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Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 x 10(-39); Region 3: rs2853677, P = 3.30 x 10(-36) and PConditional = 2.36 x 10(-8); Region 4: rs2736098, P = 3.87 x 10(-12) and PConditional = 5.19 x 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 x 10(-6); and Region 6: rs10069690, P = 7.49 x 10(-15) and PConditional = 5.35 x 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 x 10(-18) and PConditional = 7.06 x 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.

Due to its potential as a biomarker for early cancer detection, blood-based DNA methylation (DNAm) is of interest in cancer research. Specifically, highly predictive mechanisms for early detection of epithelial ovarian cancer (EOC) are desired, so previous studies have compared DNAm between EOC cases and controls. However, case-control studies are confounded by the distribution of white blood cell types through an immune response induced by the cancer. Rather than determining the distribution of the cell types manually or investigating isolated cell types, an alternative approach involves the use of complete blood count (CBC), which is routinely collected. In the analysis of an EOC case-control study of DNAm, we incorporate CBC measures to adjust for this confounding and compare DNAm between 242 EOC cases and 181 age-matched controls (assayed on the Illumina Infinium HumanMethylation27 or HumanMethylation450 Beadchips), at both the individual CpG and CpG island levels. We found that adjustment for leukocyte distribution using CBC measurements dramatically reduced confounding, with 62 single CpG sites found to be associated with EOC status after adjustment (P < 5E-8). Additionally, regional DNAm was assessed by applying principal components analysis to CpG islands. The top associated CpG island (P = 7E-6) was located in the promoter/transcription start site of the human basonuclin 2 gene (BNC2), a known susceptibility gene for EOC risk identified through GWAS. Follow-up studies are necessary to establish the role of BNC2 in blood-based DNA and EOC, including prospective studies to validate this region as a potential biomarker and predictor of EOC susceptibility.

The presence of regulatory T cells (Treg) in solid tumors is known to play a role in patient survival in ovarian cancer and other malignancies. We assessed inherited genetic variations via 749 tag single-nucleotide polymorphisms (SNP) in 25 Treg-associated genes (CD28, CTLA4, FOXP3, IDO1, IL10, IL10RA, IL15, 1L17RA, IL23A, IL23R, IL2RA, IL6, IL6R, IL8, LGALS1, LGALS9, MAP3K8, STAT5A, STAT5B, TGFB1, TGFB2, TGFB3, TGFBR1, TGRBR2, and TGFBR3) in relation to ovarian cancer survival. We analyzed genotype and overall survival in 10,084 women with invasive epithelial ovarian cancer, including 5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous carcinoma cases of European descent across 28 studies from the Ovarian Cancer Association Consortium (OCAC). The strongest associations were found for endometrioid carcinoma and IL2RA SNPs rs11256497 [HR, 1.42; 95% confidence interval (CI), 1.22–1.64; P = 5.7 × 10−6], rs791587 (HR, 1.36; 95% CI, 1.17–1.57; P = 6.2 × 10−5), rs2476491 (HR, = 1.40; 95% CI, 1.19–1.64; P = 5.6 × 10−5), and rs10795763 (HR, 1.35; 95% CI, 1.17–1.57; P = 7.9 × 10−5), and for clear cell carcinoma and CTLA4 SNP rs231775 (HR, 0.67; 95% CI, 0.54–0.82; P = 9.3 × 10−5) after adjustment for age, study site, population stratification, stage, grade, and oral contraceptive use. The rs231775 allele associated with improved survival in our study also results in an amino acid change in CTLA4 and previously has been reported to be associated with autoimmune conditions. Thus, we found evidence that SNPs in genes related to Tregs seem to play a role in ovarian cancer survival, particularly in patients with clear cell and endometrioid epithelial ovarian cancer. Cancer Immunol Res; 2(4); 332–40. ©2014 AACR.

Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

Background Three to four gene expression-based subtypes of high-grade serous ovarian cancer (HGSC) have been previously reported. We sought to systematically determine the similarity of HGSC subtypes between populations. Methods We performed k-means clustering (k = 3 and k = 4) in five publicly-available HGSC mRNA expression datasets with >130 tumors. Within each population, we summarized differential expression patterns for each cluster as moderated t statistic vectors using Significance Analysis of Microarrays. We calculated Pearsons correlations of these vectors to determine similarities and differences in expression patterns between clusters. We defined syn-clusters (SC) as sets of clusters that were strongly correlated across populations, and associated their expression patterns with biological pathways using geneset overrepresentation analyses. Results Across populations, for k = 3, moderated t score correlations for clusters 1, 2 and 3, respectively, ranged between 0.77-0.85, 0.80-0.90, and 0.65-0.77. For k = 4, correlations for clusters 1-4, respectively, ranged between 0.77-0.85, 0.83-0.89, 0.51-0.76, and 0.61-0.75. Within populations, comparing analogous clusters (k = 3 versus k = 4), correlations were high for clusters 1 and 2 (0.91-1.00), but were lower for cluster 3 (0.22-0.80). The results are similar using non-negative matrix factorization. SC1 corresponds to previously-reported mesenchymal-like, SC2 to proliferative-like, SC3 to immunoreactive-like, and SC4 to differentiated-like subtypes. Conclusions The ability to robustly identify correlated clusters across number of centroids, populations, and clustering methods provides strong evidence that at least three different HGSC subtypes exist. The mesenchymal-like and proliferative-like subtypes are remarkably consistent and could be uniquely targeted for treatment.

Ewing Sarcoma (ES) is characterized by recurrent translocations between EWSR1 and members of the ETS family of transcription factors. The transcriptional activity of the fusion oncoprotein is dependent on interaction with the nucleosome remodeling and deactylase (NuRD) co-repressor complex. While inhibitors of both histone deacetylase (HDAC) and lysine-specific demethylase-1 (LSD1) subunits of the NuRD complex demonstrate single agent activity in preclinical models, combination strategies have not been investigated. We selected 7 clinically utilized chemotherapy agents, or active metabolites thereof, for experimentation: doxorubicin, cyclophosphamide, vincristine, etoposide and irinotecan as well as the HDAC inhibitor romidepsin and the reversible LSD1 inhibitor SP2509. All agents were tested at clinically achievable concentrations in 4 ES cell lines. All possible 2 drug combinations were then tested for potential synergy. Order of addition of second-line conventional combination therapy agents was tested with the addition of SP2509. In two drug experiments, synergy was observed with several combinations, including when SP2509 was paired with topoisomerase inhibitors or romidepsin. Addition of SP2509 after treatment with second-line combination therapy agents enhanced treatment effect. Our findings suggest promising combination treatment strategies that utilize epigenetic agents in ES.

