Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Genetic risk factors for endometriosis near estrogen receptor 1 and coexpression of genes in this region in endometrium

The etiology and pathogenesis of endometriosis are complex with both genetic and environmental factors contributing to disease risk. Genome-wide association studies (GWAS) have identified multiple signals in the estrogen receptor 1 (ESR1) region associated with endometriosis and other reproductive traits and diseases. In addition, candidate gene association studies identified signals in the ESR1 region associated with endometriosis risk suggesting genetic regulation of genes in this region may be important for reproductive health. This study aimed to investigate hormonal and genetic regulation of genes in the ESR1 region in human endometrium. Changes in serum oestradiol and progesterone concentrations and expression of hormone receptors ESR1 and progesterone receptor (PGR) were assessed in endometrial samples from 135 women collected at various stages of the menstrual cycle. Correlation between hormone concentrations, receptor expression and expression of genes in the ESR1 locus was investigated. The effect of endometriosis risk variants on expression of genes in the region was analyzed to identify gene targets. Hormone concentrations and receptor expression varied significantly across the menstrual cycle. Expression of genes in the ESR1 region correlated with progesterone concentration; however, they were more strongly correlated with expression of ESR1 and PGR suggesting coregulation of genes. There was no evidence that endometriosis risk variants directly regulated expression of genes in the region. Limited sample size and cellular heterogeneity in endometrial tissue may impact the ability to detect significant genetic effects on gene expression. Effects of these variants should be validated in a larger dataset and in relevant individual cell types.

A functional variant on 20q13.33 related to glioma risk alters enhancer activity and modulates expression of multiple genes

Genome‐wide association studies (GWAS) have identified single‐nucleotide polymorphisms (SNPs) associated with glioma risk on 20q13.33, but the biological mechanisms underlying this association are unknown. We tested the hypothesis that a functional SNP on 20q13.33 impacted the activity of an enhancer, leading to an altered expression of nearby genes. To identify candidate functional SNPs, we identified all SNPs in linkage disequilibrium with the risk‐associated SNP rs2297440 that mapped to putative enhancers. Putative enhancers containing candidate functional SNPs were tested for allele‐specific effects in luciferase enhancer activity assays against glioblastoma multiforme (GBM) cell lines. An enhancer containing SNP rs3761124 exhibited allele‐specific effects on activity. Deletion of this enhancer by CRISPR‐Cas9 editing in GBM cell lines correlated with an altered expression of multiple genes, including STMN3, RTEL1, RTEL1‐TNFRSF6B, GMEB2, and SRMS. Expression quantitative trait loci (eQTL) analyses using nondiseased brain samples, isocitrate dehydrogenase 1 (IDH1) wild‐type glioma, and neurodevelopmental tissues showed STMN3 to be a consistent significant eQTL with rs3761124. RTEL1 and GMEB2 were also significant eQTLs in the context of early CNS development and/or in IDH1 wild‐type glioma. We provide evidence that rs3761124 is a functional variant on 20q13.33 related to glioma/GBM risk that modulates the expression of STMN3 and potentially other genes across diverse cellular contexts.

Supervariants identification for breast cancer

In genome-wide association studies, signals associated with rare variants and interactions between genes are hard to detect even when the sample size is in tens of thousands. To overcome these problems, we examine the concept of supervariant. Like the classic concept of the gene, a supervariant is a combination of alleles in multiple loci, but the contributing loci can be anywhere in the genome. We hypothesize that supervariants are easy to detect and the aggregated signals are more stable in their associations with the disease than that from a single nucleoid polymorphism. Using the UK Biobank databases, we develop a ranking and aggregation method for identifying supervariants. Specifically, we examine 9,377 breast cancer cases with 46,861 controls matched by sex and age. In our simulations, the use of supervariants outperforms single-nucleotide polymorphism-based association method in detecting rare variants and signals with interactive structure. In real data analysis, we identify supervariants on Chromosomes 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 16, and 22 which cover previously reported loci that have associations with breast or other cancers, and several novel loci on Chromosomes 2, 5, 9, and 12. These findings demonstrate the validity of supervariants and its potential of discovering replicable and novel results for complex disease.

Genomic Diversity in Sporadic Breast Cancer in a Latin American Population

Among Latin American women, breast cancer incidences vary across populations. Uruguay and Argentina have the highest rates in South America, which are mainly attributed to strong, genetic European contributions. Most genetic variants associated with breast cancer were described in European populations. However, the vast majority of genetic contributors to breast cancer risk remain unknown. Here, we report the results of a candidate gene association study of sporadic breast cancer in 176 cases and 183 controls in the Uruguayan population. We analyzed 141 variants from 98 loci that have been associated with overall breast cancer risk in European populations. We found weak evidence for the association of risk variants rs294174 (ESR1), rs16886165 (MAP3K1), rs2214681 (CNTNAP2), rs4237855 (VDR), rs9594579 (RANKL), rs8183919 (PTGIS), rs2981582 (FGFR2), and rs1799950 (BRCA1) with sporadic breast cancer. These results provide useful insight into the genetic susceptibility to sporadic breast cancer in the Uruguayan population and support the use of genetic risk scores for individualized screening and prevention.

