Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

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

Downregulation of MYBL1 in endothelial cells contributes to atherosclerosis by repressing PLEKHM1-inducing autophagy

MYBL1 is a strong transcriptional activator involved in the cell signaling. However, there is no systematic study on the role of MYBL1 in atherosclerosis. The aim of this study is to elucidate the role and mechanism of MYBL1 in atherosclerosis. GSE28829, GSE43292 and GSE41571 were downloaded from NCBI for differentially expressed analysis. The expression levels of MYBL1 in atherosclerotic plaque tissue and normal vessels were detected by qRT-PCR, Western blot and Immunohistochemistry. Transwell and CCK-8 were used to detect the migration and proliferation of HUVECs after silencing MYBL1. RNA-seq, Western blot, qRT-PCR, Luciferase reporter system, Immunofluorescence, Flow cytometry, ChIP and CO-IP were used to study the role and mechanism of MYBL1 in atherosclerosis. The microarray data of GSE28829, GSE43292, and GSE41571 were analyzed and intersected, and then MYBL1 were verified. MYBL1 was down-regulated in atherosclerotic plaque tissue. After silencing of MYBL1, HUVECs were damaged, and their migration and proliferation abilities were weakened. Overexpression of MYBL1 significantly enhanced the migration and proliferation of HUVECs. MYBL1 knockdown induced abnormal autophagy in HUVEC cells, suggesting that MYBL1 was involved in the regulation of HUVECs through autophagy. Mechanistic studies showed that MYBL1 knockdown inhibited autophagosome and lysosomal fusion in HUVECs by inhibiting PLEKHM1, thereby exacerbating atherosclerosis. Furthermore, MYBL1 was found to repress lipid accumulation in HUVECs after oxLDL treatment. MYBL1 knockdown in HUVECs was involved in atherosclerosis by inhibiting PLEKHM1-induced autophagy, which provided a novel target of therapy for atherosclerosis.

Investigating the shared genetic architecture between breast and ovarian cancers

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.

Large-scale genome-wide association study of 398,238 women unveils seven novel loci associated with high-grade serous epithelial ovarian cancer risk

Background

Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS).

Methods

We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan.

Results

Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10-9). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62).

Conclusions

This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.

Regulation of Cellular-Signaling Pathways by Mammalian Proteins Containing Bacterial EPIYA or EPIYA-Like Motifs Predicted to be Phosphorylated

The effector proteins of several pathogenic bacteria contain the Glu-Pro-Ile-Tyr-Ala (EPIYA) motif or other similar motifs. The EPIYA motif is delivered into the host cells by type III and IV secretion systems, through which its tyrosine residue undergoes phosphorylation by host kinases. These motifs atypically interact with a wide range of Src homology 2 (SH2) domain-containing mammalian proteins through tyrosine phosphorylation, which leads to the perturbation of multiple signaling cascades, the spread of infection, and improved bacterial colonization. Interestingly, it has been reported that EPIYA (or EPIYA-like) motifs exist in mammalian proteomes and regulate mammalian cellular-signaling pathways, leading to homeostasis and disease pathophysiology. It is possible that pathogenic bacteria have exploited EPIYA (or EPIYA-like) motifs from mammalian proteins and that the mammalian EPIYA (or EPIYA-like) motifs have evolved to have highly specific interactions with SH2 domain-containing proteins. In this review, we focus on the regulation of mammalian cellular-signaling pathways by mammalian proteins containing these motifs.

Angio-Long Noncoding RNA MALAT1 (rs3200401) and MIAT (rs1061540) Gene Variants in Ovarian Cancer

The genotyping of long non-coding RNA (lncRNA)-related single-nucleotide polymorphisms (SNPs) could be associated with cancer risk and/or progression. This study aimed to analyze the angiogenesis-related lncRNAs MALAT1 (rs3200401) and MIAT (rs1061540) variants in patients with ovarian cancer (OC) using "Real-Time allelic discrimination polymerase chain reaction" in 182 formalin-fixed paraffin-embedded (FFPE) samples of benign, borderline, and primary malignant ovarian tissues. Differences in the genotype frequencies between low-grade ovarian epithelial tumors (benign/borderline) and malignant tumors and between high-grade malignant epithelial tumors and malignant epithelial tumors other than high-grade serous carcinomas were compared. Odds ratios (ORs)/95% confidence intervals were calculated as measures of the association strength. Additionally, associations of the genotypes with the available pathological data were analyzed. The heterozygosity of MALAT1 rs3200401 was the most common genotype (47.8%), followed by C/C (36.3%). Comparing the study groups, no significant differences were observed regarding this variant. In contrast, the malignant epithelial tumors had a higher frequency of the MIAT rs1061540 C/C genotype compared to the low-grade epithelial tumor cohorts (56.7% vs. 37.6, p = 0.031). The same genotype was significantly higher in high-grade serous carcinoma than its counterparts (69.4% vs. 43.8%, p = 0.038). Multivariate Cox regression analysis showed that the age at diagnosis was significantly associated with the risk of OC development. In contrast, the MIAT T/T genotype was associated with a low risk of malignant epithelial tumors under the homozygote comparison model (OR = 0.37 (0.16-0.83), p = 0.017). Also, MIAT T allele carriers were less likely to develop high-grade serous carcinoma under heterozygote (CT vs. CC; OR = 0.33 (0.12-0.88), p = 0.027) and homozygote (TT vs. CC; OR = 0.26 (0.07-0.90), p = 0.034) comparison models. In conclusion, our data provide novel evidence for a potential association between the lncRNA MIAT rs1061540 and the malignant condition of ovarian cancer, suggesting the involvement of such lncRNAs in OC development.

