Germline copy number variations in BRCA1-associated ovarian cancer patients

Integrative Analysis of Germline Rare Variants in Clear and Non-clear Cell Renal Cell Carcinoma

Background and objective

Previous germline studies on renal cell carcinoma (RCC) have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification, which might have led to an inaccurate estimation of genetic risk. Here, we aim to analyze the major germline drivers of RCC risk and clinically relevant but underexplored germline variant types.

Methods

We first characterized germline pathogenic variants (PVs), cryptic splice variants, and copy number variants (CNVs) in 1436 unselected RCC patients. To evaluate the enrichment of PVs in RCC, we conducted a case-control study of 1356 RCC patients ancestry matched with 16 512 cancer-free controls using approaches accounting for population stratification and histological subtypes, followed by characterization of secondary somatic events.

Key findings and limitations

Clear cell RCC patients (n = 976) exhibited a significant burden of PVs in VHL compared with controls (odds ratio [OR]: 39.1, p = 4.95e-05). Non-clear cell RCC patients (n = 380) carried enrichment of PVs in FH (OR: 77.9, p = 1.55e-08) and MET (OR: 1.98e11, p = 2.07e-05). In a CHEK2-focused analysis with European participants, clear cell RCC (n = 906) harbored nominal enrichment of low-penetrance CHEK2 variants-p.Ile157Thr (OR: 1.84, p = 0.049) and p.Ser428Phe (OR: 5.20, p = 0.045), while non-clear cell RCC (n = 295) exhibited nominal enrichment of CHEK2 loss of function PVs (OR: 3.51, p = 0.033). Patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset than patients without germline PVs (mean: 46.0 vs 60.2 yr, p < 0.0001), and more than half had secondary somatic events affecting the same gene (n = 10/15, 66.7%). Conversely, CHEK2 PV carriers exhibited a similar age of onset to patients without germline PVs (mean: 60.1 vs 60.2 yr, p = 0.99), and only 30.4% carried somatic events in CHEK2 (n = 7/23). Finally, pathogenic germline cryptic splice variants were identified in SDHA and TSC1, and pathogenic germline CNVs were found in 18 patients, including CNVs in FH, SDHA, and VHL.

Conclusions and clinical implications

This analysis supports the existing link between several RCC risk genes and RCC risk manifesting in earlier age of onset. It calls for caution when assessing the role of CHEK2 due to the burden of founder variants with varying population frequency. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants.

Patient summary

In this study, we carefully compared the frequency of rare inherited mutations with a focus on patients' genetic ancestry. We discovered that subtle variations in genetic background may confound a case-control analysis, especially in evaluating the cancer risk associated with specific genes, such as CHEK2. We also identified previously less explored forms of rare inherited mutations, which could potentially increase the risk of kidney cancer.

Integrative Analysis of Germline Rare Variants in Clear and Non-Clear Cell Renal Cell Carcinoma

IMPORTANCE

RCC encompasses a set of histologically distinct cancers with a high estimated genetic heritability, of which only a portion is currently explained. Previous rare germline variant studies in RCC have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification that may significantly impact the interpretation and discovery of certain candidate risk genes.

OBJECTIVE

To evaluate the enrichment of germline PVs in established cancer-predisposing genes (CPGs) in clear cell and non-clear cell RCC patients compared to cancer-free controls using approaches that account for population stratification and to identify unconventional types of germline RCC risk variants that confer an increased risk of developing RCC.

DESIGN SETTING AND PARTICIPANTS

In 1,436 unselected RCC patients with sufficient data quality, we systematically identified rare germline PVs, cryptic splice variants, and copy number variants (CNVs). From this unselected cohort, 1,356 patients were ancestry-matched with 16,512 cancer-free controls, and gene-level enrichment of rare germline PVs were assessed in 143 CPGs, followed by an investigation of somatic events in matching tumor samples.

MAIN OUTCOMES AND MEASURES

Gene-level burden of rare germline PVs, identification of secondary somatic events accompanying the germline PVs, and characterization of less-explored types of rare germline PVs in RCC patients.