Genetic variations in estrogen and progesterone pathway genes in preeclampsia patients and controls in Bavaria

PURPOSE

Hypertensive pregnancy disorders and preeclampsia are major causes of maternal and fetal morbidity and mortality worldwide. Many different organs are involved in the diseases' clinical phenotype. The underlying mechanism is still unknown, with a possible genetic component. This case-control study investigated effects on the risk of preeclampsia of genetic variations (single nucleotide polymorphisms, SNPs) in the estrogen and progesterone pathway genes.

METHODS

The study included 167 patients with preeclampsia and 115 healthy controls from the "Franconian Maternal Health Evaluation Studies" (FRAMES). All patients completed an epidemiological questionnaire, data from which were correlated with prospective data on pregnancy and labor. DNA was isolated from blood samples and genotyping was done by PCR. Variants in the aromatase gene CYP19A1 (rs10046, rs4646), progesterone receptor gene (rs1042838, rs10895068), and estrogen receptor-α gene (rs488133) were examined, and the genotype distribution in the two groups was analyzed statistically.

RESULTS

A significant difference in the distribution frequency of genotypes between preeclampsia patients and controls was identified in one of the five SNPs. For rs10895068 in the progesterone receptor gene, genotype G/A was significantly more frequent among cases than controls (P = 0.023). No significant differences between the two cohorts were found in the other SNPs.

CONCLUSIONS

This study showed a significant association between only one SNP in the progesterone receptor and preeclampsia. Other studies have also noted genetic aspects of preeclampsia. The underlying mechanism and causal relationship are not yet known, and further research is needed to explain the extent of genetic variations and the causal relationship in preeclampsia.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Experimental Models as Refined Translational Tools for Breast Cancer Research

Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

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/.

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.

Intrinsic and Extrinsic Factors Governing the Transcriptional Regulation of ESR1

Transcriptional regulation of ESR1, the gene that encodes for estrogen receptor α (ER), is critical for regulating the downstream effects of the estrogen signaling pathway in breast cancer such as cell growth. ESR1 is a large and complex gene that is regulated by multiple regulatory elements, which has complicated our understanding of how ESR1 expression is controlled in the context of breast cancer. Early studies characterized the genomic structure of ESR1 with subsequent studies focused on identifying intrinsic (chromatin environment, transcription factors, signaling pathways) and extrinsic (tumor microenvironment, secreted factors) mechanisms that impact ESR1 gene expression. Currently, the introduction of genomic sequencing platforms and additional genome-wide technologies has provided additional insight on how chromatin structures may coordinate with these intrinsic and extrinsic mechanisms to regulate ESR1 expression. Understanding these interactions will allow us to have a clearer understanding of how ESR1 expression is regulated and eventually provide clues on how to influence its regulation with potential treatments. In this review, we highlight key studies concerning the genomic structure of ESR1, mechanisms that affect the dynamics of ESR1 expression, and considerations towards affecting ESR1 expression and hormone responsiveness in breast cancer.

Genetic, Epigenetic, and Steroidogenic Modulation Mechanisms in Endometriosis

Endometriosis is a chronic gynecological disease, affecting up to 10% of reproductive-age women. The exact cause of the disease is unknown; however, it is a heritable condition affected by multiple genetic, epigenetic, and environmental factors. Previous studies reported variations in the epigenetic patterns of numerous genes known to be involved in the aberrant modulation of cell cycle steroidogenesis, abnormal hormonal, immune and inflammatory status in endometriosis, apoptosis, adhesion, angiogenesis, proliferation, immune and inflammatory processes, response to hypoxia, steroidogenic pathway and hormone signaling are involved in the pathogenesis of endometriosis. Accumulating evidence suggest that various epigenetic aberrations may contribute to the pathogenesis of endometriosis. Among them, DNA methyltransferases, histone deacetylators, and non-coding microRNAs demonstrate differential expression within endometriotic lesions and in the endometrium of patients with endometriosis. It has been indicated that the identification of epigenetic differences within the DNA or histone proteins may contribute to the discovery of a useful prognostic biomarker, which could aid in the future earlier detection, timely diagnosis, and initiation of a new approach to the treatment of endometriosis, as well as inform us about the effectiveness of treatment and the stage of the disease. As the etiology of endometriosis is highly complex and still far from being fully elucidated, the presented review focuses on different approaches to identify the genetic and epigenetic links of endometriosis and its pathogenesis.

Risk SNP-Mediated Enhancer-Promoter Interaction Drives Colorectal Cancer through Both FADS2 and AP002754.2

Although genome-wide association studies (GWAS) have identified more than 100 colorectal cancer risk loci, most of the biological mechanisms associated with these loci remain unclear. Here we first performed a comprehensive expression quantitative trait loci analysis in colorectal cancer tissues adjusted for multiple confounders to test the determinants of germline variants in established GWAS susceptibility loci on mRNA and long noncoding RNA (lncRNA) expression. Combining integrative functional genomic/epigenomic analyses and a large-scale population study consisting of 6,024 cases and 10,022 controls, we then prioritized rs174575 with a C>G change as a potential causal candidate for colorectal cancer at 11q12.2, as its G allele was associated with an increased risk of colorectal cancer (OR = 1.26; 95% confidence interval = 1.17-1.36; P = 2.57 × 10^-9). rs174575 acted as an allele-specific enhancer to distally facilitate expression of both FADS2 and lncRNA AP002754.2 via long-range enhancer-promoter interaction loops, which were mediated by E2F1. AP002754.2 further activated a transcriptional activator that upregulated FADS2 expression. FADS2, in turn, was overexpressed in colorectal cancer tumor tissues and functioned as a potential oncogene that facilitated colorectal cancer cell proliferation and xenograft growth in vitro and in vivo by increasing the metabolism of PGE2, an oncogenic molecule involved in colorectal cancer tumorigenesis. Our findings represent a novel mechanism by which a noncoding variant can facilitate long-range genome interactions to modulate the expression of multiple genes including not only mRNA, but also lncRNA, which provides new insights into the understanding of colorectal cancer etiology. SIGNIFICANCE: This study provides an oncogenic regulatory circuit among several oncogenes including E2F1, FADS2, and AP002754.2 underlying the association of rs174575 with colorectal cancer risk, which is driven by long-range enhancer-promoter interaction loops. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/9/1804/F1.large.jpg.

