Breast Cancer Genome-wide Association Studies Research Articles

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Integrated analysis reveals FLI1 regulates the tumor immune microenvironment via its cell-type-specific expression and transcriptional regulation of distinct target genes of immune cells in breast cancer

BackgroundImmunotherapy is a practical therapeutic approach in breast cancer (BRCA), and the role of FLI1 in immune regulation has gradually been unveiled. However, the specific role of FLI1 in BRCA was conflicted; thus, additional convincing evidence is needed.MethodsWe explored the upstream regulation of FLI1 expression via summary data-based Mendelian randomization (SMR) analysis and ncRNA network construction centering on FLI1 using BRCA genome-wide association study (GWAS) summary data with expression quantitative trait loci (eQTLs) and DNA methylation quantitative trait loci (mQTLs) from the blood and a series of in silico analyses, respectively. We illuminated the downstream function of FLI1 in immune regulation by integrating a series of analyses of single-cell RNA sequence data (scRNA-seq).ResultsWe verified a causal pathway from FLI1 methylation to FLI1 gene expression to BRCA onset and demonstrated that FLI1 was downregulated in BRCA. FLI1, a transcription factor, served as myeloid and T cells’ communication regulator by targeting immune-related ligands and receptor transcription in BRCA tissues. We constructed a ceRNA network centering on FLI1 that consisted of three LncRNAs (CKMT2-AS1, PSMA3-AS1, and DIO3OS) and a miRNA (hsa-miR-324-5p), and the expression of FLI1 was positively related to a series of immune-related markers, including immune cell infiltration, biomarkers of immune cells, and immune checkpoints.ConclusionLow-methylation-induced or ncRNA-mediated downregulation of FLI1 is associated with poor prognosis, and FLI1 might regulate the tumor immune microenvironment via a cell-type-specific target genes manner in BRCA.

Multi-omics Mendelian randomization integrating GWAS, eQTL, and mQTL data identified genes associated with breast cancer.

Breast cancer (BC) remains a major disease posing a threat to women's health, but the underlying biological interpretation remains largely unknown. Here, we aimed to identify genes associated with breast cancer and analyze their pathophysiological mechanisms based on multi-omics Mendelian randomization (MR). Summary-data-based MR (SMR) was performed to estimate the causal effects of blood and breast mammary tissue expression quantitative trait loci (eQTLs) on BC. External validation analysis was used to validate the identified genes. Integration analyses BC GWAS summaries with eQTLs and DNA methylation QTLs (mQTLs) from the blood were conducted using SMR to prioritize putative blood genes and their regulatory elements associated with BC risk. Finally, two prior genes (ATG10 and RCCD1) from blood tissue reached significant levels in both BCAC (ATG10: ORBRCR = 0.91, PBRCR = 1.29 × 10-11; RCCD1: ORBRCR = 0.90, PBRCR = 3.72 × 10-15) and FinnGen cohorts (ATG10: ORFinnGen = 0.89, PFinnGen = 8.55 × 10-5; RCCD1: ORFinnGen = 0.89, PFinnGen = 2.38 × 10-8). Additionally, those two genes from breast tissues also replicated in both BCAC (ATG10: ORBRCR = 0.95, PBRCR = 1.02 × 10-9; RCCD1: ORBRCR = 0.87, PBRCR = 4.70 × 10-10) and FinnGen cohorts (ATG10: ORFinnGen = 0.93, PFinnGen = 2.38 × 10-4; RCCD1: ORFinnGen = 0.85, PFinnGen = 3.81 × 10-6). Sensitive analysis and external validation analysis validated those two identified genes. Multi-omics MR analysis showed that the SNP signals associated with ATG10 and RCCD1 were significant across the data from BC Genome-wide association study (GWAS), eQTL, and mQTL studies. In conclusion, we identified two priority genes that are potentially associated with BC. These findings improve our limited understanding of the mechanism of BC and shed light on the development of therapeutic agents for treating BC.

Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel

BackgroundPolygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European...

