Genomic profiling maps loss of heterozygosity and defines the timing and stage dependence of epigenetic and genetic events in Wilms' tumors

To understand genetic and epigenetic pathways in Wilms' tumors, we carried out a genome scan for loss of heterozygosity (LOH) using Affymetrix 10K single nucleotide polymorphism (SNP) chips and supplemented the data with karyotype information. To score loss of imprinting (LOI) of the IGF2 gene, we assessed DNA methylation of the H19 5' differentially methylated region (DMR). Few chromosomal regions other than band 11p13 (WT1) were lost in Wilms' tumors from Denys-Drash and Wilms' tumor-aniridia syndromes, whereas sporadic Wilms' tumors showed LOH of several regions, most frequently 11p15 but also 1p, 4q, 7p, 11q, 14q, 16q, and 17p. LOI was common in the sporadic Wilms' tumors but absent in the syndromic cases. The SNP chips identified novel centers of LOH in the sporadic tumors, including a 2.4-Mb minimal region on chromosome 4q24-q25. Losses of chromosomes 1p, 14q, 16q, and 17p were more common in tumors presenting at an advanced stage; 11p15 LOH was seen at all stages, whereas LOI was associated with early-stage presentation. Wilms' tumors with LOI often completely lacked LOH in the genome-wide analysis, and in some tumors with concomitant 16q LOH and LOI, the loss of chromosome 16q was mosaic, whereas the H19 DMR methylation was complete. These findings confirm molecular differences between sporadic and syndromic Wilms' tumors, define regions of recurrent LOH, and indicate that gain of methylation at the H19 DMR is an early event in Wilms' tumorigenesis that is independent of chromosomal losses. The data further suggest a biological difference between sporadic Wilms' tumors with and without LOI.     

Quality and concordance of genotyping array data of 12,064 samples from 5840 cancer patients

Genotyping arrays characterize genome-wide SNPs for a study cohort and were the primary technology behind genome wide association studies over the last decade. The Cancer Genome Atlas (TCGA) is one of the largest cancer consortium studies, and it collected genotyping data for all of its participants. Using TCGA SNP data genotyped using the Affymetrix 6.0 SNP array from 12,064 samples, we conducted a comprehensive comparisons across DNA sources (tumor tissue, normal tissue, and blood) and sample storage protocols (formalin-fixed paraffin-embedded (FFPE) vs. freshly frozen (FF)), examining genotypes, transition/transversion ratios, and mutation catalogues. During the analysis, we made important observations in relevance to the data quality issues. SNP concordance was excellent between blood and normal tissues, and slightly lower between blood and tumor tissue due to potential somatic mutations in the tumors. The observed poor SNP concordance between FFPE and FF samples suggested a batch effect. The transition/transversion ratio, a metric commonly used for quality control purpose in exome sequencing projects, appeared less applicable for genotyping array data due to the whole-genome coverage built into the array design. Moreover, there were substantially more loss of heterozygosity events than gain of heterozygosity when comparing tumors relative to normal tissues and blood. This might be a consequence of extensive copy number deletions in tumors. In summary, our thorough evaluation calls for more adequate quality control practices and provides guidelines for improved application of TCGA genotyping data.     

Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples.     

Direct inference of SNP heterozygosity rates and resolution of LOH detection

Single nucleotide polymorphisms (SNPs) have been increasingly utilized to investigate somatic genetic abnormalities in premalignancy and cancer. LOH is a common alteration observed during cancer development, and SNP assays have been used to identify LOH at specific chromosomal regions. The design of such studies requires consideration of the resolution for detecting LOH throughout the genome and identification of the number and location of SNPs required to detect genetic alterations in specific genomic regions. Our study evaluated SNP distribution patterns and used probability models, Monte Carlo simulation, and real human subject genotype data to investigate the relationships between the number of SNPs, SNP HET rates, and the sensitivity (resolution) for detecting LOH. We report that variances of SNP heterozygosity rate in dbSNP are high for a large proportion of SNPs. Two statistical methods proposed for directly inferring SNP heterozygosity rates require much smaller sample sizes (intermediate sizes) and are feasible for practical use in SNP selection or verification. Using HapMap data, we showed that a region of LOH greater than 200 kb can be reliably detected, with losses smaller than 50 kb having a substantially lower detection probability when using all SNPs currently in the HapMap database. Higher densities of SNPs may exist in certain local chromosomal regions that provide some opportunities for reliably detecting LOH of segment sizes smaller than 50 kb. These results suggest that the interpretation of the results from genome-wide scans for LOH using commercial arrays need to consider the relationships among inter-SNP distance, detection probability, and sample size for a specific study. New experimental designs for LOH studies would also benefit from considering the power of detection and sample sizes required to accomplish the proposed aims.     