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.

Chromatin interactome mapping at 139 independent breast cancer risk signals

BACKGROUND

Genome-wide association studies have identified 196 high confidence independent signals associated with breast cancer susceptibility. Variants within these signals frequently fall in distal regulatory DNA elements that control gene expression.

RESULTS

We designed a Capture Hi-C array to enrich for chromatin interactions between the credible causal variants and target genes in six human mammary epithelial and breast cancer cell lines. We show that interacting regions are enriched for open chromatin, histone marks for active enhancers, and transcription factors relevant to breast biology. We exploit this comprehensive resource to identify candidate target genes at 139 independent breast cancer risk signals and explore the functional mechanism underlying altered risk at the 12q24 risk region.

CONCLUSIONS

Our results demonstrate the power of combining genetics, computational genomics, and molecular studies to rationalize the identification of key variants and candidate target genes at breast cancer GWAS signals.

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.

Drug repurposing for cancer therapy, easier said than done

Drug repurposing for cancer therapy is currently a hot topic of research. Theoretically, in contrast to the known hurdles of developing new molecular entities, the approach of repurposing has several advantages. Mostly, it is said that it is faster, safer, easier, and cheaper. In the real world, however, there are only three repurposed drugs so far, that are listed in widely recognized cancer guidelines, but a large number of them are being studied. Among the many barriers to repurposing cancer drugs, economical-driven are the most important that difficult the clinical development of them. In this review, we provide an overview of the current status of drug repurposing for cancer therapy and the barriers that need to be overcome to realize the benefit of this approach. It means to have repositioned drugs for cancer therapy accepted as standard therapy for cancer indications at low cost.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model

Introduction

Recent studies highlight the crucial regulatory roles of transposable elements (TEs) on proximal gene expression in distinct biological contexts such as disease and development. However, computational tools extracting potential TE -proximal gene expression associations from RNA-sequencing data are still missing.

Implementation

Herein, we developed a novel R package, using a linear regression model, for studying the potential influence of TE species on proximal gene expression from a given RNA-sequencing data set. Our R package, namely TEffectR, makes use of publicly available RepeatMasker TE and Ensembl gene annotations as well as several functions of other R-packages. It calculates total read counts of TEs from sorted and indexed genome aligned BAM files provided by the user, and determines statistically significant relations between TE expression and the transcription of nearby genes under diverse biological conditions.

Availability

TEffectR is freely available at https://github.com/karakulahg/TEffectR along with a handy tutorial as exemplified by the analysis of RNA-sequencing data including normal and tumour tissue specimens obtained from breast cancer patients.

BRCA1-associated R-loop affects transcription and differentiation in breast luminal epithelial cells

BRCA1-associated basal-like breast cancer originates from luminal progenitor cells. Breast epithelial cells from cancer-free BRCA1 mutation carriers are defective in luminal differentiation. However, how BRCA1 deficiency leads to lineage-specific differentiation defect is not clear. BRCA1 is implicated in resolving R-loops, DNA-RNA hybrid structures associated with genome instability and transcriptional regulation. We recently showed that R-loops are preferentially accumulated in breast luminal epithelial cells of BRCA1 mutation carriers. Here, we interrogate the impact of a BRCA1 mutation-associated R-loop located in a putative transcriptional enhancer upstream of the ERα-encoding ESR1 gene. Genetic ablation confirms the relevance of this R-loop-containing region to enhancer-promoter interactions and transcriptional activation of the corresponding neighboring genes, including ESR1, CCDC170 and RMND1. BRCA1 knockdown in ERα+ luminal breast cancer cells increases intensity of this R-loop and reduces transcription of its neighboring genes. The deleterious effect of BRCA1 depletion on transcription is mitigated by ectopic expression of R-loop-removing RNase H1. Furthermore, RNase H1 overexpression in primary breast cells from BRCA1 mutation carriers results in a shift from luminal progenitor cells to mature luminal cells. Our findings suggest that BRCA1-dependent R-loop mitigation contributes to luminal cell-specific transcription and differentiation, which could in turn suppress BRCA1-associated tumorigenesis.