Background: Ovarian carcinoma is composed of five major histologic types, which associate with outcome and predict therapeutic response. Our aim was to evaluate histologic type assessments across the centers participating in the Ovarian Tumor Tissue Analysis (OTTA) consortium using an immunohistochemical (IHC) prediction model. Methods: Tissue microarrays (TMA) and clinical data were available for 524 pathologically confirmed ovarian carcinomas. Centralized IHC was conducted for ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1, and three histologic type assessments were compared: the original pathologic type, an IHC-based calculated type (termed TB_COSPv2), and a WT1-assisted TMA core review. Results: The concordance between TB_COSPv2 type and original type was 73%. Applying WT1-assisted core review, the remaining 27% discordant cases subdivided into unclassifiable (6%), TB_COSPv2 error (6%), and original type error (15%). The largest discordant subgroup was classified as endometrioid carcinoma by original type and as high-grade serous carcinoma (HGSC) by TB_COSPv2. When TB_COSPv2 classification was used, the difference in overall survival of endometrioid carcinoma compared with HGSC became significant [RR 0.60; 95% confidence interval (CI), 0.37–0.93; P = 0.021], consistent with previous reports. In addition, 71 cases with unclear original type could be histologically classified by TB_COSPv2. Conclusions: Research cohorts, particularly those across different centers within consortia, show significant variability in original histologic type diagnosis. Our IHC-based reclassification produced more homogeneous types with respect to outcome than original type. Impact: Biomarker-based classification of ovarian carcinomas is feasible, improves comparability of results across research studies, and can reclassify cases which lack reliable original pathology. Cancer Epidemiol Biomarkers Prev; 22(10); 1677–86. ©2013 AACR.

Few biomarkers of ovarian cancer prognosis have been established, partly because subtype-specific associations might be obscured in studies combining all histopathological subtypes. We examined whether tumour expression of the progesterone receptor (PR) and oestrogen receptor (ER) was associated with subtype-specific survival.12 studies participating in the Ovarian Tumor Tissue Analysis consortium contributed tissue microarray sections and clinical data to our study. Participants included in our analysis had been diagnosed with invasive serous, mucinous, endometrioid, or clear-cell carcinomas of the ovary. For a patient to be eligible, tissue microarrays, clinical follow-up data, age at diagnosis, and tumour grade and stage had to be available. Clinical data were obtained from medical records, cancer registries, death certificates, pathology reports, and review of histological slides. PR and ER statuses were assessed by central immunohistochemistry analysis done by masked pathologists. PR and ER staining was defined as negative (1% tumour cell nuclei), weak (1 to50%), or strong (≥50%). Associations with disease-specific survival were assessed.2933 women with invasive epithelial ovarian cancer were included: 1742 with high-grade serous carcinoma, 110 with low-grade serous carcinoma, 207 with mucinous carcinoma, 484 with endometrioid carcinoma, and 390 with clear-cell carcinoma. PR expression was associated with improved disease-specific survival in endometrioid carcinoma (log-rank p0·0001) and high-grade serous carcinoma (log-rank p=0·0006), and ER expression was associated with improved disease-specific survival in endometrioid carcinoma (log-rank p0·0001). We recorded no significant associations for mucinous, clear-cell, or low-grade serous carcinoma. Positive hormone-receptor expression (weak or strong staining for PR or ER, or both) was associated with significantly improved disease-specific survival in endometrioid carcinoma compared with negative hormone-receptor expression, independent of study site, age, stage, and grade (hazard ratio 0·33, 95% CI 0·21-0·51; p0·0001). Strong PR expression was independently associated with improved disease-specific survival in high-grade serous carcinoma (0·71, 0·55-0·91; p=0·0080), but weak PR expression was not (1·02, 0·89-1·18; p=0·74).PR and ER are prognostic biomarkers for endometrioid and high-grade serous ovarian cancers. Clinical trials, stratified by subtype and biomarker status, are needed to establish whether hormone-receptor status predicts response to endocrine treatment, and whether it could guide personalised treatment for ovarian cancer.Carraresi Foundation and others.

Although single-locus approaches have been widely applied to identify disease-associated single-nucleotide polymorphisms (SNPs), complex diseases are thought to be the product of multiple interactions between loci. This has led to the recent development of statistical methods for detecting statistical interactions between two loci. Canonical correlation analysis (CCA) has previously been proposed to detect gene–gene coassociation. However, this approach is limited to detecting linear relations and can only be applied when the number of observations exceeds the number of SNPs in a gene. This limitation is particularly important for next-generation sequencing, which could yield a large number of novel variants on a limited number of subjects. To overcome these limitations, we propose an approach to detect gene–gene interactions on the basis of a kernelized version of CCA (KCCA). Our simulation studies showed that KCCA controls the Type-I error, and is more powerful than leading gene-based approaches under a disease model with negligible marginal effects. To demonstrate the utility of our approach, we also applied KCCA to assess interactions between 200 genes in the NF-κB pathway in relation to ovarian cancer risk in 3869 cases and 3276 controls. We identified 13 significant gene pairs relevant to ovarian cancer risk (local false discovery rate

Contains fulltext : 118139.pdf (Publisher’s version ) (Closed access) PURPOSE: The majority of previous studies have observed an increased risk of mucinous ovarian tumors associated with cigarette smoking, but the association with other histological types is unclear. In a large pooled analysis, we examined the risk of epithelial ovarian cancer associated with multiple measures of cigarette smoking with a focus on characterizing risks according to tumor behavior and histology. METHODS: We used data from 21 case-control studies of ovarian cancer (19,066 controls, 11,972 invasive and 2,752 borderline cases). Study-specific odds ratios (OR) and 95 % confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio using a random effects model. RESULTS: Current cigarette smoking increased the risk of invasive mucinous (OR = 1.31; 95 % CI: 1.03-1.65) and borderline mucinous ovarian tumors (OR = 1.83; 95 % CI: 1.39-2.41), while former smoking increased the risk of borderline serous ovarian tumors (OR = 1.30; 95 % CI: 1.12-1.50). For these histological types, consistent dose-response associations were observed. No convincing associations between smoking and risk of invasive serous and endometrioid ovarian cancer were observed, while our results provided some evidence of a decreased risk of invasive clear cell ovarian cancer. CONCLUSIONS: Our results revealed marked differences in the risk profiles of histological types of ovarian cancer with regard to cigarette smoking, although the magnitude of the observed associations was modest. Our findings, which may reflect different etiologies of the histological types, add to the fact that ovarian cancer is a heterogeneous disease.