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.

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND

Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort.

METHODS

Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types.

RESULTS

We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types.

CONCLUSIONS

CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies

Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.

XGBG: A Novel Method for Identifying Ovarian Carcinoma Susceptible Genes Based on Deep Learning

Ovarian carcinomas (OCs) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors, and outcomes. OC has been regarded as the most lethal cancer among women all around the world. There are at least five main types of OCs classified by the fifth edition of the World Health Organization of tumors: high-/low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, and endometrioid carcinoma. With the improved knowledge of genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure. Moreover, pathway analyses also play an important role in exploring the underlying mechanism of complex diseases by providing curated pathway models and information about molecular dynamics and cellular processes. To investigate OCs deeper, we introduced a novel disease susceptible gene prediction method, XGBG, which could be used in identifying OC-related genes based on different omics data and deep learning methods. We first employed the graph convolutional network (GCN) to reconstruct the gene features based on both gene feature and network topological structure. Then, a boosting method is utilized to predict OC susceptible genes. As a result, our model achieved a high AUC of 0.7541 and an AUPR of 0.8051, which indicates the effectiveness of the XGPG. Based on the newly predicted OC susceptible genes, we gathered and researched related literatures to provide strong support to the results, which may help in understanding the pathogenesis and mechanisms of the disease.

The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare

BACKGROUND

Cardiorespiratory fitness (CRF) and physical activity (PA) are well-established predictors of morbidity and all-cause mortality. However, CRF is not routinely measured and PA not routinely prescribed as part of standard healthcare. The American Heart Association (AHA) recently presented a scientific case for the inclusion of CRF as a clinical vital sign based on epidemiological and clinical observation. Here, we leverage genetic data in the UK Biobank (UKB) to strengthen the case for CRF as a vital sign and make a case for the prescription of PA.

METHODS

We derived two CRF measures from the heart rate data collected during a submaximal cycle ramp test: CRF-vo2max, an estimate of the participants' maximum volume of oxygen uptake, per kilogram of body weight, per minute; and CRF-slope, an estimate of the rate of increase of heart rate during exercise. Average PA over a 7-day period was derived from a wrist-worn activity tracker. After quality control, 70,783 participants had data on the two derived CRF measures, and 89,683 had PA data. We performed genome-wide association study (GWAS) analyses by sex, and post-GWAS techniques to understand genetic architecture of the traits and prioritise functional genes for follow-up.

RESULTS

We found strong evidence that genetic variants associated with CRF and PA influenced genetic expression in a relatively small set of genes in the heart, artery, lung, skeletal muscle and adipose tissue. These functionally relevant genes were enriched among genes known to be associated with coronary artery disease (CAD), type 2 diabetes (T2D) and Alzheimer's disease (three of the top 10 causes of death in high-income countries) as well as Parkinson's disease, pulmonary fibrosis, and blood pressure, heart rate, and respiratory phenotypes. Genetic variation associated with lower CRF and PA was also correlated with several disease risk factors (including greater body mass index, body fat and multiple obesity phenotypes); a typical T2D profile (including higher insulin resistance, higher fasting glucose, impaired beta-cell function, hyperglycaemia, hypertriglyceridemia); increased risk for CAD and T2D; and a shorter lifespan.

CONCLUSIONS

Genetics supports three decades of evidence for the inclusion of CRF as a clinical vital sign. Given the genetic, clinical and epidemiological evidence linking CRF and PA to increased morbidity and mortality, regular measurement of CRF as a marker of health and routine prescription of PA could be a prudent strategy to support public health.

An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods

Ovarian cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which may reduce the survival rate greatly. Even though most of the patients are treated timely and effectively, the survival rate is still low due to the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS)-discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes based on these risk loci. However, a large number of OC-related genes remain unknown. In this study, we proposed a novel gene prediction method based on different omics data and deep learning methods to identify OC causal genes. We first employed graph attention network (GAT) to obtain a compact gene feature representation, then a deep neural network (DNN) is utilized to predict OC-related genes. As a result, our model achieved a high AUC of 0.761 and AUPR of 0.788, which proved the accuracy and effectiveness of our proposed method. At last, we conducted a gene-set enrichment analysis to further explore the mechanism of OC. Finally, we predicted 245 novel OC causal genes and 10 top related KEGG pathways.

Population-based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high-grade serous ovarian cancer

PURPOSE

The known epithelial ovarian cancer (EOC) susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting that other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes.