Bone Marrow Stromal Cells Transcriptionally Repress ESR1 but Cannot Overcome Constitutive ESR1 Mutant Activity

Estrogen receptor α (ER) is the target of endocrine therapies in ER-positive breast cancer (BC), but their therapeutic effectiveness diminishes with disease progression. Most metastatic BCs retain an ER-positive status, but ER expression levels are reduced. We asked how the bone tumor microenvironment (TME) regulates ER expression. We observed ESR1 mRNA and ER protein downregulation in BC cells treated with conditioned media (CM) from patient-derived, cancer-activated bone marrow stromal cells (BMSCs) and the BMSC cell line HS5. Decreases in ESR1 mRNA were attributed to decreases in nascent transcripts as well as decreased RNA polymerase II occupancy and H3K27Ac levels on the ESR1 promoter and/or distal enhancer (ENH1). Repression extended to neighboring genes of ESR1, including ARMT1 and SYNE1. Although ERK/MAPK signaling pathway can repress ER expression by other TME cell types, MAPK inhibition did not reverse decreases in ER expression by BMSC-CM. ESR1 mRNA and ER protein half-lives in MCF7 cells were unchanged by BMSC-CM treatment. Whereas ER phosphorylation was induced, ER activity was repressed by BMSC-CM as neither ER occupancy at known binding sites nor estrogen response element-luciferase activity was detected. BMSC-CM also repressed expression of ER target genes. In cells expressing the Y537S and D538G ESR1 mutations, BMSC-CM reduced ESR1, but expression of target genes PGR and TFF1 remained significantly elevated compared with that of control wild-type cells. These studies demonstrate that BMSCs can transcriptionally corepress ESR1 with neighboring genes and inhibit receptor activity, but the functional consequences of the BMSC TME can be limited by metastasis-associated ESR1 mutations.

RMND1 mutations in two siblings: Severe renal hypoplasia but different levels of extrarenal abnormality severity: The ethics of decision making

Mutations in the RMND1 gene, causing defects in the mitochondrial respiratory chain, result in a very heterozygous phenotype. Currently there are 36 cases reported in the literature. We report two siblings from a non-consanguineous family who were severely affected by a compound heterozygous RMND1 mutation that had not been described previously and were treated differently for their end-stage renal disease. We summarize all previous published cases and focus on the importance of extrarenal comorbidities in the context of therapeutic decision making (renal replacement therapy) and its ethical relevance.

Evidence that 6q25.1 variant rs6931104 confers susceptibility to chronic myeloid leukemia through RMND1 regulation

Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder. Our previous study reported novel loci as genetic markers associated with increased susceptibility to CML. The present study conducted an expression quantitative trait loci (eQTL) analysis to confirm that the single nucleotide polymorphisms (SNPs) at these loci affect the expression of candidate CML-susceptible genes. We identified that three SNPs (rs963193, rs6931104, and rs9371517) were related to the gene expression pattern of RMND1 (Required For Meiotic Nuclear Division 1 Homolog) in both granulocytes and mononuclear cells from 83 healthy donors. Furthermore, reduced expression of RMND1 expression was noted in CML patients compared with that in healthy individuals. We used the eQTL browsing tool to assess the regulatory information on the three associated significant SNPs, out of which rs6931104 showed strong evidence of regulatory effects. Chromatin immunoprecipitation (ChIP) assays demonstrated that A alleles of rs6931104 could significantly change the binding affinity of transcription factor (TF) RFX3 compared to the G alleles. Then, we performed in vitro experiments on BCR-ABL1-positive (BCR-ABL1+) cell lines. We found that expression of the CML-susceptible gene RMND1 is affected by the binding affinity of TF RFX3, suggesting that RFX3 plays a role in RMND1 expression. Our findings suggest potential target genes for associations of genetic susceptibility risk loci and provide further insights into the pathogenesis and mechanism of CML.

Fine Mapping in Chromosome 3q28 Identified Two Variants Associated with Lung Cancer Risk in Asian Population

Genome-wide association studies (GWASs) have consistently identified chromosome 3q28 as a lung cancer susceptibility region. To further characterize the potential genetic mechanism of the variants in this region, we conducted a fine-mapping study on chromosome 3q28 region. We performed a target resequencing in 200 lung cancer cases and 300 controls in the screening and followed by validation in multi-ethnic lung cancer GWASs with 12,843 cases and 12,639 controls. For our identified novel variants, we conducted expression quantitative trait loci (eQTL) analysis to reveal the potential target genes. Two susceptibility variants were identified (rs4396880: G>A, OR = 0.35, 95%CI: 0.20-0.62, P = 3.01×10^-4; and rs3856776: C>T, OR = 2.05, 95%CI: 1.32-3.18, P = 1.49×10^-3) and further supported in Asian population (rs4396880: OR = 0.88, P = 7.43×10^-6; and rs3856776: OR =1.17, P = 1.64×10^-4). The eQTL analysis showed the A allele of rs4396880 was significantly associated with higher mRNA expression of TP63 (P = 1.70×10^-4) in lung tissues, while rs3856776 showed significant association with the expression of LEPREL1-AS1 (P = 6.90×10^-3), which was the antisense RNA of LEPREL1 and could suppress the translation of LEPREL1. Notably, LEPREL1 was aberrantly downregulated (P = 2.54×10^-18) in lung tumor tissues based on TCGA database. In conclusion, this is the first fine-mapping analysis of 3q28 region in Han Chinese, and we found two variants associated with lung cancer susceptibility in Asian population. What's more, rs3856776 was newly identified and might modulate lung cancer susceptibility by suppressing the function of LEPREL1.

