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Comparative genomic hybridization of normal karyotype lung tumors from heavy smokers using Affymetrix SNP arrays

ABSTRACT: Whole genome analysis on normal karyotype lung tumors from heavy smokers.
Our study regarded a population of 5203 heavy smokers volunteers, enrolled in a screening program set up at the European Institute of Oncology. Each volunteer yearly undergoes low dose CT-scan. At the end of the second year, among 89 screening-identified patients with lung cancers, 57 cases with clinical localized lung malignancy were evaluated for the present analysis. These, together with 21 other asymptomatic patients (pilot population), carrying non-screening detected lung cancers, but with same characteristics of the screened population (asymptomatic, smokers, older than 50, with an occasionally detected localized cancer), constituted our study population (78 cases). At the time of surgical resection, a portion of the tumoral tissue was collected, snap-frozen and stored at -80 C, for the subsequent tumor cellularity evaluation and DNA extraction. Tumor cellularity was determined by analyzing ematossilin-eosin stained sections of the tumor samples. The evaluation was carried out by a pathologist of the Molecular Pathology Unit, at the IFOM-IEO campus. Only NK samples with an evaluated tumor cellularity of at least 70% were used for the high-resolution genomic analysis: this allowed us to restrict normal cells contamination of the tumor genome pattern in our analysis. A genomic macroscopic analysis was carried out in order to identify tumors harboring a normal karyotype, which were then subjected to a high-resolution genomic analysis, through the Affymetrix SNPs technology.

ORGANISM(S): Homo sapiens

SUBMITTER: Belloni Elena

PROVIDER: S-ECPF-MEXP-2450 | biostudies-other |

REPOSITORIES: biostudies-other

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Comparative genomic hybridization of normal karyotype lung tumors from heavy smokers using Affymetrix SNP arrays

Project description:Whole genome analysis on normal karyotype lung tumors from heavy smokers.<br> Our study regarded a population of 5203 heavy smokers volunteers, enrolled in a screening program set up at the European Institute of Oncology. Each volunteer yearly undergoes low dose CT-scan. At the end of the second year, among 89 screening-identified patients with lung cancers, 57 cases with clinical localized lung malignancy were evaluated for the present analysis. These, together with 21 other asymptomatic patients (pilot population), carrying non-screening detected lung cancers, but with same characteristics of the screened population (asymptomatic, smokers, older than 50, with an occasionally detected localized cancer), constituted our study population (78 cases). At the time of surgical resection, a portion of the tumoral tissue was collected, snap-frozen and stored at -80 C, for the subsequent tumor cellularity evaluation and DNA extraction. Tumor cellularity was determined by analyzing ematossilin-eosin stained sections of the tumor samples. The evaluation was carried out by a pathologist of the Molecular Pathology Unit, at the IFOM-IEO campus. Only NK samples with an evaluated tumor cellularity of at least 70% were used for the high-resolution genomic analysis: this allowed us to restrict normal cells contamination of the tumor genome pattern in our analysis. A genomic macroscopic analysis was carried out in order to identify tumors harboring a normal karyotype, which were then subjected to a high-resolution genomic analysis, through the Affymetrix SNPs technology.

2009-11-20 | E-MEXP-2450 | biostudies-arrayexpress

Hybridization and amplification rate correction for affymetrix SNP arrays.

Project description:BACKGROUND: Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac). METHODS: CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions. RESULTS: Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs. CONCLUSION: CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.

| S-EPMC3428662 | biostudies-literature

Analysis of wilms tumors using SNP mapping array-based comparative genomic hybridization.

Project description:Wilms tumor (WT) has been a model to study kidney embryogenesis and tumorigenesis and, although associated with hereditary, cancer predisposition syndromes, the majority of tumors occur sporadically. To analyze genetic changes in WT we have defined copy number changes and loss of heterozygosity in 56 Wilms tumors using high resolution oligonucleotide arrays at a average resolution of ~12 Kb. Consistent deletions were seen on chromosomes 1p, 4q, 7p, 9q, 11p, 11q, 14q, 16q, and 21q. High frequency gains were seen for 1q and lower frequency gains were seen on 7q and chromosomes 8, 12 and 18. The high resolution provided by the SNP mapping arrays has defined minimal regions of deletion for many of these LOH events. Analysis of CNAs by tumor stage show relatively stable karyotypes in stage 1 tumors and more complex aCGH profiles in tumors from stages 3-5.

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Cross-hybridization modeling on Affymetrix exon arrays.

