Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Investigation of common genetic risk factors between thyroid traits and breast cancer

Breast cancer (BC) risk is suspected to be linked to thyroid disorders, however observational studies exploring the association between BC and thyroid disorders gave conflicting results. We proposed an alternative approach by investigating the shared genetic risk factors between BC and several thyroid traits. We report a positive genetic correlation between BC and thyroxine (FT4) levels (corr = 0.13, p-value = 2.0 × 10-4) and a negative genetic correlation between BC and thyroid-stimulating hormone (TSH) levels (corr = -0.09, p-value = 0.03). These associations are more striking when restricting the analysis to estrogen receptor-positive BC. Moreover, the polygenic risk scores (PRS) for FT4 and hyperthyroidism are positively associated to BC risk (OR = 1.07, 95%CI: 1.00-1.13, p-value = 2.8 × 10-2 and OR = 1.04, 95%CI: 1.00-1.08, p-value = 3.8 × 10-2, respectively), while the PRS for TSH is inversely associated to BC risk (OR = 0.93, 95%CI: 0.89-0.97, p-value = 2.0 × 10-3). Using the PLACO method, we detected 49 loci associated to both BC and thyroid traits (p-value < 5 × 10-8), in the vicinity of 130 genes. An additional colocalization and gene-set enrichment analyses showed a convincing causal role for a known pleiotropic locus at 2q35 and revealed an additional one at 8q22.1 associated to both BC and thyroid cancer. We also found two new pleiotropic loci at 14q32.33 and 17q21.31 that were associated to both TSH levels and BC risk. Enrichment analyses and evidence of regulatory signals also highlighted brain tissues and immune system as candidates for obtaining associations between BC and TSH levels. Overall, our study sheds light on the complex interplay between BC and thyroid traits and provides evidence of shared genetic risk between those conditions.

Polygenic risk scores for prediction of breast cancer in Korean women

BACKGROUND

Polygenic risk scores (PRSs) for breast cancer, developed using European and Asian genome-wide association studies (GWAS), have been shown to have good discrimination in Asian women. However, prospective calibration of absolute risk prediction models, based on a PRS or PRS combined with lifestyle, clinical and environmental factors, in Asian women is limited.

METHODS

We consider several PRSs trained using European and/or Asian GWAS. For each PRS, we evaluate the discrimination and calibration of three absolute risk models among 41 031 women from the Korean Cancer Prevention Study (KCPS)-II Biobank: (i) a model using incidence, mortality and risk factor distributions (reference inputs) among US women and European relative risks; (ii) a recalibrated model, using Korean reference but European relative risks; and (iii) a fully Korean-based model using Korean reference and relative risk estimates from KCPS.

RESULTS

All Asian and European PRS improved discrimination over lifestyle, clinical and environmental (Qx) factors in Korean women. US-based absolute risk models overestimated the risks for women aged ≥50 years, and this overestimation was larger for models that only included PRS (expected-to-observed ratio E/O = 1.2 for women <50, E/O = 2.7 for women ≥50). Recalibrated and Korean-based risk models had better calibration in the large, although the risk in the highest decile was consistently overestimated. Absolute risk projections suggest that risk-reducing lifestyle changes would lead to larger absolute risk reductions among women at higher PRS.

CONCLUSIONS

Absolute risk models incorporating PRS trained in European and Asian GWAS and population-appropriate average age-specific incidences may be useful for risk-stratified interventions in Korean women.

Association between Ancestry-Specific 6q25 Variants and Breast Cancer Subtypes in Peruvian Women

BACKGROUND

Breast cancer incidence in the United States is lower in Hispanic/Latina (H/L) compared with African American/Black or Non-Hispanic White women. An Indigenous American breast cancer-protective germline variant (rs140068132) has been reported near the estrogen receptor 1 gene. This study tests the association of rs140068132 and other polymorphisms in the 6q25 region with subtype-specific breast cancer risk in H/Ls of high Indigenous American ancestry.

METHODS

Genotypes were obtained for 5,094 Peruvian women with (1,755) and without (3,337) breast cancer. Associations between genotype and overall and subtype-specific risk for the protective variant were tested using logistic regression models and conditional analyses, including other risk-associated polymorphisms in the region.

RESULTS

We replicated the reported association between rs140068132 and breast cancer risk overall [odds ratio (OR), 0.53; 95% confidence interval (CI), 0.47-0.59], as well as the lower odds of developing hormone receptor negative (HR-) versus HR+ disease (OR, 0.77; 95% CI, 0.61-0.97). Models, including HER2, showed further heterogeneity with reduced odds for HR+HER2+ (OR, 0.68; 95% CI, 0.51-0.92), HR-HER2+ (OR, 0.63; 95% CI, 0.44-0.90) and HR-HER2- (OR, 0.77; 95% CI, 0.56-1.05) compared with HR+HER2-. Inclusion of other risk-associated variants did not change these observations.

CONCLUSIONS

The rs140068132 polymorphism is associated with decreased risk of breast cancer in Peruvians and is more protective against HR- and HER2+ diseases independently of other breast cancer-associated variants in the 6q25 region.

IMPACT

These results could inform functional analyses to understand the mechanism by which rs140068132-G reduces risk of breast cancer development in a subtype-specific manner. They also illustrate the importance of including diverse individuals in genetic studies.

SLC35E2 promoter mutation as a prognostic marker of esophageal squamous cell carcinoma

AIMS

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest digestive tract cancer with poor prognosis. In our previous comprehensive genomics study, we identified that hotspot mutations in the solute carrier family 35 member E2 (SLC35E2) promoter region was significantly associated with worse prognosis in patients with ESCC. However, the biological function and molecular mechanism of SLC35E2 remains unclear. This study was to investigate the malignant function and mechanism of SLC35E2 in ESCC.