Germline Variants Impact Somatic Events during Tumorigenesis

Cancer is characterized by diverse genetic alterations in both germline and somatic genomes that disrupt normal biology and provide a selective advantage to cells during tumorigenesis. Germline and somatic genomes have been extensively studied independently, leading to numerous biological insights. Analyses integrating data from both genomes have identified genetic variants impacting somatic events in tumors, including hotspot driver mutations. Interactions among specific germline variants and somatic events influence cancer subtypes, treatment response, and clinical outcomes. Investigation of these complex interactions is increasing our understanding of aberrant pathways in tumors that may uncover novel therapeutic targets. Here, we review the literature describing the role of germline genetic variants in promoting the selection and generation of specific mutations during tumorigenesis.

Recent advances of therapeutic targets based on the molecular signature in breast cancer: genetic mutations and implications for current treatment paradigms

Breast cancer is the most common malignancy in women all over the world. Genetic background of women contributes to her risk of having breast cancer. Certain inherited DNA mutations can dramatically increase the risk of developing certain cancers and are responsible for many of the cancers that run in some families. Regarding the widespread multigene panels, whole exome sequencing is capable of providing the evaluation of genetic function mutations for development novel strategy in clinical trials. Targeting the mutant proteins involved in breast cancer can be an effective therapeutic approach for developing novel drugs. This systematic review discusses gene mutations linked to breast cancer, focusing on signaling pathways that are being targeted with investigational therapeutic strategies, where clinical trials could be potentially initiated in the future are being highlighted.

Specific allelic variants of SNPs in the MDM2 and MDMX genes are associated with earlier tumor onset and progression in Caucasian breast cancer patients

Background

Genetic factors play a substantial role in breast cancer etiology. Genes encoding proteins that have key functions in the DNA damage response, such as p53 and its inhibitors MDM2 and MDMX, are most likely candidates to harbor allelic variants that influence breast cancer susceptibility. The aim of our study was to comprehensively analyze the impact of SNPs in the TP53, MDM2, and MDMX genes in conjunction with TP53 mutational status regarding the onset and progression of breast cancer.

Methods

In specimen from 815 breast cancer patients, five SNPs within the selected genes were analyzed: TP53 – Arg72Pro (rs1042522), MDM2 – SNP285 (rs2279744), SNP309 (rs117039649); MDMX – SNP31826 (rs1563828), and SNP34091 (rs4245739). Classification of the tumors was evaluated by histomorphology. Subtyping according hormone receptor status, HER2-status and proliferation rate enabled provision of the clinico-pathological surrogate of intrinsic subtypes.

Results

The homozygous C-allele of MDM2 SNP285 was significantly associated with a younger age-at-diagnosis of 44.2 years, in contrast to G/G- and G/C-patients (62.4, 62.7 yrs., respectively; p = 0.0007; log-Rank-test). In contrast, there was no difference regarding the age-at-diagnosis for patients with the respective genotypes of MDM2 SNP309 (p = 0.799; log-Rank-test). In patients with estrogen receptor (ER)-positive and TP53-mutated tumors, however, the T/T-genotype of the MDM2 SNP309 was significantly associated with an earlier average age-at-diagnosis compared with T/G+G/G-patients (53.5 vs. 68.2 yrs; p = 0.002; log-Rank-test). In the triple-negative subgroup, the G/G-patients had an average age-at-diagnosis of 51 years compared with 63 years for SNP309T carriers (p = 0.004; log-Rank-test) indicating a susceptibility of the G/G genotype for the development of triple negative breast cancer. Patients with the A/A-genotype of MDMX SNP31826 with ER-negative tumors were diagnosed 11 years earlier compared with patients and ER-positive tumors (53.2 vs. 64.4 yrs; p = 0.025, log-Rank-test). Furthermore, in luminal B-like patients (HER2-independent) the C/C-genotype of MDMX SNP34091 was significantly correlated with a decreased event-free survival compared with the A/A-genotype (p < 0.001; log-Rank-test).

Conclusions

We showed that SNPs in the MDM2 and MDMX genes affect at least in part the onset and progression of breast cancer dependent on the ER-status. Our findings provide further evidence for the distinct etiological pathways in ER-negative and ER-positive breast cancers.

Functional Analysis of Promoter Variants in Genes Involved in Sex Steroid Action, DNA Repair and Cell Cycle Control

Genetic variants affecting the regulation of gene expression are among the main causes of human diversity. The potential importance of regulatory polymorphisms is underscored by results from Genome Wide Association Studies, which have already implicated such polymorphisms in the susceptibility to complex diseases such as breast cancer. In this study, we re-sequenced the promoter regions of 24 genes involved in pathways related to breast cancer including sex steroid action, DNA repair, and cell cycle control in 60 unrelated Caucasian individuals. We constructed haplotypes and assessed the functional impact of promoter variants using gene reporter assays and electrophoretic mobility shift assays. We identified putative functional variants within the promoter regions of estrogen receptor 1 (ESR1), ESR2, forkhead box A1 (FOXA1), ubiquitin interaction motif containing 1 (UIMC1) and cell division cycle 7 (CDC7). The functional polymorphism on CDC7, rs13447455, influences CDC7 transcriptional activity in an allele-specific manner and alters DNA–protein complex formation in breast cancer cell lines. Moreover, results from the Breast Cancer Association Consortium show a marginal association between rs13447455 and breast cancer risk (p = 9.3 × 10⁻⁵), thus warranting further investigation. Furthermore, our study has helped provide methodological solutions to some technical difficulties that were encountered with gene reporter assays, particularly regarding inter-clone variability and statistical consistency.