RESULTS

In clear cell RCC (n = 976 patients), patients exhibited significantly higher prevalence of PVs in VHL compared to controls (OR: 39.1, 95% CI: 7.01-218.07, p-value:4.95e-05, q-value:0.00584). In non-clear cell RCC (n = 380 patients), patients carried enriched burden of PVs in FH (OR: 77.9, 95% CI: 18.68-324.97, p-value:1.55e-08, q-value: 1.83e-06) and MET (OR: 1.98e11, 95% CI: 0-inf, p-value: 2.07e-05, q-value: 3.50e-07). In a CHEK2-focused analysis with European cases and controls, clear cell RCC patients (n=906 European patients) harbored nominal enrichment of the previously reported low-penetrance CHEK2 variants, p.Ile157Thr (OR:1.84, 95% CI: 1.00-3.36, p-value:0.049) and p.Ser428Phe (OR:5.20, 95% CI: 1.00-26.40, p-value:0.045) while non-clear cell RCC patients (n=295 European patients) exhibited nominal enrichment of CHEK2 LOF germline PVs (OR: 3.51, 95% CI: 1.10-11.10, p-value: 0.033). RCC patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset compared to patients without any germline PVs in CPGs (Mean: 46.0 vs 60.2 years old, Tukey adjusted p-value < 0.0001), and more than half had secondary somatic events affecting the same gene (n=10/15, 66.7%, 95% CI: 38.7-87.0%). Conversely, patients with rare germline PVs in CHEK2 exhibited a similar age of disease onset to patients without any identified germline PVs in CPGs (Mean: 60.1 vs 60.2 years old, Tukey adjusted p-value: 0.99), and only 30.4% of the patients carried secondary somatic events in CHEK2 (n=7/23, 95% CI: 14.1-53.0%). Finally, rare pathogenic germline cryptic splice variants underexplored in RCC were identified in SDHA and TSC1, and rare pathogenic germline CNVs were found in 18 patients, including CNVs in FH, SDHA, and VHL.

CONCLUSIONS AND RELEVANCE

This systematic analysis supports the existing link between several RCC risk genes and elevated RCC risk manifesting in earlier age of RCC onset. Our analysis calls for caution when assessing the role of germline PVs in CHEK2 due to the burden of founder variants with varying population frequency in different ancestry groups. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants, such as cryptic splice variants and CNVs.

Determining the accuracy of next generation sequencing based copy number variation analysis in Hereditary Breast and Ovarian Cancer

BACKGROUND

Copy number variations (CNVs) are commonly associated with malignancies, including hereditary breast and ovarian cancers. Next generation sequencing (NGS) provides solutions for CNV detection in a single run. This study aimed to compare the accuracy of CNV detection by NGS analyzing tool against Multiplex Ligation Dependent Probe Amplification (MLPA).

RESEARCH DESIGN AND METHODS

In total, 1276 cases were studied by targeted NGS panels and 691 cases (61 calls in 58 NGS-CNV positive and 633 NGS-CNV negative cases) were validated by MLPA.

RESULTS

Twenty-eight (46%) NGS-CNV positive calls were consistent, whereas 33 (54%) calls showed discordance with MLPA. Two cases were detected as SNV by the NGS and CNV by the MLPA analysis. In total, 2% of the cases showed an MLPA confirmed CNV region in BRCA1/2. The results of this study showed that despite the high false positive call rate of the NGS-CNV algorithm, there were no false negative calls. The cases that were determined to be negative by the NGS and positive by the MLPA were actually carrying SNVs that were located on the MLPA probe binding sites.

CONCLUSION

The diagnostic performance of NGS-CNV analysis is promising; however, the need for confirmation by different methods remains.

Short somatic alterations at the site of copy number variation in breast cancer

Copy number variation (CNV) is a polymorphism in the human genome involving DNA fragments larger than 1 kb. Copy number variation sites provide hotspots of somatic alterations in cancers. Herein, we examined somatic alterations at sites of CNV in DNA from 20 invasive breast cancers using a Comparative Genomic Hybridization array specifically designed to detect the genome-wide CNV status of approximately 412 000 sites. Somatic copy number alterations (CNAs) were detected in 39.9% of the CNV probes examined. The most frequently altered regions were gains of 1q21-22 (90%), 8q21-24 (85%), 1q44 (85%), and 3q11 (85%) or losses of 16q22-24 (80%). Gene ontology analyses of genes within the CNA fragments revealed that cascades related to transcription and RNA metabolism correlated significantly with human epidermal growth factor receptor 2 positivity and menopausal status. Thirteen of 20 tumors showed CNAs in more than 35% of sites examined and a high prevalence of CNAs correlated significantly with estrogen receptor (ER) negativity, higher nuclear grade (NG), and higher Ki-67 labeling index. Finally, when CNA fragments were categorized according to their size, CNAs smaller than 10 kb correlated significantly with ER positivity and lower NG, whereas CNAs exceeding 10 Mb correlated with higher NG, ER negativity, and a higher Ki-67 labeling index. Most of these findings were confirmed or supported by quantitative PCR of representative DNA fragments in 72 additional breast cancers. These results suggest that most CNAs are caused by gain or loss of large chromosomal fragments and correlate with NG and several malignant features, whereas solitary CNAs of less than 10 kb could be involved in ER-positive breast carcinogenesis.

