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Quantification of normal cell fraction and copy number neutral LOH in clinical lung cancer samples using SNP array data.

ABSTRACT: BACKGROUND: Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells. PRINCIPAL FINDING: Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection. CONCLUSION: Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.

SUBMITTER: Goransson H

PROVIDER: S-EPMC2699026 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Quantification of normal cell fraction and copy number neutral LOH in clinical lung cancer samples using SNP array data.

Göransson Hanna H Edlund Karolina K Rydåker Maria M Rasmussen Markus M Winquist Johan J Ekman Simon S Bergqvist Michael M Thomas Andrew A Lambe Mats M Rosenquist Richard R Holmberg Lars L Micke Patrick P Botling Johan J Isaksson Anders A

PloS one 20090626 6

<h4>Background</h4>Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.<h4>Principal finding</h4>Using SNP array data from 44 non-small cell lung cancer samples we h ...[more]

PMID: 19557126

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Project description:BackgroundNeuroblastoma is a very heterogeneous pediatric tumor of the sympathetic nervous system showing clinically significant patterns of genetic alterations. Favorable tumors usually have near-triploid karyotypes with few structural rearrangements. Aggressive stage 4 tumors often have near-diploid or near-tetraploid karyotypes and structural rearrangements. Whole genome approaches for analysis of genome-wide copy number have been used to analyze chromosomal abnormalities in tumor samples. We have used array-based copy number analysis using oligonucleotide single nucleotide polymorphisms (SNP) arrays to analyze the chromosomal structure of a large number of neuroblastoma tumors of different clinical and biological subsets.ResultsNinety-two neuroblastoma tumors were analyzed with 50 K and/or 250 K SNP arrays from Affymetrix, using CNAG3.0 software. Thirty percent of the tumors harbored 1p deletion, 22% deletion of 11q, 26% had MYCN amplification and 45% 17q gain. Most of the tumors with 1p deletion were found among those with MYCN amplification. Loss of 11q was most commonly seen in tumors without MYCN amplification. In the case of MYCN amplification, two types were identified. One type displayed simple continuous amplicons; the other type harbored more complex rearrangements. MYCN was the only common gene in all cases with amplification. Complex amplification on chromosome 12 was detected in two tumors and three different overlapping regions of amplification were identified. Two regions with homozygous deletions, four cases with CDKN2A deletions in 9p and one case with deletion on 3p (the gene RBMS3) were also detected in the tumors.ConclusionSNP arrays provide useful tools for high-resolution characterization of significant chromosomal rearrangements in neuroblastoma tumors. The mapping arrays from Affymetrix provide both copy number and allele-specific information at a resolution of 10-12 kb. Chromosome 9p, especially the gene CDKN2A, is subject to homozygous (four cases) and heterozygous deletions (five cases) in neuroblastoma tumors.

Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.

Project description:BACKGROUND: Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events. RESULTS: To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation. CONCLUSIONS: We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.

Dataset Information

Quantification of normal cell fraction and copy number neutral LOH in clinical lung cancer samples using SNP array data.

Publications

Quantification of normal cell fraction and copy number neutral LOH in clinical lung cancer samples using SNP array data.

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