Integrated logistic ridge regression and random forest for phenotype-genotype association analysis in categorical genomic data containing non-ignorable missing values

Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS.

Abstract 5227: Cross-trait genome-wide association meta-analyses of clonal hematopoiesis and solid tumor risk

Abstract Clonal hematopoiesis (CH) is an age-related phenomenon where hematopoietic stem cells and their progeny acquire specific somatic mutations in a small, well-defined set of leukemogenic genes conferring them with fitness advantage leading to clonal proliferation. Mosaic chromosomal alterations (mCAs) are somatic structural variants that are manifestations of genomic instability and a sign of CH when they occur in blood. The recent availability of large-scale cohorts with blood exome sequence and genotype data has powered genome-wide association studies (GWAS) investigating the germline genetic contribution to CH and mCA risk. However, phenotypes of somatic mutation acquisition in driver genes, clonal selection and expansion, and chromosomal mosaicism are processes that are not confined to blood and contribute to neoplastic transformation across tissue types. GWAS of solid tumor susceptibility have so far identified hundreds of germline variants associated with cancer risk and it is likely that a subset of these variants contributes to solid tumor risk by affecting these somatic phenotypes in the corresponding tissues of origin and that some of the variants exert these somatic effects across tissues. We aimed to identify this subset of solid tumor risk variants by undertaking fixed effects inverse-variance weighted meta-analyses combining breast, prostate, ovarian, and endometrial cancer GWAS (total n = 237,483 cases/317,006 controls; no UK Biobank samples included) with UK Biobank-based CH (10,203 “cases”/173,918 controls) and mCA (66,011 “cases”/378,188 controls) risk GWAS in pairs where each pair involved one solid tumor type and one of either CH or mCA (for example, breast cancer and mCA constituted a pair). These analyses collectively identified 61 independent (r2<0.05) lead variants at combined cross-trait genome-wide significance (P<5 × 10−8) across 22 genomic loci where each lead variant was associated with each trait individually at P<10−3, had the same direction of allelic effect across traits, and little evidence of heterogeneity in effect (I2<40%). New loci (>1 Mb away from previously reported risk loci for a given trait) counts were: 14 new loci in the context of mCA risk, 6 new loci for CH risk, 2 for breast cancer risk, and one each for prostate, ovarian, and endometrial cancer risks. In silico functional annotation of these loci suggested lead variant-target genes that could be assigned to the discrete pathways of mitotic spindle assembly (INCENP, CENPN, ZWILCH, MAD1L1), p53 signaling (TP53, MDM4, SPI1), DNA damage repair (ATM, RAD51C, CHEK2, USP28, PARP8), and myeloid oncogenesis (WT1, HOXA9, L3MBTL3). Our novel approach combining the latest and largest blood somatic genomic trait GWAS with large-scale solid tumor risk GWAS thus yielded clear mechanistic insights into the shared inherited genetic basis of solid tumor risk and somatic genomic traits. Citation Format: Victoria Gray, George Richenberg, Pedro Quirós, Matthew Freedman, Paul Pharoah, Simon Gayther, Michelle Jones, George Vassiliou, Kate Lawrenson, Siddhartha Kar. Cross-trait genome-wide association meta-analyses of clonal hematopoiesis and solid tumor risk. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5227.

Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study.

This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study. Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9years). For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with PT = 0.05 and R2 = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586). This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.

Mendelian randomization study on the effect of tumor necrosis factor on schizophrenia.