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

BACKGROUND

Germline DNA copy number variation (CNV) is a ubiquitous source of genetic variation and remains largely unexplored in association with epithelial ovarian cancer (EOC) risk.

METHODS

CNV was quantified in the DNA of approximately 3,500 cases and controls genotyped with the Illumina 610k and HumanOmni2.5M arrays. We performed a genome-wide association study of common (>1%) CNV regions (CNVRs) with EOC and high-grade serous (HGSOC) risk and, using The Cancer Genome Atlas (TCGA), performed in silico analyses of tumor-gene expression.

RESULTS

Three CNVRs were associated (P < 0.01) with EOC risk: two large (∼100 kb) regions within the 610k set and one small (<5 kb) region with the higher resolution 2.5M data. Large CNVRs included a duplication at LILRA6 (OR = 2.57; P = 0.001) and a deletion at CYP2A7 (OR = 1.90; P = 0.007) that were strongly associated with HGSOC risk (OR = 3.02; P = 8.98 × 10^-5). Somatic CYP2A7 alterations correlated with EGLN2 expression in tumors (P = 2.94 × 10^-47). An intronic ERBB4/HER4 deletion was associated with reduced EOC risk (OR = 0.33; P = 9.5 × 10^-2), and somatic deletions correlated with ERBB4 downregulation (P = 7.05 × 10^-5). Five CNVRs were associated with HGSOC, including two reduced-risk deletions: one at 1p36.33 (OR = 0.28; P = 0.001) that correlated with lower CDKIIA expression in TCGA tumors (P = 2.7 × 10^-7), and another at 8p21.2 (OR = 0.52; P = 0.002) that was present somatically where it correlated with lower GNRH1 expression (P = 5.9 × 10^-5).

CONCLUSIONS

Though CNV appears to not contribute largely to EOC susceptibility, a number of low-to-common frequency variants may influence the risk of EOC and tumor-gene expression.

IMPACT

Further research on CNV and EOC susceptibility is warranted, particularly with CNVs estimated from high-density arrays.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

Germline DNA copy number variations as potential prognostic markers for non-muscle invasive bladder cancer progression

Accumulating evidence has suggested that germline DNA copy number variations (CNVs) affect various disorders, including human malignancies. However, the significance of CNVs in non-muscle invasive bladder cancer (NMIBC) remains unclear. The purpose of the present study was to identify the role of CNVs in NMIBC. Array comparative genomic hybridization (CGH) analysis was performed to search for candidate CNVs associated with NMIBC susceptibility. Quantitative polymerase chain reaction was carried out to evaluate CNVs associated with patient outcome in 189 NMIBC cases. In total, 11 CNVs were associated with NMIBC risk in array CGH analysis. Out of the 189 CNVs examined, family with sequence similarity 81 member A (FAM81A) and proprotein convertase subtilisin/kexin type 6 (PCSK6) CNVs exhibited a significant association with recurrence and disease progression in NMIBC. PCSK6 has been reported to regulate proliferation and tumor progression in breast and prostate malignancies. Notably, patients with pT1 stage had significantly lower PCSK6 relative copy number than those with pTa (P=0.0196). In multivariate analyses, PCSK6 copy number was an independent prognostic factor for progression-free survival (P=0.0456; risk ratio, 2.17; 95% confidence interval, 1.02-4.82). These data suggest that PCSK6 CNV is a potential new tumor marker for estimating disease progression in NMIBC.

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1. Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2. This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.