FKBP51 (51 kDa immunophilin) acts as a modulator of the glucocorticoid receptor and a negative regulator of the Akt pathway. Genetic variation in FKBP5 plays a role in antidepressant response. The aim of this study was to comprehensively assess the role of genetic variation in FKBP5, identified by both Sanger and Next Generation DNA resequencing, as well as genome-wide single nucleotide polymorphisms (SNPs) associated with FKBP5 expression in the response to the selective serotonin reuptake inhibitor (SSRI) treatment of major depressive disorder.We identified 657 SNPs in FKBP5 by Next Generation sequencing of 96 DNA samples from white patients, and 149 SNPs were selected for the genotyping together with 235 SNPs that were trans-associated with variation in FKBP5 expression in lymphoblastoid cells. A total of 529 DNA samples from the Mayo Clinic PGRN-SSRI Pharmacogenomic trial for which genome-wide SNPs had already been obtained were genotyped for these 384 SNPs, and associations with treatment outcomes were determined. The most significant SNPs were genotyped using 96 DNA samples from white non-Hispanic patients of the NIMH-supported Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to attempt replication, followed by functional genomic studies.Genotype-phenotype association analysis indicated that rs352428 was associated with both 8-week treatment response in the Mayo study (odds ratio=0.49; P=0.003) and 6-week response in the STAR*D replication study (odds ratio=0.74; P=0.05). The electrophoresis mobility shift assay and the reporter gene assay confirmed the possible role of this SNP in transcription regulation.This comprehensive FKBP5 sequence study provides insight into the role of common genetic polymorphisms that might influence SSRI treatment outcomes in major depressive disorder patients.

We and others first reported several inherited variants that increase glioma risk in 2009.1,2 Additional confirmation and new risk loci have been identified since then.3–8 We9 and Simon et al.10 showed that the glioma risk loci in 8q24 and 11q23 first identified by Shete et al.1 were associated with risk of lower grade infiltrating gliomas but not with glioblastomas. It is now well established that mutation in the isocitrate dehydrogenase (IDH) 1 and 2 genes leads to genome-wide histone and DNA methylation changes that result in abnormal gene expression and gliomagenesis, defining a distinct subclass of gliomas.11,12 IDH mutations occur in ∼50%–80% of grade II-III gliomas and secondary glioblastomas but in fewer than 10% of primary glioblastomas.13–17 Tumor IDH mutations are associated with younger age of onset and better overall survival among patients with gliomas of all grades and histologies18,19 and are also associated with other somatic, genetic, and epigenetic alterations.17,20 Given these observations, we hypothesized that the 8q24 and 11q23 glioma risk loci might be specific to IDH-mutated, but not to IDH-wild-type gliomas. In a separate recent publication,21 we showed that the 8q24 locus is specifically associated with risk for oligodendroglial tumors and IDH-mutated astrocytomas of all grades. Here, we report that the 11q23 glioma risk variant rs498872 T allele is associated with risk of IDH-mutated gliomas but not with risk of IDH-wild-type gliomas, regardless of grade or histology.

Background: Ovarian cancer is a leading cause of cancer-related death among women. In an effort to understand contributors to disease outcome, we evaluated single-nucleotide polymorphisms (SNP) previously associated with ovarian cancer recurrence or survival, specifically in angiogenesis, inflammation, mitosis, and drug disposition genes. Methods: Twenty-seven SNPs in VHL, HGF, IL18, PRKACB, ABCB1, CYP2C8, ERCC2, and ERCC1 previously associated with ovarian cancer outcome were genotyped in 10,084 invasive cases from 28 studies from the Ovarian Cancer Association Consortium with over 37,000-observed person-years and 4,478 deaths. Cox proportional hazards models were used to examine the association between candidate SNPs and ovarian cancer recurrence or survival with and without adjustment for key covariates. Results: We observed no association between genotype and ovarian cancer recurrence or survival for any of the SNPs examined. Conclusions: These results refute prior associations between these SNPs and ovarian cancer outcome and underscore the importance of maximally powered genetic association studies. Impact: These variants should not be used in prognostic models. Alternate approaches to uncovering inherited prognostic factors, if they exist, are needed. Cancer Epidemiol Biomarkers Prev; 22(5); 987–. ©2013 AACR.

Supplemental Digital Content is available in the text., Background Pancreatic cancer is a rapidly fatal disease with gemcitabine remaining the first-line therapy. We performed a genotype–phenotype association study to identify biomarkers for predicting gemcitabine treatment outcome. Materials and methods We selected the top 200 single nucleotide polymorphisms (SNPs) identified from our previous genome-wide association study to associate with overall survival using 400 patients treated with/or without gemcitabine, followed by imputation analysis for regions around the identified SNPs and a replication study using an additional 537 patients by the TaqMan genotyping assay. Functional validation was performed using quantitative reverse transcription-PCR for gemcitabine-induced expression in genotyped lymphoblastoid cell lines and siRNA knockdown for candidate genes in pancreatic cancer cell lines. Results Four SNPs in chromosome 1, 3, 9, and 20 showed an interaction with gemcitabine from the discovery cohort of 400 patients (P

Contains fulltext : 167177.pdf (Publisher’s version ) (Open Access) 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.

Background Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients. Methods We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines. Results Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability. Conclusions Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.

Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor.

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.

Metformin is currently considered as a promising anticancer agent in addition to its anti-diabetic effect. To better individualize metformin therapy and explore novel molecular mechanisms in cancer treatment, we conducted a pharmacogenomic study using 266 lymphoblastoid cell lines (LCLs). Metformin cytotoxicity assay was performed using the MTS assay. Genome-wide association (GWA) analyses were performed in LCLs using 1.3 million SNPs, 485k DNA methylation probes, 54k mRNA expression probe sets, and metformin cytotoxicity (IC50s). Top candidate genes were functionally validated using siRNA screening, followed by MTS assay in breast cancer cell lines. Further study of one top candidate, STUB1, was performed to elucidate the mechanisms by which STUB1 might contribute to metformin action. GWA analyses in LCLs identified 198 mRNA expression probe sets, 12 SNP loci, and 5 DNA methylation loci associated with metformin IC50 with P-values

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.

Contains fulltext : 171782.pdf (Publisher’s version ) (Closed access) Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian Cancer Association Consortium genotyped in the iCOGS array (211,155 SNPs). We estimate the array heritability (attributable to variants tagged on arrays) of each subtype and their genetic correlations. We also look for genetic overlaps with factors such as obesity, smoking behaviors, diabetes, age at menarche and height. We estimated the array heritabilities of high-grade serous disease ([Formula: see text] = 8.8 +/- 1.1 %), endometrioid ([Formula: see text] = 3.2 +/- 1.6 %), clear cell ([Formula: see text] = 6.7 +/- 3.3 %) and all EOC ([Formula: see text] = 5.6 +/- 0.6 %). Known associated loci contributed approximately 40 % of the total array heritability for each subtype. The contribution of each chromosome to the total heritability was not proportional to chromosome size. Through bivariate and cross-trait LD score regression, we found evidence of shared genetic backgrounds between the three high-grade subtypes: serous, endometrioid and undifferentiated. Finally, we found significant genetic correlations of all EOC with diabetes and obesity using a polygenic prediction approach.