Previous observational studies have shown that the levels of tumor necrosis factor (TNF) increased in patients with schizophrenia. The present two-sample Mendelian randomization (MR) study aims to identify the causal link between TNF and schizophrenia. To date, the largest genome-wide association study (GWAS) for TNF (n = 23 141) and for schizophrenia (53 386 cases and 77 258 controls) was used. All participants were of European ancestry. The MR-egger_intercept test and Cochran's Q statistic were used to determine the pleiotropy and heterogeneity, respectively. Weighted median and inverse variance weighted (IVW) were used to evaluate the causal association of TNF with schizophrenia. We found no significant pleiotropy or heterogeneity of all three selected plasma TNF genetic instrumental variants in breast cancer GWAS. Interestingly, the odds ratio (OR) = 1.517 with 95% confidence interval (CI), 1.006-2.288 and P = 0.047 of schizophrenia correspond to one unit increase in natural log-transformed TNF levels using IVW method. The increased trend was further proven using weighted median (OR = 1.585; 95% CI, 1.017-2.469; P = 0.042). Reverse MR analysis shows no causal effect of schizophrenia on plasma TNF levels. Our analysis suggested a causal association between genetically increased TNF signaling and increased risk of schizophrenia in the European population. Thus, TNF may be a potential risk for schizophrenia.

The African Female Breast Cancer Epidemiology Study Protocol.

Breast cancer is now the commonest cancer in most sub-Saharan African countries. Few studies of the epidemiology and genomics of breast cancer and its molecular subtypes in these countries have been done. The African Female Breast Cancer Epidemiology (AFBRECANE) study, a part of the Human Heredity and Health in Africa (H3Africa) initiative, is designed to study the genomics and epidemiology of breast cancer and its molecular subtypes in Nigerian women. We link recruitment of breast cancer cases at study sites with population-based cancer registries activities to enable ascertainment of the incidence of breast cancer and its molecular subtypes. We use centralized laboratory processing to characterize the histopathological and molecular diagnosis of breast cancer and its subtypes using multiple technologies. By combining genome-wide association study (GWAS) data from this study with that generated from 12,000 women participating in our prospective cohort study of cervical cancer, we conduct GWAS of breast cancer in an entirely indigenous African population. We test associations between dietary intakes and breast cancer and focus on vitamin D which we measure using dietary intakes, serum vitamin D, and Mendelian randomization. This paper describes the AFBRECANE project, its design, objectives and anticipated contributions to knowledge and understanding of breast cancer.

Abstract P3-13-02: A genome-wide association study of mammographic texture variation

Abstract Background: Breast parenchymal texture features capture the patterns of texture variation on a mammogram. One such texture feature is called V, which reflects the gray scale variation on the image. Recent studies have shown that texture features, including V, are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of texture features and the shared genetic components between these features, MD, and breast cancer is limited. Methods: We conducted a genome-wide association study (GWAS) of breast texture variation, including four different assessments of V, in 7,040 European-ancestry women within the Nurses’ Health Studies and Mayo Mammography Health Study cohorts. Genotype data were imputed to the 1000 Genomes Phase 3 version 5 reference panel. The four V assessments, differ by amount of breast edge erosion and image resolution, were generated from digitized film mammograms using fully automated methods. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes (percent density, dense area, and nondense area), and the top four principal components. Multivariate phenotype association tests were performed to estimate summary test statistics of the four V assessments. We compared our results to GWAS of breast cancer and MD phenotypes, and identified shared susceptibility loci. We calculated genetic correlations of V measures with MD phenotypes, breast cancer, overall and by estrogen receptor status, BMI, childhood body fatness, age at menarche, and age at natural menopause. Aggregate tests were performed to assess the relationship between V measures and the above traits using genome-wide significant SNPs for these traits. Results: We identified three novel genome-wide significant loci that are associated with V measures: rs79670367 (8q24.22) in LINC01591, rs113174754 (12q22) near PGAM1P5, and rs138141444 (6q24.1) in ECT2L. 12q22 is a known locus for both MD (nondense area) and breast cancer. 8q24.22 and 6q24.1 have not previously been associated with MD or breast cancer. We identified four additional loci that are associated with MD or breast cancer, or both and are significantly associated with the V measures at the Bonferroni-corrected thresholds accounting for the number of MD or breast cancer SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) in LOC105369705, and rs17817449 (16q12.2) in FTO, the directions of association are consistent with MD or breast cancer for all four loci. We observed significant genetic correlations between V measures and dense area (rg = 0.79, p = 6.0 × 10-5), percent density (rg = 0.73, p = 1.0 × 10-4), and BMI (rg = -0.36, p = 3.9 × 10-7). Additional significant relationships were observed for nondense area (z = -4.14, p = 3.5 × 10-5) and childhood body fatness (z = -4.91, p = 9.1 × 10-7) from the aggregate tests. Conclusions: This is the first GWAS of breast parenchymal texture features. These findings provide new insights into the genetic basis of texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors. Citation Format: Yuxi Liu, Xia Jiang, Constance Turman, Celine M Vachon, Rulla Tamimi, Peter Kraft. A genome-wide association study of mammographic texture variation [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-13-02.