Classification of cancers based on copy number variation landscapes

Genomic alterations in DNA can cause human cancer. DNA copy number variants (CNV), as one of the types of DNA mutations, have been considered to be associated with various human cancers. CNVs vary in size from 1bp up to one complete chromosome arm. In order to understand the difference between different human cancers on CNVs, in this study, we developed a method to computationally classify six human cancer types by using only CNV level values. The CNVs of 23,082 genes were used as features to construct the classifier. Then the features are carefully selected by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) methods. An accuracy of over 0.75 was reached by using only the CNVs of 19 genes based on Dagging method in 10-fold cross validation. It was indicated that these 19 genes may play important roles in differentiating cancer types. We also analyzed the biological functions of several top genes within the 19 gene list. The statistical results and biological analysis of these genes from this work might further help understand different human cancer types and provide guidance for related validation experiments. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Genomic-Wide Analysis with Microarrays in Human Oncology

DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research.

Identification of large-scale genomic variation in cancer genomes using in silico reference models

Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.

Rare germline copy number deletions of likely functional importance are implicated in endometrial cancer predisposition

Endometrial cancer is the most common invasive gynaecological cancer in women, and relatively little is known about inherited risk factors for this disease. This is the first genome-wide study to explore the role of common and rare germline copy number variants (CNVs) in predisposition to endometrial cancer. CNVs were called from germline DNA of 1,209 endometrioid endometrial cancer cases and 528 cancer-unaffected female controls. Overall CNV load of deletions or DNA gains did not differ significantly between cases and controls (P > 0.05), but cases presented with an excess of rare germline deletions overlapping likely functional genomic regions including genes (P = 8 × 10(-10)), CpG islands (P = 1 × 10(-7)) and sno/miRNAs regions (P = 3 × 10(-9)). On average, at least one additional gene and two additional CpG islands were disrupted by rare deletions in cases compared to controls. The most pronounced difference was that over 30 sno/miRNAs were disrupted by rare deletions in cases for every single disruption event in controls. A total of 13 DNA repair genes were disrupted by rare deletions in 19/1,209 cases (1.6%) compared to one gene in 1/528 controls (0.2%; P = 0.007), and this increased DNA repair gene loss in cases persisted after excluding five individuals carrying CNVs disrupting mismatch repair genes MLH1, MSH2 and MSH6 (P = 0.03). There were 34 miRNA regions deleted in at least one case but not in controls, the most frequent of which encompassed hsa-mir-661 and hsa-mir-203. Our study implicates rare germline deletions of functional and regulatory regions as possible mechanisms conferring endometrial cancer risk, and has identified specific regulatory elements as candidates for further investigation.

Molecular characterization of an intact p53 pathway subtype in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most aggressive histological type of epithelial ovarian cancer, which is characterized by a high frequency of somatic TP53 mutations. We performed exome analyses of tumors and matched normal tissues of 34 Japanese patients with HGSOC and observed a substantial number of patients without TP53 mutation (24%, 8/34). Combined with the results of copy number variation analyses, we subdivided the 34 patients with HGSOC into subtypes designated ST1 and ST2. ST1 showed intact p53 pathway and was characterized by fewer somatic mutations and copy number alterations. In contrast, the p53 pathway was impaired in ST2, which is characterized by abundant somatic mutations and copy number alterations. Gene expression profiles combined with analyses using the Gene Ontology resource indicate the involvement of specific biological processes (mitosis and DNA helicase) that are relevant to genomic stability and cancer etiology. In particular we demonstrate the presence of a novel subtype of patients with HGSOC that is characterized by an intact p53 pathway, with limited genomic alterations and specific gene expression profiles.

Germline DNA variations in breast cancer predisposition and prognosis: a systematic review of the literature

Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The disease is caused by a combination of genetic, environmental, lifestyle, and reproductive risk factors. Linkage and family-based studies have identified many pathological germline mutations, which account for around 20% of the genetic risk of familial breast cancer. In recent years, single nucleotide polymorphism-based genetic association studies, especially genome-wide association studies (GWASs), have been very successful in uncovering low-penetrance common variants associated with breast cancer risk. These common variants alone may explain up to an additional 30% of the familial risk of breast cancer. With the advent of available genetic resources and growing collaborations among researchers across the globe, the much needed large sample size to capture variants with small effect sizes and low population frequencies is being addressed, and hence many more common variants are expected to be discovered in the coming days. Here, major GWASs conducted for breast cancer predisposition and prognosis until 2013 are summarized. Few studies investigating other forms of genetic variations contributing to breast cancer predisposition and disease outcomes are also discussed. Finally, the potential utility of the GWAS-identified variants in disease risk models and some future perspectives are presented.