METHODS

We sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases versus controls were further examined in an independent set of 14 135 EOC cases and 28 655 controls from the Ovarian Cancer Association Consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics.

RESULTS

The ORs associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 to 5.68; p=0.00068), 1.99 for POLK (95% CI 1.15 to 3.43; p=0.014) and 4.07 for SLX4 (95% CI 1.34 to 12.4; p=0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 to 1.00, p=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive.

CONCLUSIONS

We have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling.

Functional Interrogation of Enhancer Connectome Prioritizes Candidate Target Genes at Ovarian Cancer Susceptibility Loci

Identifying causal regulatory variants and their target genes from the majority of non-coding disease-associated genetic loci is the main challenge in post-Genome-Wide Association Studies (GWAS) functional studies. Although chromosome conformation capture (3C) and its derivative technologies have been successfully applied to nominate putative causal genes for non-coding variants, many GWAS target genes have not been identified yet. This study generated a high-resolution contact map from epithelial ovarian cancer (EOC) cells with two H3K27ac-HiChIP libraries and analyzed the underlying gene networks for 15 risk loci identified from the largest EOC GWAS. By combinatory analysis of 4,021 fine-mapped credible variants of EOC GWAS and high-resolution contact map, we obtained 162 target genes that mainly enriched in cancer related pathways. Compared with GTEx eQTL genes in ovarian tissue and annotated proximal genes, 132 HiChIP targets were first identified for EOC causal variants. More than half of the credible variants (CVs) involved interactions that were over 185 kb in distance, indicating that long-range transcriptional regulation is an important mechanism for the function of GWAS variants in EOC. We also found that many HiChIP gene targets showed significantly differential expressions between normal ovarian and EOC tumor samples. We validated one of these targets by manipulating the rs9303542 located region with CRISPR-Cas9 deletion and dCas9-VP64 activation experiments and found altered expression of HOXB7 and HOXB8 at 17q21.32. This study presents a systematic analysis to identify putative target genes for causal variants of EOC, providing an in-depth investigation of the mechanisms of non-coding regulatory variants in the etiology and pathogenesis of ovarian cancer.

Germline copy number variations in BRCA1/2 negative families: Role in the molecular etiology of hereditary breast cancer in Tunisia

Hereditary breast cancer accounts for 5-10% of all breast cancer cases. So far, known genetic risk factors account for only 50% of the breast cancer genetic component and almost a quarter of hereditary cases are carriers of pathogenic mutations in BRCA1/2 genes. Hence, the genetic basis for a significant fraction of familial cases remains unsolved. This missing heritability may be explained in part by Copy Number Variations (CNVs). We herein aimed to evaluate the contribution of CNVs to hereditary breast cancer in Tunisia. Whole exome sequencing was performed for 9 BRCA negative cases with a strong family history of breast cancer and 10 matched controls. CNVs were called using the ExomeDepth R-package and investigated by pathway analysis and web-based bioinformatic tools. Overall, 483 CNVs have been identified in breast cancer patients. Rare CNVs affecting cancer genes were detected, of special interest were those disrupting APC2, POU5F1, DOCK8, KANSL1, TMTC3 and the mismatch repair gene PMS2. In addition, common CNVs known to be associated with breast cancer risk have also been identified including CNVs on APOBECA/B, UGT2B17 and GSTT1 genes. Whereas those disrupting SULT1A1 and UGT2B15 seem to correlate with good clinical response to tamoxifen. Our study revealed new insights regarding CNVs and breast cancer risk in the Tunisian population. These findings suggest that rare and common CNVs may contribute to disease susceptibility. Those affecting mismatch repair genes are of interest and require additional attention since it may help to select candidates for immunotherapy leading to better outcomes.

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

The prospect of discovering new biomarkers for ovarian cancer based on current knowledge of susceptibility loci and genetic variation (Review)

Ovarian cancer is the most lethal gynaecological malignancy. The cancer initially presents with non‑specific symptoms; thus, it is typically not discovered until the patient has reached the late, considerably more lethal, stages of the disease. Research focus is currently on finding novel biomarkers, especially for early detection and stratification of the disease. One promising approach has been to focus on mutations or variations in the genetic code that are associated with the risk of developing ovarian cancer. A certain heritable component is already known regarding genes such as BRCA1/2, TP53, MSH6, BRIP1 and RAD51C, yet these are estimated to only account for ~3.1% of the total risk. Recent advances in sequencing technologies have enabled the investigation of hundreds of thousands of genetic variants in genome‑wide association studies in tens of thousands of patients, which has led to the discovery of 108 (39 loci with P<5.0x10‑8) novel susceptibility loci for ovarian cancer, presented in this review. Using the published variants in a patient cohort screening, together with variants identified in our ongoing whole exome sequencing project, future aims are to ascertain whether certain of the novel variants could be used as biomarkers for early diagnosis and/or treatment decisions.