A Meta-Analysis of Genome-Wide Association Studies of Multiple Myeloma in Persons of African Ancestry [African-American Multiple Myeloma Study (AAMMS)] Identifies Suggestive Risk Variants

Background: Multiple myeloma (MM) is 2-3 times more common among persons of African ancestry (AA) compared to those of European ancestry (EA). The 2-3-fold increased risk among family members of cases suggests a genetic contribution to risk. Genome-wide association studies (GWAS) in EA populations have identified 16 novel risk loci. We previously showed that 5 of these replicated in populations of AA (Rand et al., CEBP, 2016). In order to increase the power to identify additional novel common genetic risk variants, we conducted a GWAS meta-analysis using two sets of AA MM patients recruited into the African American Multiple Myeloma Study (AAMMS) and the University of Arkansas for Medical Sciences (UAMS)) Myeloma Institute.Methods: The first GWAS included 1,305 incident AA MM cases enrolled in AAMMS, recruited from 10 clinical centers and population-based cancer registries in the NCI SEER Program from 2011 to 2013 and genotyped using the Illumina HumanCore GWAS array. Control data were obtained from 4425 male controls from the African Ancestry Prostate Cancer Consortium (consisting of 14 independent studies) and 2632 female controls from a breast cancer GWAS of African-American women (consisting of 9 independent studies) and genotyped using the Illumina 1M-Duo. The second GWAS included 95 additional AAMMS patients and 435 AA MM patients enrolled from UAMS, genotyped using the Illumina MegaBead Chip and compared to 2,390 AA controls from the Multiethnic Cohort. Both GWAS were imputed with the CAAPA Haplotype Reference panel on the University of Michigan Imputation Server http://www.caapa-project.org/. Single nucleotide polymorphisms (SNPs) with a minor allele frequency of less than 0.01 and an imputation score of less than 0.8 were excluded. Associations between SNPs and MM risk in each GWAS were calculated using unconditional logistic regression adjusted for sex, age at diagnosis (cases) or cohort sample collection (controls), and the first 10 genetic ancestry principal components. A meta-analysis of the two GWAS was conducted using a fixed effects model.Results: There were 8,715,278 overlapping SNPs in the meta-analysis with a lambda (inflation) = 1.066. Seven loci were associated with MM risk at p<10-6, but none reached genome-wide significance. We found several novel SNPs at the previously reported locus 6p21.33, with odds ratios (ORs) ranging from 1.20-1.28 and p-values ranging from 3.72-8.46 x 10-7 (Figure 1). We also identified six additional suggestive novel loci at chr4q32.1, chr9p24.3, chr12q12, chr12p13.3, chr16p12.2, chr18q12.1 with ORs ranging from 1.27-2.13 and p-values from 1.2x10-7-9.39x10-7.Of the 16 known risk loci in EA MM patients, eight replicated at p<0.05 in the AA MM GWAS meta-analysis. We previously reported better localized signals in AA patients for 7 of these known risk SNPs; five of these replicated at p< 0.05 in this meta-analysis. The highest p-value for the previously identified EA or AA SNPs was 1.90 x10-5.Conclusions: We have found suggestive loci associated with MM risk in AA's that appear distinct from risk loci identified in EA's, that may explain some of the disparity for MM risk in these two populations. Further follow-up with replication is warranted.Figure 1. A Manhattan plot showing results of a fixed effects meta-analysis of two genome wide association scans based on a total of 1,835 multiple myeloma patients and 9,468 controls of African ancestry, with genome-wide significance of p= 5x10-8 (red line). Y-axis= -Log10 of the p-value; X-axis= chromosomes; green dots= previously reported risk loci, excluding those with imputation scores<0.8 and a minor allele frequency of <1%. [Display omitted] DisclosuresAilawadhi:Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pharmacyclics: Research Funding. Nooka:Amgen, Novartis, Spectrum, Adaptive: Consultancy. Anderson:Millenium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; MedImmune: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Other: scientific founder; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopep: Other: scientific founder. Vij:Amgen: Honoraria, Research Funding; Jazz: Honoraria; Konypharma: Honoraria; Bristol-Meyers-Squibb: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Celgene: Honoraria; Takeda: Honoraria, Research Funding. Zonder:Prothena: Consultancy; Pharmacyclics: Other: Data Safety Monitoring Committee; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Orlowski:BioTheryX: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Risk of Breast Cancer Among Carriers of Pathogenic Variants in Breast Cancer Predisposition Genes Varies by Polygenic Risk Score.

