Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues

Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10^-8 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.

Moving from one to many: insights from the growing list of pleiotropic cancer risk genes

Pleiotropy, a phenomenon in which a single gene affects multiple phenotypes, is becoming very common among different cancer types and cancer-related phenotypes, such as those in hormonal, cardiometabolic and inflammatory/immune conditions. The discovery of pleiotropic associations can improve our understanding of cancer and help to target investigation of genes with greater clinical relevance.

Inverse Association Between Height-Increasing Alleles and Extreme Longevity in Japanese Women

Growth hormone (GH)/insulin-like growth factor-1 (IGF-1)/insulin signaling is one of the most plausible biological pathways regulating aging and longevity. Previous studies have demonstrated that several single nucleotide polymorphisms (SNPs) in the GH/IGF-1/insulin signaling-associated genes influence both longevity and adult height, suggesting the possibility of a shared genetic architecture between longevity and height. We therefore examined the relationship between 30 height-associated SNPs and extreme longevity in a Japanese population consisting of 428 centenarians and 4,026 younger controls. We confirmed that height-increasing genetic scores (HGSs) constructed based on 30 SNPs were significantly associated with height in the controls (p = 6.95 × 10-23). HGS was significantly and inversely associated with extreme longevity in women (p = .011), but not in men, although no SNPs were significantly associated with extreme longevity after Bonferroni correction. The odds ratio for extreme longevity in the lowest HGS group (≤27) and the second lowest HGS group (28-30) relative to the highest HGS group (≥37) was 1.71 (p = .056) and 1.69 (p = .034), respectively, for women. In conclusion, the present study demonstrated an inverse association between height-increasing alleles with extreme longevity in Japanese women, providing novel insight into the genetic architecture of longevity and aging.

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

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10-8), breast and ovarian cancer (rg = 0.24, p = 7 × 10-5), breast and lung cancer (rg = 0.18, p =1.5 × 10-6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Bridging the gap by discerning SNPs in linkage disequilibrium and their role in breast cancer

Breast Cancer is the most common cancer among women with several genes involved in disease susceptibility. As majority of genome-wide significant variants fall outside the coding region, it is likely that some of them alter specific gene functions. GWAS database was used to interpret the regulatory functions of these genetic variants. A total of 320 SNPs for breast cancer were selected via GWAS, which were entered into the SNAP web portal tool, to determine the one's found to be in Linkage Disequilibrium (r² < 0.80). The resulting 2024 proxy SNP's were processed in RegulomeDB to predict their regulatory role. Of these, 1440 produced a score ranging from 1-6, whereas the remaining produced no data. Only the variants under score 4 (cut-off value) in RegulomeDB has been studied further. From these variants, 221 had scores of less than 4, indicating a high degree of potential regulatory role associated with them. Further study revealed that 61 of the 221 SNPs were reported to be genome-wide significant for breast cancer, 52 to be associated with other diseases, 99 as unconfirmed for association with breast cancer, leaving only 9 to be novel proxy SNPs linked to breast cancer. Therefore, the study further confirmed postulation of non-coding variants being linked to disease risk thereby, requiring additional validation through genome-wide association studies to substantiate their underlying mechanism.

Identifying disease-associated copy number variations by a doubly penalized regression model

Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Most of the current researchers reply on an ad hoc two-stage procedure by first identifying CNVs in each individual genome and then performing an association test using these identified CNVs. This potentially leads to information loss and as a result a lower power to identify disease associated CNVs. In this article, we describe a new method that combines the two steps into a single coherent model to identify the common CNV across patients that are associated with certain diseases. We use a double penalty model to capture CNVs' association with both the intensities and the disease trait. We validate its performance in simulated datasets and a data example on platinum resistance and CNV in ovarian cancer genome.

