Citations

Abstract 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.

AbstractEpidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.

Abstract Background: Comorbidities can affect survival of ovarian cancer patients by influencing treatment efficacy. However, little evidence exists on the association between individual concurrent comorbidities and prognosis in ovarian cancer patients. Methods: Among patients diagnosed with invasive ovarian carcinoma who participated in 23 studies included in the Ovarian Cancer Association Consortium, we explored associations between histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, and neurological diseases and overall and progression-free survival. Using Cox proportional hazards regression models adjusted for age at diagnosis, stage of disease, histology, and study site, we estimated pooled HRs and 95% confidence intervals to assess associations between each comorbidity and ovarian cancer outcomes. Results: None of the comorbidities were associated with ovarian cancer outcome in the overall sample nor in strata defined by histologic subtype, weight status, age at diagnosis, or stage of disease (local/regional vs. advanced). Conclusions: Histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, or neurologic diseases were not associated with ovarian cancer overall or progression-free survival. Impact: These previously diagnosed chronic diseases do not appear to affect ovarian cancer prognosis. Cancer Epidemiol Biomarkers Prev; 26(9); 1470–3. ©2017 AACR.

Abstract Familial studies were among the first to indicate that breast cancer prognosis has a heritable component. Subsequently many variants associated with prognosis have been identified using a range of techniques including genome-wide association studies (GWAs). Despite these advances, much of the heritability remains unexplained. In young women, breast cancer is characterised by a higher incidence of adverse pathological features, unique tumour gene expression profiles and worse survival. In addition, the association of risk with conventional epidemiological exposures is less clear in women with early onset. We hypothesise that some of these difference between early and late onset could be influenced by germline variation. To identify additional variants that influence breast cancer prognosis we conducted a two stage meta-analysis of four GWAs consisting of 6,042 patients from the UK (POSH), Finland (HEBCS), Germany (SUCCESS-A) and Australia (ABCFS). Cox-regression analyses were used to investigate overall survival (OS, n=1,101 events) and disease-free survival (DFS, n=1,316 events) with correction for oestrogen status (ER). These survival analyses were repeated in a subset of patients with early onset (aged ≤40 at diagnosis, n=2,315 patients, OS n=604 events, DFS n=716 events). Meta-analysis identified two intronic SNPs in ADAMTSL1 that were associated exclusively with early onset DFS, rs715212 (Pmeta=3.54x10-5) and rs10963755 (Pmeta=3.91x10-4) without heterogeneity between cohorts. Multivariable Cox-regression demonstrated that the effect of these SNPs were independent of the classical prognostic factors. Most importantly, rs715212 reached genome-wide significance (Pmultivariate=5.37x10-8) in the multivariate model. ADAMTSL1 encodes a glycoprotein that forms part of the extracellular matrix (ECM) and may function in cell-cell or cell-matrix interactions or may regulate other ADAMTS proteases. Previous studies have shown that ADAMTSL1 is hypermethylated in ER positive breast cancer tumours. Using GTEx to perform eQTL analysis, we found that rs715212 is associated with the expression of several biologically relevant genes AREG (P=0.035), TNF (P=0.015), FASLG (P=0.0031) and EGF (P=0.0018) in breast mammary tissue. Interestingly, separate studies have shown that AREG is overexpressed in ER-positive breast tumours from pre-menopausal women versus post-menopausal women. Furthermore, AREG is differentially expressed between parous and non-parous mammary glands and is persistently downregulated by parity, which suggests it may contribute to the susceptibility of the nulliparous gland to breast cancer. We conclude that rs715212 is associated with an increased risk of disease progression in patients with early onset and speculate that this could be due to an interaction with the expression of AREG. Note: This abstract was not presented at the meeting. Citation Format: Latha Kadalayil, Sofia Khan, Heli Nevanlinna, Peter A. Fasching, Fergus J. Couch, John Hopper, Jianjun Liu, Tom Maishman, Lorraine Durcan, Carl Bloomqvist, Andy Collins, Dianna Eccles, William Tapper. A meta-analysis of genome-wide association studies identifies novel loci that influence breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1322. doi:10.1158/1538-7445.AM2017-1322

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