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.

BackgroundIt is unclear whether the transcriptional subtypes of high grade serous ovarian cancer (HGSOC) apply to high grade clear cell (HGCCOC) or high grade endometrioid ovarian cancer (HGEOC). We aim to delineate transcriptional profiles of HGCCOCs and HGEOCs.MethodsWe used Agilent microarrays to determine gene expression profiles of 276 well annotated ovarian cancers (OCs) including 37 HGCCOCs and 66 HGEOCs. We excluded low grade OCs as these are known to be distinct molecular entities. We applied the prespecified TCGA and CLOVAR gene signatures using consensus non-negative matrix factorization (NMF).ResultsWe confirm the presence of four TCGA transcriptional subtypes and their significant prognostic relevance (p

Background: The standard treatment for epithelial ovarian cancer (EOC) patients with advanced disease is carboplatin-paclitaxel combination therapy following initial debulking surgery, yet there is wide inter-patient variation in clinical response. We sought to identify pharmacogenomic markers related to carboplatin-paclitaxel therapy. Methods: The lymphoblastoid cell lines, derived from 74 invasive EOC patients seen at the Mayo Clinic, were treated with increasing concentrations of carboplatin and/or paclitaxel and assessed for in vitro drug response using MTT viability and caspase3/7 apoptosis assays. Drug response phenotypes IC50 (effective dose at which 50% of cells are viable) and EC50 (dose resulting in 50 % induction of caspase 3/7 activity) were estimated for each patient to paclitaxel and carboplatin (alone and in combination). For each of the six drug response phenotypes, a genome-wide association study was conducted. Results: Statistical analysis found paclitaxel in vitro drug response phenotypes to be moderately associated with time to EOC recurrence (p = 0.008 IC50; p = 0.058 EC50). Although no pharmacogenomic associations were significant at p

Background: The strongest known risk factor for endometrial cancer is obesity. To determine whether SNPs associated with increased body mass index (BMI) or waist–hip ratio (WHR) are associated with endometrial cancer risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and endometrial cancer in 6,609 cases and 37,926 country-matched controls. Methods: Logistic regression analysis and fixed effects meta-analysis were used to test for associations between endometrial cancer risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for endometrial cancer was assessed using Mendelian randomization, with BMIwGRS as instrumental variable. Results: The BMIwGRS was significantly associated with endometrial cancer risk (P = 3.4 × 10−17). Scaling the effect of the BMIwGRS on endometrial cancer risk by its effect on BMI, the endometrial cancer OR per 5 kg/m2 of genetically predicted BMI was 2.06 [95% confidence interval (CI), 1.89–2.21], larger than the observed effect of BMI on endometrial cancer risk (OR = 1.55; 95% CI, 1.44–1.68, per 5 kg/m2). The association attenuated but remained significant after adjusting for BMI (OR = 1.22; 95% CI, 1.10–1.39; P = 5.3 × 10−4). There was evidence of directional pleiotropy (P = 1.5 × 10−4). BMI SNP rs2075650 was associated with endometrial cancer at study-wide significance (P < 4.0 × 10−4), independent of BMI. Endometrial cancer was not significantly associated with individual WHR SNPs or the WHRwGRS. Conclusions: BMI, but not WHR, is causally associated with endometrial cancer risk, with evidence that some BMI-associated SNPs alter endometrial cancer risk via mechanisms other than measurable BMI. Impact: The causal association between BMI SNPs and endometrial cancer has possible implications for endometrial cancer risk modeling. Cancer Epidemiol Biomarkers Prev; 25(11); 1503–10. ©2016 AACR.

We conducted a meta-analysis of three endometrial cancer genome-wide association studies (GWAS) and two follow-up phases totaling 7,737 endometrial cancer cases and 37,144 controls of European ancestry. Genome-wide imputation and meta-analysis identified five new risk loci of genome-wide significance at likely regulatory regions on chromosomes 13q22.1 (rs11841589, near KLF5), 6q22.31 (rs13328298, in LOC643623 and near HEY2 and NCOA7), 8q24.21 (rs4733613, telomeric to MYC), 15q15.1 (rs937213, in EIF2AK4, near BMF) and 14q32.33 (rs2498796, in AKT1, near SIVA1). We also found a second independent 8q24.21 signal (rs17232730). Functional studies of the 13q22.1 locus showed that rs9600103 (pairwise r2 = 0.98 with rs11841589) is located in a region of active chromatin that interacts with the KLF5 promoter region. The rs9600103[T] allele that is protective in endometrial cancer suppressed gene expression in vitro, suggesting that regulation of the expression of KLF5, a gene linked to uterine development, is implicated in tumorigenesis. These findings provide enhanced insight into the genetic and biological basis of endometrial cancer.

Ovarian Cancer Association Consortium, Breast Cancer Association Consortium, and Consortium of Modifiers of BRCA1 and BRCA2: et al., [Objective]: Clinical genetic testing is commercially available for rs61764370, an inherited variant residing in a KRAS 3' UTR microRNA binding site, based on suggested associations with increased ovarian and breast cancer risk as well as with survival time. However, prior studies, emphasizing particular subgroups, were relatively small. Therefore, we comprehensively evaluated ovarian and breast cancer risks as well as clinical outcome associated with rs61764370. [Methods]: Centralized genotyping and analysis were performed for 140,012 women enrolled in the Ovarian Cancer Association Consortium (15,357 ovarian cancer patients; 30,816 controls), the Breast Cancer Association Consortium (33,530 breast cancer patients; 37,640 controls), and the Consortium of Modifiers of BRCA1 and BRCA2 (14,765 BRCA1 and 7904 BRCA2 mutation carriers). [Results]: We found no association with risk of ovarian cancer (OR = 0.99, 95% CI 0.94-1.04, p = 0.74) or breast cancer (OR = 0.98, 95% CI 0.94-1.01, p = 0.19) and results were consistent among mutation carriers (BRCA1, ovarian cancer HR = 1.09, 95% CI 0.97-1.23, p = 0.14, breast cancer HR = 1.04, 95% CI 0.97-1.12, p = 0.27; BRCA2, ovarian cancer HR = 0.89, 95% CI 0.71-1.13, p = 0.34, breast cancer HR = 1.06, 95% CI 0.94-1.19, p = 0.35). Null results were also obtained for associations with overall survival following ovarian cancer (HR = 0.94, 95% CI 0.83-1.07, p = 0.38), breast cancer (HR = 0.96, 95% CI 0.87-1.06, p = 0.38), and all other previously-reported associations. [Conclusions]: rs61764370 is not associated with risk of ovarian or breast cancer nor with clinical outcome for patients with these cancers. Therefore, genotyping this variant has no clinical utility related to the prediction or management of these cancers., The COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 — HEALTH-F2-2009-223175). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). Funding for the project was provided by the Wellcome Trust under award 076113. This work was partially supported by Spanish Association against Cancer (AECC08), RTICC 06/0020/1060, FISPI08/1120, Mutua Madrileña Foundation (FMMA) and SAF2010-20493. Was supported by a grant RD12/00369/0006 and 12/00539 from ISCIII (Spain), partially supported by European Regional Development FEDER funds. Contract grant sponsor: Asociación Española Contra el Cáncer, Spanish Health Research Fund; Carlos III Health Institute; Catalan Health Institute and Autonomous Government of Catalonia. Contract grant numbers: ISCIIIRETIC RD06/0020/1051, RD12/0036/008, PI10/01422, PI10/00748, PI13/00285 and 2009SGR290.