MAIN METHODS

Western blotting and qRT-PCR were used to assess the expression of SLC35E2 in ESCC cell lines. Luciferase assay and chromatin immunoprecipitation (ChIP) assay were used to assess the transcriptional inhibition of KLF4. Incucyte cell proliferation assay, colony formation assay and subcutaneous tumor formation in nude mice were used to assess the malignant function of SLC35E2.

KEY FINDINGS

SLC35E2 can promote ESCC cell proliferation in vitro and in vivo. Krüppel-like factor 4 (KLF4), a transcriptional repressor in ESCC, binds to the SLC35E2 promoter and represses the expression of SLC35E2. The transcriptional suppression of KLF4 can be blocked by the mutation at -118 site of the SLC35E2 promoter. Besides, the accumulation of SLC35E2 expression contributes to the malignant phenotype of ESCC.

SIGNIFICANCE

These results indicate that SLC35E2 may be used as a biomarker for prognosis as well as a therapeutic target for patients with ESCC.

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.

Genome-Wide Scanning for Signatures of Selection Revealed the Putative Genomic Regions and Candidate Genes Controlling Milk Composition and Coat Color Traits in Sahiwal Cattle

Background

In the evolutionary time scale, selection shapes the genetic variation and alters the architecture of genome in the organisms. Selection leaves detectable signatures at the genomic coordinates that provide clues about the protein-coding regions. Sahiwal is a valuable indicine cattle adapted to tropical environments with desirable milk attributes. Insights into the genomic regions under putative selection may reveal the molecular mechanisms affecting the quantitative and other important traits. To understand this, the present investigation was undertaken to explore signatures of selection in the genome of Sahiwal cattle using a medium-density genotyping INDUS chip.

Result

De-correlated composite of multiple selection signals (DCMS), which combines five different univariate statistics, was computed in the dataset to detect the signatures of selection in the Sahiwal genome. Gene annotations, Quantitative Trait Loci (QTL) enrichment, and functional analyses were carried out for the identification of significant genomic regions. A total of 117 genes were identified, which affect a number of important economic traits. The QTL enrichment analysis highlighted 14 significant [False Discovery Rate (FDR)-corrected p-value ≤ 0.05] regions on chromosomes BTA 1, 3, 6, 11, 20, and 21. The top three enriched QTLs were found on BTA 6, 20, and 23, which are associated with exterior, health, milk production, and reproduction traits. The present study on selection signatures revealed some key genes related with coat color (PDGFRA, KIT, and KDR), facial pigmentation (LEF), milk fat percent (MAP3K1, HADH, CYP2U1, and SGMS2), sperm membrane integrity (OSTC), lactation persistency (MRPS30, NNT, CCL28, HMGCS1, NIM1K, ZNF131, and CCDC152), milk yield (GHR and ZNF469), reproduction (NKX2-1 and DENND1A), and bovine tuberculosis susceptibility (RNF144B and PAPSS1). Further analysis of candidate gene prioritization identified four hub genes, viz., KIT, KDR, MAP3K1, and LEF, which play a role in coat color, facial pigmentation, and milk fat percentage in cattle. Gene enrichment analysis revealed significant Gene ontology (GO) terms related to breed-specific coat color and milk fat percent.

Conclusion

The key candidate genes and putative genomic regions associated with economic traits were identified in Sahiwal using single nucleotide polymorphism data and the DCMS method. It revealed selection for milk production, coat color, and adaptability to tropical climate. The knowledge about signatures of selection and candidate genes affecting phenotypes have provided a background information that can be further utilized to understand the underlying mechanism involved in these traits in Sahiwal cattle.

Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants.

Genetic modifiers regulating DNA replication and double-strand break repair are associated with differences in mammary tumors in mouse models of Li-Fraumeni syndrome

Breast cancer is the most common tumor among women with inherited variants in the TP53 tumor suppressor, but onset varies widely suggesting interactions with genetic or environmental factors. Rodent models haploinsufficent for Trp53 also develop a wide variety of malignancies associated with Li-Fraumeni Syndrome, but BALB/c mice are uniquely susceptible to mammary tumors and is genetically linked to the Suprmam1 locus on chromosome 7. To define mechanisms that interact with deficiencies in p53 to alter susceptibility to mammary tumors, we fine-mapped the Suprmam1 locus in females from an N2 backcross of BALB/cMed and C57BL/6J mice. A major modifier was localized within a 10 cM interval on chromosome 7. The effect of the locus on DNA damage responses was examined in the parental strains and mice that are congenic for C57BL/6J alleles on the BALB/cMed background (SM1-Trp53^+/−). The mammary epithelium of C57BL/6J-Trp53^+/− females exhibited little radiation-induced apoptosis compared to BALB/cMed-Trp53^+/− and SM1-Trp53^+/− females indicating that the Suprmam1^B6/B6 alleles could not rescue repair of radiation-induced DNA double-strand breaks mostly relying on non-homologous end joining. In contrast, the Suprmam1^B6/B6 alleles in SM1-Trp53+/− mice were sufficient to confer the C57BL/6J-Trp53^+/− phenotypes in homology-directed repair and replication fork progression. The Suprmam1^B6/B6 alleles in SM1-Trp53^+/− mice appear to act in trans to regulate a panel of DNA repair and replication genes which lie outside the locus.

From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

Background

There has been huge progress over the last 30 years in identifying the familial component of breast cancer.

