Displaying publications 1 - 20 of 30 in total

  1. Rebbeck TR, Mitra N, Wan F, Sinilnikova OM, Healey S, McGuffog L, et al.
    JAMA, 2015 Apr 07;313(13):1347-61.
    PMID: 25849179 DOI: 10.1001/jama.2014.5985
    IMPORTANCE: Limited information about the relationship between specific mutations in BRCA1 or BRCA2 (BRCA1/2) and cancer risk exists.

    OBJECTIVE: To identify mutation-specific cancer risks for carriers of BRCA1/2.

    DESIGN, SETTING, AND PARTICIPANTS: Observational study of women who were ascertained between 1937 and 2011 (median, 1999) and found to carry disease-associated BRCA1 or BRCA2 mutations. The international sample comprised 19,581 carriers of BRCA1 mutations and 11,900 carriers of BRCA2 mutations from 55 centers in 33 countries on 6 continents. We estimated hazard ratios for breast and ovarian cancer based on mutation type, function, and nucleotide position. We also estimated RHR, the ratio of breast vs ovarian cancer hazard ratios. A value of RHR greater than 1 indicated elevated breast cancer risk; a value of RHR less than 1 indicated elevated ovarian cancer risk.

    EXPOSURES: Mutations of BRCA1 or BRCA2.

    MAIN OUTCOMES AND MEASURES: Breast and ovarian cancer risks.

    RESULTS: Among BRCA1 mutation carriers, 9052 women (46%) were diagnosed with breast cancer, 2317 (12%) with ovarian cancer, 1041 (5%) with breast and ovarian cancer, and 7171 (37%) without cancer. Among BRCA2 mutation carriers, 6180 women (52%) were diagnosed with breast cancer, 682 (6%) with ovarian cancer, 272 (2%) with breast and ovarian cancer, and 4766 (40%) without cancer. In BRCA1, we identified 3 breast cancer cluster regions (BCCRs) located at c.179 to c.505 (BCCR1; RHR = 1.46; 95% CI, 1.22-1.74; P = 2 × 10(-6)), c.4328 to c.4945 (BCCR2; RHR = 1.34; 95% CI, 1.01-1.78; P = .04), and c. 5261 to c.5563 (BCCR2', RHR = 1.38; 95% CI, 1.22-1.55; P = 6 × 10(-9)). We also identified an ovarian cancer cluster region (OCCR) from c.1380 to c.4062 (approximately exon 11) with RHR = 0.62 (95% CI, 0.56-0.70; P = 9 × 10(-17)). In BRCA2, we observed multiple BCCRs spanning c.1 to c.596 (BCCR1; RHR = 1.71; 95% CI, 1.06-2.78; P = .03), c.772 to c.1806 (BCCR1'; RHR = 1.63; 95% CI, 1.10-2.40; P = .01), and c.7394 to c.8904 (BCCR2; RHR = 2.31; 95% CI, 1.69-3.16; P = .00002). We also identified 3 OCCRs: the first (OCCR1) spanned c.3249 to c.5681 that was adjacent to c.5946delT (6174delT; RHR = 0.51; 95% CI, 0.44-0.60; P = 6 × 10(-17)). The second OCCR spanned c.6645 to c.7471 (OCCR2; RHR = 0.57; 95% CI, 0.41-0.80; P = .001). Mutations conferring nonsense-mediated decay were associated with differential breast or ovarian cancer risks and an earlier age of breast cancer diagnosis for both BRCA1 and BRCA2 mutation carriers.

    CONCLUSIONS AND RELEVANCE: Breast and ovarian cancer risks varied by type and location of BRCA1/2 mutations. With appropriate validation, these data may have implications for risk assessment and cancer prevention decision making for carriers of BRCA1 and BRCA2 mutations.