Contains fulltext : 171697.pdf (Publisher’s version ) (Closed access) PURPOSE: Socioeconomic status (SES) is a known predictor of survival for several cancers and it has been suggested that SES differences affecting tumour stage at diagnosis may be the most important explanatory factor for this. However, only a limited number of studies have investigated SES differences in tumour stage at diagnosis of ovarian cancer. In a pooled analysis, we investigated whether SES as represented by level of education is predictive for advanced tumour stage at diagnosis of ovarian cancer, overall and by histotype. The effect of cigarette smoking and body mass index (BMI) on the association was also evaluated. METHODS: From 18 case-control studies, we obtained information on 10,601 women diagnosed with epithelial ovarian cancer. Study specific odds ratios (ORs) with corresponding 95% confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio (pOR) using a random effects model. RESULTS: Overall, women who completed high school (pOR 1.15; 95% CI 1.03-1.28). The risk estimates for the different histotypes of ovarian cancer resembled that observed for ovarian cancers combined but did not reach statistical significance. Our results were unchanged when we included BMI and cigarette smoking. CONCLUSION: Lower level of education was associated with an increased risk of advanced tumour stage at diagnosis of ovarian cancer. The observed socioeconomic difference in stage at diagnosis of ovarian cancer calls for further studies on how to reduce this diagnostic delay.

Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world, accounting for 4% of the deaths from cancer in women. We performed a three-phase genome-wide association study of EOC survival in 8,951 individuals with EOC (cases) with available survival time data and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.11, rs8170 and rs2363956, showed evidence of association with survival (overall P = 5 × 10⁻⁴ and P = 6 × 10⁻⁴, respectively), but they did not replicate in phase 3. However, the same two SNPs demonstrated genome-wide significance for risk of serous EOC (P = 3 × 10⁻⁹ and P = 4 × 10⁻¹¹, respectively). Expression analysis of candidate genes at this locus in ovarian tumors supported a role for the BRCA1-interacting gene C19orf62, also known as MERIT40, which contains rs8170, in EOC development.

Contains fulltext : 171904.pdf (Publisher’s version ) (Closed access) BACKGROUND: In vitro and observational epidemiological studies suggest that vitamin D may play a role in cancer prevention. However, the relationship between vitamin D and ovarian cancer is uncertain, with observational studies generating conflicting findings. A potential limitation of observational studies is inadequate control of confounding. To overcome this problem, we used Mendelian randomization (MR) to evaluate the association between single nucleotide polymorphisms (SNPs) associated with circulating 25-hydroxyvitamin D [25(OH)D] concentration and risk of ovarian cancer. METHODS: We employed SNPs with well-established associations with 25(OH)D concentration as instrumental variables for MR: rs7944926 (DHCR7), rs12794714 (CYP2R1) and rs2282679 (GC). We included 31 719 women of European ancestry (10 065 cases, 21 654 controls) from the Ovarian Cancer Association Consortium, who were genotyped using customized Illumina Infinium iSelect (iCOGS) arrays. A two-sample (summary data) MR approach was used and analyses were performed separately for all ovarian cancer (10 065 cases) and for high-grade serous ovarian cancer (4121 cases). RESULTS: The odds ratio for epithelial ovarian cancer risk (10 065 cases) estimated by combining the individual SNP associations using inverse variance weighting was 1.27 (95% confidence interval: 1.06 to 1.51) per 20 nmol/L decrease in 25(OH)D concentration. The estimated odds ratio for high-grade serous epithelial ovarian cancer (4121 cases) was 1.54 (1.19, 2.01). CONCLUSIONS: Genetically lowered 25-hydroxyvitamin D concentrations were associated with higher ovarian cancer susceptibility in Europeans. These findings suggest that increasing plasma vitamin D levels may reduce risk of ovarian cancer.

Variants at 8q24.21 have been shown to be associated with glioma development. By means of tag SNP genotyping and imputation, pooled next-generation sequencing using long-range PCR and subsequent validation SNP genotyping, we identified seven low-frequency SNPs at 8q24.21 that were strongly associated with glioma risk (P=1×10(-25) to 1×10(-14)). The most strongly associated SNP, rs55705857, remained highly significant after individual adjustment for the other top six SNPs and two previously published SNPs. After stratifying by histological and tumor genetic subtype, the most significant associations of rs55705857 were with oligodendroglial tumors and gliomas with mutant IDH1 or IDH2 (odds ratio (OR)=5.1, P=1.1×10(-31) and OR=4.8, P=6.6×10(-22), respectively). Strong associations were observed for astrocytomas with mutated IDH1 or IDH2 (grades 2-4) (OR=5.16-6.66, P=4.7×10(-12) to 2.2×10(-8)) but not for astrocytomas with wild-type IDH1 and IDH2 (smallest P=0.26). The conserved sequence block that includes rs55705857 is consistently modeled as a microRNA.

Gliomas account for approximately 80% of all primary malignant brain tumors and, despite improvements in clinical care over the last 20years, remain among the most lethal tumors, underscoring the need for gaining new insights that could translate into clinical advances. Recent genome-wide association studies (GWAS) have identified seven new susceptibility regions. We conducted a new independent GWAS of glioma using 1,856 cases and 4,955 controls (from 14 cohort studies, 3 case-control studies, and 1 population-based case-only study) and found evidence of strong replication for three of the seven previously reported associations at 20q13.33 (RTEL), 5p15.33 (TERT), and 9p21.3 (CDKN2BAS), and consistent association signals for the remaining four at 7p11.2 (EGFR both loci), 8q24.21 (CCDC26) and 11q23.3 (PHLDB1). The direction and magnitude of the signal were consistent for samples from cohort and case-control studies, but the strength of the association was more pronounced for loci rs6010620 (20q,13.33; RTEL) and rs2736100 (5p15.33, TERT) in cohort studies despite the smaller number of cases in this group, likely due to relatively more higher grade tumors being captured in the cohort studies. We further examined the 85 most promising single nucleotide polymorphism (SNP) markers identified in our study in three replication sets (5,015 cases and 11,601 controls), but no new markers reached genome-wide significance. Our findings suggest that larger studies focusing on novel approaches as well as specific tumor subtypes or subgroups will be required to identify additional common susceptibility loci for glioma risk.