Summary

Currently around 20% is explained by the high-risk genes BRCA1 and BRCA2, a further 2% by other high-penetrance genes, and around 5% by the moderate risk genes ATM and CHEK2. In contrast, the more than 300 low-penetrance single-nucleotide polymorphisms (SNP) now account for around 28% and they are predicted to account for most of the remaining 45% yet to be found. Even for high-risk genes which confer a 40-90% risk of breast cancer, these SNP can substantially affect the level of breast cancer risk. Indeed, the strength of family history and hormonal and reproductive factors is very important in assessing risk even for a BRCA carrier. The risks of contralateral breast cancer are also affected by SNP as well as by the presence of high or moderate risk genes. Genetic testing using gene panels is now commonplace.

Key-Messages

There is a need for a more parsimonious approach to panels only testing those genes with a definite 2-fold increased risk and only testing those genes with challenging management implications, such as CDH1 and TP53, when there is strong clinical indication to do so. Testing of SNP alongside genes is likely to provide a more accurate risk assessment.

Impact of ESR1 Polymorphisms on Risk of Breast Cancer in the Chinese Han Population

BACKGROUND

The estrogen receptor-1 (ESR1) gene encodes estrogen receptor-α, which is a major biomarker in the development of breast cancer. This study aimed to investigate the effect of ESR1 polymorphisms on breast cancer in Chinese Han women.

MATERIALS AND METHODS

We genotyped 4 candidate single nucleotide polymorphisms (SNPs) in ESR1 among 503 patients with breast cancer and 503 healthy people using the Agena MassARRAY platform. The association between ESR1 polymorphisms and breast cancer risk was evaluated using odds ratios (ORs) and 95% confidence intervals (95% CIs) under 4 genetic models. The HaploReg v4.1 and GEPIA database were used for SNP functional annotation and ESR1 expression analysis, respectively.

RESULTS

The T allele of rs9383938 in ESR1 was significantly associated with an increased breast cancer risk (OR, 1.26; 95% CI, 1.05-1.50; P = .013). In genetic models, rs9383938 increased breast cancer risk in the codominant model (OR, 1.54; 95% CI, 1.07-2.22; P = .021), the dominant model (OR, 1.31; 95% CI, 1.01-1.68; P = .040), and the additive model (OR, 1.24; 95% CI, 1.04-1.48; P = .017). Stratification analysis showed that rs9383938 and rs2228480 raised the breast cancer susceptibility in individuals aged younger than 52 years old. Rs1801132 of ESR1 was significantly associated with the status of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 in the allele model and genetic models (P < .05).

CONCLUSIONS

This study demonstrated that ESR1 polymorphisms might influence breast cancer susceptibility in the Chinese Han population. Further mechanism studies are needed to confirm the contribution of ESR1.

Integrative genomic analysis implicates ERCC6 and its interaction with ERCC8 in susceptibility to breast cancer

Up to 30% of all breast cancer cases may be inherited and up to 85% of those may be due to segregation of susceptibility genes with low and moderate risk [odds ratios (OR) ≤ 3] for (mostly peri- and post-menopausal) breast cancer. The majority of low/moderate-risk genes, particularly those with minor allele frequencies (MAF) of < 30%, have not been identified and/or validated due to limitations of conventional association testing approaches, which include the agnostic nature of Genome Wide Association Studies (GWAS). To overcome these limitations, we used a hypothesis-driven integrative genomics approach to test the association of breast cancer with candidate genes by analyzing multi-omics data. Our candidate-gene association analyses of GWAS datasets suggested an increased risk of breast cancer with ERCC6 (main effect: 1.29 ≤ OR ≤ 2.91, 0.005 ≤ p ≤ 0.04, 11.8 ≤ MAF ≤ 40.9%), and implicated its interaction with ERCC8 (joint effect: 3.03 ≤ OR ≤ 5.31, 0.01 ≤ pinteraction ≤ 0.03). We found significant upregulation of ERCC6 (p = 7.95 × 10-6) and ERCC8 (p = 4.67 × 10-6) in breast cancer and similar frequencies of ERCC6 (1.8%) and ERCC8 (0.3%) mutations in breast tumors to known breast cancer susceptibility genes such as BLM (1.9%) and LSP1 (0.3%). Our integrative genomics approach suggests that ERCC6 may be a previously unreported low- to moderate-risk breast cancer susceptibility gene, which may also interact with ERCC8.

New insight into the significance of KLF4 PARylation in genome stability, carcinogenesis, and therapy

KLF4 plays a critical role in determining cell fate responding to various stresses or oncogenic signaling. Here, we demonstrated that KLF4 is tightly regulated by poly(ADP‐ribosyl)ation (PARylation). We revealed the subcellular compartmentation for KLF4 is orchestrated by PARP1‐mediated PARylation. We identified that PARylation of KLF4 is critical to govern KLF4 transcriptional activity through recruiting KLF4 from soluble nucleus to the chromatin. We mapped molecular motifs on KLF4 and PARP1 that facilitate their interaction and unveiled the pivotal role of the PBZ domain YYR motif (Y430, Y451 and R452) on KLF4 in enabling PARP1‐mediated PARylation of KLF4. Disruption of KLF4 PARylation results in failure in DNA damage response. Depletion of KLF4 by RNA interference or interference with PARP1 function by KLF4^YYR/AAA (a PARylation‐deficient mutant) significantly sensitizes breast cancer cells to PARP inhibitors. We further demonstrated the role of KLF4 in modulating homologous recombination through regulating BRCA1 transcription. Our work points to the synergism between KLF4 and PARP1 in tumorigenesis and cancer therapy, which provides a potential new therapeutic strategy for killing BRCA1‐proficient triple‐negative breast cancer cells.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Critical Analysis of Genome-Wide Association Studies: Triple Negative Breast Cancer Quae Exempli Causa

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.