Proteolysis of methylated SOX2 protein is regulated by L3MBTL3 and CRL4DCAF5 ubiquitin ligase

SOX2 is a dose-dependent master stem cell protein that controls the self-renewal and pluripotency or multipotency of embryonic stem (ES) cells and many adult stem cells. We have previously found that SOX2 protein is monomethylated at lysine residues 42 and 117 by SET7 methyltransferase to promote SOX2 proteolysis, whereas LSD1 and PHF20L1 act on both methylated Lys-42 and Lys-117 to prevent SOX2 proteolysis. However, the mechanism by which the methylated SOX2 protein is degraded remains unclear. Here, we report that L3MBTL3, a protein with the malignant-brain-tumor (MBT) methylation-binding domain, is required for SOX2 proteolysis. Our studies showed that L3MBTL3 preferentially binds to the methylated Lys-42 in SOX2, although mutation of Lys-117 also partially reduces the interaction between SOX2 and L3MBTL3. The direct binding of L3MBTL3 to the methylated SOX2 protein leads to the recruitment of the CRL4^DCAF5 ubiquitin E3 ligase to target SOX2 protein for ubiquitin-dependent proteolysis. Whereas loss of either LSD1 or PHF20L1 destabilizes SOX2 protein and impairs the self-renewal and pluripotency of mouse ES cells, knockdown of L3MBTL3 or DCAF5 is sufficient to restore the protein levels of SOX2 and rescue the defects of mouse ES cells caused by LSD1 or PHF20L1 deficiency. We also found that retinoic acid-induced differentiation of mouse ES cells is accompanied by the enhanced degradation of the methylated SOX2 protein at both Lys-42 and Lys-117. Our studies provide novel insights into the mechanism by which the methylation-dependent degradation of SOX2 protein is controlled by the L3MBTL3-CRL4^DCAF5 ubiquitin ligase complex.

Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers

Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology.

Comprehensive Analysis of Germline Variants in Mexican Patients with Hereditary Breast and Ovarian Cancer Susceptibility

Hereditary breast and ovarian cancer syndrome (HBOC) represents 5⁻10% of all patients with breast cancer and is associated with high-risk pathogenic alleles in BRCA1/2 genes, but only for 25% of cases. We aimed to find new pathogenic alleles in a panel of 143 cancer-predisposing genes in 300 Mexican cancer patients with suspicion of HBOC and 27 high-risk patients with a severe family history of cancer, using massive parallel sequencing. We found pathogenic variants in 23 genes, including BRCA1/2. In the group of cancer patients 15% (46/300) had a pathogenic variant; 11% (33/300) harbored variants with unknown clinical significance (VUS) and 74% (221/300) were negative. The high-risk group had 22% (6/27) of patients with pathogenic variants, 4% (1/27) had VUS and 74% (20/27) were negative. The most recurrent mutations were the Mexican founder deletion of exons 9-12 and the variant p.G228fs in BRCA1, each found in 5 of 17 patients with alterations in this gene. Rare VUS with potential impact at the protein level were found in 21 genes. Our results show for the first time in the Mexican population a higher contribution of pathogenic alleles in other susceptibility cancer genes (54%) than in BRCA1/2 (46%), highlighting the high locus heterogeneity of HBOC and the necessity of expanding genetic tests for this disease to include broader gene panels.

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

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

Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls

We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER-) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis.

Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study

BACKGROUND

Genome-wide association studies have identified >30 common SNPs associated with epithelial ovarian cancer (EOC). We evaluated the combined effects of EOC susceptibility SNPs on predicting EOC risk in an independent prospective cohort study.

METHODS

We genotyped ovarian cancer susceptibility single nucleotide polymorphisms (SNPs) in a nested case-control study (750 cases and 1428 controls) from the UK Collaborative Trial of Ovarian Cancer Screening trial. Polygenic risk scores (PRSs) were constructed and their associations with EOC risk were evaluated using logistic regression. The absolute risk of developing ovarian cancer by PRS percentiles was calculated.

RESULTS

The association between serous PRS and serous EOC (OR 1.43, 95% CI 1.29 to 1.58, p=1.3×10-11) was stronger than the association between overall PRS and overall EOC risk (OR 1.32, 95% CI 1.21 to 1.45, p=5.4×10-10). Women in the top fifth percentile of the PRS had a 3.4-fold increased EOC risk compared with women in the bottom 5% of the PRS, with the absolute EOC risk by age 80 being 2.9% and 0.9%, respectively, for the two groups of women in the population.