The "methionine cycle" plays a critical role in the regulation of concentrations of (S)-adenosylmethionine (AdoMet), the major biological methyl donor. We set out to study sequence variation in genes encoding the enzyme that synthesizes AdoMet in liver, methionine adenosyltransferase 1A (MAT1A) and the major hepatic AdoMet using enzyme, glycine N-methyltransferase (GNMT), as well as functional implications of that variation. We resequenced MAT1A and GNMT using DNA from 288 subjects of three ethnicities, followed by functional genomic and genotype-phenotype correlation studies performed with 268 hepatic biopsy samples. We identified 44 and 42 polymorphisms in MAT1A and GNMT, respectively. Quantitative Western blot analyses for the human liver samples showed large individual variation in MAT1A and GNMT protein expression. Genotype-phenotype correlation identified two genotyped single-nucleotide polymorphisms (SNPs), reference SNP (rs) 9471976 (corrected p = 3.9 × 10(-10)) and rs11752813 (corrected p = 1.8 × 10(-5)), and 42 imputed SNPs surrounding GNMT that were significantly associated with hepatic GNMT protein levels (corrected p values0.01). Reporter gene studies showed that variant alleles for both genotyped SNPs resulted in decreased transcriptional activity. Correlation analyses among hepatic protein levels for methionine cycle enzymes showed significant correlations between GNMT and MAT1A (p = 1.5 × 10(-3)) and between GNMT and betaine homocysteine methyltransferase (p = 1.6 × 10(-7)). Our discovery of SNPs that are highly associated with hepatic GNMT protein expression as well as the "coordinate regulation" of methionine cycle enzyme protein levels provide novel insight into the regulation of this important human liver biochemical pathway.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types.

Background: Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes. Methods: To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype. Results: Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10−5) and macrolide binding (P = 6.2 × 10−4) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10−4) and macrolide binding (P = 1.0 × 10−3). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS. Conclusion: This research detected novel gene sets associated with EOC survival. Impact: Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies. Cancer Epidemiol Biomarkers Prev; 21(3); 529–36. ©2012 AACR.

Previous studies have examined the association between ABO blood group and ovarian cancer risk, with inconclusive results.In eight studies participating in the Ovarian Cancer Association Consortium, we determined ABO blood groups and diplotypes by genotyping 3 SNPs in the ABO locus. Odds ratios and 95 % confidence intervals were calculated in each study using logistic regression; individual study results were combined using random effects meta-analysis.Compared to blood group O, the A blood group was associated with a modestly increased ovarian cancer risk: (OR: 1.09; 95 % CI: 1.01-1.18; p = 0.03). In diplotype analysis, the AO, but not the AA diplotype, was associated with increased risk (AO: OR: 1.11; 95 % CI: 1.01-1.22; p = 0.03; AA: OR: 1.03; 95 % CI: 0.87-1.21; p = 0.76). Neither AB nor the B blood groups were associated with risk. Results were similar across ovarian cancer histologic subtypes.Consistent with most previous reports, the A blood type was associated modestly with increased ovarian cancer risk in this large analysis of multiple studies of ovarian cancer. Future studies investigating potential biologic mechanisms are warranted.

Disruption in circadian gene expression, whether due to genetic variation or environmental factors (e.g., light at night, shiftwork), is associated with increased incidence of breast, prostate, gastrointestinal and hematologic cancers and gliomas. Circadian genes are highly expressed in the ovaries where they regulate ovulation; circadian disruption is associated with several ovarian cancer risk factors (e.g., endometriosis). However, no studies have examined variation in germline circadian genes as predictors of ovarian cancer risk and invasiveness. The goal of the current study was to examine single nucleotide polymorphisms (SNPs) in circadian genes BMAL1, CRY2, CSNK1E, NPAS2, PER3, REV1 and TIMELESS and downstream transcription factors KLF10 and SENP3 as predictors of risk of epithelial ovarian cancer (EOC) and histopathologic subtypes. The study included a test set of 3,761 EOC cases and 2,722 controls and a validation set of 44,308 samples including 18,174 (10,316 serous) cases and 26,134 controls from 43 studies participating in the Ovarian Cancer Association Consortium (OCAC). Analysis of genotype data from 36 genotyped SNPs and 4600 imputed SNPs indicated that the most significant association was rs117104877 in BMAL1 (OR = 0.79, 95% CI = 0.68-0.90, p = 5.59 × 10(-4)]. Functional analysis revealed a significant down regulation of BMAL1 expression following cMYC overexpression and increasing transformation in ovarian surface epithelial (OSE) cells as well as alternative splicing of BMAL1 exons in ovarian and granulosa cells. These results suggest that variation in circadian genes, and specifically BMAL1, may be associated with risk of ovarian cancer, likely through disruption of hormonal pathways. ispartof: Journal of Genetics and Genome Research vol:2 issue:2 pages:017- ispartof: location:United States status: published

Gene-set analysis (GSA) evaluates the overall evidence of association between a phenotype and all genotyped single nucleotide polymorphisms (SNPs) in a set of genes, as opposed to testing for association between a phenotype and each SNP individually. We propose using the Gamma Method (GM) to combine gene-level P-values for assessing the significance of GS association. We performed simulations to compare the GM with several other self-contained GSA strategies, including both one-step and two-step GSA approaches, in a variety of scenarios. We denote a ‘one-step' GSA approach to be one in which all SNPs in a GS are used to derive a test of GS association without consideration of gene-level effects, and a ‘two-step' approach to be one in which all genotyped SNPs in a gene are first used to evaluate association of the phenotype with all measured variation in the gene and then the gene-level tests of association are aggregated to assess the GS association with the phenotype. The simulations suggest that, overall, two-step methods provide higher power than one-step approaches and that combining gene-level P-values using the GM with a soft truncation threshold between 0.05 and 0.20 is a powerful approach for conducting GSA, relative to the competing approaches assessed. We also applied all of the considered GSA methods to data from a pharmacogenomic study of cisplatin, and obtained evidence suggesting that the glutathione metabolism GS is associated with cisplatin drug response.