Associations between TAB2 Gene Polymorphisms and Epithelial Ovarian Cancer in a Chinese Population

Background

Epithelial ovarian cancer (EOC) is highly lethal worldwide. Factors involved in the inflammation and hormone-associated signaling pathway play vital roles in EOC carcinogenesis. The transforming growth factor-β- (TGF-β-) activated kinase 1 (MAP3K7) binding protein 2 (TAB2), mediating convergence of inflammatory and estrogen, may be implicated in EOC. The present study is aimed at exploring the association between the TAB2 gene polymorphisms and EOC.

Methods

Three single nucleotide polymorphisms (SNPs) (rs237028, rs521845, and rs652921) of TAB2 were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 221 patients and 252 healthy controls. Associations between SNPs and clinical characteristics were performed either with the χ ² test or with Fisher's exact test. The Kaplan-Meier method and Cox proportional hazard models were used to detect associations between genotypes and overall survival.

Results

The rs237028 polymorphism was significantly associated with an increased risk of EOC with an allelic genetic model (A vs. G; OR = 1.45; 95%CI = 1.07-1.96; P = 0.016), dominant genetic model (AA vs. AG-GG; OR = 1.66; CI 1.14-2.41; P = 0.008), and overdominant genetic model (AA-GG vs. AG; OR = 1.60; CI 1.08-2.36; P = 0.017). However, no significant association was observed between rs237028 polymorphism and overall survival.

Conclusions

Our study indicated that the rs237028 polymorphism in the TAB2 gene was associated with EOC susceptibility and the TAB2 gene might contribute to the initiation of EOC.

Integrated analyses of murine breast cancer models reveal critical parallels with human disease

Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. Here, we complete whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. Through integrative in vitro and in vivo studies, we identify copy number alterations in key extracellular matrix proteins including collagen 1 type 1 alpha 1 (COL1A1) and chondroadherin (CHAD) that drive metastasis in these mouse models. In addition to copy number alterations, we observe a propensity of the tumors to modulate tyrosine kinase-mediated signaling through mutation of phosphatases such as PTPRH in the MMTV-PyMT mouse model. Mutation in PTPRH leads to increased phospho-EGFR levels and decreased latency. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers.

Mouse models are an essential tool in breast cancer research. Here, the authors present the genomic and transcriptomic profiles of two widely used mouse models, revealing parallels with the human disease specifically with metastasis and treatment response.

A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants

We sought to identify susceptibility genes for high-grade serous ovarian cancer (HGSOC) by performing a transcriptome-wide association study of gene expression and splice junction usage in HGSOC-relevant tissue types (N = 2,169) and the largest genome-wide association study available for HGSOC (N = 13,037 cases and 40,941 controls). We identified 25 transcriptome-wide association study significant genes, 7 at the junction level only, including LRRC46 at 19q21.32, (P = 1 × 10-9), CHMP4C at 8q21 (P = 2 × 10-11) and a PRC1 junction at 15q26 (P = 7 × 10-9). In vitro assays for CHMP4C showed that the associated variant induces allele-specific exon inclusion (P = 0.0024). Functional screens in HGSOC cell lines found evidence of essentiality for three of the new genes we identified: HAUS6, KANSL1 and PRC1, with the latter comparable to MYC. Our study implicates at least one target gene for 6 out of 13 distinct genome-wide association study regions, identifying 23 new candidate susceptibility genes for HGSOC.

Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women

OBJECTIVE

Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women.

METHODS

Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations.

RESULTS

At chromosome 6p25.2, SNP rs7748275 was associated with risk of serous EOC (odds ratio [OR] = 1.34, P = 8.7 × 10-9) and high-grade serous EOC (HGSOC) (OR = 1.34, P = 4.3 × 10-9). SNP rs6902488 at 6p25.2 (r2 = 0.97 with rs7748275) lies in an active enhancer and is predicted to impact binding of STAT3, P300 and ELF1. We identified additional risk loci with low Bayesian false discovery probability (BFDP) scores, indicating they are likely to be true risk associations (BFDP <10%). At chromosome 20q11.22, rs74272064 was associated with HGSOC risk (OR = 1.27, P = 9.0 × 10-8). Overall EOC risk was associated with rs10260419 at chromosome 7p21.3 (OR = 1.33, P = 1.2 × 10-7) and rs74917072 at chromosome 2q37.3 (OR = 1.25, P = 4.7 × 10-7). At 2q37.3, expression quantitative trait locus analysis in 404 HGSOC tissues identified ESPNL as a putative candidate susceptibility gene (P = 1.2 × 10-7).

CONCLUSION

While some risk loci were shared between East Asian and European populations, others were population-specific, indicating that the landscape of EOC risk in Asian women has both shared and unique features compared to women of European ancestry.

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.

Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10-7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

Functional Analysis and Fine Mapping of the 9p22.2 Ovarian Cancer Susceptibility Locus

Genome-wide association studies have identified 40 ovarian cancer risk loci. However, the mechanisms underlying these associations remain elusive. In this study, we conducted a two-pronged approach to identify candidate causal SNPs and assess underlying biological mechanisms at chromosome 9p22.2, the first and most statistically significant associated locus for ovarian cancer susceptibility. Three transcriptional regulatory elements with allele-specific effects and a scaffold/matrix attachment region were characterized and, through physical DNA interactions, BNC2 was established as the most likely target gene. We determined the consensus binding sequence for BNC2 in vitro, verified its enrichment in BNC2 ChIP-seq regions, and validated a set of its downstream target genes. Fine-mapping by dense regional genotyping in over 15,000 ovarian cancer cases and 30,000 controls identified SNPs in the scaffold/matrix attachment region as among the most likely causal variants. This study reveals a comprehensive regulatory landscape at 9p22.2 and proposes a likely mechanism of susceptibility to ovarian cancer. SIGNIFICANCE: Mapping the 9p22.2 ovarian cancer risk locus identifies BNC2 as an ovarian cancer risk gene.See related commentary by Choi and Brown, p. 439.

A Transcriptome-Wide Association Study Among 97,898 Women to Identify Candidate Susceptibility Genes for Epithelial Ovarian Cancer Risk

Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10-6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10-7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10-3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis.Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419-30. ©2018 AACR.

Modulation of the Tumor Microenvironment by CXCR4 Antagonist-Armed Viral Oncotherapy Enhances the Antitumor Efficacy of Dendritic Cell Vaccines against Neuroblastoma in Syngeneic Mice

The induction of antitumor immune responses in tumor-bearing hosts depends on efficient uptake and processing of native or modified tumors/self-antigens by dendritic cells (DCs) to activate immune effector cells, as well as the extent of the immunosuppressive network in the tumor microenvironment (TME). Because the C-X-C motif chemokine receptor 4 (CXCR4) for the C-X-C motif chemokine 12 (CXCL12) is involved in signaling interactions between tumor cells and their TME, we used oncolytic virotherapy with a CXCR4 antagonist to investigate whether targeting of the CXCL12/CXCR4 signaling axis in murine neuroblastoma cells (NXS2)-bearing syngeneic mice affects the efficacy of bone marrow (BM)-derived DCs loaded with autologous tumor cells treated with doxorubicin for induction of immunogenic cell death. Here, we show that CXCR4 antagonist expression from an oncolytic vaccinia virus delivered intravenously to mice with neuroblastoma tumors augmented efficacy of the DC vaccines compared to treatments mediated by a soluble CXCR4 antagonist or oncolysis alone. This study is the first demonstration that modulating the tumor microenvironment by an armed oncolytic virus could have a significant impact on the efficacy of DC vaccines, leading to the generation of effective protection against neuroblastoma challenge.

Early transcriptional response of human ovarian and fallopian tube surface epithelial cells to norepinephrine

Evidence from human and animal studies suggests that chronic behavioral stress and resulting activation of the sympathetic nervous system may influence initiation and progression of tumors. However, the underlying mechanisms for these observations are poorly understood. The purpose of this study is to explore the effects of adrenergic signaling on cell line models derived from normal cells presumed to originate epithelial ovarian cancers. Here we explored the effects of the stress-related hormone, norepinephrine, on the transcriptional program of normal immortalized ovarian (iOSE) and fallopian tube (iFTSEC) surface epithelial cells. Analysis of RNA-Seq data of treated and untreated cells revealed a significant overlap between the responses in iOSE and iFTSEC cells. Most genes modulated by norepinephrine in ovarian and fallopian tube epithelial cells are already expressed in normal ovarian and fallopian tissue and cells. For several genes, expression changes were reflected at the protein level. Genes in immune-related and developmental pathways were enriched in the set of genes modulated by norepinephrine. We identified HOXA5, SPIB, REL, SRF, SP1, NFKB1, MEF2A, E2F1, and EGR1 transcription factor binding sites to be highly enriched in our dataset. These data represent the early transcriptional response to norepinephrine in cells postulated to originate epithelial ovarian cancer.

Characterization of fusion genes in common and rare epithelial ovarian cancer histologic subtypes

Gene fusions play a critical role in some cancers and can serve as important clinical targets. In epithelial ovarian cancer (EOC), the contribution of fusions, especially by histological type, is unclear. We therefore screened for recurrent fusions in a histologically diverse panel of 220 EOCs using RNA sequencing. The Pipeline for RNA-Sequencing Data Analysis (PRADA) was used to identify fusions and allow for comparison with The Cancer Genome Atlas (TCGA) tumors. Associations between fusions and clinical prognosis were evaluated using Cox proportional hazards regression models. Nine recurrent fusions, defined as occurring in two or more tumors, were observed. CRHR1-KANSL1 was the most frequently identified fusion, identified in 6 tumors (2.7% of all tumors). This fusion was not associated with survival; other recurrent fusions were too rare to warrant survival analyses. One recurrent in-frame fusion, UBAP1-TGM7, was unique to clear cell (CC) EOC tumors (in 10%, or 2 of 20 CC tumors). We found some evidence that CC tumors harbor more fusions on average than any other EOC histological type, including high-grade serous (HGS) tumors. CC tumors harbored a mean of 7.4 fusions (standard deviation [sd] = 7.4, N = 20), compared to HGS EOC tumors mean of 2.0 fusions (sd = 3.3, N = 141). Few fusion genes were detected in endometrioid tumors (mean = 0.24, sd = 0.74, N = 55) or mucinous tumors (mean = 0.25, sd = 0.5, N = 4) tumors. To conclude, we identify one fusion at 10% frequency in the CC EOC subtype, but find little evidence for common (> 5% frequency) recurrent fusion genes in EOC overall, or in HGS subtype-specific EOC tumors.

