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A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data.

ABSTRACT: We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.

SUBMITTER: Yau C

PROVIDER: S-EPMC2965384 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data.

Yau Christopher C Mouradov Dmitri D Jorissen Robert N RN Colella Stefano S Mirza Ghazala G Steers Graham G Harris Adrian A Ragoussis Jiannis J Sieber Oliver O Holmes Christopher C CC

Genome biology 20100921 9

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours. ...[more]

PMID: 20858232

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A statistical method for detecting genomic aberrations in heterogeneous tumour samples from single nucleotide polymorphism genotyping data

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A statistical method for detecting genomic aberrations in heterogeneous tumour samples from single nucleotide polymorphism genotyping data

Project description:SNP array analysis of microdissected tumor vs stroma vs surgically obtained tumor DNA was extracted from total tumor material, from microdissected stroma and microdissected, pure tumor tissue

2010-08-25 | E-GEOD-23785 | biostudies-arrayexpress

Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels.

Project description:The genomic era has led to an unprecedented increase in the availability of genome-wide data for a broad range of taxa. Wildlife management strives to make use of these vast resources to enable refined genetic assessments that enhance biodiversity conservation. However, as new genomic platforms emerge, problems remain in adapting the usually complex approaches for genotyping of noninvasively collected wildlife samples. Here, we provide practical guidelines for the standardized development of reduced single nucleotide polymorphism (SNP) panels applicable for microfluidic genotyping of degraded DNA samples, such as faeces or hairs. We demonstrate how microfluidic SNP panels can be optimized to efficiently monitor European wildcat (Felis silvestris S.) populations. We show how panels can be set up in a modular fashion to accommodate informative markers for relevant population genetics questions, such as individual identification, hybridization assessment and the detection of population structure. We discuss various aspects regarding the implementation of reduced SNP panels and provide a framework that will allow both molecular ecologists and practitioners to help bridge the gap between genomics and applied wildlife conservation.

| S-EPMC7199164 | biostudies-literature

Wavelet-based identification of DNA focal genomic aberrations from single nucleotide polymorphism arrays.

Project description:BackgroundCopy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs.ResultsIn this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12).ConclusionsOur results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets.

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High-resolution single-nucleotide polymorphism array-profiling in myeloproliferative neoplasms identifies novel genomic aberrations.

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Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.

Project description:BackgroundThe identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.ResultsThe performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.ConclusionsWe demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.

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Detecting genomic aberrations using products in a multiscale analysis.

Project description:Genomic instability, such as copy-number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy-number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale-based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change point and adjusted p-values for the significance of a change point using a step-down maxT permutation algorithm to control the family-wise error rate. A simulation study investigates the finite sample performance of the proposed method and compares it with a more standard sequential testing method. The method is illustrated using two real data sets.

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HCV genotyping using statistical classification approach.

Project description:The genotype of Hepatitis C Virus (HCV) strains is an important determinant of the severity and aggressiveness of liver infection as well as patient response to antiviral therapy. Fast and accurate determination of viral genotype could provide direction in the clinical management of patients with chronic HCV infections. Using publicly available HCV nucleotide sequences, we built a global Position Weight Matrix (PWM) for the HCV genome. Based on the PWM, a set of genotype specific nucleotide sequence "signatures" were selected from the 5' NCR, CORE, E1, and NS5B regions of the HCV genome. We evaluated the predictive power of these signatures for predicting the most common HCV genotypes and subtypes. We observed that nucleotide sequence signatures selected from NS5B and E1 regions generally demonstrated stronger discriminant power in differentiating major HCV genotypes and subtypes than that from 5' NCR and CORE regions. Two discriminant methods were used to build predictive models. Through 10 fold cross validation, over 99% prediction accuracy was achieved using both support vector machine (SVM) and random forest based classification methods in a dataset of 1134 sequences for NS5B and 947 sequences for E1. Prediction accuracy for each genotype is also reported.

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Heterogeneous genomic aberrations in esophageal squamous cell carcinoma: a review.

Project description:Esophageal cancer (EC) causes hundreds of thousands of deaths a year worldwide, especially the major subtype esophageal squamous cell carcinoma (ESCC). With the advent of next-generation sequencing and the availability of commercial microarrays, abnormities in genetic levels have been revealed in various independent researches. High frequencies of structure variations (SVs), single nucleotide variations (SNVs) and copy-number alterations (CNAs) in ESCCs are uncovered, and ESCC shows high levels of inter- and intratumor heterogeneity, implying diverse evolutionary trajectories. This review tries to explain the pathogenesis of ESCC on the scope of most often mutated genes based on prior studies, hopes to offer some hints for diagnosis and therapy in clinic.

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Detection of genomic aberrations in molecularly defined Burkitt's lymphoma by array-based, high resolution, single nucleotide polymorphism analysis.

Project description:BACKGROUND: Knowledge about the genetic lesions that occur in Burkitt's lymphoma, besides the pathognomonic IG-MYC translocations, is limited. DESIGN AND METHODS: Thirty-nine molecularly-defined Burkitt's lymphomas were analyzed with high-resolution single-nucleotide polymorphism chips for genomic imbalances and uniparental disomy. Imbalances were correlated to expression profiles and selected micro-RNA analysis. Translocations affecting the MYC locus were studied by fluorescence in situ hybridization. RESULTS: We detected 528 copy number changes, defining 29 recurrently imbalanced regions. Five hundred and eighteen regions of uniparental disomy were found, but these were rarely recurrent. Combined imbalance mapping and expression profiling revealed a strong correlation between copy number and expression. Several recurrent imbalances affected the MYC pathway: the micro-RNA-supercluster 17-92 was frequently gained and the transcription factor E2F2 was recurrently deleted. Molecular Burkitt's lymphoma lacking MYC translocations showed MYC gains. Amplifications of the polymerase iota gene were associated with increased frequency of positions scored as aberrant. CONCLUSIONS: The present findings suggest that uniparental disomies do not play a major role in the pathogenesis of Burkitt's lymphoma, whereas some genes may contribute to the development of this lymphoma through gene dosage effects. Amplifications of the polymerase iota gene may be functionally linked with increased genomic alterations in Burkitt's lymphoma. The pattern and rarity of chromosomal changes detectable, even at the high resolution employed here, together with aberrations of genes regulating MYC activity, support the hypothesis that deregulation of the MYC pathway is the major force driving the pathogenesis of Burkitt's lymphoma, but show that this deregulation is more complex than previously known.

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