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Fulltext Author Correction: Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk

Wilcox N, Dumont M, González-Neira A, Carvalho S, Joly Beauparlant C, Crotti M, et al.

Nat Genet, 2023 Nov;55(11):2009.
PMID: 37752376 DOI: 10.1038/s41588-023-01549-x
Fulltext Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk

Wilcox N, Dumont M, González-Neira A, Carvalho S, Joly Beauparlant C, Crotti M, et al.

Nat Genet, 2023 Sep;55(9):1435-1439.
PMID: 37592023 DOI: 10.1038/s41588-023-01466-z

Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P
Fulltext Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer

Shi J, Zhang Y, Zheng W, Michailidou K, Ghoussaini M, Bolla MK, et al.

Int J Cancer, 2016 Sep 15;139(6):1303-1317.
PMID: 27087578 DOI: 10.1002/ijc.30150

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2) = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.
Fulltext A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2

Zanti M, O'Mahony DG, Parsons MT, Li H, Dennis J, Aittomäkkiki K, et al.

Hum Mutat, 2023;2023.
PMID: 38725546 DOI: 10.1155/2023/9961341

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.
Fulltext Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium

Morra A, Jung AY, Behrens S, Keeman R, Ahearn TU, Anton-Culver H, et al.

Cancer Epidemiol Biomarkers Prev, 2021 Apr;30(4):623-642.
PMID: 33500318 DOI: 10.1158/1055-9965.EPI-20-0924

BACKGROUND: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype.
METHODS: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype.
RESULTS: There was no evidence of heterogeneous associations between risk factors and mortality by subtype (P adj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking.
CONCLUSIONS: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype.
IMPACT: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

Dareng EO, Coetzee SG, Tyrer JP, Peng PC, Rosenow W, Chen S, et al.

Am J Hum Genet, 2024 May 07.
PMID: 38723632 DOI: 10.1016/j.ajhg.2024.04.011

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.
Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Mueller SH, Lai AG, Valkovskaya M, Michailidou K, Bolla MK, Wang Q, et al.

Genome Med, 2023 Jan 26;15(1):7.
PMID: 36703164 DOI: 10.1186/s13073-022-01152-5

BACKGROUND: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes.
METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry.
RESULTS: In European ancestry samples, 14 genes were significantly associated (q
Fulltext Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai Z, et al.

Cancer Discov, 2016 Sep;6(9):1052-67.
PMID: 27432226 DOI: 10.1158/2159-8290.CD-15-1227

Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.
SIGNIFICANCE: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
Fulltext Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, et al.

Nat Genet, 2020 01;52(1):56-73.
PMID: 31911677 DOI: 10.1038/s41588-019-0537-1

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.
Fulltext Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindström S, et al.

Nat Genet, 2017 Dec;49(12):1767-1778.
PMID: 29058716 DOI: 10.1038/ng.3785

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.