Common Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions

In this review, we summarize current progress in the genetic epidemiology of epithelial ovarian cancer (EOC), focusing exclusively on elucidating the role of common germline genetic variation in conferring susceptibility to EOC. We provide an overview of the more than 30 EOC risk loci identified to date by genome-wide association studies (GWAS) and describe the contribution of large-scale, cross-cancer type, custom genotyping projects, such as the OncoArray and the Collaborative Oncological Gene-Environment Study, to locus discovery and replication. We discuss the histotype-specific nature of these EOC risk loci, pleiotropy, or overlapping genetic effects between EOC and other hormone-related cancer types, and the application of findings to polygenic risk prediction for EOC. The second part of the article offers a concise review of primarily laboratory-based studies that have led to the identification of several putative EOC susceptibility genes using common variants at the known EOC risk loci as starting points. More global biological insights emerging from network- and pathway-based analyses of GWAS for EOC susceptibility are also highlighted. Finally, we delve into potential future directions, including the need to identify EOC risk loci in non-European populations and the next generation of GWAS functional studies that are likely to involve genome editing to establish the cell type-specific carcinogenic effects of EOC risk variants Cancer Epidemiol Biomarkers Prev; 27(4); 395-404. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction

Epithelial ovarian cancer (EOC) is a heterogeneous disease with a major heritable component. The different histotypes of invasive disease - high grade serous, clear cell, endometrioid and mucinous - are associated with different underlying genetic susceptibility and epidemiological and lifestyle risk factors, all of which contribute to the different biology and clinical characteristics of each histotype. A combination of familial and population based sequencing studies, and genome wide association studies (GWAS) have identified a range of genetic susceptibility alleles for EOC comprising rare but highly penetrant genes (e.g. BRCA1, BRCA2) that are responsible for familial clustering of ovarian cancer cases; more moderate penetrance susceptibility genes (e.g. BRIP1, RAD51C/D, MSH6); and multiple common but low penetrance susceptibility alleles identified by GWAS. Identifying genetic risk alleles for ovarian cancer has had a significant impact on disease prevention strategies; for example it is now routine clinical practice for individuals with germline BRCA1 and BRCA2 mutations to undergo risk reducing salpingo-oophorectomy. Because ovarian cancers are commonly diagnosed at a late clinical stage when the prognosis is poor, the continued development of genetic risk prediction and prevention strategies will represent an important approach to reduce mortality due to ovarian cancer. Advances in genomics technologies that enable more high-throughput genetic testing, combined with research studies that identify additional EOC risk alleles will likely provide further opportunities to establish polygenic risk prediction approaches, based on combinations of rare high/moderate penetrance susceptibility genes and common, low penetrance susceptibility alleles. This article reviews the current literature describing the genetic and epidemiological components of ovarian cancer risk, and discusses both the opportunities and challenges in using this information for clinical risk prediction and prevention.

Mutational heterogeneity in non-serous ovarian cancers

Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes.

Mutational heterogeneity in non-serous ovarian cancers

Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes.

Mutational heterogeneity in non-serous ovarian cancers

Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes.

Germline whole exome sequencing and large-scale replication identifies FANCM as a likely high grade serous ovarian cancer susceptibility gene

We analyzed whole exome sequencing data in germline DNA from 412 high grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas Project and identified 5,517 genes harboring a predicted deleterious germline coding mutation in at least one HGSOC case. Gene-set enrichment analysis showed enrichment for genes involved in DNA repair (p = 1.8×10-3). Twelve DNA repair genes - APEX1, APLF, ATX, EME1, FANCL, FANCM, MAD2L2, PARP2, PARP3, POLN, RAD54L and SMUG1 - were prioritized for targeted sequencing in up to 3,107 HGSOC cases, 1,491 cases of other epithelial ovarian cancer (EOC) subtypes and 3,368 unaffected controls of European origin. We estimated mutation prevalence for each gene and tested for associations with disease risk. Mutations were identified in both cases and controls in all genes except MAD2L2, where we found no evidence of mutations in controls. In FANCM we observed a higher mutation frequency in HGSOC cases compared to controls (29/3,107 cases, 0.96 percent; 13/3,368 controls, 0.38 percent; P=0.008) with little evidence for association with other subtypes (6/1,491, 0.40 percent; P=0.82). The relative risk of HGSOC associated with deleterious FANCM mutations was estimated to be 2.5 (95% CI 1.3 - 5.0; P=0.006). In summary, whole exome sequencing of EOC cases with large-scale replication in case-control studies has identified FANCM as a likely novel susceptibility gene for HGSOC, with mutations associated with a moderate increase in risk. These data may have clinical implications for risk prediction and prevention approaches for high-grade serous ovarian cancer in the future and a significant impact on reducing disease mortality.