Identification of novel common breast cancer risk variants at the 6q25 locus among Latinas

Background

Breast cancer is a partially heritable trait and genome-wide association studies (GWAS) have identified over 180 common genetic variants associated with breast cancer. We have previously performed breast cancer GWAS in Latinas and identified a strongly protective single nucleotide polymorphism (SNP) at 6q25, with the protective minor allele originating from indigenous American ancestry. Here we report on fine mapping of the 6q25 locus in an expanded sample of Latinas.

Methods

We performed GWAS in 2385 cases and 6416 controls who were either US Latinas or Mexican women. We replicated the top SNPs in 2412 cases and 1620 controls of US Latina, Mexican, and Colombian women. In addition, we validated the top novel variants in studies of African, Asian and European ancestry. In each dataset we used logistic regression models to test the association between SNPs and breast cancer risk and corrected for genetic ancestry using either principal components or genetic ancestry inferred from ancestry informative markers using a model-based approach.

Results

We identified a novel set of SNPs at the 6q25 locus associated with genome-wide levels of significance (p = 3.3 × 10^− 8 - 6.0 × 10^− 9) not in linkage disequilibrium (LD) with variants previously reported at this locus. These SNPs were in high LD (r² > 0.9) with each other, with the top SNP, rs3778609, associated with breast cancer with an odds ratio (OR) and 95% confidence interval (95% CI) of 0.76 (0.70–0.84). In a replication in women of Latin American origin, we also observed a consistent effect (OR 0.88; 95% CI 0.78–0.99; p = 0.037). We also performed a meta-analysis of these SNPs in East Asians, African ancestry and European ancestry populations and also observed a consistent effect (rs3778609, OR 0.95; 95% CI 0.91–0.97; p = 0.0017).

Conclusion

Our study adds to evidence about the importance of the 6q25 locus for breast cancer susceptibility. Our finding also highlights the utility of performing additional searches for genetic variants for breast cancer in non-European populations.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1085-9) contains supplementary material, which is available to authorized users.

Complex genetics of female fertility

Variation in reproductive lifespan and female fertility have implications for health, population size and ageing. Fertility declines well before general signs of menopause and is also adversely affected by common reproductive diseases, including polycystic ovarian syndrome (PCOS) and endometriosis. Understanding the factors that regulate the timing of puberty and menopause, and the relationships with fertility are important for individuals and for policy. Substantial genetic variation exists for common traits associated with reproductive lifespan and for common diseases influencing female fertility. Genetic studies have identified mutations in genes contributing to disorders of reproduction, and in the last ten years, genome-wide association studies (GWAS) have transformed our understanding of common genetic contributions to these complex traits and diseases. These studies have made great progress towards understanding the genetic factors contributing to variation in traits and diseases influencing female fertility. The data emerging from GWAS demonstrate the utility of genetics to explain epidemiological observations, revealing shared biological pathways linking puberty timing, fertility, reproductive ageing and health outcomes. Many variants implicate DNA damage/repair genes in variation in the age at menopause with implications for follicle health and ageing. In addition to the discovery of individual genes and pathways, the increasingly powerful studies on common genetic risk factors help interpret the underlying relationships and direction of causation in the regulation of reproductive lifespan, fertility and related traits.

Variants associating with uterine leiomyoma highlight genetic background shared by various cancers and hormone-related traits

Uterine leiomyomas are common benign tumors of the myometrium. We performed a meta-analysis of two genome-wide association studies of leiomyoma in European women (16,595 cases and 523,330 controls), uncovering 21 variants at 16 loci that associate with the disease. Five variants were previously reported to confer risk of various malignant or benign tumors (rs78378222 in TP53, rs10069690 in TERT, rs1800057 and rs1801516 in ATM, and rs7907606 at OBFC1) and four signals are located at established risk loci for hormone-related traits (endometriosis and breast cancer) at 1q36.12 (CDC42/WNT4), 2p25.1 (GREB1), 20p12.3 (MCM8), and 6q26.2 (SYNE1/ESR1). Polygenic score for leiomyoma, computed using UKB data, is significantly correlated with risk of cancer in the Icelandic population. Functional annotation suggests that the non-coding risk variants affect multiple genes, including ESR1. Our results provide insights into the genetic background of leiomyoma that are shared by other benign and malignant tumors and highlight the role of hormones in leiomyoma growth.

Uterine leiomyomas are common benign tumors. Here, a meta-analysis of two European leiomyoma GWAS uncovers 21 leiomyoma risk variants at 16 loci, providing evidence of genetic overlap between leiomyoma and various benign and malignant tumors and highlighting the role of estrogen in tumor growth.

Exploring the Role of Fallopian Ciliated Cells in the Pathogenesis of High-Grade Serous Ovarian Cancer

High-grade serous epithelial ovarian cancer (HGSOC) is the fifth leading cause of cancer death in women and the first among gynecological malignancies. Despite an initial response to standard chemotherapy, most HGSOC patients relapse. To improve treatment options, we must continue investigating tumor biology. Tumor characteristics (e.g., risk factors and epidemiology) are valuable clues to accomplish this task. The two most frequent risk factors for HGSOC are the lifetime number of ovulations, which is associated with increased oxidative stress in the pelvic area caused by ovulation fluid, and a positive family history due to genetic factors. In the attempt to identify novel genetic factors (i.e., genes) associated with HGSOC, we observed that several genes in linkage with HGSOC are expressed in the ciliated cells of the fallopian tube. This finding made us hypothesize that ciliated cells, despite not being the cell of origin for HGSOC, may take part in HGSOC tumor initiation. Specifically, malfunction of the ciliary beat impairs the laminar fluid flow above the fallopian tube epithelia, thus likely reducing the clearance of oxidative stress caused by follicular fluid. Herein, we review the up-to-date findings dealing with HGSOC predisposition with the hypothesis that fallopian ciliated cells take part in HGSOC onset. Finally, we review the up-to-date literature concerning genes that are located in genomic loci associated with epithelial ovarian cancer (EOC) predisposition that are expressed by the fallopian ciliated cells.