CONCLUSION

PRSs can be used to predict future risk of developing ovarian cancer for women in the general population. Incorporation of PRSs into risk prediction models for EOC could inform clinical decision-making and health management.

RNA-modifying proteins as anticancer drug targets

All major biological macromolecules (DNA, RNA, proteins and lipids) undergo enzyme-catalysed covalent modifications that impact their structure, function and stability. A variety of covalent modifications of RNA have been identified and demonstrated to affect RNA stability and translation to proteins; these mechanisms of translational control have been termed epitranscriptomics. Emerging data suggest that some epitranscriptomic mechanisms are altered in human cancers as well as other human diseases. In this Review, we examine the current understanding of RNA modifications with a focus on mRNA methylation, highlight their possible roles in specific cancer indications and discuss the emerging potential of RNA-modifying proteins as therapeutic targets.

Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects

Motivation

Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. We previously developed a modified random effects model (RE2) that can achieve higher power to detect heterogeneous effects than the commonly used fixed effects model (FE). However, RE2 cannot perform meta-analysis of correlated statistics, which are found in recent research designs, and the identified variants often overlap with those found by FE.

Results

Here, we propose RE2C, which increases the power of RE2 in two ways. First, we generalized the likelihood model to account for correlations of statistics to achieve optimal power, using an optimization technique based on spectral decomposition for efficient parameter estimation. Second, we designed a novel statistic to focus on the heterogeneous effects that FE cannot detect, thereby, increasing the power to identify new associations. We developed an efficient and accurate p-value approximation procedure using analytical decomposition of the statistic. In simulations, RE2C achieved a dramatic increase in power compared with the decoupling approach (71% vs. 21%) when the statistics were correlated. Even when the statistics are uncorrelated, RE2C achieves a modest increase in power. Applications to real genetic data supported the utility of RE2C. RE2C is highly efficient and can meta-analyze one hundred GWASs in one day.

Availability and implementation

The software is freely available at http://software.buhmhan.com/RE2C.

Supplementary information

Supplementary data are available at Bioinformatics online.

Methylated DNMT1 and E2F1 are targeted for proteolysis by L3MBTL3 and CRL4DCAF5 ubiquitin ligase

Many non-histone proteins are lysine methylated and a novel function of this modification is to trigger the proteolysis of methylated proteins. Here, we report that the methylated lysine 142 of DNMT1, a major DNA methyltransferase that preserves epigenetic inheritance of DNA methylation patterns during DNA replication, is demethylated by LSD1. A novel methyl-binding protein, L3MBTL3, binds the K142-methylated DNMT1 and recruits a novel CRL4^DCAF5 ubiquitin ligase to degrade DNMT1. Both LSD1 and PHF20L1 act primarily in S phase to prevent DNMT1 degradation by L3MBTL3-CRL4^DCAF5. Mouse L3MBTL3/MBT-1 deletion causes accumulation of DNMT1 protein, increased genomic DNA methylation, and late embryonic lethality. DNMT1 contains a consensus methylation motif shared by many non-histone proteins including E2F1, a key transcription factor for S phase. We show that the methylation-dependent E2F1 degradation is also controlled by L3MBTL3-CRL4^DCAF5. Our studies elucidate for the first time a novel mechanism by which the stability of many methylated non-histone proteins are regulated.

Qianlie Xiaozheng Decoction Induces Autophagy in Human Prostate Cancer Cells via Inhibition of the Akt/mTOR Pathway