Methylation of the central transcriptional regulator KLF4 by PRMT5 is required for DNA end resection and recombination

Cell fitness and survival upon exposure to DNA damage depends on the repair of DNA lesions. Interestingly, cellular identity does affect and finetunes such response, although the molecular basis of such differences between tissues and cell types is not well understood. Thus, a possibility is that DNA repair itself is controlled by the mechanisms that govern cell identity. Here we show that the KLF4, involved in cellular homeostasis, proliferation, cell reprogramming and cancer development, directly regulates resection and homologous recombination proficiency. Indeed, resection efficiency follows KLF4 protein levels, i.e. decreases upon KLF4 downregulation and increases when is overexpressed. Moreover, KLF4 role in resection requires its methylation by the methyl-transferase PRMT5. Thus, PRMT5 depletion not only mimics KLF4 downregulation, but also showed an epistatic genetic relationship. Our data support a model in which the methylation of KLF4 by PRMT5 is a priming event required to license DNA resection and homologous recombination.

Regulation of KLF4 by posttranslational modification circuitry in endocrine resistance

KLF4 plays an important role in orchestrating a variety of cellular events, including cell-fate decision, genome stability and apoptosis. Its deregulation is correlated with human diseases such as breast cancer and gastrointestinal cancer. Results from recent biochemical studies have revealed that KLF4 is tightly regulated by posttranslational modifications. Here we report a new finding that KLF4 orchestrates estrogen receptor signaling and facilitates endocrine resistance. We also uncovered the underlying mechanism that alteration of KLF4 by posttranslational modifications such as phosphorylation and ubiquitylation changes tumor cell response to endocrine therapy drugs. IHC analyses using based on human breast cancer specimens showed the accumulation of KLF4 protein in ER-positive breast cancer tissues. Elevated KLF4 expression significantly correlated with prognosis and endocrine resistance. Our drug screening for suppressing KLF4 protein expression led to identification of Src kinase to be a critical player in modulating KLF4-mediated tamoxifen resistance. Depletion of VHL (von Hippel-Lindau tumor suppressor), a ubiquitin E3 ligase for KLF4, reduces tumor cell sensitivity to tamoxifen. We demonstrated phosphorylation of VHL by Src enhances proteolysis of VHL that in turn leads to upregulation of KLF4 and increases endocrine resistance. Suppression of Src-VHL-KLF4 cascade by Src inhibitor or enhancement of VHL-KLF4 ubiquitination by TAT-KLF4 (371-420AAa) peptides re-sensitizes tamoxifen-resistant breast cancer cells to tamoxifen treatment. Taken together, our findings demonstrate a novel role for KLF4 in modulating endocrine resistance via the Src-VHL-KLF4 axis.

A Possible Link of Genetic Variations in ER/IGF1R Pathway and Risk of Melanoma

The mechanism of gender disparity in cutaneous melanoma incidence remains unclear. Steroid hormones including estrogens have long been implicated in the course of melanoma, but the conclusion is controversial. Estrogen receptors (ERs) and insulin-like growth factor 1 receptor (IGF1R) show extensive crosstalk in cancer development, but how the ER/IGF1R network impacts melanoma is currently unclear. Here we studied the melanoma associations of selected SNPs from the ER/IGF1R network. Part of the International Genes, Environment, and Melanoma (GEM) cohort was used as a discovery set, and the Gene Environment Association Studies Initiative (GENEVA) dataset served as a validation set. Based on the associations with other malignant disease conditions, thirteen single nucleotide polymorphism (SNP) variants in ESR1, ESR2, IGF1, and IGF1R were selected for candidate gene association analyses. The rs1520220 in IGF1 and rs2229765 in IGF1R variants were significantly associated with melanoma risk in the GEM dataset after Benjamini-Hochberg multiple comparison correction, although they were not validated in the GENEVA set. The discrepancy may be caused by the multiple melanoma characteristics in the GEM patients. Further analysis of gender disparity was carried out for IGF1 and IGF1R SNPs in the GEM dataset. The GG phenotype in IGF1 rs1520220 (recessive model) presented an increased risk of melanoma (OR = 8.11, 95% CI: 2.20, 52.5, p = 0.006) in men but a significant opposite effect in women (OR = 0.15, 95% CI: 0.018, 0.86, p = 0.045). The AA genotype in IGF1R rs2229765 (recessive model) showed a significant protective effect in men (OR = 0.24, 95% CI: 0.07, 0.64, p = 0.008) and no effect in women. Results from the current study are warranted for further validation.

Recursive Feature Elimination by Sensitivity Testing

There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With membership queries, one can check whether changing the value of a feature in an example changes the label. In the real-world, we usually cannot get answers to such queries, so our approach instead makes these queries to a trained (imperfect) non-linear model. Because SVMs are widely used in bioinformatics, our empirical results use a real-world cancer genomics problem; because ground truth is not known for this task, we discuss the potential insights provided. We also evaluate on synthetic data where ground truth is known.

Identification of two novel breast cancer loci through large-scale genome-wide association study in the Japanese population

Genome-wide association studies (GWAS) have successfully identified about 70 genomic loci associated with breast cancer. Owing to the complexity of linkage disequilibrium and environmental exposures in different populations, it is essential to perform regional GWAS for better risk prediction. This study aimed to investigate the genetic architecture and to assess common genetic risk model of breast cancer with 6,669 breast cancer patients and 21,930 female controls in the Japanese population. This GWAS identified 11 genomic loci that surpass genome-wide significance threshold of P < 5.0 × 10⁻⁸ with nine previously reported loci and two novel loci that include rs9862599 on 3q13.11 (ALCAM) and rs75286142 on 21q22.12 (CLIC6-RUNX1). Validation study was carried out with 981 breast cancer cases and 1,394 controls from the Aichi Cancer Center. Pathway analyses of GWAS signals identified association of dopamine receptor medicated signaling and protein amino acid deacetylation with breast cancer. Weighted genetic risk score showed that individuals who were categorized in the highest risk group are approximately 3.7 times more likely to develop breast cancer compared to individuals in the lowest risk group. This well-powered GWAS is a representative study to identify SNPs that are associated with breast cancer in the Japanese population.