This study assessed the joint association of pathogenic variants (PVs) in breast cancer (BC) predisposition genes and polygenic risk scores (PRS) with BC in the general population. A total of 26,798 non-Hispanic white BC cases and 26,127 controls from predominately population-based studies in the Cancer Risk Estimates Related to Susceptibility consortium were evaluated for PVs in BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, BRIP1, CDH1, and NF1. PRS based on 105 common variants were created using effect estimates from BC genome-wide association studies; the performance of an overall BC PRS and estrogen receptor-specific PRS were evaluated. The odds of BC based on the PVs and PRS were estimated using penalized logistic regression. The results were combined with age-specific incidence rates to estimate 5-year and lifetime absolute risks of BC across percentiles of PRS by PV status and first-degree family history of BC. The estimated lifetime risks of BC among general-population noncarriers, based on 10th and 90th percentiles of PRS, were 9.1%-23.9% and 6.7%-18.2% for women with or without first-degree relatives with BC, respectively. Taking PRS into account, more than 95% of BRCA1, BRCA2, and PALB2 carriers had > 20% lifetime risks of BC, whereas, respectively, 52.5% and 69.7% of ATM and CHEK2 carriers without first-degree relatives with BC, and 78.8% and 89.9% of those with a first-degree relative with BC had > 20% risk. PRS facilitates personalization of BC risk among carriers of PVs in predisposition genes. Incorporating PRS into BC risk estimation may help identify > 30% of CHEK2 and nearly half of ATM carriers below the 20% lifetime risk threshold, suggesting the addition of PRS may prevent overscreening and enable more personalized risk management approaches.

Mendelian randomization analyses of genetically predicted circulating levels of cytokines with risk of breast cancer

To determine whether genetically predicted circulating levels of cytokines are associated with risk of overall breast cancer (BC), estrogen receptor (ER)-positive and ER-negative BC, we conducted two-sample MR analyses using data from the most comprehensive genome-wide association studies (GWAS) on cytokines in 8293 Finnish participants and the largest BC GWAS from the Breast Cancer Association Consortium (BCAC) with totally 122,977 BC cases and 105,974 healthy controls. We systematically screened 41 cytokines (of which 24 cytokines have available instruments) and identified that genetically predicted circulating levels (1-SD increase) of MCP1 (OR: 1.08; 95% CIs: 1.03–1.12; P value: 3.55 × 10−4), MIP1b (OR: 1.02; 95% CIs: 1.01–1.04; P value: 2.70 × 10−3) and IL13 (OR: 1.06; 95% CIs: 1.03–1.10; P value: 3.33 × 10−4) were significantly associated with increased risk of overall BC, as well as ER-positive BC. In addition, higher levels of MIP1b and IL13 were also significantly associated with increased risk of ER-negative BC. These findings suggest the crucial role of cytokines in BC carcinogenesis and potential of targeting specific inflammatory cytokines for BC prevention.

EQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene

Breast cancer genome-wide association studies (GWASs) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes. Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation invitro and tumor growth invivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition.

Abstract 28: Integrating genomic and transcriptomic data to identify genetic loci associated with breast cancer risk in women of African ancestry

Abstract Background: To date, most genome-wide association studies (GWAS) of breast cancer have been conducted only among women of Asian and European ancestry. It is difficult to generalize results from those studies to women of African ancestry (AA). We conducted a large genetic association study of breast cancer in women of AA by analyzing both genetic and transcriptomic data. Methods: This collaborative study included 11,073 cases and 11,095 controls of AA who were participants in more than 15 studies conducted in the U.S. and Africa. Genotyping data were harmonized and imputed using the 1000 Genomes Project database as the reference. Imputed genotypes were used for GWAS to identify novel genetic risk loci for breast cancer. To search for susceptibility genes, we conducted a transcriptome-wide association study (TWAS), in which gene expression prediction models were built using genetic and tumor tissue RNA sequencing data from ~400 AA patients and used to impute expression levels of genes across the transcriptome for association analyses in all cases and controls included in the GWAS mentioned above. Results: We identified five loci (5p15.33, 5q31.3, 10q26.13, 18q12.1, and 19p13.11) associated with breast cancer risk at P &amp;lt; 5 × 10−8, including a novel locus at 5q31.3 (allelic odds ratio, OR = 1.18, 95% CI = 1.11-1.25, P = 4.65 × 10−8, nearby gene, ARHGAP26). This locus was also identified in association with estrogen receptor (ER) positive breast cancer at P &amp;lt; 5 × 10−8. Analyses stratified by ER status replicated known loci associated specifically with ER-positive (10q26.13) or ER-negative (2q14.2, 2p11.2, 5p15.33) breast cancer at P &amp;lt; 5 × 10−8. Of the 165 lead risk SNPs reported from previous breast cancer GWAS, 35 SNPs were replicated with the same association direction at P &amp;lt; 0.05. We constructed a polygenic risk score using these 35 replicated SNPs and the lead risk SNP at the novel locus and estimated the AUC to be 0.575. Of the 7,592 genes tested in the TWAS, we identified one gene, AC091053.1, with an association at a Bonferroni-corrected threshold of 6.64 × 10−6 (0.05/7,592). AC091053.1 is a long non-coding RNA gene at locus 11p15.4, where no risk variants have been identified in any previous breast cancer GWAS. AC091053.1 is located in the region of protein coding gene DENND2B, which acts as a regulator of MAPK1/ERK2 kinase and reduces the tumorigenic phenotype in cells. The gene AC091053.1 was associated with ER-positive breast cancer with P = 4.11 × 10−5 and ER-negative breast cancer with P = 0.032. Conclusions: Our study, the largest genetic study conducted to date in AA, identified novel breast cancer risk loci at 5q31.3 and 11p15.4 (AC091053.1) among women of AA and replicated &amp;gt;30 associations reported in previous studies. Studies with a larger sample size are needed to further investigate genetic variants and genes associated with breast cancer risk in AA women. Citation Format: Guochong Jia, Jie Ping, Yaohua Yang, Maureen Sanderson, Qiuyin Cai, Xingyi Guo, William J. Blot, Bingshan Li, Elisa V. Bandera, Manjeet K. Bolla, Montserrat García-Closas, Douglas F. Easton, Mary K. Fadden, Jian Gu, Dezheng Huo, Esther M. John, Kathryn L. Lunetta, Olufunmilayo I. Olopade, Xiang Shu, Melissa A. Troester, Song Yao, Breast Cancer Association Consortium, Andrew F. Olshan, Christine B. Ambrosone, Christopher A. Haiman, Jirong Long, Julie R. Palmer, Wei Zheng. Integrating genomic and transcriptomic data to identify genetic loci associated with breast cancer risk in women of African ancestry [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 28.