Qianlie Xiaozheng decoction (QLXZD), a traditional Chinese medicinal formula, has been used clinically to treat advanced prostate cancer (PCa) for more than 10 years. However, experimental evidence supporting its efficacy is lacking. Here, we investigated the anticancer properties and molecular mechanism of QLXZD in vitro in a human PCa cell line (PC3) and in vivo using PC3 xenografts in nude mice. We confirmed the antineoplastic activity of QLXZD by analyzing cell viability and tumor volume growth, which decreased significantly compared to that of the controls. Autophagy following QLXZD treatment was detected morphologically using transmission electron microscopy and was confirmed by measuring the expression of autophagy markers (LC3-II and p62) using fluorescence analysis, flow cytometry, and western blotting. Increasing autophagic flux induced by QLXZD was monitored via pmCherry-GFP-LC3 fluorescence analysis. QLXZD-induced autophagic cell death was alleviated by the autophagy inhibitors, 3-methyl adenine and hydroxychloroquine. We evaluated the total expression and phosphorylation levels of proteins involved in the Akt/mTOR pathway regulating autophagy. Phosphorylation of Akt, mTOR, and p70S6K, but not total protein levels, decreased following treatment. This is the first study to demonstrate the autophagy-related mechanistic pathways utilized during QLXZD-mediated antitumor activity both in vitro and in vivo. These findings support the clinical use of QLXZD for PCa treatment.

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

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

The integrated landscape of causal genes and pathways in schizophrenia

Genome-wide association studies (GWAS) have identified more than 100 loci that show robust association with schizophrenia risk. However, due to the complexity of linkage disequilibrium and gene regulatory, it is challenging to pinpoint the causal genes at the risk loci and translate the genetic findings from GWAS into disease mechanism and clinical treatment. Here we systematically predicted the plausible candidate causal genes for schizophrenia at genome-wide level. We utilized different approaches and strategies to predict causal genes for schizophrenia, including Sherlock, SMR, DAPPLE, Prix Fixe, NetWAS, and DEPICT. By integrating the results from different prediction approaches, we identified six top candidates that represent promising causal genes for schizophrenia, including CNTN4, GATAD2A, GPM6A, MMP16, PSMA4, and TCF4. Besides, we also identified 35 additional high-confidence causal genes for schizophrenia. The identified causal genes showed distinct spatio-temporal expression patterns in developing and adult human brain. Cell-type-specific expression analysis indicated that the expression level of the predicted causal genes was significantly higher in neurons compared with oligodendrocytes and microglia (P < 0.05). We found that synaptic transmission-related genes were significantly enriched among the identified causal genes (P < 0.05), providing further support for the dysregulation of synaptic transmission in schizophrenia. Finally, we showed that the top six causal genes are dysregulated in schizophrenia cases compared with controls and knockdown of these genes impaired the proliferation of neuronal cells. Our study depicts the landscape of plausible schizophrenia causal genes for the first time. Further genetic and functional validation of these genes will provide mechanistic insights into schizophrenia pathogenesis and may facilitate to provide potential targets for future therapeutics and diagnostics.

IntLIM: integration using linear models of metabolomics and gene expression data

BACKGROUND

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites.

RESULTS

The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally.

CONCLUSIONS

IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies

Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research.

Subunits of human condensins are potential therapeutic targets for cancers

The main role of condensins is to regulate chromosome condensation and segregation during cell cycles. Recently, it has been suggested in the literatures that subunits of condensin I and condensin II are involved in some human cancers. This paper will first briefly discuss discoveries of human condensins, their components and structures, and their multiple cellular functions. This will be followed by reviews of most recent studies on subunits of human condensins and their dysregulations or mutations in human cancers. It can be concluded that many of these subunits have potentials to be novel targets for cancer therapies. However, hCAP-D2, a subunit of human condensin I, has not been directly documented to be associated with any human cancers to date. This review hypothesizes that hCAP-D2 can also be a potential therapeutic target for human cancers, and therefore that all subunits of human condensins are potential therapeutic targets for human cancers.