Association between mitochondrial genetic variation and breast cancer risk: The Multiethnic Cohort

Background

The mitochondrial genome encodes for thirty-seven proteins, among them thirteen are essential for the oxidative phosphorylation (OXPHOS) system. Inherited variation in mitochondrial genes may influence cancer development through changes in mitochondrial proteins, altering the OXPHOS process and promoting the production of reactive oxidative species.

Methods

To investigate the association between mitochondrial genetic variation and breast cancer risk, we tested 314 mitochondrial SNPs (mtSNPs), capturing four complexes of the mitochondrial OXPHOS pathway and mtSNP groupings for rRNA and tRNA, in 2,723 breast cancer cases and 3,260 controls from the Multiethnic Cohort Study.

Results

We examined the collective set of 314 mtSNPs as well as subsets of mtSNPs grouped by mitochondrial OXPHOS pathway, complexes, and genes, using the sequence kernel association test and adjusting for age, sex, and principal components of global ancestry. We also tested haplogroup associations using unconditional logistic regression and adjusting for the same covariates. Stratified analyses were conducted by self-reported maternal race/ethnicity. No significant mitochondrial OXPHOS pathway, gene, and haplogroup associations were observed in African Americans, Asian Americans, Latinos, and Native Hawaiians. In European Americans, a global test of all genetic variants of the mitochondrial genome identified an association with breast cancer risk (P = 0.017, q = 0.102). In mtSNP-subset analysis, the gene MT-CO2 (P = 0.001, q = 0.09) in Complex IV (cytochrome c oxidase) and MT-ND2 (P = 0.004, q = 0.19) in Complex I (NADH dehydrogenase (ubiquinone)) were significantly associated with breast cancer risk.

Conclusions

In summary, our findings suggest that collective mitochondrial genetic variation and particularly in the MT-CO2 and MT-ND2 may play a role in breast cancer risk among European Americans. Further replication is warranted in larger populations and future studies should evaluate the contribution of mitochondrial proteins encoded by both the nuclear and mitochondrial genomes to breast cancer risk.

Evaluation of significant genome-wide association studies risk - SNPs in young breast cancer patients

Purpose

Genome-wide-association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that are associated with an increased risk of breast cancer. Most of these studies were conducted primarily in postmenopausal breast cancer patients. Therefore, we set out to assess whether or not these breast cancer variants are also associated with an elevated risk of breast cancer in young premenopausal patients.

Methods

In 451 women of European ancestry who had prospectively enrolled in a longitudinal cohort study for women diagnosed with breast cancer at or under age 40, we genotyped 44 SNPs that were previously associated with breast cancer risk. A control group was comprised of 1142 postmenopausal healthy women from the Nurses’ Health Study (NHS). We assessed if the frequencies of the adequately genotyped SNPs differed significantly (p≤0.05) between the cohort of young breast cancer patients and postmenopausal controls, and then we corrected for multiple testing.

Results

Genotyping of the controls or cases was inadequate for comparisons between the groups for seven of the 44 SNPs. 9 of the remaining 37 were associated with breast cancer risk in young women with a p-value <0.05: rs10510102, rs1219648, rs13387042, rs1876206, rs2936870, rs2981579, rs3734805, rs3803662 and rs4973768. The directions of these associations were consistent with those in postmenopausal women. However, after correction for multiple testing (Benjamini Hochberg) none of the results remained statistically significant.

Conclusion

After correction for multiple testing, none of the alleles for postmenopausal breast cancer were clearly associated with risk of premenopausal breast cancer in this relatively small study.

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts

BACKGROUND

Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers.

METHODS

Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts.

RESULTS

Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P_-trend  < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers.

CONCLUSION

Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.

netDx: interpretable patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.

Genetic Epidemiology of Breast Cancer in Latin America

The last 10 years witnessed an acceleration of our understanding of what genetic factors underpin the risk of breast cancer. Rare high- and moderate-penetrance variants such as those in the BRCA genes account for a small proportion of the familial risk of breast cancer. Low-penetrance alleles are expected to underlie the remaining heritability. By now, there are about 180 genetic polymorphisms that are associated with risk, most of them of modest effect. In combination, they can be used to identify women at the lowest or highest ends of the risk spectrum, which might lead to more efficient cancer prevention strategies. Most of these variants were discovered in populations of European descent. As a result, we might be failing to discover additional polymorphisms that could explain risk in other groups. This review highlights breast cancer genetic epidemiology studies conducted in Latin America, and summarizes the information that they provide, with special attention to similarities and differences with studies in other populations. It includes studies of common variants, as well as moderate- and high-penetrance variants. In addition, it addresses the gaps that need to be bridged in order to better understand breast cancer genetic risk in Latin America.

Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer

INTRODUCTION

Heritage is the most important risk factor for breast cancer. About 15-20% of breast cancer is familial, referring to affected women who have one or more first- or second-degree relatives with the disease. The heritable component in these families is substantial, especially in families with aggregation of breast cancer with low age at onset. Identifying breast cancer susceptibility genes: Since the discovery of the highly penetrant autosomal dominant susceptibility genes BRCA1 and BRCA2 in the 1990s, several more breast cancer genes that confer a moderate to high risk of breast cancer have been identified. Furthermore, during the last decade, advances in genomic technologies have led to large scale genotyping in genome-wide association studies that have identified a considerable amount of common low penetrance loci. In total, the high risk genes, BRCA1, BRCA2, TP53, STK11, CD1 and PTEN account for approximately 20% of the familial risk. Moderate risk variants account for up to 5% of the inherited familial risk. The more than 180 identified low-risk loci explain 18% of the familial risk. Altogether more than half of the genetic background in familial breast cancer remains unclear. Other genes and low risk loci that explain a part the remaining fraction will probably be identified. Clinical aspects and future perspectives: Definitive clinical recommendations can be drawn only for carriers of germline variants in a limited number of high and moderate risk genes for which an association with breast cancer has been established. Future progress in evaluating previously identified breast cancer candidate variants and low risk loci as well as exploring new ones can play an important role in improving individual risk prediction in familial breast cancer.