Project description:MotivationMicroarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts.ResultsTo study cross-hybridization, we map Affymetrix exon array probes to a set of annotated mRNA transcripts, allowing a small number of mismatches or insertion/deletions between the two sequences. Based on a systematic study of the degree to which probes with a given match type to a transcript are affected by cross-hybridization, we developed a strategy to correct for cross-hybridization biases of gene-level expression estimates. Comparison with Solexa ultra high-throughput sequencing data demonstrates that correction for cross-hybridization leads to a significant improvement of gene expression estimates.AvailabilityWe provide mappings between human and mouse exon array probes and off-target transcripts and provide software extending the GeneBASE program for generating gene-level expression estimates including the cross-hybridization correction http://biogibbs.stanford.edu/~kkapur/GeneBase/.

| S-EPMC2639301 | biostudies-literature

Glioblastoma Xenograft Comparative Genomic Hybridization Arrays

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Comparative genomic hybridization of human breast cancer cell lines (45) on Affymetrix 100K SNP arrays

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2009-05-01 | E-GEOD-13696 | biostudies-arrayexpress

Comparative genomic hybridization of BRCAX breast tumors

Project description:Only about 25% of familial breast cancer is explained by mutations in BRCA1 and BRCA2, fewer by moderate penetrance genes like P53, PTEN, CHEK2, ATM and PALB2 and an unknown fraction by common variants of genes with low penetrance. Evidence suggests that additional dominant breast cancer genes exist and these are referred to as BRCAX. Clinical presentation of families with highly increased incidence of breast cancer that are non-BRCA1/BRCA2, suggests dominant inheritance of such high penetrance breast cancer genes. Because cancer genes often confer a specific clinical presentation (e.g. age of onset, sex-ratio, tissue spectrum) it seems useful to initiate their discovery by such clinical criteria. An earlier linkage study of BRCAX / non-BRCA1/2 breast cancer families aimed to enrich for a common genetic defect by setting stringent inclusion criteria, failed to identify new breast cancer susceptibility loci. Motivated by results of BRCA1 and BRCA2 breast tumors that have characteristic genomic signatures (array-CGH 'phenotypes'), we present the largest dataset to date showing the genomic profiles of 58 BRCAX primary breast tumors by array-CGH and show by unsupervised hierarchical clustering that they form a heterogeneous group with 4 distinct subtypes that are different from (n = 48) sporadic controls. This provides a possible explanation for the lack of high LOD scores in linkage studies. The presence of more than one BRCAX sub-type suggests the existence of more than one BRCAX gene. We propose approaches that can be employed to stratify BRCAX families based on array-CGH data.

2011-02-01 | GSE18626 | GEO

Copy number profiling of 36 ovarian tumors on Affymetrix 100K SNP arrays and 16 ovarian tumors on 500K SNP arrays

Project description:Copy number profiling of 36 ovarian tumors on Affymetrix 100K SNP arrays Thirty-six ovarian tumors were profiled for copy-number alterations with the Affymetrix 100K Mapping Array. Copy number profiling of 36 ovarian tumors on Affymetrix 500K SNP arrays Sixteen ovary tumors were profiled for copy-number alterations with the high-resolution Affymetrix 500K Mapping Array.

2010-12-16 | GSE26075 | GEO

Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

Project description:BackgroundMicroarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms with similar design features. This procedure deals with both extreme and subtle biases and is intended to be applied before standard genotype calling algorithms.ResultsApplication of the spatial bias adjustments on HapMap samples resulted in higher genotype call rates with equal or even better accuracy for thousands of SNPs. Consequently the normalization procedure is expected to lead to more meaningful biological inferences and could be valuable for genome-wide SNP analysis.ConclusionsSpatial normalization can potentially rescue thousands of SNPs in a genetic study at the small cost of computational time. The approach is implemented in R and available from the authors upon request.

| S-EPMC2910027 | biostudies-literature

Optimizing comparative genomic hybridization probes for genotyping and SNP detection in Plasmodium falciparum.

Project description:Microarray-based comparative genomic hybridizations (CGH) interrogate genomic DNA to identify structural differences such as amplifications and deletions that are easily detected as large signal aberrations. Subtle signal deviations caused by single nucleotide polymorphisms (SNPs) can also be detected but is challenged by a high AT content (81%) in P. falciparum. We compared genome-wide CGH signal to sequence polymorphisms between parasite strains 3D7, HB3, and Dd2 using NimbleGen microarrays. From 23,191 SNPs (excluding var/rif/stevor genes), our CGH probe set detected SNPs with >99.9% specificity but low (<10%) sensitivity. Probe length, melting temperature, GC content, SNP location in the probe, mutation type, and hairpin structures affected SNP sensitivity. Previously unrecognized variable number tandem repeats (VNTRs) also were detected by this method. These findings will guide the redesign of a probe set to optimize an openly available CGH microarray platform for high-resolution genotyping suitable for population genomics studies.

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