Primary squamous cell carcinoma arising from endometriosis of the ovary: A case report and literature review

Cases of squamous cell carcinoma (SCC) arising from ovarian endometriosis have been rarely reported. Most of the patients show a poor prognosis and present with a diminished survival time. We present a SCC case arising from endometriosis, and analyzed the clinical, therapeutic, and pathologic features through a comprehensive literature review. A 43-year premenopausal woman (gravida 2, para 1) presented to our hospital due to sudden pain in lower abdomen. Exploratory laparotomy indicated rupture of left ovarian cyst and intra-abdominal hemorrhage were observed. Frozen section pathologic examination indicated malignant ovarian (left-sided) epithelial tumor (poorly differentiated squamous carcinoma). She received hysterectomy, adnexectomy, and omentectomy. Initially, the patient received 3 cycles of chemotherapy using paclitaxel and cisplatin via peritoneal injection. Subsequently, the regimen was altered to 2 cycles of paclitaxel or cisplatin chemotherapy through intravenous injection due to poor tolerance. Upon diagnosis of vaginal metastasis, 2 cycles of chemotherapy was performed using cisplatin, doxorubicin, and cyclophosphamide. Pathologic analysis revealed massive poorly differentiated squamous carcinoma in the fibrous tissues. Besides, cancer embolus was noticed in the lymphatic vessels. Besides our case, 20 cases of infiltrating SCC of the ovary associated with or arising from endometriosis were found. The tumor was associated with 80% patient mortality in the first few months. Adjuvant chemotherapy with paclitaxel and carboplatin or cisplatin appeared to contribute to the survival duration. The best outcomes were obtained in patients received paclitaxel and carboplatin or cisplatin after radical surgery. In future, further studies are needed to validate the efficiency of such regimen.

Exome sequencing and case-control analyses identify RCC1 as a candidate breast cancer susceptibility gene

Breast cancer is a genetic disease but the known genes explain a minority of cases. To elucidate the molecular basis of breast cancer in the Tunisian population, we performed exome sequencing on six BRCA1/BRCA2 mutation-negative patients with familial breast cancer and identified a novel frameshift mutation in RCC1, encoding the Regulator of Chromosome Condensation 1. Subsequent genotyping detected the 19-bp deletion in additional 5 out of 153 (3%) breast cancer patients but in none of 400 female controls (p = 0.0015). The deletion was enriched in patients with a positive family history (5%, p = 0.0009) and co-segregated with breast cancer in the initial pedigree. The mutant allele was lost in 4/6 breast tumors from mutation carriers which may be consistent with the hypothesis that RCC1 dysfunction provides a selective disadvantage at the stage of tumor progression. In summary, we propose RCC1 as a likely breast cancer susceptibility gene in the Tunisian population.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects

Motivation

In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level.

Results

We identify and report a phenomenon that these methods for overlapping subjects can yield low power. For instance, in our simulation involving a meta-analysis of five studies that share 20% of individuals, whereas the traditional splitting method achieved 80% power, none of the new methods exceeded 32% power. We found that this low power resulted from the unaccounted differences between shared and unshared individuals in terms of their contributions towards the final statistic. Here, we propose an optimal summary-statistic-based method termed as FOLD that increases the power of meta-analysis involving studies with overlapping subjects.

Availability and implementation

Our method is available at http://software.buhmhan.com/FOLD.

Supplementary information

Supplementary data are available at Bioinformatics online.

miR-200c regulates the proliferation, apoptosis and invasion of gastric carcinoma cells through the downregulation of EDNRA expression