Bridging the gap by discerning SNPs in linkage disequilibrium and their role in breast cancer

Breast Cancer is the most common cancer among women with several genes involved in disease susceptibility. As majority of genome-wide significant variants fall outside the coding region, it is likely that some of them alter specific gene functions. GWAS database was used to interpret the regulatory functions of these genetic variants. A total of 320 SNPs for breast cancer were selected via GWAS, which were entered into the SNAP web portal tool, to determine the one's found to be in Linkage Disequilibrium (r² < 0.80). The resulting 2024 proxy SNP's were processed in RegulomeDB to predict their regulatory role. Of these, 1440 produced a score ranging from 1-6, whereas the remaining produced no data. Only the variants under score 4 (cut-off value) in RegulomeDB has been studied further. From these variants, 221 had scores of less than 4, indicating a high degree of potential regulatory role associated with them. Further study revealed that 61 of the 221 SNPs were reported to be genome-wide significant for breast cancer, 52 to be associated with other diseases, 99 as unconfirmed for association with breast cancer, leaving only 9 to be novel proxy SNPs linked to breast cancer. Therefore, the study further confirmed postulation of non-coding variants being linked to disease risk thereby, requiring additional validation through genome-wide association studies to substantiate their underlying mechanism.

Update Breast Cancer 2018 (Part 3) - Genomics, Individualized Medicine and Immune Therapies - in the Middle of a New Era: Prevention and Treatment Strategies for Early Breast Cancer

In primary early breast cancer, the aim of treatment planning is to obtain an increasingly better understanding of the disease. The identification of patients with an excellent prognosis could help this group avoid unnecessary treatments. Furthermore, the planning of treatment is becoming increasingly patient-focussed. There is a growing understanding of those patients who benefit particularly from chemotherapy, as well as of those who could benefit from immunotherapy. Studies conducted on immunotherapies will be published shortly. Smaller individual studies offer an initial insight into the efficacy of checkpoint inhibitors (anti-PD1/PDL1 therapies). Not least, one of the largest breast cancer studies of all times has recently come to an end. The use of a multigene test has shown that it is sufficient to identify patients with such a good prognosis that chemotherapy is unnecessary. This review article is intended to summarise the current studies and give an outlook on current developments.

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.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer

Background: Although germline genetics influences breast cancer incidence, published research only explains approximately half of the expected association. Moreover, the accuracy of prediction models remains low. For women who develop breast cancer early, the genetic architecture is less established.Methods: To identify loci associated with early-onset breast cancer, gene-based tests were carried out using exome array data from 3,479 women with breast cancer diagnosed before age 50 and 973 age-matched controls. Replication was undertaken in a population that developed breast cancer at all ages of onset.Results: Three gene regions were associated with breast cancer incidence: FGFR2 (P = 1.23 × 10^-5; replication P < 1.00 × 10^-6), NEK10 (P = 3.57 × 10^-4; replication P < 1.00 × 10^-6), and SIVA1 (P = 5.49 × 10^-4; replication P < 1.00 × 10^-6). Of the 151 gene regions reported in previous literature, 19 (12.5%) showed evidence of association (P < 0.05) with the risk of early-onset breast cancer in the early-onset population. To predict incidence, whole-genome prediction was implemented on a subset of 3,076 participants who were additionally genotyped on a genome wide array. The whole-genome prediction outperformed a polygenic risk score [AUC, 0.636; 95% confidence interval (CI), 0.614-0.659 compared with 0.601; 95% CI, 0.578-0.623], and when combined with known epidemiologic risk factors, the AUC rose to 0.662 (95% CI, 0.640-0.684).Conclusions: This research supports a role for variation within FGFR2 and NEK10 in breast cancer incidence, and suggests SIVA1 as a novel risk locus.Impact: This analysis supports a shared genetic etiology between women with early- and late-onset breast cancer, and suggests whole-genome data can improve risk assessment. Cancer Epidemiol Biomarkers Prev; 27(9); 1057-64. ©2018 AACR.

Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data

Over the last two decades genetic testing for mutations in BRCA1 and BRCA2 has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.

Japanese GWAS identifies variants for bust-size, dysmenorrhea, and menstrual fever that are eQTLs for relevant protein-coding or long non-coding RNAs

Traits related to primary and secondary sexual characteristics greatly impact females during puberty and day-to-day adult life. Therefore, we performed a GWAS analysis of 11,348 Japanese female volunteers and 22 gynecology-related phenotypic variables, and identified significant associations for bust-size, menstrual pain (dysmenorrhea) severity, and menstrual fever. Bust-size analysis identified significant association signals in CCDC170-ESR1 (rs6557160; P = 1.7 × 10-16) and KCNU1-ZNF703 (rs146992477; P = 6.2 × 10-9) and found that one-third of known European-ancestry associations were also present in Japanese. eQTL data points to CCDC170 and ZNF703 as those signals' functional targets. For menstrual fever, we identified a novel association in OPRM1 (rs17181171; P = 2.0 × 10-8), for which top variants were eQTLs in multiple tissues. A known dysmenorrhea signal near NGF replicated in our data (rs12030576; P = 1.1 × 10-19) and was associated with RP4-663N10.1 expression, a putative lncRNA enhancer of NGF, while a novel dysmenorrhea signal in the IL1 locus (rs80111889; P = 1.9 × 10-16) contained SNPs previously associated with endometriosis, and GWAS SNPs were most significantly associated with IL1A expression. By combining regional imputation with colocalization analysis of GWAS/eQTL signals along with integrated annotation with epigenomic data, this study further refines the sets of candidate causal variants and target genes for these known and novel gynecology-related trait loci.