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.

Survival and Disease-Free Survival by Breast Density and Phenotype in Interval Breast Cancers

Background: We aimed to evaluate survival and disease-free survival in different subtypes of interval cancers by breast density, taking into account clinical and biological characteristics.Methods: We included 374 invasive breast tumors (195 screen-detected cancers; 179 interval cancers, classified into true interval, false-negatives, occult tumors and minimal-sign cancers) diagnosed in women ages 50-69 years undergoing biennial screening from 2000-2009, followed up to 2014. Breast density was categorized into non-dense (<25% dense tissue) and mixed dense breasts (≥25%). Survival curves were generated by the Kaplan-Meier method and compared by the log-rank test. Cox proportional hazard regression models were computed to estimate the adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) for death and recurrences by comparing women with interval and true interval cancers versus women with screen-detected cancers, controlling for tumor and patient characteristics. All analyses were stratified by breast density.Results: Interval cancers were detected in younger women, at more advanced stages, in denser breasts and showed a higher proportion of triple-negative cancers, especially among true interval cancers. Women with interval cancer and non-dense breasts had an aHR for death of 3.40 (95% CI, 0.92-12.62). Women with true interval cancers detected in non-dense breasts had the highest adjusted risk of death (aHR, 6.55; 95% CI, 1.37-31.39).Conclusions: Women with true interval cancer in non-dense breasts had a higher risk of death than women with screen-detected cancers.Impact: These results support the advisability of routinely collecting information on breast density, both for further tailoring of screening strategies and as a prognostic factor for diagnosed breast cancers. Cancer Epidemiol Biomarkers Prev; 27(8); 908-16. ©2018 AACR.

Fine mapping in TERT-CLPTM1L region identified three independent lung cancer susceptibility signals: A large-scale multi-ethnic population study

Genome-wide association studies (GWAS) and fine mapping studies have identified multiple lung cancer susceptibility variants in TERT-CLPTM1L region. However, it is still unclear about the relationship between these risk variants and the independent lung cancer risk signals in this region. Therefore, we evaluated the independent susceptibility signals for lung cancer and explored the potential functional variants in this region. Sequential conditional analysis was used to detect the independent susceptibility loci based on four lung cancer GWAS datasets with 12 843 lung cases and 12 639 controls. Comprehensively functional annotations were performed for each independent signal. Three independent susceptibility signals were identified in multi-ethnic population. For the first signal, rs2736100 showed the most significant association with lung cancer risk (C > A, OR = 0.82, 95%CI: 0.79-0.85, P = 1.98 × 10^-25 ). Rs36019446 was the top-ranked site (A > G, OR = 0.88, 95%CI: 0.84-0.92, P = 1.74 × 10^-9 ) in the second signal. For the third signal, rs326048 was the leading SNP (A > G, OR = 0.91, 95%CI: 0.87-0.95, P = 1.38 × 10^-5 ). The following subgroup analysis found the same three loci among Asian population. Further, we compared the difference between various subgroup populations. Functional annotations revealed that rs2736100, rs27996 (r²  = 0.85 with rs36019446) and rs326049 (r²  = 0.73 with rs326048) could be potential functional variants in these three risk signals, respectively. In conclusion, although multiple variants have been found associated with lung cancer risk in TERT-CLPTM1L region, our findings indicated that there are three independent lung cancer susceptibility signals in this region.

Genetic Modifiers of the Breast Tumor Microenvironment

Multiple nonmalignant cell types in the tumor microenvironment (TME) impact breast cancer risk, metastasis, and response to therapy, yet most heritable mechanisms that influence TME cell function and breast cancer outcomes are largely unknown. Breast cancer risk is ∼30% heritable and >170 genetic loci have been associated with breast cancer traits. However, the majority of candidate genes have poorly defined mechanistic roles in breast cancer biology. Research indicates that breast cancer risk modifiers directly impact cancer cells, yet it is equally plausible that some modifier alleles impact the nonmalignant TME. The objective of this review is to examine the list of current breast cancer candidate genes that may modify breast cancer risk and outcome through the TME.

A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER⁺) and negative (ER^-) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer.