Up-regulation of 91H promotes tumor metastasis and predicts poor prognosis for patients with colorectal cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) play widespread roles in gene regulation and cellular processes. However, the functional roles of lncRNAs in colorectal cancer (CRC) are not yet well elucidated. The aim of the present study was to measure the levels of lncRNA 91H expression in CRC and evaluate its clinical significance and biological roles in the development and progression of CRC.

METHODS

91H expression and copy number variation (CNV) were measured in 72 CRC tumor tissues and adjacent normal tissues by real-time PCR. The biological roles of 91H were evaluated by MTT, scratch wound assay, migration and invasion assays, and flow cytometry.

RESULTS

91H was significantly overexpressed in cancerous tissue and CRC cell lines compared with adjacent normal tissue and a normal human intestinal epithelial cell line. Moreover, 91H overexpression was closely associated with distant metastasis and poor prognosis in patients with CRC, except for CNV of 91H. Multivariate analysis indicated that 91H expression was an independent prognostic indicator, as well as distant metastasis. Our in vitro data indicated that knockdown of 91H inhibited the proliferation, migration, and invasiveness of CRC cells.

CONCLUSIONS

91H played an important role in the molecular etiology of CRC and might be regarded as a novel prognosis indicator in patients with CRC.

Expanding the genetic basis of copy number variation in familial breast cancer

Introduction

Familial breast cancer (fBC) is generally associated with an early age of diagnosis and a higher frequency of disease among family members. Over the past two decades a number of genes have been identified that are unequivocally associated with breast cancer (BC) risk but there remain a significant proportion of families that cannot be accounted for by these genes. Copy number variants (CNVs) are a form of genetic variation yet to be fully explored for their contribution to fBC. CNVs exert their effects by either being associated with whole or partial gene deletions or duplications and by interrupting epigenetic patterning thereby contributing to disease development. CNV analysis can also be used to identify new genes and loci which may be associated with disease risk.

Methods

The Affymetrix Cytogenetic Whole Genome 2.7 M (Cyto2.7 M) arrays were used to detect regions of genomic re-arrangement in a cohort of 129 fBC BRCA1/BRCA2 mutation negative patients with a young age of diagnosis (<50 years) compared to 40 unaffected healthy controls (>55 years of age).

Results

CNV analysis revealed the presence of 275 unique rearrangements that were not present in the control population suggestive of their involvement in BC risk. Several CNVs were found that have been previously reported as BC susceptibility genes. This included CNVs in RPA3, NBN (NBS1), MRE11A and CYP19A1 in five unrelated fBC patients suggesting that these genes are involved in BC initiation and/or progression. Of special interest was the identification of WWOX and FHIT rearrangements in three unrelated fBC patients.

Conclusions

This study has identified a number of CNVs that potentially contribute to BC initiation and/or progression. The identification of CNVs that are associated with known tumour suppressor genes is of special interest that warrants further larger studies to understand their precise role in fBC.

Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view

A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases.

A computational framework to infer human disease-associated long noncoding RNAs

As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs. In this paper, we developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, we showed that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of our algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment. The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA-disease.html.

Copy number polymorphism in plant genomes

Copy number variants (CNVs) are genomic rearrangements resulting from gains or losses of DNA segments. Typically, the term refers to rearrangements of sequences larger than 1 kb. This type of polymorphism has recently been shown to be a key contributor to intra-species genetic variation, along with single-nucleotide polymorphisms and short insertion-deletion polymorphisms. Over the last decade, a growing number of studies have highlighted the importance of copy number variation (CNV) as a factor affecting human phenotype and individual CNVs have been linked to risks for severe diseases. In plants, the exploration of the extent and role of CNV is still just beginning. Initial genomic analyses indicate that CNVs are prevalent in plants and have greatly affected plant genome evolution. Many CNV events have been observed in outcrossing and autogamous species. CNVs are usually found on all chromosomes, with CNV hotspots interspersed with regions of very low genetic variation. Although CNV is mainly associated with intergenic regions, many CNVs encompass protein-coding genes. The collected data suggest that CNV mainly affects the members of large families of functionally redundant genes. Thus, the effects of individual CNV events on phenotype are usually modest. Nevertheless, there are many cases in which CNVs for specific genes have been linked to important traits such as flowering time, plant height and resistance to biotic and abiotic stress. Recent reports suggest that CNVs may form rapidly in response to stress.