Loss-of-heterozygosity analysis of small-cell lung carcinomas using single-nucleotide polymorphism arrays

Human cancers arise by a combination of discrete mutations and chromosomal alterations. Loss of heterozygosity (LOH) of chromosomal regions bearing mutated tumor suppressor genes is a key event in the evolution of epithelial and mesenchymal tumors. Global patterns of LOH can be understood through allelotyping of tumors with polymorphic genetic markers. Simple sequence length polymorphisms (SSLPs, or microsatellites) are reliable genetic markers for studying LOH, but only a modest number of SSLPs are used in LOH studies because the genotyping procedure is rather tedious. Here, we report the use of a highly parallel approach to genotype large numbers of single-nucleotide polymorphisms (SNPs) for LOH, in which samples are genotyped for nearly 1,500 loci by performing 24 polymerase chain reactions (PCR), pooling the resulting amplification products and hybridizing the mixture to a high-density oligonucleotide array. We characterize the results of LOH analyses on human small-cell lung cancer (SCLC) and control DNA samples by hybridization. We show that the patterns of LOH are consistent with those obtained by analysis with both SSLPs and comparative genomic hybridization (CGH), whereas amplifications rarely are detected by the SNP array. The results validate the use of SNP array hybridization for tumor studies.     

Differential loss of heterozygosity in familial, sporadic, and uremic hyperparathyroidism

Various genetic loci harboring oncogenes, tumor suppressor genes, and genes for calcium receptors have been implicated in the development of parathyroid tumors. We have carried out loss of heterozygosity (LOH) studies in chromosomes 1p, 1q, 3q, 6q, 11q, 13q, 15q, and X in a total of 89 benign parathyroid tumors. Of these, 28 were sporadic parathyroid adenomas from patients with no family history of the disease, 41 were secondary parathyroid tumors, 5 were from patients with a history of previous irradiation to the neck, 12 were from patients with a family history of hyperparathyroidism, and 3 were parathyroid tumors related to multiple endocrine neoplasia type 1 (MEN1). In addition, we determined the chromosomal localization of a second putative calcium-sensing receptor, CaS, for inclusion in the LOH studies. Based on analysis of somatic cell hybrids and fluorescent in situ hybridization to metaphase chromsomes, the gene for CaS was mapped to chromosomal region 2q21-q22. The following results were obtained from the LOH studies: (1) out of the 24 tumors that showed LOH, only 4 had more than one chromosomal region involved, (2) in the tumours from uremic patients, LOH of chromosome 3q was detected in a subset of the tumors, (3) LOH of the MEN1 region at 11q13 was the most common abnormality found in both MEN1-related and sporadic parathyroid tumours but was not a feature of the other forms of parathyroid tumors, (4) LOH in 1p and 6q was not as frequent as previously reported, and (5) tumor suppressor genes in 1q and X might have played a role, particularly on the X chromosome, in the case of familial parathyroid adenomas. We therefore conclude that the tumorigenesis of familial, sporadic, and uremic hyperparathyroidism involves different genetic triggers in a non-progressive pattern. Received: 28 October 1996 / Revised: 16 November 1996     

High-resolution analysis of chromosomal imbalances using the Affymetrix 10K SNP genotyping chip

Array-based comparative genome hybridization is a powerful tool for detecting chromosomal imbalances at high resolution. However, the design and setup of such arrays are time consuming and expensive and thus worthwhile only when large numbers of arrays will be processed. To provide a feasible solution, we have developed an algorithm that renders the publicly available Affymetrix 10K SNP genotyping chip useful for high-resolution analysis of chromosomal imbalances. We have used our newly developed algorithm to analyze data from Affymetrix 10K chips that were hybridized with DNA probes from a variety of different sources, such as primary tumors, cell lines, and blood from patients with unbalanced translocations. In summary, we were able to (i) demonstrate the capability of our method by reproduction of published and unpublished data obtained with alternative methods and (ii) identify novel imbalances that were not shown before.     

