Michailidou, Kyriaki

Abstract Purpose: Germline pathogenic variants in CHEK2 confer moderately elevated breast cancer risk (odds ratio, OR ∼ 2.5), qualifying carriers for enhanced breast cancer screening. Besides pathogenic variants, dozens of missense CHEK2 variants of uncertain significance (VUS) have been identified, hampering the clinical utility of germline genetic testing (GGT). Experimental Design: We collected 460 CHEK2 missense VUS identified by the ENIGMA consortium in 15 countries. Their functional characterization was performed using CHEK2-complementation assays quantifying KAP1 phosphorylation and CHK2 autophosphorylation in human RPE1–CHEK2-knockout cells. Concordant results in both functional assays were used to categorize CHEK2 VUS from 12 ENIGMA case–control datasets, including 73,048 female patients with breast cancer and 88,658 ethnicity-matched controls. Results: A total of 430/460 VUS were successfully analyzed, of which 340 (79.1%) were concordant in both functional assays and categorized as functionally impaired (N = 102), functionally intermediate (N = 12), or functionally wild-type (WT)–like (N = 226). We then examined their association with breast cancer risk in the case–control analysis. The OR and 95% CI (confidence intervals) for carriers of functionally impaired, intermediate, and WT-like variants were 2.83 (95% CI, 2.35–3.41), 1.57 (95% CI, 1.41–1.75), and 1.19 (95% CI, 1.08–1.31), respectively. The meta-analysis of population-specific datasets showed similar results. Conclusions: We determined the functional consequences for the majority of CHEK2 missense VUS found in patients with breast cancer (3,660/4,436; 82.5%). Carriers of functionally impaired missense variants accounted for 0.5% of patients with breast cancer and were associated with a moderate risk similar to that of truncating CHEK2 variants. In contrast, 2.2% of all patients with breast cancer carried functionally wild-type/intermediate missense variants with no clinically relevant breast cancer risk in heterozygous carriers.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P < 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data.ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci.ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

In this study we aim to examine gene–environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10−3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10−4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

AbstractPrevious 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.

AbstractEpidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.