A survey of copy-number variation detection tools based on high-throughput sequencing data

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.

Germline DNA copy number aberrations identified as potential prognostic factors for breast cancer recurrence

Breast cancer recurrence (BCR) is a common treatment outcome despite curative-intent primary treatment of non-metastatic breast cancer. Currently used prognostic and predictive factors utilize tumor-based markers, and are not optimal determinants of risk of BCR. Germline-based copy number aberrations (CNAs) have not been evaluated as determinants of predisposition to experience BCR. In this study, we accessed germline DNA from 369 female breast cancer subjects who received curative-intent primary treatment following diagnosis. Of these, 155 experienced BCR and 214 did not, after a median duration of follow up after breast cancer diagnosis of 6.35 years (range = 0.60–21.78) and 8.60 years (range = 3.08–13.57), respectively. Whole genome CNA genotyping was performed on the Affymetrix SNP array 6.0 platform. CNAs were identified using the SNP-Fast Adaptive States Segmentation Technique 2 algorithm implemented in Nexus Copy Number 6.0. Six samples were removed due to poor quality scores, leaving 363 samples for further analysis. We identified 18,561 CNAs with ≥1 kb as a predefined cut-off for observed aberrations. Univariate survival analyses (log-rank tests) identified seven CNAs (two copy number gains and five copy neutral-loss of heterozygosities, CN-LOHs) showing significant differences (P<2.01×10⁻⁵) in recurrence-free survival (RFS) probabilities with and without CNAs.We also observed three additional but distinct CN-LOHs showing significant differences in RFS probabilities (P<2.86×10⁻⁵) when analyses were restricted to stratified cases (luminal A, n = 208) only. After adjusting for tumor stage and grade in multivariate analyses (Cox proportional hazards models), all the CNAs remained strongly associated with the phenotype of BCR. Of these, we confirmed three CNAs at 17q11.2, 11q13.1 and 6q24.1 in representative samples using independent genotyping platforms. Our results suggest further investigations on the potential use of germline DNA variations as prognostic markers in cancer-associated phenotypes.

Germline copy number variations associated with breast cancer susceptibility in a Japanese population

Although copy number variations (CNVs) are expected to affect various diseases, little is known about the association between CNVs and breast cancer susceptibility. Therefore, we investigated this relation. Array comparative genomic hybridization was performed to search for candidate CNVs related to breast cancer susceptibility. Subsequent quantitative real-time polymerase chain reaction was carried out for confirmation. We found seven CNV markers associated with breast cancer risk. The means of the relative copy numbers of patients with a history of breast cancer and women in the control group were 0.8 and 1.8 for Hs06535529_cn on 1p36.12 (P < 0.0001), 2.9 and 2.2 for Hs03103056_cn on 3q26.1 (P < 0.0001), 1.2 and 1.8 for Hs03899300_cn on 15q26.3 (P < 0.0001), 1.0 and 1.5 for Hs03908783_cn on 15q26.3 (P < 0.0001), and 1.1 and 1.7 for Hs03898338_cn on 15q26.3 (P < 0.0001), respectively. Interestingly, nine or more copies of Hs04093415_cn on 22q12.3 were found only in 8/193 (4.1 %) patients with a history of breast cancer and in none of the controls (P = 0.0081). Similarly, 12 or more copies of Hs040908898_cn on 22q12.3 were found only in 7/193 (3.6 %) patients with a history of breast cancer and in none of the controls (P = 0.016). A combination of two CNVs resulted in 80.3 % sensitivity, 80.6 % specificity, 82.4 % positive predictive value, and 78.3 % negative predictive value for the prediction of breast cancer susceptibility. These findings may lead to a new means of risk assessment for breast cancer. Confirmatory studies using independent data sets are needed to support our findings.