    Matched MeSH terms: Ovarian Neoplasms/genetics*
  2. Vigorito E, Kuchenbaecker KB, Beesley J, Adlard J, Agnarsson BA, Andrulis IL, et al.
    PLoS ONE, 2016;11(7):e0158801.
    PMID: 27463617 DOI: 10.1371/journal.pone.0158801
    Population-based genome wide association studies have identified a locus at 9p22.2 associated with ovarian cancer risk, which also modifies ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. We conducted fine-scale mapping at 9p22.2 to identify potential causal variants in BRCA1 and BRCA2 mutation carriers. Genotype data were available for 15,252 (2,462 ovarian cancer cases) BRCA1 and 8,211 (631 ovarian cancer cases) BRCA2 mutation carriers. Following genotype imputation, ovarian cancer associations were assessed for 4,873 and 5,020 SNPs in BRCA1 and BRCA 2 mutation carriers respectively, within a retrospective cohort analytical framework. In BRCA1 mutation carriers one set of eight correlated candidate causal variants for ovarian cancer risk modification was identified (top SNP rs10124837, HR: 0.73, 95%CI: 0.68 to 0.79, p-value 2× 10-16). These variants were located up to 20 kb upstream of BNC2. In BRCA2 mutation carriers one region, up to 45 kb upstream of BNC2, and containing 100 correlated SNPs was identified as candidate causal (top SNP rs62543585, HR: 0.69, 95%CI: 0.59 to 0.80, p-value 1.0 × 10-6). The candidate causal in BRCA1 mutation carriers did not include the strongest associated variant at this locus in the general population. In sum, we identified a set of candidate causal variants in a region that encompasses the BNC2 transcription start site. The ovarian cancer association at 9p22.2 may be mediated by different variants in BRCA1 mutation carriers and in the general population. Thus, potentially different mechanisms may underlie ovarian cancer risk for mutation carriers and the general population.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  3. Kuchenbaecker KB, Ramus SJ, Tyrer J, Lee A, Shen HC, Beesley J, et al.
    Nat. Genet., 2015 Feb;47(2):164-71.
    PMID: 25581431 DOI: 10.1038/ng.3185
    Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  4. Li J, Wen WX, Eklund M, Kvist A, Eriksson M, Christensen HN, et al.
    Int. J. Cancer, 2019 03 01;144(5):1195-1204.
    PMID: 30175445 DOI: 10.1002/ijc.31841
    Breast cancer patients with BRCA1/2-driven tumors may benefit from targeted therapy. It is not clear whether current BRCA screening guidelines are effective at identifying these patients. The purpose of our study was to evaluate the prevalence of inherited BRCA1/2 pathogenic variants in a large, clinically representative breast cancer cohort and to estimate the proportion of BRCA1/2 carriers not detected by selectively screening individuals with the highest probability of being carriers according to current clinical guidelines. The study included 5,122 unselected Swedish breast cancer patients diagnosed from 2001 to 2008. Target sequence enrichment (48.48 Fluidigm Access Arrays) and sequencing were performed (Illumina Hi-Seq 2,500 instrument, v4 chemistry). Differences in patient and tumor characteristics of BRCA1/2 carriers who were already identified as part of clinical BRCA1/2 testing routines and additional BRCA1/2 carriers found by sequencing the entire study population were compared using logistic regression models. Ninety-two of 5,099 patients with valid variant calls were identified as BRCA1/2 carriers by screening all study participants (1.8%). Only 416 study participants (8.2%) were screened as part of clinical practice, but this identified 35 out of 92 carriers (38.0%). Clinically identified carriers were younger, less likely postmenopausal and more likely to be associated with familiar ovarian cancer compared to the additional carriers identified by screening all patients. More BRCA2 (34/42, 81.0%) than BRCA1 carriers (23/50, 46%) were missed by clinical screening. In conclusion, BRCA1/2 mutation prevalence in unselected breast cancer patients was 1.8%. Six in ten BRCA carriers were not detected by selective clinical screening of individuals.
    Matched MeSH terms: Ovarian Neoplasms/genetics
  5. Lawrenson K, Li Q, Kar S, Seo JH, Tyrer J, Spindler TJ, et al.
    Nat Commun, 2015 Sep 22;6:8234.
    PMID: 26391404 DOI: 10.1038/ncomms9234
    Genome-wide association studies have reported 11 regions conferring risk of high-grade serous epithelial ovarian cancer (HGSOC). Expression quantitative trait locus (eQTL) analyses can identify candidate susceptibility genes at risk loci. Here we evaluate cis-eQTL associations at 47 regions associated with HGSOC risk (P≤10(-5)). For three cis-eQTL associations (P<1.4 × 10(-3), FDR<0.05) at 1p36 (CDC42), 1p34 (CDCA8) and 2q31 (HOXD9), we evaluate the functional role of each candidate by perturbing expression of each gene in HGSOC precursor cells. Overexpression of HOXD9 increases anchorage-independent growth, shortens population-doubling time and reduces contact inhibition. Chromosome conformation capture identifies an interaction between rs2857532 and the HOXD9 promoter, suggesting this SNP is a leading causal variant. Transcriptomic profiling after HOXD9 overexpression reveals enrichment of HGSOC risk variants within HOXD9 target genes (P=6 × 10(-10) for risk variants (P<10(-4)) within 10 kb of a HOXD9 target gene in ovarian cells), suggesting a broader role for this network in genetic susceptibility to HGSOC.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  6. Lawrenson K, Kar S, McCue K, Kuchenbaeker K, Michailidou K, Tyrer J, et al.
    Nat Commun, 2016 09 07;7:12675.
    PMID: 27601076 DOI: 10.1038/ncomms12675
    A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10(-20)), ER-negative BC (P=1.1 × 10(-13)), BRCA1-associated BC (P=7.7 × 10(-16)) and triple negative BC (P-diff=2 × 10(-5)). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10(-3)) and ABHD8 (P<2 × 10(-3)). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3'-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  7. Chatterjee J, Dai W, Aziz NHA, Teo PY, Wahba J, Phelps DL, et al.