This study aimed to investigate the regulatory effects of microRNA (miR)‑200c on the proliferation, apoptosis and invasion of gastric carcinoma cells and to elucidate the underlying mechanisms involving the possible role of endothelin receptor A (EDNRA). The expression levels of miR‑200c and EDNRA in the gastric carcinoma cell lines, BGC‑823, SGC‑7901 and HGC‑27, and in GES‑1 normal gastric cells were evaluated by RT‑PCR and western blot analysis. The gastric carcinoma cells, particularly the BGC‑823 cells, expressed significantly lower levels of miR‑200c than the normal gastric cells (P<0.01). Thus, the BGC‑823 cells were employed as model cells. In comparison to the normal gastric cells, EDNRA was overexpressed in the gastric carcinoma cells (P<0.01). Following the transfection of the gastric carcinoma cells with miR‑200c mimics, or negative control vector (miR‑200c NC), or with siRNA targeting EDNRA (siRNA EDNRA) or negatvie control siRNA (siRNA NC), the expression levels were assessed again by RT‑PCR and western blot analysis. The successful transfection of miR‑200c mimics was observed and this markedly elevated the expression of miR‑200c (P<0.01). The transfection of miR‑200c mimics or siRNA EDNRA notably decreased the EDNRA mRNA and protein expression levels (both P<0.01). In additoin, dual‑luciferase reporter gene analysis was performed to reveal the targeting relationship between miR‑200c and EDNRA. EDNRA was found to be the downstream target gene of miR‑200c. Moreover, methyl thiazolyl tetrazolium (MTT) assay, Hoechst staining and Transwell assay were conducted to demonstrate the effects of miR‑200c mimics or siRNA EDNRA on the proliferation, apoptosis and invasion of the gastric carcinoma cells, respectively. We found that transfection with miR‑200c mimics and siRNA EDNRA were able to markedly suppress the proliferation and invasive capacity, and to promote the apoptosis of the gastric carcinoma cells (all P<0.01). On the whole, our data indicate that miR‑200c regulates the proliferation, apoptosis and invasion of gastric carcinoma cells through the downregulation of EDNRA expression.

Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction

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

Cancer risk susceptibility loci in a Swedish population

A germline mutation in cancer predisposing genes is known to increase the risk of more than one tumor type. In order to find loci associated with many types of cancer, a genome-wide association study (GWAS) was conducted, and 3,555 Swedish cancer cases and 15,581 controls were analyzed for 226,883 SNPs. The study used haplotype analysis instead of single SNP analysis in order to find putative founder effects. Haplotype association studies identified seven risk loci associated with cancer risk, on chromosomes 1, 7, 11, 14, 16, 17 and 21. Four of the haplotypes, on chromosomes 7, 14, 16 and 17, were confirmed in Swedish familial cancer cases. It was possible to perform exome sequencing in one patient for each of those four loci. No clear disease-causing exonic mutation was found in any of the four loci. Some of the candidate loci hold several cancer genes, suggesting that the risk associated with one locus could involve more than one gene associated with cancer risk. In summary, this study identified seven novel candidate loci associated with cancer risk. It was also suggested that cancer risk at one locus could depend on multiple contributing risk mutations/genes.

Exploring the Link between the Germline and Somatic Genome in Cancer

<b/> Carter and colleagues propose a systematic analysis of the germline and somatic genome in cancer. They identify interactions that occur between germline and somatic variants. This elucidates the function of the germline genome in the context of cancer risk and development. Cancer Discov; 7(4); 354-5. ©2017 AACRSee related article by Carter et al., p. 410.

Genome-wide association study evaluating single-nucleotide polymorphisms and outcomes in patients with advanced stage serous ovarian or primary peritoneal cancer: An NRG Oncology/Gynecologic Oncology Group study

OBJECTIVE

This study evaluated single nucleotide polymorphisms (SNPs) associated with progression free (PFS) and overall survival (OS) in patients with advanced stage serous EOC.

METHODS

Patients enrolled in GOG-172 and 182 who provided specimens for translational research and consent were included. Germline DNA was evaluated with the Illumina's HumanOMNI1-Quad beadchips and scanned using Illumina's iScan optical imaging system. SNPs with allele frequency>0.05 and genotyping rate>0.98 were included. Analysis of SNPs for PFS and OS was done using Cox regression. Statistical significance was determined using Bonferroni corrected p-values with genomic control adjustment.

RESULTS

The initial GWAS analysis included 1,124,677 markers in 396 patients. To obtain the final data set, quality control checks were performed and limited to serous tumors and self-identified Caucasian race. In total 636,555 SNPs and 289 patients passed all the filters. The pre-specified statistical level of significance was 7.855e-08. No SNPs met this criteria for PFS or OS, however, two SNPs were close to significance (rs10899426 p-2.144e-08) (rs6256 p-9.774e-07) for PFS and 2 different SNPs were identified (rs295315 p-7.536e-07; rs17693104 p-7.734e-07) which were close to significance for OS.