Germline copy number variations and cancer predisposition

We present an overview of the role of germline copy number variations (CNVs) in cancer predisposition. CNVs represent a significant source of genetic diversity, although the mechanisms by which they influence cancer susceptibility still remain largely unknown. Approximately 100 highly penetrant germline mutant genes are now known to cause cancer predisposition inherited in a Mendelian fashion; in this review, we show that nearly half of these genes have also been observed as rare CNVs associated with cancer. However, these highly penetrant alleles seem to account for less than 5% of all familial cancers. We surmise that most of the genetic risk of cancer in the general population must largely involve genes of low or moderate penetrance. In the last 5 years, studies have demonstrated that although common low penetrant CNVs are modest contributors to cancer individually, their combined impact on cancer predisposition must be taken into account in estimating cancer risk.

Identification of germline genomic copy number variation in familial pancreatic cancer

Adenocarcinoma of the pancreas is a significant cause of cancer mortality, and up to 10 % of cases appear to be familial. Heritable genomic copy number variants (CNVs) can modulate gene expression and predispose to disease. Here, we identify candidate predisposition genes for familial pancreatic cancer (FPC) by analyzing germline losses or gains present in one or more high-risk patients and absent in a large control group. A total of 120 FPC cases and 1,194 controls were genotyped on the Affymetrix 500K array, and 36 cases and 2,357 controls were genotyped on the Affymetrix 6.0 array. Detection of CNVs was performed by multiple computational algorithms and partially validated by quantitative PCR. We found no significant difference in the germline CNV profiles of cases and controls. A total of 93 non-redundant FPC-specific CNVs (53 losses and 40 gains) were identified in 50 cases, each CNV present in a single individual. FPC-specific CNVs overlapped the coding region of 88 RefSeq genes. Several of these genes have been reported to be differentially expressed and/or affected by copy number alterations in pancreatic adenocarcinoma. Further investigation in high-risk subjects may elucidate the role of one or more of these genes in genetic predisposition to pancreatic cancer.

Germline DNA copy number variation in familial and early-onset breast cancer

Introduction

Genetic factors predisposing individuals to cancer remain elusive in the majority of patients with a familial or clinical history suggestive of hereditary breast cancer. Germline DNA copy number variation (CNV) has recently been implicated in predisposition to cancers such as neuroblastomas as well as prostate and colorectal cancer. We evaluated the role of germline CNVs in breast cancer susceptibility, in particular those with low population frequencies (rare CNVs), which are more likely to cause disease."

Methods

Using whole-genome comparative genomic hybridization on microarrays, we screened a cohort of women fulfilling criteria for hereditary breast cancer who did not carry BRCA1/BRCA2 mutations.

Results

The median numbers of total and rare CNVs per genome were not different between controls and patients. A total of 26 rare germline CNVs were identified in 68 cancer patients, however, a proportion that was significantly different (P = 0.0311) from the control group (23 rare CNVs in 100 individuals). Several of the genes affected by CNV in patients and controls had already been implicated in cancer.

Conclusions

This study is the first to explore the contribution of germline CNVs to BRCA1/2-negative familial and early-onset breast cancer. The data suggest that rare CNVs may contribute to cancer predisposition in this small cohort of patients, and this trend needs to be confirmed in larger population samples.

Optimizing molecular-targeted therapies in ovarian cancer: the renewed surge of interest in ovarian cancer biomarkers and cell signaling pathways

The hallmarks of ovarian cancer encompass the development of resistance, disease recurrence and poor prognosis. Ovarian cancer cells express gene signatures which pose significant challenges for cancer drug development, therapeutics, prevention and management. Despite enhancements in contemporary tumor debulking surgery, tentative combination regimens and abdominal radiation which can achieve beneficial response rates, the majority of ovarian cancer patients not only experience adverse effects, but also eventually relapse. Therefore, additional therapeutic possibilities need to be explored to minimize adverse events and prolong progression-free and overall response rates in ovarian cancer patients. Currently, a revival in cancer drug discovery is devoted to identifying diagnostic and prognostic ovarian cancer biomarkers. However, the sensitivity and reliability of such biomarkers may be complicated by mutations in the BRCA1 or BRCA2 genes, diverse genetic risk factors, unidentified initiation and progression elements, molecular tumor heterogeneity and disease staging. There is thus a dire need to expand existing ovarian cancer therapies with broad-spectrum and individualized molecular targeted approaches. The aim of this review is to profile recent developments in our understanding of the interrelationships among selected ovarian tumor biomarkers, heterogeneous expression signatures and related molecular signal transduction pathways, and their translation into more efficacious targeted treatment rationales.