The Post-GWAS Era: From Association to Function

During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

Breast cancer family history and allele-specific DNA methylation in the legacy girls study

Family history, a well-established risk factor for breast cancer, can have both genetic and environmental contributions. Shared environment in families as well as epigenetic changes that also may be influenced by shared genetics and environment may also explain familial clustering of cancers. Epigenetic regulation, such as DNA methylation, can change the activity of a DNA segment without a change in the sequence; environmental exposures experienced across the life course can induce such changes. However, genetic-epigenetic interactions, detected as methylation quantitative trait loci (mQTLs; a.k.a. meQTLs) and haplotype-dependent allele-specific methylation (hap-ASM), can also contribute to inter-individual differences in DNA methylation patterns. To identify differentially methylated regions (DMRs) associated with breast cancer susceptibility, we examined differences in white blood cell DNA methylation in 29 candidate genes in 426 girls (ages 6-13 years) from the LEGACY Girls Study, 239 with and 187 without a breast cancer family history (BCFH). We measured methylation by targeted massively parallel bisulfite sequencing (bis-seq) and observed BCFH DMRs in two genes: ESR1 (Δ4.9%, P = 0.003) and SEC16B (Δ3.6%, P = 0.026), each of which has been previously implicated in breast cancer susceptibility and pubertal development. These DMRs showed high inter-individual variability in methylation, suggesting the presence of mQTLs/hap-ASM. Using single nucleotide polymorphisms data in the bis-seq amplicon, we found strong hap-ASM in SEC16B (with allele specific-differences ranging from 42% to 74%). These findings suggest that differential methylation in genes relevant to breast cancer susceptibility may be present early in life, and that inherited genetic factors underlie some of these epigenetic differences.

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.

Genetic variation in sensitivity to estrogens and breast cancer risk

Breast cancer risk is intimately intertwined with exposure to estrogens. While more than 160 breast cancer risk loci have been identified in humans, genetic interactions with estrogen exposure remain to be established. Strains of rodents exhibit striking differences in their responses to endogenous ovarian estrogens (primarily 17β-estradiol). Similar genetic variation has been observed for synthetic estrogen agonists (ethinyl estradiol) and environmental chemicals that mimic the actions of estrogens (xenoestrogens). This review of literature highlights the extent of variation in responses to estrogens among strains of rodents and compiles the genetic loci underlying pathogenic effects of excessive estrogen signaling. Genetic linkage studies have identified a total of the 35 quantitative trait loci (QTL) affecting responses to 17β-estradiol or diethylstilbestrol in five different tissues. However, the QTL appear to act in a tissue-specific manner with 9 QTL affecting the incidence or latency of mammary tumors induced by 17β-estradiol or diethylstilbestrol. Mammary gland development during puberty is also exquisitely sensitive to the actions of endogenous estrogens. Analysis of mammary ductal growth and branching in 43 strains of inbred mice identified 20 QTL. Regions in the human genome orthologous to the mammary development QTL harbor loci associated with breast cancer risk or mammographic density. The data demonstrate extensive genetic variation in regulation of estrogen signaling in rodent mammary tissues that alters susceptibility to tumors. Genetic variants in these pathways may identify a subset of women who are especially sensitive to either endogenous estrogens or environmental xenoestrogens and render them at increased risk of breast cancer.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

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

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.

A Dementia-Associated Risk Variant near TMEM106B Alters Chromatin Architecture and Gene Expression

Neurodegenerative diseases pose an extraordinary threat to the world's aging population, yet no disease-modifying therapies are available. Although genome-wide association studies (GWASs) have identified hundreds of risk loci for neurodegeneration, the mechanisms by which these loci influence disease risk are largely unknown. Here, we investigated the association between common genetic variants at the 7p21 locus and risk of the neurodegenerative disease frontotemporal lobar degeneration. We showed that variants associated with disease risk correlate with increased expression of the 7p21 gene TMEM106B and no other genes; co-localization analyses implicated a common causal variant underlying both association with disease and association with TMEM106B expression in lymphoblastoid cell lines and human brain. Furthermore, increases in the amount of TMEM106B resulted in increases in abnormal lysosomal phenotypes and cell toxicity in both immortalized cell lines and neurons. We then combined fine-mapping, bioinformatics, and bench-based approaches to functionally characterize all candidate causal variants at this locus. This approach identified a noncoding variant, rs1990620, that differentially recruits CTCF in lymphoblastoid cell lines and human brain to influence CTCF-mediated long-range chromatin-looping interactions between multiple cis-regulatory elements, including the TMEM106B promoter. Our findings thus provide an in-depth analysis of the 7p21 locus linked by GWASs to frontotemporal lobar degeneration, nominating a causal variant and causal mechanism for allele-specific expression and disease association at this locus. Finally, we show that genetic variants associated with risk of neurodegenerative diseases beyond frontotemporal lobar degeneration are enriched in CTCF-binding sites found in brain-relevant tissues, implicating CTCF-mediated gene regulation in risk of neurodegeneration more generally.

Gene-environment interactions involving functional variants: Results from the Breast Cancer Association Consortium

Investigating the most likely causal variants identified by fine-mapping analyses may improve the power to detect gene-environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine-scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR-rs7558475 and current smoking (ORint  = 0.77, 95% CI: 0.67-0.88, pint  = 1.8 × 10-4 ). The interaction with the strongest statistical evidence was found between 5q14-rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16-1.59, pint  = 1.9 × 10-5 ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint  = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10-4 ) and between 8q23-rs13267382 and age at first full-term pregnancy (ORint  = 0.89, 95% CI: 0.83-0.95, pint  = 5.2 × 10-4 ). While these results do not suggest any strong gene-environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.

Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores

Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10-6). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10-9). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

Background

Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates.

Methods

We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS.

Results

The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P =  8.2×10 -53 ). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P =  7.2×10 -20 ). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS.

Conclusions

BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management.