CONCLUSIONS

Using the pre-specified level of significance of 1×10-08, we did not identify any SNPs of statistical significance for OS or PFS, however several were close. The SNP's identified in this GWAS study will require validation and these preliminary findings may lead to identification of novel pathways and biomarkers.

Tumor cells and their crosstalk with endothelial cells in 3D spheroids

Recapitulating the tumor microenvironment is a central challenge in the development of experimental model for cancer. To provide a reliable tool for drug development and for personalized cancer therapy, it is critical to maintain key features that  exist in the original tumor. Along with this effort, 3-dimentional (3D) cellular models are being extensively studied. Spheroids are self-assembled cell aggregates that possess many important components of the physiological spatial growth and cell-cell interactions. In this study we aimed to investigate the interconnection between tumor and endothelial cells (EC) in hybrid spheroids containing either tumor cell (TC) lines or patient derived cancer cells. Preparation protocols of hybrid spheroids were optimized and their morphology and tissue-like features were analyzed. Our finding show that capillary-like structures are formed upon assembly and growth of TC:EC spheroids and that spheroids’ shape and surface texture may be an indication of spatial invasiveness of cells in the extra-cellular matrix (ECM). Establishing a model of hybrid tumor/stroma spheroids has a crucial importance in the experimental approach for personalized medicine, and may offer a reliable and low-cost method for the goal of predicting drug effects.

Mutational heterogeneity in non-serous ovarian cancers

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

Mutational heterogeneity in non-serous ovarian cancers

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

Mutational heterogeneity in non-serous ovarian cancers

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

Mechanisms of Breast Cancer in Shift Workers: DNA Methylation in Five Core Circadian Genes in Nurses Working Night Shifts

Shift work has been suggested to be associated with breast cancer risk, and circadian disruption in shift workers is hypothesized as one of the mechanisms of increased cancer risk. There is, however, insufficient molecular evidence supporting this hypothesis. Using the quantitative methodology of pyrosequencing, epigenetic changes in 5-methyl cytosine (5mC) in five circadian genes CLOCK, BMAL1, CRY1, PER1 and PER2 in female nurses working night shift work (278 breast cancer cases, 280 controls) were analyzed. In breast cancer cases, a medium exposure to night work was associated with increased methylation levels of the CLOCK (p=0.050), BMAL1 (p=0.001) and CRY1 (p=0.040) genes, compared with controls. Within the cases, analysis of the effects of shift work on the methylation patterns showed that methylation of CRY1 was lower in those who had worked night shift and had a high exposure (p=0.006) compared with cases that had worked only days. For cases with a medium exposure to night work, an increase in BMAL1 (p=0.003) and PER1 (p=0.035) methylation was observed compared with day working (unexposed) cases. The methylation levels of the five core circadian genes were also analyzed in relation to the estrogen and progesterone receptors status of the tumors in the cases, and no correlations were observed. Furthermore, nineteen polymorphisms in the five circadian genes were assessed for their effects on the methylation levels of the respective genes, but no associations were found. In summary, our data suggest that epigenetic regulation of CLOCK, BMAL1, CRY1 and PER1 may contribute to breast cancer in shift workers.

Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility

The Y chromosome is frequently lost in hematopoietic cells, which represents the most common somatic alteration in men. However, the mechanisms that regulate mosaic loss of chromosome Y (mLOY), and its clinical relevance, are unknown. We used genotype-array-intensity data and sequence reads from 85,542 men to identify 19 genomic regions (P < 5 × 10-8) that are associated with mLOY. Cumulatively, these loci also predicted X chromosome loss in women (n = 96,123; P = 4 × 10-6). Additional epigenome-wide methylation analyses using whole blood highlighted 36 differentially methylated sites associated with mLOY. The genes identified converge on aspects of cell proliferation and cell cycle regulation, including DNA synthesis (NPAT), DNA damage response (ATM), mitosis (PMF1, CENPN and MAD1L1) and apoptosis (TP53). We highlight the shared genetic architecture between mLOY and cancer susceptibility, in addition to inferring a causal effect of smoking on mLOY. Collectively, our results demonstrate that genotype-array-intensity data enables a measure of cell cycle efficiency at population scale and identifies genes implicated in aneuploidy, genome instability and cancer susceptibility.

