; p = 5.81 × 10-10 ; ORC-allele 1.40) in European populations. SNP rs1429142 was associated with premenopausal breast cancer risk in women of African (T/C; p-value 1.45 × 10-02 ; ORC-allele 1.2) but not from Chinese ancestry. Fine-mapping of the locus revealed several potential causal variants which are present within a single association signal, revealed from the conditional regression analysis. Functional annotation of the potential causal variants revealed three putative SNPs rs1366691, rs1429139 and rs7667633 with active enhancer functions inferred based on histone marks, DNase hypersensitive sites in breast cell line data. These putative variants were bound by transcription factors (C-FOS, STAT1/3 and POL2/3) with known roles in inflammatory pathways. Furthermore, Hi-C data revealed several short-range interactions in the fine-mapped locus harboring the putative variants. The fine mapped locus was predicted to be within a single topologically associated domain, potentially facilitating enhancer-promoter interactions possibly leading to the regulation of nearby genes.

Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium

BACKGROUND

Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions.

METHODS

Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions.

RESULTS

Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth.

CONCLUSIONS

Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors.

Elucidation of WW domain ligand binding specificities in the Hippo pathway reveals STXBP4 as YAP inhibitor

The Hippo pathway, which plays a critical role in organ size control and cancer, features numerous WW domain-based protein-protein interactions. However, ~100 WW domains and 2,000 PY motif-containing peptide ligands are found in the human proteome, raising a "WW-PY" binding specificity issue in the Hippo pathway. In this study, we have established the WW domain binding specificity for Hippo pathway components and uncovered a unique amino acid sequence required for it. By using this criterion, we have identified a WW domain-containing protein, STXBP4, as a negative regulator of YAP. Mechanistically, STXBP4 assembles a protein complex comprising α-catenin and a group of Hippo PY motif-containing components/regulators to inhibit YAP, a process that is regulated by actin cytoskeleton tension. Interestingly, STXBP4 is a potential tumor suppressor for human kidney cancer, whose downregulation is correlated with YAP activation in clear cell renal cell carcinoma. Taken together, our study not only elucidates the WW domain binding specificity for the Hippo pathway, but also reveals STXBP4 as a player in actin cytoskeleton tension-mediated Hippo pathway regulation.

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

Elucidating the Underlying Functional Mechanisms of Breast Cancer Susceptibility Through Post-GWAS Analyses

Genome-wide association studies (GWAS) have identified more than 170 single nucleotide polymorphisms (SNPs) associated with the susceptibility to breast cancer. Together, these SNPs explain 18% of the familial relative risk, which is estimated to be nearly half of the total familial breast cancer risk that is collectively explained by low-risk susceptibility alleles. An important aspect of this success has been the access to large sample sizes through collaborative efforts within the Breast Cancer Association Consortium (BCAC), but also collaborations between cancer association consortia. Despite these achievements, however, understanding of each variant's underlying mechanism and how these SNPs predispose women to breast cancer remains limited and represents a major challenge in the field, particularly since the vast majority of the GWAS-identified SNPs are located in non-coding regions of the genome and are merely tags for the causal variants. In recent years, fine-scale mapping studies followed by functional evaluation of putative causal variants have begun to elucidate the biological function of several GWAS-identified variants. In this review, we discuss the findings and lessons learned from these post-GWAS analyses of 22 risk loci. Identifying the true causal variants underlying breast cancer susceptibility and their function not only provides better estimates of the explained familial relative risk thereby improving polygenetic risk scores (PRSs), it also increases our understanding of the biological mechanisms responsible for causing susceptibility to breast cancer. This will facilitate the identification of further breast cancer risk alleles and the development of preventive medicine for those women at increased risk for developing the disease.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10-6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

Common Genetic Variation and Breast Cancer Risk-Past, Present, and Future

Breast cancer is the most common cancer among women in the United States, with up to 30% of those diagnosed displaying a family history of breast cancer. To date, 18% of the familial risk of breast cancer can be explained by SNPs. This review summarizes the discovery of risk-associated SNPs using candidate gene and genome-wide association studies (GWAS), including discovery and replication in large collaborative efforts such as The Collaborative Oncologic Gene-environment Study and OncoArray. We discuss the evolution of GWAS studies, efforts to discover additional SNPs, and methods for identifying causal variants. We summarize findings associated with overall breast cancer, pathologic subtypes, and mutation carriers (BRCA1, BRCA2, and CHEK2). In addition, we summarize the development of polygenic risk scores (PRS) using the risk-associated SNPs and show how PRS can contribute to estimation of individual risks for developing breast cancer. Cancer Epidemiol Biomarkers Prev; 27(4); 380-94. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Capture Hi-C identifies putative target genes at 33 breast cancer risk loci

Genome-wide association studies (GWAS) have identified approximately 100 breast cancer risk loci. Translating these findings into a greater understanding of the mechanisms that influence disease risk requires identification of the genes or non-coding RNAs that mediate these associations. Here, we use Capture Hi-C (CHi-C) to annotate 63 loci; we identify 110 putative target genes at 33 loci. To assess the support for these target genes in other data sources we test for associations between levels of expression and SNP genotype (eQTLs), disease-specific survival (DSS), and compare them with somatically mutated cancer genes. 22 putative target genes are eQTLs, 32 are associated with DSS and 14 are somatically mutated in breast, or other, cancers. Identifying the target genes at GWAS risk loci will lead to a greater understanding of the mechanisms that influence breast cancer risk and prognosis.