Objective Responses to therapies, either with regard to toxicities or efficacy, are expected to involve complex relationships of gene products within the same molecular pathway or functional gene set. Therefore, pathways or gene sets, as opposed to single genes, may better reflect the true underlying biology and may be more appropriate units for analysis of pharmacogenomic studies. Application of such methods to pharmacogenomic studies may enable the detection of more subtle effects of multiple genes in the same pathway that may be missed by assessing each gene individually. Methods A gene set analysis of 3821 gene sets is presented assessing the association between basal messenger RNA expression and drug cytotoxicity using ethnically defined human lymphoblastoid cell lines for two classes of drugs: pyrimidines [gemcitabine (dFdC) and arabinoside] and purines [6-thioguanine and 6-mercaptopurine]. Results The gene set nucleoside-diphosphatase activity was found to be significantly associated with both dFdC and arabinoside, whereas gene set γ-aminobutyric acid catabolic process was associated with dFdC and 6-thioguanine. These gene sets were significantly associated with the phenotype even after adjusting for multiple testing. In addition, five associated gene sets were found in common between the pyrimidines and two gene sets for the purines (3',5'-cyclic-AMP phosphodiesterase activity and γ-aminobutyric acid catabolic process) with a P value of less than 0.0001. Functional validation was attempted with four genes each in gene sets for thiopurine and pyrimidine antimetabolites. All four genes selected from the pyrimidine gene sets (PSME3, CANT1, ENTPD6, ADRM1) were validated, but only one (PDE4D) was validated for the thiopurine gene sets. Conclusion In summary, results from the gene set analysis of pyrimidine and purine therapies, used often in the treatment of various cancers, provide novel insight into the relationship between genomic variation and drug response.

OBJECTIVE To investigate the association between 347 single-nucleotide polymorphisms within candidate genes of the tumor necrosis factor, interleukin 1 and interleukin 6 families with neutrophil count. PATIENTS AND METHODS Four hundred cases with heart failure after myocardial infarction (MI) were matched by age, sex, and date of incident MI to 694 controls (MI without post-MI heart failure). Both genotypes and neutrophil count at admission for incident MI were available in 314 cases and 515 controls. RESULTS We found significant associations between the TNFSF8 polymorphisms rs927374 ( P =5.1 × 10 5 ) and rs2295800 ( P =1.3 × 10 −4 ) and neutrophil count; these single-nucleotide polymorphisms are in high linkage disequilibrium ( r 2 =0.97). Associations persisted after controlling for clinical characteristics and were unchanged after adjusting for case-control status. For rs927374, the neutrophil count of GG homozygotes (7.6±5.1) was 16% lower than that of CC homozygotes (9.0±5.2). CONCLUSION The TNFSF8 polymorphisms rs927374 and rs2295800 were associated with neutrophil count. This finding suggests that post-MI inflammatory response is genetically modulated.

Purpose: Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy. Experimental Design: We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas. Results: In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P < 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (Pper-allele = 2 × 10−2), with a stronger effect in the subset of women with optimally debulked tumors (Pper-allele = 4 × 10−3). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (Pper-allele = 9 × 10−3). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts. Conclusion: This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect. Clin Cancer Res; 17(16); 5490–500. ©2011 AACR.

Background: Mitochondria contribute to oxidative stress, a phenomenon implicated in ovarian carcinogenesis. We hypothesized that inherited variants in mitochondrial-related genes influence epithelial ovarian cancer (EOC) susceptibility. Methods: Through a multicenter study of 1,815 Caucasian EOC cases and 1,900 controls, we investigated associations between EOC risk and 128 single nucleotide polymorphisms (SNPs) from 22 genes/regions within the mitochondrial genome (mtDNA) and 2,839 nuclear-encoded SNPs localized to 138 genes involved in mitochondrial biogenesis (BIO, n = 35), steroid hormone metabolism (HOR, n = 13), and oxidative phosphorylation (OXP, n = 90) pathways. Unconditional logistic regression was used to estimate OR and 95% CI between genotype and case status. Overall significance of each gene and pathway was evaluated by using Fisher's method to combine SNP-level evidence. At the SNP level, we investigated whether lifetime ovulation, hormone replacement therapy (HRT), and cigarette smoking were confounders or modifiers of associations. Results: Interindividual variation involving BIO was most strongly associated with EOC risk (empirical P = 0.050), especially for NRF1, MTERF, PPARGC1A, ESRRA, and CAMK2D. Several SNP-level associations strengthened after adjustment for nongenetic factors, particularly for MTERF. Statistical interactions with cigarette smoking and HRT use were observed with MTERF and CAMK2D SNPs, respectively. Overall variation within mtDNA, HOR, and OXP was not statistically significant (empirical P > 0.10). Conclusion: We provide novel evidence to suggest that variants in mitochondrial biogenesis genes may influence EOC susceptibility. Impact: A deeper understanding of the complex mechanisms implicated in mitochondrial biogenesis and oxidative stress may aid in developing strategies to reduce morbidity and mortality from EOC. Cancer Epidemiol Biomarkers Prev; 20(6); 1131–45. ©2011 AACR.

Betaine-homocysteine methyltransferase (BHMT) catalyzes the remethylation of homocysteine. BHMT is highly expressed in the human liver. In the liver, BHMT catalyzes up to 50% of homocysteine metabolism. Understanding the relationship between BHMT genetic polymorphisms and function might increase our understanding of the role of this reaction in homocysteine remethylation and in S-adenosylmethionine-dependent methylation. To help achieve those goals, we measured levels of BHMT enzyme activity and immunoreactive protein in 268 human hepatic surgical biopsy samples from adult subjects as well as 73 fetal hepatic tissue samples obtained at different gestational ages. BHMT protein levels were correlated significantly (p0.001) with levels of enzyme activity in both fetal and adult tissues, but both were decreased in fetal tissue when compared with levels in the adult hepatic biopsies. To determine possible genotype-phenotype correlations, 12 tag SNPs for BHMT and the closely related BHMT2 gene were selected from SNPs observed during our own gene resequencing studies as well as from HapMap. These SNPs data were used to genotype DNA from the adult hepatic surgical biopsy samples, and genotype-phenotype association analysis was performed. Three SNPs (rs41272270, rs16876512, and rs6875201), located 28kb upstream, in the 5'-UTR and in intron 1 of BHMT, respectively, were significantly correlated with both BHMT activity (p=3.41E-8, 2.55E-9 and 2.46E-10, respectively) and protein levels (p=5.78E-5, 1.08E-5 and 6.92E-6, respectively). We also imputed 230 additional SNPs across the BHMT and BHMT2 genes, identifying an additional imputed SNP, rs7700790, that was also highly associated with hepatic BHMT enzyme activity and protein. However, none of the 3 genotyped or one imputed SNPs displayed a "shift" during electrophoretic mobility shift assays. These observations may help us to understand individual variation in the regulation of BHMT in the human liver and its possible relationship to variation in methylation.