    Clin. Cancer Res., 2017 07 01;23(13):3453-3460.
    PMID: 27986748 DOI: 10.1158/1078-0432.CCR-16-2366
    Purpose: We aimed to establish whether programmed cell death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) expression, in ovarian cancer tumor tissue and blood, could be used as biomarkers for discrimination of tumor histology and prognosis of ovarian cancer.Experimental Design: Immune cells were separated from blood, ascites, and tumor tissue obtained from women with suspected ovarian cancer and studied for the differential expression of possible immune biomarkers using flow cytometry. PD-L1 expression on tumor-associated inflammatory cells was assessed by immunohistochemistry and tissue microarray. Plasma soluble PD-L1 was measured using sandwich ELISA. The relationships among immune markers were explored using hierarchical cluster analyses.Results: Biomarkers from the discovery cohort that associated with PD-L1+ cells were found. PD-L1+ CD14+ cells and PD-L1+ CD11c+ cells in the monocyte gate showed a distinct expression pattern when comparing benign tumors and epithelial ovarian cancers (EOCs)-confirmed in the validation cohort. Receiver operating characteristic curves showed PD-L1+ and PD-L1+ CD14+ cells in the monocyte gate performed better than the well-established tumor marker CA-125 alone. Plasma soluble PD-L1 was elevated in patients with EOC compared with healthy women and patients with benign ovarian tumors. Low total PD-1+ expression on lymphocytes was associated with improved survival.Conclusions: Differential expression of immunological markers relating to the PD-1/PD-L1 pathway in blood can be used as potential diagnostic and prognostic markers in EOC. These data have implications for the development and trial of anti-PD-1/PD-L1 therapy in ovarian cancer. Clin Cancer Res; 23(13); 3453-60. ©2016 AACR.
    Matched MeSH terms: Ovarian Neoplasms/genetics
  8. Nordin N, Majid NA, Othman R, Omer FAA, Nasharuddin MNA, Hashim NM
    Apoptosis, 2018 02;23(2):152-169.
    PMID: 29430581 DOI: 10.1007/s10495-018-1447-x
    Plagioneurin B belongs to acetogenin group has well-established class of compounds. Acetogenin group has attracted worldwide attention in the past few years due their biological abilities as inhibitors for several types of tumour cells. Plagioneurin B was isolated via conventional chromatography and tested for thorough mechanistic apoptosis activity on human ovarian cancer cells (CAOV-3). Its structure was also docked at several possible targets using Autodock tools software. Our findings showed that plagioneurin B successfully inhibits the growth of CAOV-3 cells at IC50 of 0.62 µM. The existence of apoptotic bodies, cell membrane blebbing and chromatin condensation indicated the hallmark of apoptosis. Increase of Annexin V-FITC bound to phosphatidylserine confirmed the apoptosis induction in the cells. The apoptosis event was triggered through the extrinsic and intrinsic pathways via activation of caspases 8 and 9, respectively. Stimulation of caspase 3 and the presence of DNA ladder suggested downstream apoptotic signalling were initiated. Further confirmation of apoptosis was conducted at the molecular levels where up-regulation in Bax, as well as down-regulation of Bcl-2, Hsp-70 and survivin were observed. Plagioneurin B was also seen to arrest CAOV-3 cells cycle at the G2/M phase. Docking simulation of plagioneurin B with CD95 demonstrated that the high binding affinity and hydrogen bonds formation may explain the capability of plagioneurin B to trigger apoptosis. This study is therefore importance in finding the effective compound that may offer an alternative drug for ovarian cancer treatment.
    Matched MeSH terms: Ovarian Neoplasms/genetics
  9. Briggs MT, Condina MR, Ho YY, Everest-Dass AV, Mittal P, Kaur G, et al.
    Proteomics, 2019 11;19(21-22):e1800482.
    PMID: 31364262 DOI: 10.1002/pmic.201800482
    Epithelial ovarian cancer is one of the most fatal gynecological malignancies in adult women. As studies on protein N-glycosylation have extensively reported aberrant patterns in the ovarian cancer tumor microenvironment, obtaining spatial information will uncover tumor-specific N-glycan alterations in ovarian cancer development and progression. matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is employed to investigate N-glycan distribution on formalin-fixed paraffin-embedded ovarian cancer tissue sections from early- and late-stage patients. Tumor-specific N-glycans are identified and structurally characterized by porous graphitized carbon-liquid chromatography-electrospray ionization-tandem mass spectrometry (PGC-LC-ESI-MS/MS), and then assigned to high-resolution images obtained from MALDI-MSI. Spatial distribution of 14 N-glycans is obtained by MALDI-MSI and 42 N-glycans (including structural and compositional isomers) identified and structurally characterized by LC-MS. The spatial distribution of oligomannose, complex neutral, bisecting, and sialylated N-glycan families are localized to the tumor regions of late-stage ovarian cancer patients relative to early-stage patients. Potential N-glycan diagnostic markers that emerge include the oligomannose structure, (Hex)6 + (Man)3 (GlcNAc)2 , and the complex neutral structure, (Hex)2 (HexNAc)2 (Deoxyhexose)1 + (Man)3 (GlcNAc)2 . The distribution of these markers is evaluated using a tissue microarray of early- and late-stage patients.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  10. Sng JH, Ali AB, Lee SC, Zahar D, Wong JE, Blake V, et al.
    J. Med. Genet., 2003 Oct;40(10):e117.
    PMID: 14569140
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  11. Sng JH, Ali AB, Lee SC, Zahar D, Wong JE, Cross G, et al.
    Ann. Acad. Med. Singap., 2003 Sep;32(5 Suppl):S53-5.
    PMID: 14968737
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  12. Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai Z, et al.
    Cancer Discov, 2016 09;6(9):1052-67.
    PMID: 27432226 DOI: 10.1158/2159-8290.CD-15-1227
    Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.

