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A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.

ABSTRACT: Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique. The algorithm has an optimal solution, which means all maximal cliques can be identified, and guarantees that the identified CNV regions are the most frequent and that the minimal regions have been delineated among tumor samples. The algorithm has successfully been applied to analyze a large cohort of breast cancer samples and identified some breast cancer-associated genes and pathways.

SUBMITTER: Chi C

PROVIDER: S-EPMC5063805 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.

Chi Chen C Ajwad Rasif R Kuang Qin Q Hu Pingzhao P

Cancer informatics 20161009 Suppl 2

Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the m ...[more]

PMID: 27773988

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Estimates of penetrance for recurrent pathogenic copy-number variations.

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| S-EPMC3664238 | biostudies-literature

A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations.

Project description:The discovery of cancer of unknown primary (CUP) is of great significance in designing more effective treatments and improving the diagnostic efficiency in cancer patients. In the study, we develop an appropriate machine learning model for tracing the tissue of origin of CUP with high accuracy after feature engineering and model evaluation. Based on a copy number variation data consisting of 4,566 training cases and 1,262 independent validation cases, an XGBoost classifier is applied to 10 types of cancer. Extremely randomized tree (Extra tree) is used for dimension reduction so that fewer variables replace the original high-dimensional variables. Features with top 300 weights are selected and principal component analysis is applied to eliminate noise. We find that XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10-fold cross-validation for training samples and 0.7421 on independent validation datasets for predicting tumor tissue of origin. Furthermore, by contrasting various performance indices, such as precision and recall rate, the experimental results show that XGBoost classifier significantly improves the classification performance of various tumors with less prediction error, as compared to other classifiers, such as K-nearest neighbors (KNN), Bayes, support vector machine (SVM), and Adaboost. Our method can infer tissue of origin for the 10 cancer types with acceptable accuracy in both cross-validation and independent validation data. It may be used as an auxiliary diagnostic method to determine the actual clinicopathological status of specific cancer.

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Project description:DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least O(n2), where n is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.

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Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations.

Project description:BackgroundCopy number variation (CNV) analysis has become one of the most important research areas for understanding complex disease. With increasing resolution of array-based comparative genomic hybridization (aCGH) arrays, more and more raw copy number data are collected for multiple arrays. It is natural to realize the co-existence of both recurrent and individual-specific CNVs, together with the possible data contamination during the data generation process. Therefore, there is a great need for an efficient and robust statistical model for simultaneous recovery of both recurrent and individual-specific CNVs.ResultWe develop a penalized weighted low-rank approximation method (WPLA) for robust recovery of recurrent CNVs. In particular, we formulate multiple aCGH arrays into a realization of a hidden low-rank matrix with some random noises and let an additional weight matrix account for those individual-specific effects. Thus, we do not restrict the random noise to be normally distributed, or even homogeneous. We show its performance through three real datasets and twelve synthetic datasets from different types of recurrent CNV regions associated with either normal random errors or heavily contaminated errors.ConclusionOur numerical experiments have demonstrated that the WPLA can successfully recover the recurrent CNV patterns from raw data under different scenarios. Compared with two other recent methods, it performs the best regarding its ability to simultaneously detect both recurrent and individual-specific CNVs under normal random errors. More importantly, the WPLA is the only method which can effectively recover the recurrent CNVs region when the data is heavily contaminated.

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Characterization of Copy-Number Variations and Possible Candidate Genes in Recurrent Pregnancy Losses.

Project description:It is well established that embryonic chromosomal abnormalities (both in the number of chromosomes and the structure) account for 50% of early pregnancy losses. However, little is known regarding the potential differences in the incidence and distribution of chromosomal abnormalities between patients with sporadic abortion (SA) and recurrent pregnancy loss (RPL), let alone the role of submicroscopic copy-number variations (CNVs) in these cases. The aim of the present study was to systematically evaluate the role of embryonic chromosomal abnormalities and CNVs in the etiology of RPL compared with SA. Over a 3-year period, 1556 fresh products of conception (POCs) from miscarriage specimens were investigated using single nucleotide polymorphism array (SNP-array) and CNV sequencing (CNV-seq) in this study, along with further functional enrichment analysis. Chromosomal abnormalities were identified in 57.52% (895/1556) of all cases. Comparisons of the incidence and distributions of chromosomal abnormalities within the SA group and RPL group and within the different age groups were performed. Moreover, 346 CNVs in 173 cases were identified, including 272 duplications, 2 deletions and 72 duplications along with deletions. Duplications in 16q24.3 and 16p13.3 were significantly more frequent in RPL cases, and thereby considered to be associated with RPL. There were 213 genes and 131 signaling pathways identified as potential RPL candidate genes and signaling pathways, respectively, which were centered primarily on six functional categories. The results of the present study may improve our understanding of the etiologies of RPL and assist in the establishment of a population-based diagnostic panel of genetic markers for screening RPL amongst Chinese women.

| S-EPMC7911754 | biostudies-literature

Copy number variations among silkworms.