ABC-GWAS: Functional Annotation of Estrogen Receptor-Positive Breast Cancer Genetic Variants.

Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created “Analysis of Breast Cancer GWAS” (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.

Functional genomics based on germline genome-wide association studies of endocrine therapy for breast cancer.

Breast cancer is the most common invasive cancer in women worldwide. Functional follow-up of breast cancer genome-wide association studieshas led to the discovery of genes that regulate endocrine therapy response in a SNP-and drug-dependent manner. Here, we will present four examples in which functional genomic studies from breast cancer clinical trials led to novel pharmacogenomic insights and molecular mechanisms of selective estrogen receptor modulatorsand aromatase inhibitors. The approach utilized for studying genetic variability described in this review offers substantial potential for meaningful discoveries that move the field toward precision medicine for patients.

Abstract PD3-02: Polygenic risk scores provide clinically meaningful risk stratification among women carrying moderate penetrance pathogenic variants in breast cancer predisposition genes: Results from the CARRIERS study

Abstract Background: Pathogenic variants detected in multi-gene cancer predisposition panels are increasingly used to counsel women regarding their risk for breast cancer. However, the clinical implications of moderate penetrance genes (e.g. CHEK2, ATM) remain unclear. Breast MRI is indicated for women with a lifetime risk of breast cancer of &amp;gt;20%, and polygenic risk scores (PRS) based on common variants discovered in recent genome-wide association studies (GWAS) may identify women with pathogenic variants in CHEK2 and ATM who are above or below this level of risk. PRS may also stratify carriers with substantially elevated lifetime risks of breast cancer. Previous work found that PRS modified breast cancer risk in carriers of pathogenic variant in BRCA1 or BRCA2, but the joint effects of pathogenic variants and PRS have not been studied in samples drawn from the general population. There is also no published study evaluating the effect of PRS among women with pathogenic variants in genes other than BRCA1/2 and CHEK2. Here, we evaluated the joint effects of PRS and pathogenic variants in ten established breast cancer predisposition genes in a population-based case-control sample. Method: We analyzed 43,144 non-Hispanic European-ancestry individuals (20,988 controls and 22,120 cases) drawn from 7 cohorts and 2 population-based case-control studies in the CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) consortium. Targeted sequencing was performed using a dual bar-coded QIAseq panel of breast cancer predisposition genes. Variant calling was conducted with Haplotype Caller and Vardict. The PRS was calculated based on 106 common variants using effect estimates from the largest published breast cancer GWAS. Genetic status was evaluated for 10 breast cancer predisposition genes: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, NF1, PALB2 and TP53. Lasso Penalized regression was performed to assess the combined effect on overall breast cancer of pathogenic variants and common variants (including main and interaction effects between age, family history, carrier status and PRS). The fraction of women at &amp;gt;2-fold increased risk (which is associated with approximately 20% lifetime risk of breast cancer) was calculated for pathogenic variant carriers and non-carriers of different family history and age groups (&amp;lt;40, 40-50, 50-60, 60-70, &amp;gt;70yrs old). Results: Among non-carriers, one standard deviation change in the PRS was associated with a 1.63x change in odds of breast cancer. The best-fitting risk model did not include any PRS-by-carrier interaction terms, implying the relative risk gradient associated with PRS among women with pathogenic variants is similar to that among non-carriers. For carriers of pathogenic variants in moderate penetrance genes with no family history of breast cancer, the PRS can identify 60.6% of ATM pathogenic variant carriers and 30% of CHEK2 pathogenic variant carriers that have less than 2-fold risk of breast cancer; when carriers have a family history of breast cancer, the PRS can identify 30% of ATM pathogenic variant carriers and 11.8% of CHEK2 pathogenic variant carriers with less than 2-fold risk of breast cancer. Conclusion: In our large, population-based case-control study, the effect of the PRS among pathogenic variant carriers was not significantly different than among non-carriers. However, the PRS may be particularly important for estimating breast cancer risk among carriers of pathogenic variants in moderate penetrance genes such as CHEK2 and ATM, enabling a more precise approach for MRI screening strategy and breast cancer risk management. Citation Format: Chi Gao, Eric C. Polley, Chunling Hu, Steven N. Hart, Rohan Gnanaolivu, Kun Y. Lee, Jie Na, Nicholas J. Boddicker, Raed Samara, Paul Auer, Leslie Bernstein, Mia Gaudet, Amy Trentham-Dietz, Song Yao, Christopher Haiman, Janet E. Olson, Susan Neuhausen, Jeffrey N. Weitzel, David E. Goldgar, Susan Domchek, Katherine L. Nathanson, Fergus J. Couch, Peter Kraft, on behalf of the CARRIERS consortium. Polygenic risk scores provide clinically meaningful risk stratification among women carrying moderate penetrance pathogenic variants in breast cancer predisposition genes: Results from the CARRIERS study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD3-02.