Because selected xenobiotic-metabolizing enzymes process pro-carcinogens that could initiate ovarian carcinogenesis, we hypothesized that single-nucleotide polymorphisms (SNPs) in the genes encoding xenobiotic-metabolizing enzymes are associated with risk of ovarian cancer. Cases with invasive epithelial ovarian cancer (N = 1,571 including 956 of serous sub-type) and controls (N = 2,046) from three studies were genotyped at 11 SNPs in EPHX1, ADH4, ADH1A, NQO2, NAT2, GSTP1, CYP1A1, and NQO1, following an initial SNP screen in a subset of participants. Logistic regression analysis of genotypes obtained via Illumina GoldenGate and Sequenom iPlex technologies revealed the following age- and study-adjusted associations: EPHX1 rs1051740 with increased serous ovarian cancer risk (per-allele odds ratio (OR) 1.17, 95% confidence interval (95% CI) 1.04–1.32, p = 0.01), ADH4 r1042364 with decreased ovarian cancer risk (OR 0.90, 95% CI 0.81–1.00, p = 0.05), and NQO1 rs291766 with increased ovarian cancer risk (OR 1.11, 95% CI 1.00–1.23, p = 0.04). These findings are consistent with prior studies implicating these genes in carcinogenesis and suggest that this collection of variants is worthy of follow-up in additional studies.

Often, analysis for pharmacogenomic studies involving multiple drugs from the same class is completed by analyzing each drug individually for association with genomic variation. However, by completing the analysis of each drug individually, we may be losing valuable information. When studying multiple drugs from the same drug class, one may wish to determine genomic variation that explains the difference in response between individuals for the drug class, as opposed to each individual drug. Therefore, we have developed a multivariate model to assess whether genomic variation impacts a class of drugs. In addition to determine genomic effects that are similar for the drugs, we will also be able to determine genomic effects that differ between the drugs (that is, interaction). We will illustrate the utility of this multivariate model for cytotoxicity and genomic data collected on the Coriell Human Variation Panel for the class of anti-purine metabolites (6-mercaptopurine and 6-thioguanine).

Thiopurine drugs such as 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG) are used to treat acute lymphoblastic leukemia of childhood. To test the hypothesis that variation in the expression of genes within the “thiopurine pathway” might influence 6-MP and 6-TG sensitivity, we generated basal gene expression profiles and IC50 values for both of these thiopurine drugs using a model system consisting of 194 Human Variation Panel lymphoblastoid cell lines. Association analysis showed that thiopurine S-methyltransferase, ecto-5′-nucleotidase (NT5E), and multidrug resistance protein 4 (ABCC4) expression were correlated with thiopurine cytotoxicity. Those observations suggested the possible existence of a “thiopurine cellular circulation” involving nucleotide efflux by ABCC4, hydrolysis of thiopurine nucleotide monophosphates outside of the cell by NT5E, and subsequent transport of thiopurine nucleosides back into the cell by nucleoside transporters. The existence of this cellular circulation was confirmed by a series of functional experiments performed with cultured cells stably or transiently transfected with ABCC4 and/or NT5E. Because of the central role of NT5E in this cellular circulation, the NT5E gene was resequenced using 287 DNA samples from three ethnic groups, with the identification of 68 single nucleotide polymorphisms (SNPs), 46 of which were novel. Several SNPs in the 5′-flanking region of NT5E were highly correlated with expression, rs9450278 having the lowest p value (p = 2.4 × 10−10, R = −0.376). The thiopurine cellular circulation and genetic polymorphisms for genes encoding the proteins involved should be incorporated into future studies of thiopurine drug therapy and effect.

Background: Deleterious TP53 mutations are found in 99% of patients with high-grade serous ovarian cancer (HGSOC). TP53 missense mutations, found in two-thirds of HGSOC tumors, endow the mutant protein with new gain-of-function (GOF) activities leading to altered expression of genes involved in maintaining controlled cellular metabolism and the development of drug resistance. Identification of specific altered pathways could be exploited therapeutically. We investigated whether all missense mutations alter the same metabolic pathways. Methods: We used publicly available data from The Cancer Genome Atlas (TCGA) and the Australia Ovarian Cancer Study (AOCS). TCGA and AOCS gene expression datasets were downloaded from the Curated Ovarian Data, a resource of uniformly prepared microarray data from 23 studies with curated and documented clinical metadata. We merged gene expression data from TCGA (Affymetrix HT_HG-U133A) and AOCS (Affymetrix HG-U133Plus2), subset to 12,211 features common to both datasets and included non-missing values of invasive HGSOC. TP53 mutations were downloaded from TCGA and obtained for AOCS and merged with the curated datasets. The final datasets consisted of 295 patients in TCGA (N=184 with missense mutations with putative GOF activity, and N=111 nonsense mutations with putative loss of function (LOF) activity and 21 wild-type) and 142 patients in AOCS (N=83 missense mutations with putative GOF activity, N=59 nonsense mutations with putative LOF activity and N=13 wild-type). Gene expression values were normalized in each dataset separately by subtracting the mean value of each gene and dividing by the standard deviation. Mutations were categorized according to missense vs nonsense mutation class and also according to specific mutations. We evaluated all gene sets in KEGG but focused a priori on the association of Oxidative Phosphorylation (OXPHOS), Fatty Acid Metabolism (FA), Glycolysis and Gluconeogenesis (GLY), and the P53 pathway with overall (OS) and progression-free survival (PFS) using Cox regression models stratified by mutation class and adjusted for age and stage. Results: There were no significant differences between TP53 missense vs nonsense mutation class for gene set expressions for a priori pathways of interest in TCGA, and a nominal difference for the P53 gene set expression (P=0.07) in AOCS. Comparing TCGA, AOCS, and the combined datasets, differential gene set expressions by TP53 mutation class were observed in all three datasets at P Conclusions: Specific TP53 missense mutations are associated with different metabolic pathways and may lead to differences in survival. Citation Format: Linda E. Kelemen, James D. Brenton, David D. Bowtell, Brooke L. Fridley. TP53 missense mutations associate with different metabolic pathways. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr A14.

Genetic risk factors are important contributors to the development of colorectal cancer. Following the definition of a linkage signal at 9q22-31, we fine mapped this region in an independent collection of colon cancer families. We used a custom array of single-nucleotide polymorphisms (SNP) densely spaced across the candidate region, performing both single-SNP and moving-window association analyses to identify a colon neoplasia risk haplotype. Through this approach, we isolated the association effect to a five-SNP haplotype centered at 98.15 Mb on chromosome 9q. This haplotype is in strong linkage disequilibrium with the haplotype block containing HABP4 and may be a surrogate for the effect of this CD30 Ki-1 antigen. It is also in close proximity to GALNT12, also recently shown to be altered in colon tumors. We used a predictive modeling algorithm to show the contribution of this risk haplotype and surrounding candidate genes in distinguishing between colon cancer cases and healthy controls. The ability to replicate this finding, the strength of the haplotype association (odds ratio, 3.68), and the accuracy of our prediction model (∼60%) all strongly support the presence of a locus for familial colon cancer on chromosome 9q. Cancer Res; 70(13); 5409–18. ©2010 AACR.