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

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

Epidemiology of ovarian cancer: a review

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

Epidemiology of ovarian cancer: a review

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

Precancer Atlas to Drive Precision Prevention Trials

Cancer development is a complex process driven by inherited and acquired molecular and cellular alterations. Prevention is the holy grail of cancer elimination, but making this a reality will take a fundamental rethinking and deep understanding of premalignant biology. In this Perspective, we propose a national concerted effort to create a Precancer Atlas (PCA), integrating multi-omics and immunity - basic tenets of the neoplastic process. The biology of neoplasia caused by germline mutations has led to paradigm-changing precision prevention efforts, including: tumor testing for mismatch repair (MMR) deficiency in Lynch syndrome establishing a new paradigm, combinatorial chemoprevention efficacy in familial adenomatous polyposis (FAP), signal of benefit from imaging-based early detection research in high-germline risk for pancreatic neoplasia, elucidating early ontogeny in BRCA1-mutation carriers leading to an international breast cancer prevention trial, and insights into the intricate germline-somatic-immunity interaction landscape. Emerging genetic and pharmacologic (metformin) disruption of mitochondrial (mt) respiration increased autophagy to prevent cancer in a Li-Fraumeni mouse model (biology reproduced in clinical pilot) and revealed profound influences of subtle changes in mt DNA background variation on obesity, aging, and cancer risk. The elaborate communication between the immune system and neoplasia includes an increasingly complex cellular microenvironment and dynamic interactions between host genetics, environmental factors, and microbes in shaping the immune response. Cancer vaccines are in early murine and clinical precancer studies, building on the recent successes of immunotherapy and HPV vaccine immune prevention. Molecular monitoring in Barrett's esophagus to avoid overdiagnosis/treatment highlights an important PCA theme. Next generation sequencing (NGS) discovered age-related clonal hematopoiesis of indeterminate potential (CHIP). Ultra-deep NGS reports over the past year have redefined the premalignant landscape remarkably identifying tiny clones in the blood of up to 95% of women in their 50s, suggesting that potentially premalignant clones are ubiquitous. Similar data from eyelid skin and peritoneal and uterine lavage fluid provide unprecedented opportunities to dissect the earliest phases of stem/progenitor clonal (and microenvironment) evolution/diversity with new single-cell and liquid biopsy technologies. Cancer mutational signatures reflect exogenous or endogenous processes imprinted over time in precursors. Accelerating the prevention of cancer will require a large-scale, longitudinal effort, leveraging diverse disciplines (from genetics, biochemistry, and immunology to mathematics, computational biology, and engineering), initiatives, technologies, and models in developing an integrated multi-omics and immunity PCA - an immense national resource to interrogate, target, and intercept events that drive oncogenesis. Cancer Res; 77(7); 1510-41. ©2017 AACR.

The recurrence risk of genetic complex diseases

Complex inherited diseases affected by an interaction between collective effects of the genotype at one or multiple loci either to increase or to lower susceptibility to disease, combined with a variety of environmental exposures that may trigger, accelerate, exacerbate, or protect against the disease process. The new aspects of genetic techniques have been opened for diagnosis and analysis of inherited disorders. While appropriate Mendelian laws is applied to estimate the recurrence risk of single gene diseases, using empirical recurrence risks are the most important and available method to evaluate pedigree of complex (multifactorial), chromosomal, and unknown etiology disorders. Although, generally, empirical recurrent risks are not accurate, either because of the difference of gene frequencies and environmental factors among populations or heterogeneity of disease; using results of plenty family population studies, computerized estimating programs, genotyping technologies, and Genome-wide association studies (GWASs) of single nucleotide polymorphisms (SNPs), can make it possible nowadays to estimate these risks. The specific family situation and importance recurrence risks of some common complex genetic diseases will be presented in this review and some important multifactorial disorders’ recurrence risks will be summarized to help genetic counselors for supporting families and representing better view of genetic disorders.

Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk

Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute's "Up for a Challenge" (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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