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.

Breast Cancer Family History and Contralateral Breast Cancer Risk in Young Women: An Update From the Women's Environmental Cancer and Radiation Epidemiology Study

Purpose The Women's Environmental Cancer and Radiation Epidemiology (WECARE) study demonstrated the importance of breast cancer family history on contralateral breast cancer (CBC) risk, even for noncarriers of deleterious BRCA1/2 mutations. With the completion of WECARE II, updated risk estimates are reported. Additional analyses that exclude women negative for deleterious mutations in ATM, CHEK2*1100delC, and PALB2 were performed. Patients and Methods The WECARE Study is a population-based case-control study that compared 1,521 CBC cases with 2,212 individually matched unilateral breast cancer (UBC) controls. Participants were younger than age 55 years when diagnosed with a first invasive breast cancer between 1985 and 2008. Women were interviewed about breast cancer risk factors, including family history. A subset of women was screened for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2. Rate ratios (RRs) were estimated using multivariable conditional logistic regression. Cumulative absolute risks (ARs) were estimated by combining RRs from the WECARE Study and population-based SEER*Stat cancer incidence data. Results Women with any first-degree relative with breast cancer had a 10-year AR of 8.1% for CBC (95% CI, 6.7% to 9.8%). Risks also were increased if the relative was diagnosed at an age younger than 40 years (10-year AR, 13.5%; 95% CI, 8.8% to 20.8%) or with CBC (10-year AR, 14.1%; 95% CI, 9.5% to 20.7%). These risks are comparable with those seen in BRCA1/2 deleterious mutation carriers (10-year AR, 18.4%; 95% CI, 16.0% to 21.3%). In the subset of women who tested negative for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2, estimates were unchanged. Adjustment for known breast cancer single-nucleotide polymorphisms did not affect estimates. Conclusion Breast cancer family history confers a high CBC risk, even after excluding women with deleterious mutations. Clinicians are urged to use detailed family histories to guide treatment and future screening decisions for young women with breast cancer.

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.

Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese

Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.

Genome-wide association studies in women of African ancestry identified 3q26.21 as a novel susceptibility locus for oestrogen receptor negative breast cancer

Multiple breast cancer loci have been identified in previous genome-wide association studies, but they were mainly conducted in populations of European ancestry. Women of African ancestry are more likely to have young-onset and oestrogen receptor (ER) negative breast cancer for reasons that are unknown and understudied. To identify genetic risk factors for breast cancer in women of African descent, we conducted a meta-analysis of two genome-wide association studies of breast cancer; one study consists of 1,657 cases and 2,029 controls genotyped with Illumina’s HumanOmni2.5 BeadChip and the other study included 3,016 cases and 2,745 controls genotyped using Illumina Human1M-Duo BeadChip. The top 18,376 single nucleotide polymorphisms (SNP) from the meta-analysis were replicated in the third study that consists of 1,984 African Americans cases and 2,939 controls. We found that SNP rs13074711, 26.5 Kb upstream of TNFSF10 at 3q26.21, was significantly associated with risk of oestrogen receptor (ER)-negative breast cancer (odds ratio [OR]=1.29, 95% CI: 1.18-1.40; P = 1.8 × 10 − 8). Functional annotations suggest that the TNFSF10 gene may be involved in breast cancer aetiology, but further functional experiments are needed. In addition, we confirmed SNP rs10069690 was the best indicator for ER-negative breast cancer at 5p15.33 (OR = 1.30; P = 2.4 × 10 − 10) and identified rs12998806 as the best indicator for ER-positive breast cancer at 2q35 (OR = 1.34; P = 2.2 × 10 − 8) for women of African ancestry. These findings demonstrated additional susceptibility alleles for breast cancer can be revealed in diverse populations and have important public health implications in building race/ethnicity-specific risk prediction model for breast cancer.

Rat models of 17β-estradiol-induced mammary cancer reveal novel insights into breast cancer etiology and prevention

Numerous laboratory and epidemiologic studies strongly implicate endogenous and exogenous estrogens in the etiology of breast cancer. Data summarized herein suggest that the ACI rat model of 17β-estradiol (E2)-induced mammary cancer is unique among rodent models in the extent to which it faithfully reflects the etiology and biology of luminal types of breast cancer, which together constitute ~70% of all breast cancers. E2 drives cancer development in this model through mechanisms that are largely dependent upon estrogen receptors and require progesterone and its receptors. Moreover, mammary cancer development appears to be associated with generation of oxidative stress and can be modified by multiple dietary factors, several of which may attenuate the actions of reactive oxygen species. Studies of susceptible ACI rats and resistant COP or BN rats provide novel insights into the genetic bases of susceptibility and the biological processes regulated by genetic determinants of susceptibility. This review summarizes research progress resulting from use of these physiologically relevant rat models to advance understanding of breast cancer etiology and prevention.

Women's preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment

BACKGROUND

Single-nucleotide polymorphism (SNP) gene test is a potential tool for improving the accuracy of breast cancer risk prediction. We seek to measure women's preferences and marginal willingness-to-pay (mWTP) for this new technology.