    SIGNIFICANCE: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.

    Matched MeSH terms: Ovarian Neoplasms/genetics*
  13. Velapasamy S, Alex L, Chahil JK, Lye SH, Munretnam K, Hashim NA, et al.
    Genet Test Mol Biomarkers, 2013 Jan;17(1):62-8.
    PMID: 23113749 DOI: 10.1089/gtmb.2012.0223
    The identification of high-risk individuals can help to improve early cancer detection and patient survival. Risk assessment, however, can only be accomplished if the risk factors are known. To date, the genetic risk factors for ovarian cancer, other than mutations in the BRCA1/2 genes, have never been systematically explored in Malaysia. The present study aims to identify from a panel of cancer-associated single-nucleotide polymorphisms (SNPs), those associated with ovarian cancer risk in Malaysia.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  14. Yarmolinsky J, Relton CL, Lophatananon A, Muir K, Menon U, Gentry-Maharaj A, et al.
    PLoS Med., 2019 08;16(8):e1002893.
    PMID: 31390370 DOI: 10.1371/journal.pmed.1002893
    BACKGROUND: Various risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational studies. We used MR to evaluate the association of 12 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) with risk of invasive epithelial ovarian cancer, invasive epithelial ovarian cancer histotypes, and low malignant potential tumours.

    METHODS AND FINDINGS: Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10-8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case-control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR-Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06-1.15; P = 6.94 × 10-7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04-1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15-1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02-1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82-0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.

    CONCLUSIONS: Our comprehensive examination of possible aetiological drivers of ovarian carcinogenesis using germline genetic variants to proxy risk factors supports a role for few of these factors in invasive epithelial ovarian cancer overall and suggests distinct aetiologies across histotypes. The identification of novel risk factors remains an important priority for the prevention of epithelial ovarian cancer.

