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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. A multi-tissue transcriptome-wide association study based on genetic predictors of expression level and alternative splicing in relevant tissues identifies 25 candidate genes associated with high-grade serous ovarian cancer.

ObjectiveWhile oesophageal squamous cell carcinoma remains infrequent in Western populations, the incidence of oesophageal adenocarcinoma (EAC) has increased sixfold to eightfold over the past four decades. We aimed to characterise oesophageal cancer-specific and subtypes-specific gene regulation patterns and their upstream transcription factors (TFs). DesignTo identify regulatory elements, we profiled fresh-frozen oesophageal normal samples, tumours and cell lines with chromatin immunoprecipitation sequencing (ChIP-Seq). Mathematical modelling was performed to establish (super)-enhancers landscapes and interconnected transcriptional circuitry formed by master TFs. Coregulation and cooperation between master TFs were investigated by ChIP-Seq, circularised chromosome conformation capture sequencing and luciferase assay. Biological functions of candidate factors were evaluated both in vitro and in vivo.ResultsWe found widespread and pervasive alterations of the (super)-enhancer reservoir in both subtypes of oesophageal cancer, leading to transcriptional activation of a myriad of novel oncogenes and signalling pathways, some of which may be exploited pharmacologically (eg, leukemia inhibitory factor (LIF) pathway). Focusing on EAC, we bioinformatically reconstructed and functionally validated an interconnected circuitry formed by four master TFs—ELF3, KLF5, GATA6 and EHF—which promoted each other’s expression by interacting with each super-enhancer. Downstream, these master TFs occupied almost all EAC super-enhancers and cooperatively orchestrated EAC transcriptome. Each TF within the transcriptional circuitry was highly and specifically expressed in EAC and functionally promoted EAC cell proliferation and survival.ConclusionsBy establishing cancer-specific and subtype-specific features of the EAC epigenome, our findings promise to transform understanding of the transcriptional dysregulation and addiction of EAC, while providing molecular clues to develop novel therapeutic modalities against this malignancy.

Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis. By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74-0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72-0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands. Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.

Germline mutations of the BRCA1 and BRCA2 genes confer a high life-time risk of ovarian cancer. They represent the most significant and well characterised genetic risk factors so far identified for the disease. The frequency with which BRCA1/2 mutations occur in families containing multiple cases of ovarian cancer or breast and ovarian cancer, and in population-based ovarian cancer series varies geographically and between different ethnic groups. There are differences in the frequency of common mutations and in the presence of specific founder mutations in different populations. BRCA1 and BRCA2 are responsible for half of all families containing two or more ovarian cancer cases. In population-based studies, BRCA1 and BRCA2 mutations are present in 5–15% of all ovarian cancer cases. Often, individuals in which mutations are identified in unselected cases have no family history of either ovarian or breast cancer. The ability to identify BRCA1/ 2 mutations has been one of the few major success stories over the last few years in the clinical management of ovarian cancer. Currently, unaffected individuals can be screened for mutations if they have a family history of the disease. If a mutation is identified in the family, and if an individual is found be a mutation carrier, they can be offered clinical intervention strategies that can dramatically reduce their ovarian cancer risks. In some populations with frequent founder mutations screening may not be dependant on whether a mutation is identified in an affected relative.

Background As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method. Results The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics. Conclusions Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development. The role of non-coding somatic mutations in ovarian cancer is unclear. Here, the authors integrate genomic and epigenomic data from patient samples to show that these mutations frequently converge on the PAX8 transcriptional network.

Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Penetrance for breast cancer, ovarian cancer, or both in carriers of BRCA1/BRCA2 mutations is disproportionately high. Sex hormone dysregulation and altered end-organ hormone sensitivity might explain this organ-specific penetrance. We sought to identify differences in hormone regulation between carriers of BRCA1/2 and women who are negative for BRCA1/2 mutations. We assessed endometrial thickness for each menstrual cycle day (as an index of hormone regulation) in 393 scans from 228 women in the UK Familial Ovarian Cancer Screening Study (UK FOCSS) known to carry either mutation and 1573 scans from 754 women known to be negative for the mutations. To quantify differences in endometrial thickness we focused on days 10–14 and days 21–26, and calculated the area under the curve. We then compared serum oestradiol and progesterone titres during these days of the menstrual cycle in the same groups. Follicular and luteal oestradiol and progesterone serum titres were grouped into quartiles and odds ratios were calculated with logistic regression. Follicular phase endometrial thickness of carriers of the mutations adjusted for age and day of the menstrual cycle was higher (odds ratio [OR] 1·11, 95% CI 1·03–1·20; p=0·0063) and luteal phase endometrial thickness lower (0·90, 0·83–0·98; p=0·027) than for women negative for the mutations. Median luteal phase titres of progesterone were 121% higher (p=0·00037) in carriers than in women negative for the mutations, and for oestradiol were 33% higher (p=0·007)—ie, 59% of carriers had concentrations of serum progesterone that would have been in the top quartile of concentrations in the control group (OR 8·0, 95% CI 2·1–52·57; p=0·008). Carriers of BRCA1/BRCA2 mutations are exposed to higher titres of oestradiol and progesterone—known risk-factors for breast cancer. Higher titres of oestradiol in carriers are compatible with this hormone having a role in ovarian carcinogenesis in such women. Our findings could not be explained by differential contraceptive pill use. Eve Appeal, European Union, Cancer Research UK, and US National Institutes of Health.

To date, single-nucleotide polymorphisms (SNPs) have been the most intensively investigated class of polymorphisms in genome wide associations studies (GWAS), however, other classes such as insertion-deletion or multiple nucleotide length polymorphism (MNLPs) may also confer disease risk. Multiple reports have shown that the 5p15.33 prostate cancer risk region is a particularly strong expression quantitative trait locus (eQTL) for Iroquois Homeobox 4 ( IRX4) transcripts. Here, we demonstrate using epigenome and genome editing that a biallelic (21 and 47 base pairs (bp)) MNLP is the causal variant regulating IRX4 transcript levels. In LNCaP prostate cancer cells (homozygous for the 21 bp short allele), a single copy knock-in of the 47 bp long allele potently alters the chromatin state, enabling de novo functional binding of the androgen receptor ( AR ) associated with increased chromatin accessibility, Histone 3 lysine 27 acetylation (H3K27ac), and ~3-fold upregulation of IRX4 expression. We further show that an MNLP is amongst the strongest candidate susceptibility variants at two additional prostate cancer risk loci. We estimated that at least 5% of prostate cancer risk loci could be explained by functional non-SNP causal variants, which may have broader implications for other cancers GWAS. More generally, our results underscore the importance of investigating other classes of inherited variation as causal mediators of human traits. Here, the authors functionally characterize a complex genetic variant relevant in prostate cancer that regulates IRX4 expression through epigenetic activation. This work highlights the significance of non-single nucleotide polymorphism causal variants in explaining disease risk.

The molecular biology and cellular origins of mixed type endometrial carcinomas (MT-ECs) are poorly understood, and a Type II component of 10 percent or less may confer poorer prognoses. We studied 10 cases of MT-EC (containing endometrioid and serous differentiation), 5 pure low-grade endometrioid adenocarcinoma (EAC) and 5 pure uterine serous carcinoma (USC). Endometrioid and serous components of the MT-ECs were macrodissected and the expression of 60 candidate genes compared between MT-EC, pure USC and pure EAC. We found that four genes were differentially expressed when MT-ECs were compared to pure low-grade EAC: CDKN2A (P = 0.006), H19 (P = 0.010), HOMER2 (P = 0.009) and TNNT1 (P = 0.006). Also while we found that even though MT-ECs closely resembled the molecular profiles of pure USCs, they also exhibit lower expression of PAX8 compared to all pure cases combined (P = 0.035). Our data suggest that MT-EC exhibits the closest molecular and epidemiological similarities to pure USC and supports clinical observations that suggest patients with MT-EC should receive the same treatment as patients with pure serous carcinoma. Novel specific markers of MT-EC could be of diagnostic utility and could represent novel therapeutic targets in the future.