MATERIALS AND METHODS

We administered a discrete choice experiment (DCE) to English-speaking Singaporean women aged 40-69 years without any history of breast cancer, enrolled via door-to-door recruitment with quota sampling by age and ethnicity. DCE attributes comprise: 1) sample type (buccal swab and dried blood spot), 2) person conducting pretest discussion (specialist doctor, non-specialist doctor, and nurse educator), 3) test location (private family clinic, public primary-care clinic, and hospital), and 4) out-of-pocket cost (S$50, S$175, and S$300). Mixed logit model was used to estimate the effect of attribute levels on women's preferences and mWTP. Interactions between significant attributes and respondent characteristics were investigated. Predicted uptake rates for various gene testing scenarios were studied.

RESULTS

A total of 300 women aged 52.6±7.6 years completed the survey (100 Chinese, Malay, and Indian women, respectively). Sample type (P=0.046), person conducting pretest discussion, and out-of-pocket cost (P<0.001) are significantly associated with going for SNP gene testing. Women with higher income and education levels are more willing to pay higher prices for the test. Preferences in terms of mWTP across ethnic groups appear similar, but Chinese women have greater preference heterogeneity for the attributes. Predicted uptake for a feasible scenario consisting of buccal swab, pretest discussion with nurse educator at the hospital costing S$50 is 60.5%. Only 3.3% of women always opted out of the SNP gene test in real life. Reasons include high cost, poor awareness, and indifference toward test results.

CONCLUSION

SNP gene testing may be tailored according to individual preferences to encourage uptake. Future research should focus on outcomes and cost-effectiveness of personalized breast cancer screening using SNP gene testing.

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.

Making Sense of SNPs: Women's Understanding and Experiences of Receiving a Personalized Profile of Their Breast Cancer Risks

Genome wide association studies have identified a number of common genetic variants - single nucleotide polymorphisms (SNPs) - that combine to increase breast cancer risk. SNP profiling may enhance the accuracy of risk assessment and provides a personalized risk estimate. SNP testing for breast cancer risks may supplement other genetic tests in the future, however, before it can be implemented in the clinic we need to know how it will be perceived and received. Semi-structured qualitative interviews were conducted with 39 women who had previously had a breast cancer diagnosis and undergone BRCA1/2 testing, participated in the Variants in Practice (ViP) study and received personalized risk (SNP) profiles. Interviews explored their understanding and experiences of receiving this SNP information. Women reported feeling positive about receiving their personalized risk profile, because it: provided an explanation for their previous diagnosis of cancer, vindicated previous risk management decisions and clarified their own and other family members' risks. A small group was initially shocked to learn of the increased risk of a second primary breast cancer. This study suggests that the provision of personalized risk information about breast cancer generated by SNP profiling is understood and well received. However, a model of genetic counseling that incorporates monogenic and polygenic genetic information will need to be developed prior to clinical implementation.

A qualitative study on Singaporean women's views towards breast cancer screening and Single Nucleotide Polymorphisms (SNPs) gene testing to guide personalised screening strategies

Background

Breast cancer is the top cancer by incidence and mortality in Singaporean women. Mammography is by far its best screening tool, but current recommended age and interval may not yield the most benefit. Recent studies have demonstrated the potential of single nucleotide polymorphisms (SNPs) to improve discriminatory accuracy of breast cancer risk assessment models. This study was conducted to understand Singaporean women’s views towards breast cancer screening and SNPs gene testing to guide personalised screening strategies.

Methods

Focus group discussions were conducted among English-speaking women (n = 27) between 40 to 65 years old, both current and lapsed mammogram users. Women were divided into four groups based on age and mammogram usage. Discussions about breast cancer and screening experience, as well as perception and attitude towards SNPs gene testing were conducted by an experienced moderator. Women were also asked for factors that will influence their uptake of the test. Transcripts were analysed using thematic analysis to captured similarities and differences in views expressed.

Results

Barriers to repeat mammogram attendance include laziness to make appointment and painful and uncomfortable screening process. However, the underlying reason may be low perceived susceptibility to breast cancer. Facilitators to repeat mammogram attendance include ease of making appointment and timely reminders. Women were generally receptive towards SNPs gene testing, but required information on accuracy, cost, invasiveness, and side effects before they decide whether to go for it. Other factors include waiting time for results and frequency interval. On average, women gave a rating of 7.5 (range 5 to 10) when asked how likely they will go for the test.

Conclusion

Addressing concerns such as pain and discomfort during mammogram, providing timely reminders and debunking breast cancer myths can help to improve screening uptake. Women demonstrated a spectrum of responses towards a novel test like SNPs gene testing, but need more information to make an informed decision. Future public health education on predictive genetic testing should adequately address both benefits and risks. Findings from this study is used to inform a discrete choice experiment to empirically quantify women preferences and willingness-to-pay for SNPs gene testing.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3781-8) contains supplementary material, which is available to authorized users.

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10^-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10^-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.

Although individual genetic variant-based genome-wide association studies have greatly increased our understanding of the genetic susceptibility to breast cancer, the genetic variants identified to date account for a relatively small proportion of the heritability. Shifting the focus of analysis from individual genetic variants to genes or gene sets could lead to the identification of novel genes involved in breast cancer risk. Here, we take advantage of a recently developed gene-level expression-based association method MetaXcan to examine the association of genetically-predicted expression levels for 11,536 genes across the human genome with breast cancer risk. The MetaXcan method uses external information on the effects of genetic variants on gene expression. We show that the TP53INP2 gene on human chromosome 20 is significantly associated with estrogen-receptor negative breast cancer (P = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association is consistent across analyses of four datasets, representing European, African and Asian ancestry populations. As a downstream gene of p53, TP53INP2 may affect breast cancer risk through p53 signaling pathway. Furthermore, TP53INP2, also known as DOR (Diabetes And Obesity-Regulated Gene), has been linked to obesity and diabetes, suggesting a novel biological pathway for the known association between obesity and breast cancer risk.