Genome-wide Analysis of Common Copy Number Variation and Epithelial Ovarian Cancer Risk

A variant in the 5'UTR of ERBB4 is associated with lifespan in Golden Retrievers

Genome-wide association studies (GWAS) in long-lived human populations have led to identification of variants associated with Alzheimer's disease and cardiovascular disease, the latter being the most common cause of mortality in people worldwide. In contrast, naturally occurring cancer represents the leading cause of death in pet dogs, and specific breeds like the Golden Retriever (GR) carry up to a 65% cancer-related death rate. We hypothesized that GWAS of long-lived GRs might lead to the identification of genetic variants capable of modifying longevity within this cancer-predisposed breed. A GWAS was performed comparing GR dogs ≥ 14 years to dogs dying prior to age 12 which revealed a significant association to ERBB4, the only member of the epidermal growth factor receptor family capable of serving as both a tumor suppressor gene and an oncogene. No coding variants were identified, however, distinct haplotypes in the 5'UTR were associated with reduced lifespan in two separate populations of GR dogs. When all GR dogs were analyzed together (n = 304), the presence of haplotype 3 was associated with shorter survival (11.8 years vs. 12.8 years, p = 0.024). GRs homozygous for haplotype 3 had the shortest survival, and GRs homozygous for haplotype 1 had the longest survival (11.6 years vs. 13.5 years, p = 0.0008). Sub-analyses revealed that the difference in lifespan for GRs carrying at least 1 copy of haplotype 3 was specific to female dogs (p = 0.009), whereas survival remained significantly different in both male and female GRs homozygous for haplotype 1 or haplotype 3 (p = 0.026 and p = 0.009, respectively). Taken together, these findings implicate a potential role for ERBB4 in GR longevity and provide evidence that within-breed canine lifespan studies could serve as a mechanism to identify favorable or disease-modifying variants important to the axis of aging and cancer.

Associating CYP2A6 structural variants with ovarian and lung cancer risk in the UK Biobank: replication and extension

CYP2A6 is a polymorphic enzyme that inactivates nicotine; structural variants (SVs) include gene deletions and hybrids with the neighboring pseudogene CYP2A7. Two studies found that CYP2A7 deletions were associated with ovarian cancer risk. Using their methodology, we aimed to characterize CYP2A6 SVs (which may be misidentified by prediction software as CYP2A7 SVs), then assess CYP2A6 SV-associated risk for ovarian cancer, and extend analyses to lung cancer. An updated reference panel was created to impute CYP2A6 SVs from UK Biobank array data. Logistic regression models analyzed the association between CYP2A6 SVs and cancer risk, adjusting for covariates. Software-predicted CYP2A7 deletions were concordant with known CYP2A6 SVs. Deleterious CYP2A6 SVs were not associated with ovarian cancer (OR = 1.06; 95% CI: 0.80-1.37; p = 0.7) but did reduce the risk of lung cancer (OR = 0.44; 95% CI: 0.29-0.64; p < 0.0001), and a lung cancer subtype. Replication of known lung cancer associations indicates the validity of array-based SV analyses.

Human leukocyte immunoglobulin-like receptors in health and disease

Human leukocyte immunoglobulin (Ig)-like receptors (LILR) are a family of 11 innate immunomodulatory receptors, primarily expressed on lymphoid and myeloid cells. LILRs are either activating (LILRA) or inhibitory (LILRB) depending on their associated signalling domains (D). With the exception of the soluble LILRA3, LILRAs mediate immune activation, while LILRB1-5 primarily inhibit immune responses and mediate tolerance. Abnormal expression and function of LILRs is associated with a range of pathologies, including immune insufficiency (infection and malignancy) and overt immune responses (autoimmunity and alloresponses), suggesting LILRs may be excellent candidates for targeted immunotherapies. This review will discuss the biology and clinical relevance of this extensive family of immune receptors and will summarise the recent developments in targeting LILRs in disease settings, such as cancer, with an update on the clinical trials investigating the therapeutic targeting of these receptors.

Chromosome instability region analysis and identification of the driver genes of the epithelial ovarian cancer cell lines A2780 and SKOV3

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole-exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single-nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

Role of epithelial-mesenchymal transition factor SNAI1 and its targets in ovarian cancer aggressiveness

Ovarian cancer remains the most lethal gynecologic malignancy in the USA. For over twenty years, epithelial-mesenchymal transition (EMT) has been characterized extensively in development and disease. The dysregulation of this process in cancer has been identified as a mechanism by which epithelial tumors become more aggressive, allowing them to survive and invade distant tissues. This occurs in part due to the increased expression of the EMT transcription factor, SNAI1 (Snail). In the case of epithelial ovarian cancer, Snail has been shown to contribute to cancer invasion, stemness, chemoresistance, and metabolic changes. Thus, in this review, we focus on summarizing current findings on the role of EMT (specifically, factors downstream of Snail) in determining ovarian cancer aggressiveness.