    Matched MeSH terms: Ovarian Neoplasms/genetics
  15. Yang Y, Wu L, Shu X, Lu Y, Shu XO, Cai Q, et al.
    Cancer Res., 2019 02 01;79(3):505-517.
    PMID: 30559148 DOI: 10.1158/0008-5472.CAN-18-2726
    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.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  16. Lu Y, Beeghly-Fadiel A, Wu L, Guo X, Li B, Schildkraut JM, et al.
    Cancer Res., 2018 09 15;78(18):5419-5430.
    PMID: 30054336 DOI: 10.1158/0008-5472.CAN-18-0951
    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.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  17. Ab Mutalib NS, Syafruddin SE, Md Zain RR, Mohd Dali AZ, Mohd Yunos RI, Saidin S, et al.
    BMC Res Notes, 2014;7:805.
    PMID: 25404506 DOI: 10.1186/1756-0500-7-805
    High grade serous ovarian cancer is one of the poorly characterized malignancies. This study aimed to elucidate the mutational events in Malaysian patients with high grade serous ovarian cancer by performing targeted sequencing on 50 cancer hotspot genes.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  18. Hampras SS, Sucheston-Campbell LE, Cannioto R, Chang-Claude J, Modugno F, Dörk T, et al.
    Oncotarget, 2016 10 25;7(43):69097-69110.
    PMID: 27533245 DOI: 10.18632/oncotarget.10215
    BACKGROUND: Regulatory T (Treg) cells, a subset of CD4+ T lymphocytes, are mediators of immunosuppression in cancer, and, thus, variants in genes encoding Treg cell immune molecules could be associated with ovarian cancer.

    METHODS: In a population of 15,596 epithelial ovarian cancer (EOC) cases and 23,236 controls, we measured genetic associations of 1,351 SNPs in Treg cell pathway genes with odds of ovarian cancer and tested pathway and gene-level associations, overall and by histotype, for the 25 genes, using the admixture likelihood (AML) method. The most significant single SNP associations were tested for correlation with expression levels in 44 ovarian cancer patients.

    RESULTS: The most significant global associations for all genes in the pathway were seen in endometrioid ( p = 0.082) and clear cell ( p = 0.083), with the most significant gene level association seen with TGFBR2 ( p = 0.001) and clear cell EOC. Gene associations with histotypes at p < 0.05 included: IL12 ( p = 0.005 and p = 0.008, serous and high-grade serous, respectively), IL8RA ( p = 0.035, endometrioid and mucinous), LGALS1 ( p = 0.03, mucinous), STAT5B ( p = 0.022, clear cell), TGFBR1 ( p = 0.021 endometrioid) and TGFBR2 ( p = 0.017 and p = 0.025, endometrioid and mucinous, respectively).

    CONCLUSIONS: Common inherited gene variation in Treg cell pathways shows some evidence of germline genetic contribution to odds of EOC that varies by histologic subtype and may be associated with mRNA expression of immune-complex receptor in EOC patients.

    Matched MeSH terms: Ovarian Neoplasms/genetics*
  19. Permuth JB, Pirie A, Ann Chen Y, Lin HY, Reid BM, Chen Z, et al.
    Hum. Mol. Genet., 2016 08 15;25(16):3600-3612.
    PMID: 27378695 DOI: 10.1093/hmg/ddw196
    Rare and low frequency variants are not well covered in most germline genotyping arrays and are understudied in relation to epithelial ovarian cancer (EOC) risk. To address this gap, we used genotyping arrays targeting rarer protein-coding variation in 8,165 EOC cases and 11,619 controls from the international Ovarian Cancer Association Consortium (OCAC). Pooled association analyses were conducted at the variant and gene level for 98,543 variants directly genotyped through two exome genotyping projects. Only common variants that represent or are in strong linkage disequilibrium (LD) with previously-identified signals at established loci reached traditional thresholds for exome-wide significance (P  P≥5.0 ×10 -  7) were detected for rare and low-frequency variants at 16 novel loci. Four rare missense variants were identified (ACTBL2 rs73757391 (5q11.2), BTD rs200337373 (3p25.1), KRT13 rs150321809 (17q21.2) and MC2R rs104894658 (18p11.21)), but only MC2R rs104894668 had a large effect size (OR = 9.66). Genes most strongly associated with EOC risk included ACTBL2 (PAML = 3.23 × 10 -  5; PSKAT-o = 9.23 × 10 -  4) and KRT13 (PAML = 1.67 × 10 -  4; PSKAT-o = 1.07 × 10 -  5), reaffirming variant-level analysis. In summary, this large study identified several rare and low-frequency variants and genes that may contribute to EOC susceptibility, albeit with possible small effects. Future studies that integrate epidemiology, sequencing, and functional assays are needed to further unravel the unexplained heritability and biology of this disease.
    Matched MeSH terms: Ovarian Neoplasms/genetics*
  20. Kar SP, Tyrer JP, Li Q, Lawrenson K, Aben KK, Anton-Culver H, et al.
    Cancer Epidemiol. Biomarkers Prev., 2015 Oct;24(10):1574-84.
    PMID: 26209509 DOI: 10.1158/1055-9965.EPI-14-1270
    BACKGROUND: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations.

    METHODS: We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).

    RESULTS: Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.

    CONCLUSION: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.

    IMPACT: Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization.

    Matched MeSH terms: Ovarian Neoplasms/genetics*
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