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC.

Epidemiology of ovarian cancer: a review

Ovarian cancer (OC) is the seventh most commonly diagnosed cancer among women in the world and the tenth most common in China. Epithelial OC is the most predominant pathologic subtype, with five major histotypes that differ in origination, pathogenesis, molecular alterations, risk factors, and prognosis. Genetic susceptibility is manifested by rare inherited mutations with high to moderate penetrance. Genome-wide association studies have additionally identified 29 common susceptibility alleles for OC, including 14 subtype-specific alleles. Several reproductive and hormonal factors may lower risk, including parity, oral contraceptive use, and lactation, while others such as older age at menopause and hormone replacement therapy confer increased risks. These associations differ by histotype, especially for mucinous OC, likely reflecting differences in etiology. Endometrioid and clear cell OC share a similar, unique pattern of associations with increased risks among women with endometriosis and decreased risks associated with tubal ligation. OC risks associated with other gynecological conditions and procedures, such as hysterectomy, pelvic inflammatory disease, and polycystic ovarian syndrome, are less clear. Other possible risk factors include environmental and lifestyle factors such as asbestos and talc powder exposures, and cigarette smoking. The epidemiology provides clues on etiology, primary prevention, early detection, and possibly even therapeutic strategies.

Epidemiology of ovarian cancer: a review

Ovarian cancer (OC) is the seventh most commonly diagnosed cancer among women in the world and the tenth most common in China. Epithelial OC is the most predominant pathologic subtype, with five major histotypes that differ in origination, pathogenesis, molecular alterations, risk factors, and prognosis. Genetic susceptibility is manifested by rare inherited mutations with high to moderate penetrance. Genome-wide association studies have additionally identified 29 common susceptibility alleles for OC, including 14 subtype-specific alleles. Several reproductive and hormonal factors may lower risk, including parity, oral contraceptive use, and lactation, while others such as older age at menopause and hormone replacement therapy confer increased risks. These associations differ by histotype, especially for mucinous OC, likely reflecting differences in etiology. Endometrioid and clear cell OC share a similar, unique pattern of associations with increased risks among women with endometriosis and decreased risks associated with tubal ligation. OC risks associated with other gynecological conditions and procedures, such as hysterectomy, pelvic inflammatory disease, and polycystic ovarian syndrome, are less clear. Other possible risk factors include environmental and lifestyle factors such as asbestos and talc powder exposures, and cigarette smoking. The epidemiology provides clues on etiology, primary prevention, early detection, and possibly even therapeutic strategies.

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

BACKGROUND

Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis.

METHODS

All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals).

RESULTS

The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation.

CONCLUSIONS

Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.

Genetic risk factors for ovarian cancer and their role for endometriosis risk

OBJECTIVE

Several genetic variants have been validated as risk factors for ovarian cancer. Endometriosis has also been described as a risk factor for ovarian cancer. Identifying genetic risk factors that are common to the two diseases might help improve our understanding of the molecular pathogenesis potentially linking the two conditions.

METHODS

In a hospital-based case-control analysis, 12 single nucleotide polymorphisms (SNPs), validated by the Ovarian Cancer Association Consortium (OCAC) and the Collaborative Oncological Gene-environment Study (COGS) project, were genotyped using TaqMan® OpenArray™ analysis. The cases consisted of patients with endometriosis, and the controls were healthy individuals without endometriosis. A total of 385 cases and 484 controls were analyzed. Odds ratios and P values were obtained using simple logistic regression models, as well as from multiple logistic regression models with adjustment for clinical predictors.

RESULTS

rs11651755 in HNF1B was found to be associated with endometriosis in this case-control study. The OR was 0.66 (95% CI, 0.51 to 0.84) and the P value after correction for multiple testing was 0.01. None of the other genotypes was associated with a risk for endometriosis.

CONCLUSIONS

As rs11651755 in HNF1B modified both the ovarian cancer risk and also the risk for endometriosis, HNF1B may be causally involved in the pathogenetic pathway leading from endometriosis to ovarian cancer.

Epidemiology of ovarian cancer: a review

Ovarian cancer (OC) is the seventh most commonly diagnosed cancer among women in the world and the tenth most common in China. Epithelial OC is the most predominant pathologic subtype, with five major histotypes that differ in origination, pathogenesis, molecular alterations, risk factors, and prognosis. Genetic susceptibility is manifested by rare inherited mutations with high to moderate penetrance. Genome-wide association studies have additionally identified 29 common susceptibility alleles for OC, including 14 subtype-specific alleles. Several reproductive and hormonal factors may lower risk, including parity, oral contraceptive use, and lactation, while others such as older age at menopause and hormone replacement therapy confer increased risks. These associations differ by histotype, especially for mucinous OC, likely reflecting differences in etiology. Endometrioid and clear cell OC share a similar, unique pattern of associations with increased risks among women with endometriosis and decreased risks associated with tubal ligation. OC risks associated with other gynecological conditions and procedures, such as hysterectomy, pelvic inflammatory disease, and polycystic ovarian syndrome, are less clear. Other possible risk factors include environmental and lifestyle factors such as asbestos and talc powder exposures, and cigarette smoking. The epidemiology provides clues on etiology, primary prevention, early detection, and possibly even therapeutic strategies.

Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci

BACKGROUND

Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P < 5 × 10-8). Most risk SNPs lie within non-protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets.

METHODS

Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10-5 > P > 5 × 10-8) overlapping lncRNAs.

RESULTS

Of 5,294 EOC-associated SNPs (P < 1.0 × 10-5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2 > 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10-4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression.

CONCLUSIONS

lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC.

IMPACT

lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116-25. ©2016 AACR.

A splicing variant of TERT identified by GWAS interacts with menopausal estrogen therapy in risk of ovarian cancer

Menopausal estrogen-alone therapy (ET) is a well-established risk factor for serous and endometrioid ovarian cancer. Genetics also plays a role in ovarian cancer, which is partly attributable to 18 confirmed ovarian cancer susceptibility loci identified by genome-wide association studies. The interplay among these loci, ET use and ovarian cancer risk has yet to be evaluated. We analyzed data from 1,414 serous cases, 337 endometrioid cases and 4,051 controls across 10 case-control studies participating in the Ovarian Cancer Association Consortium (OCAC). Conditional logistic regression was used to determine the association between the confirmed susceptibility variants and risk of serous and endometrioid ovarian cancer among ET users and non-users separately and to test for statistical interaction. A splicing variant in TERT, rs10069690, showed a statistically significant interaction with ET use for risk of serous ovarian cancer (pint  = 0.013). ET users carrying the T allele had a 51% increased risk of disease (OR = 1.51, 95% CI 1.19-1.91), which was stronger for long-term ET users of 10+ years (OR = 1.85, 95% CI 1.28-2.66, pint  = 0.034). Non-users showed essentially no association (OR = 1.08, 95% CI 0.96-1.21). Two additional genomic regions harboring rs7207826 (C allele) and rs56318008 (T allele) also had significant interactions with ET use for the endometrioid histotype (pint  = 0.021 and pint  = 0.037, respectively). Hence, three confirmed susceptibility variants were identified whose associations with ovarian cancer risk are modified by ET exposure; follow-up is warranted given that these interactions are not adjusted for multiple comparisons. These findings, if validated, may elucidate the mechanism of action of these loci.

PLEKHM1/DEF8/RAB7 complex regulates lysosome positioning and bone homeostasis

Mutations of the Plekhm1 gene in humans and rats cause osteopetrosis, an inherited bone disease characterized by diminished bone resorption by osteoclasts. PLEKHM1 binds to RAB7 and is critical for lysosome trafficking. However, the molecular mechanisms by which PLEKHM1 regulates lysosomal pathways remain unknown. Here, we generated germline and conditional Plekhm1-deficient mice. These mice displayed no overt abnormalities in major organs, except for an increase in trabecular bone mass. Furthermore, loss of PLEKHM1 abrogated the peripheral distribution of lysosomes and bone resorption in osteoclasts. Mechanistically, we indicated that DEF8 interacts with PLEKHM1 and promotes its binding to RAB7, whereas the binding of FAM98A and NDEL1 with PLEKHM1 connects lysosomes to microtubules. Importantly, suppression of these proteins results in lysosome positioning and bone resorption defects similar to those of Plekhm1-null osteoclasts. Thus, PLHKEM1, DEF8, FAM98A, and NDEL1 constitute a molecular complex that regulates lysosome positioning and secretion through RAB7.

A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking

Environmental health studies have examined associations between context and health with individuals as statistical units. However, investigators have been unable to investigate momentary exposures, and such studies are often vulnerable to confounding from, for example, individual-level preferences. We present a Global Positioning System (GPS)-based methodology for segmenting individuals' observation periods into visits to places and trips, enabling novel life-segment investigations and case-crossover analysis for improved inferences. We analyzed relationships between built environments and walking in trips. Participants were tracked for 7 days with GPS receivers and accelerometers and surveyed with a Web-based mapping application about their transport modes during each trip (Residential Environment and Coronary Heart Disease (RECORD) GPS Study, France, 2012-2013; 6,313 trips made by 227 participants). Contextual factors were assessed around residences and the trips' origins and destinations. Conditional logistic regression modeling was used to estimate associations between environmental factors and walking or accelerometry-assessed steps taken in trips. In case-crossover analysis, the probability of walking during a trip was 1.37 (95% confidence interval: 1.23, 1.61) times higher when trip origin was in the fourth (vs. first) quartile of service density and 1.47 (95% confidence interval: 1.23, 1.68) times higher when trip destination was in the fourth (vs. first) quartile of service density. Green spaces at the origin and destination of trips were also associated with within-individual, trip-to-trip variations in walking. Our proposed approach using GPS and Web-based surveys enables novel life-segment epidemiologic investigations.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.