Genotyping, characterization, and imputation of known and novel CYP2A6 structural variants using SNP array data

CYP2A6 metabolically inactivates nicotine. Faster CYP2A6 activity is associated with heavier smoking and higher lung cancer risk. The CYP2A6 gene is polymorphic, including functional structural variants (SV) such as gene deletions (CYP2A6*4), duplications (CYP2A6*1 × 2), and hybrids with the CYP2A7 pseudogene (CYP2A6*12, CYP2A6*34). SVs are challenging to genotype due to their complex genetic architecture. Our aims were to develop a reliable protocol for SV genotyping, functionally phenotype known and novel SVs, and investigate the feasibility of CYP2A6 SV imputation from SNP array data in two ancestry populations. European- (EUR; n = 935) and African- (AFR; n = 964) ancestry individuals from smoking cessation trials were genotyped for SNPs using an Illumina array and for CYP2A6 SVs using Taqman copy number (CN) assays. SV-specific PCR amplification and Sanger sequencing was used to characterize a novel SV. Individuals with SVs were phenotyped using the nicotine metabolite ratio, a biomarker of CYP2A6 activity. SV diplotype and SNP array data were integrated and phased to generate ancestry-specific SV reference panels. Leave-one-out cross-validation was used to investigate the feasibility of CYP2A6 SV imputation. A minimal protocol requiring three Taqman CN assays for CYP2A6 SV genotyping was developed and known SV associations with activity were replicated. The first domain swap CYP2A6-CYP2A7 hybrid SV, CYP2A6*53, was identified, sequenced, and associated with lower CYP2A6 activity. In both EURs and AFRs, most SV alleles were identified using imputation (>70% and >60%, respectively); importantly, false positive rates were <1%. These results confirm that CYP2A6 SV imputation can identify most SV alleles, including a novel SV.

Genetic Risk Scores and Missing Heritability in Ovarian Cancer

Ovarian cancers are curable by surgical resection when discovered early. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk. This opinion article summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

Shallow Whole-Genome Sequencing of Cell-Free DNA (cfDNA) Detects Epithelial Ovarian Cancer and Predicts Patient Prognosis

Despite the progress in diagnostics and therapeutics, epithelial ovarian cancer (EOC) remains a fatal disease. Using shallow whole-genome sequencing of plasma cell-free DNA (cfDNA), we investigated biomarkers that could detect EOC and predict survival. Plasma cfDNA from 40 EOC patients and 20 healthy subjects were analyzed by shallow whole-genome sequencing (WGS) to identify copy number variations (CNVs) and determine the Z-scores of genes. In addition, we also calculated the genome-wide scores (Gi scores) to quantify chromosomal instability. We found that the Gi scores could distinguish EOC patients from healthy subjects and identify various EOC histological subtypes (e.g., high-grade serous carcinoma). In addition, we characterized EOC CNVs and demonstrated a relationship between RAB25 amplification (alone or with CA125), and disease-free survival and overall survival. This study identified RAB25 amplification as a predictor of EOC patient survival. Moreover, we showed that Gi scores could detect EOC. These data demonstrated that cfDNA, detected by shallow WGS, represented a potential tool for diagnosing EOC and predicting its prognosis.

Human orphan cytochromes P450: An update

Orphan cytochromes P450 (CYP) are enzymes whose biological functions and substrates are unknown. However, the use of new experimental strategies has allowed obtaining more information about their relevance in the metabolism of endogenous and exogenous compounds. Likewise, the modulation of their expression and activity has been associated with pathogenesis and prognosis in different diseases. In this work, we review the regulatory pathways and the possible role of orphan CYP to provide evidence that allow us to stop considering some of them as orphan enzymes and to propose them as possible therapeutic targets in the design of new strategies for the treatment of diseases associated with CYP-mediated metabolism.

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND

Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort.

METHODS

Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types.

RESULTS

We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types.

CONCLUSIONS

CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.

Polishing copy number variant calls on exome sequencing data via deep learning

Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.

Cadherin‑16 inhibits thyroid carcinoma cell proliferation and invasion

Cadherin-16 (CDH16), a member of the cadherin family of adhesion molecules, serves an important role in the formation and maintenance of the thyroid follicular lumen. Decreased expression of CDH16 has been reported to be associated with tumor stage in papillary thyroid cancer (PTC); however, previous analyses have been limited and the biological role of CDH16 in different subtypes of TC is unknown. To investigate the role of CDH16 in the occurrence and development of TC, bioinformatic analysis of three TC subtypes (PTC, follicular cell-derived TC and anaplastic TC) was performed using an extended data set from the Gene Expression Omnibus database, with additional confirmation using data from The Cancer Genome Atlas, as well as biopsies from 35 patients with PTC and TC or follicular cell lines. According to the dataset analysis, CDH16 was downregulated in PTC and follicular cell-derived and anaplastic TC; the downregulation in PTC was independent of DNA copy number variation. Furthermore, low expression levels of CDH16 were significantly correlated with tumor size, lymph node metastasis status and disease stage in 35 patients with PTC. Gene Set Enrichment Analysis suggested that CDH16 participated in DNA replication and cell adhesion pathways. To evaluate CDH16 activity, CDH16 was overexpressed in TC-derived BCPAP cells. CDH16 overexpression inhibited cell proliferation, migration and invasion and induced apoptosis by downregulating proteins associated with DNA replication and cell adhesion. These results support the identification of CDH16 as a valuable target for TC prognosis and therapy and, to the best of our knowledge, represent the first direct demonstration of its mechanistic role in TC.