Project description:BACKGROUND: Copy number variations (CNVs), which are important source for genetic and phenotypic variation, have been shown to be associated with disease as well as important QTLs, especially in domesticated animals. However, little is known about the CNVs in silkworm. RESULTS: In this study, we have constructed the first CNVs map based on genome-wide analysis of CNVs in domesticated silkworm. Using next-generation sequencing as well as quantitative PCR (qPCR), we identified ~319 CNVs in total and almost half of them (~ 49%) were distributed on uncharacterized chromosome. The CNVs covered 10.8 Mb, which is about 2.3% of the entire silkworm genome. Furthermore, approximately 61% of CNVs directly overlapped with SDs in silkworm. The genes in CNVs are mainly related to reproduction, immunity, detoxification and signal recognition, which is consistent with the observations in mammals. CONCLUSIONS: An initial CNVs map for silkworm has been described in this study. And this map provides new information for genetic variations in silkworm. Furthermore, the silkworm CNVs may play important roles in reproduction, immunity, detoxification and signal recognition. This study provided insight into the evolution of the silkworm genome and an invaluable resource for insect genomics research.

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Copy number variations and stroke.

Project description:Stroke is the third leading cause of death worldwide after heart disease and all forms of cancers. Monogenic disorders, genetic, and environmental risk factors contribute to damaging cerebral blood vessels and, consequently, cause stroke. Developments in genomic research led to the discovery of numerous copy number variants (CNVs) that have been recently identified as a new tool for understanding the genetic basis of many diseases. This review discusses the current understanding of the types of stroke, the existing knowledge on the involvement of specific CNVs in stroke as well as the limitations of the methods used for detecting CNVs like SNP-microarray. To confirm an unequivocally association between CNVs and stroke and extend the current findings, it would be desirable to use another methodology to detect smaller CNVs or CNVs in genomic regions poorly covered by this technique, for instance, CGH-array.

| S-EPMC5110597 | biostudies-literature

Human subtelomeric copy number variations.

Project description:Copy number variation is a defining characteristic of human subtelomeres. Human subtelomeric segmental duplication regions ('Subtelomeric Repeats') comprise about 25% of the most distal 500 kb and 80% of the most distal 100 kb in human DNA. Huge allelic disparities seen in subtelomeric DNA sequence content and organization are postulated to have an impact on the dosage of transcripts embedded within the duplicated sequences, on the transcription of genes in adjacent single copy DNA regions, and on the chromatin structures mediating telomere functions including chromosome stability. In addition to the complex duplicon substructure and huge allelic variations in extended subtelomere regions, both copy number variation and alternative sequence organizations for DNA characterize the sequences immediately adjacent to terminal (TTAGGG)n tracts ('subterminal DNA'). The structural variation in subterminal DNA is likely to have important consequences for expression of subterminal transcripts such as a newly-discovered gene family encoding actin-interacting proteins and a non-coding telomeric repeat containing RNA (TERRA) transcript family critical for telomere integrity. Major immediate challenges include discovering the full extent and nature of subtelomeric structural and copy number variation in humans, and developing methods for tracking individual allelic variants in the context of total genomic DNA.

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Next-generation sequencing identifies recurrent copy number variations in invasive breast carcinomas from Ghana.

Project description:African and African-American (AA) women have higher incidence of triple-negative breast cancers (TNBC) with high histological grade and aggressive clinical behavior, but the reasons are not fully understood. We recently found that the oncogenic protein EZH2 is overexpressed in Ghanaian breast cancer patients, with 16% of the tumors expressing cytoplasmic EZH2. Understanding the molecular underpinnings of these aggressive tumors may lead to the identification of potential targetable oncogenic drivers. We characterized the copy number variations of 11 Ghanaian breast tumor patients by targeted multiplexed PCR-based DNA next-generation sequencing (NGS) over 130 cancer-relevant genes. While the DNA quality was not optimal for mutation analysis, 90% of the tumors had frequent recurrent copy number alterations (CNAs) of 17 genes: SDHC, RECQL4, TFE3, BCL11A, BCL2L1, PDGFRA, DEK, SMUG1, AKT3, SMARCA4, VHL, KLF6, CCNE1, G6PD, FGF3, ABL1, and CCND1, with the top oncogenic functions being mitotic G1-G1/S-phase regulation, gene transcription, apoptosis, and PI3K/AKT pathway. The most common recurrent high-level CNAs were gains of RECQL4 and SDHC, in 50% and 60% of cases, respectively. Network analyses revealed a significant predicted interaction among 12 of the 17 (70.6%) genes with high-level CNAs (p = 5.7E-07), which was highly correlated with EZH2 expression (r = 0.4-0.75). By immunohistochemistry, RECQL4 and SDHC proteins were upregulated in 53 of 86 (61.6%) and 48 of 86 (56%) of Ghanaian invasive carcinoma tissue samples. In conclusion, our data show that invasive carcinomas from Ghana exhibit recurrent CNAs in 17 genes, with functions in oncogenic pathways, including PI3K/AKT and G1-G1/S regulation, which may have implications for the biology and treatment of invasive carcinomas in African and AA women.

| S-EPMC7390688 | biostudies-literature

Whole genome sequencing-based copy number variations reveal novel pathways and targets in Alzheimer's disease.

Project description:IntroductionA few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.MethodsWe used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.ResultsWe identified 3,012 rare AD-specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.DiscussionWe identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

| S-EPMC9264340 | biostudies-literature

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