Non-coding RNAs underlie genetic predisposition to breast cancer

BackgroundGenetic variants identified through genome-wide association studies (GWAS) are predominantly non-coding and typically attributed to altered regulatory elements such as enhancers and promoters. However, the contribution of non-coding RNAs to complex traits is not clear.ResultsUsing targeted RNA sequencing, we systematically annotated multi-exonic non-coding RNA (mencRNA) genes transcribed from 1.5-Mb intervals surrounding 139 breast cancer GWAS signals and assessed their contribution to breast cancer risk. We identify more than 4000 mencRNA genes and show their expression distinguishes normal breast tissue from tumors and different breast cancer subtypes. Importantly, breast cancer risk variants, identified through genetic fine-mapping, are significantly enriched in mencRNA exons, but not the promoters or introns. eQTL analyses identify mencRNAs whose expression is associated with risk variants. Furthermore, chromatin interaction data identify hundreds of mencRNA promoters that loop to regions that contain breast cancer risk variants.ConclusionsWe have compiled the largest catalog of breast cancer-associated mencRNAs to date and provide evidence that modulation of mencRNAs by GWAS variants may provide an alternative mechanism underlying complex traits.

Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies

BackgroundThe principal line of investigation in Genome Wide Association Studies (GWAS) is the identification of main effects, that is individual Single Nucleotide Polymorphisms (SNPs) which are associated with the trait of interest, independent of other factors. A variety of methods have been proposed to this end, mostly statistical in nature and differing in assumptions and type of model employed. Moreover, for a given model, there may be multiple choices for the SNP genotype encoding. As an alternative to statistical methods, machine learning methods are often applicable. Typically, for a given GWAS, a single approach is selected and utilized to identify potential SNPs of interest. Even when multiple GWAS are combined through meta-analyses within a consortium, each GWAS is typically analyzed with a single approach and the resulting summary statistics are then utilized in meta-analyses.ResultsIn this work we use as case studies a Type 2 Diabetes (T2D) and a breast cancer GWAS to explore a diversity of applicable approaches spanning different methods and encoding choices. We assess similarity of these approaches based on the derived ranked lists of SNPs and, for each GWAS, we identify a subset of representative approaches that we use as an ensemble to derive a union list of top SNPs. Among these are SNPs which are identified by multiple approaches as well as several SNPs identified by only one or a few of the less frequently used approaches. The latter include SNPs from established loci and SNPs which have other supporting lines of evidence in terms of their potential relevance to the traits.ConclusionsNot every main effect analysis method is suitable for every GWAS, but for each GWAS there are typically multiple applicable methods and encoding options. We suggest a workflow for a single GWAS, extensible to multiple GWAS from consortia, where representative approaches are selected among a pool of suitable options, to yield a more comprehensive set of SNPs, potentially including SNPs that would typically be missed with the most popular analyses, but that could provide additional valuable insights for follow-up.