A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources

Copy number variation (CNV) may contribute to the development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene, and disease label data provides us with an opportunity to design a new machine learning framework to predict potential disease-related CNVs. In this paper, we developed a novel machine learning approach, namely, IHI-BMLLR (Integrating Heterogeneous Information sources with Biweight Mid-correlation and L1-regularized Logistic Regression under stability selection), to predict the CNV-disease path associations by using a data set containing CNV, disease state labels, and gene data. CNVs, genes, and diseases are connected through edges and then constitute a biological association network. To construct a biological network, we first used a self-adaptive biweight mid-correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self-adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find top D path associations and important CNVs. The experimental results on both simulation and prostate cancer data show that IHI-BMLLR is significantly better than two state-of-the-art CNV detection methods (i.e., CCRET and DPtest) under false-positive control. Furthermore, we applied IHI-BMLLR to prostate cancer data and found significant path associations. Three new cancer-related genes were discovered in the paths, and these genes need to be verified by biological research in the future.

Genome-wide copy number variation analysis of hepatitis B infection in a Japanese population

Genome-wide association studies have been performed to identify common genetic variants associated with hepatitis B (HB). However, little is known about copy number variations (CNVs) in HB. In this study, we performed a genome-wide CNV analysis between 1830 healthy controls and 1031 patients with HB infection after quality control. Using signal calling by the Axiom Analysis Suite and CNV detection by PennCNV software, we obtained a total of 4494 CNVs across all individuals. The genes with CNVs that were found only in the HB patients were associated with the immune system, such as antigen processing. A gene-level CNV association test revealed statistically significant CNVs in the contactin 6 (CNTN6) gene. Moreover, we also performed gene-level CNV association tests in disease subgroups, including hepatocellular carcinoma patients, liver cirrhosis patients, and HBV carriers, including asymptomatic carriers and patients with HBV-derived chronic hepatitis. Our findings from germline cells suggested that patient-specific CNVs may be inherent genetic risk factors for HB.

The risk of contracting the hepatitis B virus may be linked to the number of copies of certain genes in an individual’s genome. A Japanese team led by Masataka Kikuchi, Osaka University, and Akihiro Nakaya, University of Tokyo, looked for repeated segments of the genome, known as copy number variants (CNVs), that differed between people with hepatitis B infections and those without. Studying around 3000 individuals of Japanese descent, the researchers identified several rare CNVs associated with immune function in hepatitis-affected individuals. They also found a common CNV in a gene called CNTN6 that the hepatitis B virus often uses to integrate itself into the genome of liver cells, a process that can lead to cancer. The findings underscore the importance of CNVs as inherited risk factors for hepatitis B and other viral infections.

A genetic risk score for glioblastoma multiforme based on copy number variations

Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer. Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations. We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile. The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

Background

It is estimated that more than 20% of ovarian cancer cases are associated with a genetic predisposition that is only partially explained by germline mutations in the BRCA1 and BRCA2 genes. Recently, several pieces of evidence showed that mutations in three genes involved in the homologous recombination DNA repair pathway, i.e., BRIP1, RAD51C, and RAD51D, are associated with a high risk of ovarian cancer. To more precisely estimate the ovarian cancer risk attributed to BRIP1, RAD51C, and RAD51D mutations, we performed a meta-analysis based on a comparison of a total of ~ 29,400 ovarian cancer patients from 63 studies and a total of ~ 116,000 controls from the gnomAD database.

Results

The analysis allowed precise estimation of ovarian cancer risks attributed to mutations in BRIP1, RAD51C, and RAD51D, confirming that all three genes are ovarian cancer high-risk genes (odds ratio (OR) = 4.94, 95%CIs:4.07–6.00, p < 0.0001; OR = 5.59, 95%CIs:4.42–7.07, p < 0.0001; and OR = 6.94, 95%CIs:5.10–9.44, p < 0.0001, respectively). In the present report, we show, for the first time, a mutation-specific risk analysis associated with distinct, recurrent, mutations in the genes.

Conclusions

The meta-analysis provides evidence supporting the pathogenicity of BRIP1, RAD51C, and RAD51D mutations in relation to ovarian cancer. The level of ovarian cancer risk conferred by these mutations is relatively high, indicating that after BRCA1 and BRCA2, the BRIP1, RAD51C, and RAD51D genes are the most important ovarian cancer risk genes, cumulatively contributing to ~ 2% of ovarian cancer cases. The inclusion of the genes into routine diagnostic tests may influence both the prevention and the potential treatment of ovarian cancer.