A model for genetic complementation controlling the chromosomal abnormalities and loss of heterozygosity formation in cancer

The relationship between the apparently random chromosomal changes found in aneuploidy and the genetic instability driving the progression of cancer is not clear. We report a test of the hypothesis that aneuploid chromosomal abnormalities might be selected to preserve cell-survival genes during loss of heterozygosity (LOH) formations which eliminate tumor suppressor genes. The LOHs and structurally abnormal chromosomes present in the aneuploid LoVo (colon), A549 (lung), SUIT-2 (pancreas), and LN-18 (glioma) cancer cell lines were identified by single nucleotide polymorphisms (SNPs) and Spectral Karyotyping (SKY). The Mann-Whitney U and chi square tests were used to evaluate possible differences in chromosome numbers and abnormalities between the cell lines, with two-tailed P values of <0.01 being considered significant. The cell lines differed significantly in chromosome numbers and frequency of structurally abnormal chromosomes. The SNP analysis revealed that each cell line contained at least a haploid set of somatic chromosomes, consistent with our hypothesis that cell-survival genes are widely scattered throughout the genome. Further, over 90% of the chromosomal abnormalities seemed to be selected, often after LOH formation, for gene-dosage compensation or to provide heterozygosity for specific chromosomal regions. These results suggest that the chromosomal changes of aneuploidy are not random, but may be selected to provide gene-dosage compensation and/or retain functional alleles of cell-survival genes during LOH formation.     

Conditional random pattern algorithm for LOH inference and segmentation

Motivation: Loss of heterozygosity (LOH) is one of the mostimportant mechanisms in the tumor evolution. LOH can be detectedfrom the genotypes of the tumor samples with or without pairednormal samples. In paired sample cases, LOH detection for informativesingle nucleotide polymorphisms (SNPs) is straightforward ifthere is no genotyping error. But genotyping errors are alwaysunavoidable, and there are about 70% non-informative SNPs whoseLOH status can only be inferred from the neighboring informativeSNPs. Results: This article presents a novel LOH inference and segmentationalgorithm based on the conditional random pattern (CRP) model.The new model explicitly considers the distance between twoneighboring SNPs, as well as the genotyping error rate and theheterozygous rate. This new method is tested on the simulatedand real data of the Affymetrix Human Mapping 500K SNP arrays.The experimental results show that the CRP method outperformsthe conventional methods based on the hidden Markov model (HMM). Availability: Software is available upon request. Contact: xzhou{at}tmhs.org Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Alex Bateman     

Angiomyolipoma have common mutations in TSC2 but no other common genetic events

Renal angiomyolipoma are part of the PEComa family of neoplasms, and occur both in association with Tuberous Sclerosis Complex (TSC) and independent of that disorder. Previous studies on the molecular genetic alterations that occur in angiomyolipoma are very limited. We evaluated 9 angiomyolipoma for which frozen tissue was available from a consecutive surgical series. Seven of 8 samples subjected to RT-PCR-cDNA sequencing showed mutations in TSC2; none showed mutations in TSC1 or RHEB. Six of the seven mutations were deletions. We searched for 983 activating and inactivating mutations in 115 genes, and found none in these tumors. Similarly analysis for genomic regions of loss or gain, assessed by Affymetrix SNP6.0 analysis, showed no abnormalities. Loss of heterozygosity in the TSC2 region was commonly seen, except in patients with low frequency TSC2 mutations. We conclude that sporadic renal angiomyolipoma usually have mutations in TSC2, but not TSC1 or RHEB, and have no other common genomic events, among those we searched for. However, chromosomal translocations and gene fusion events were not assessed here. TSC2 inactivation by mutation is a consistent and likely necessary genetic event in the pathogenesis of most angiomyolipoma.     

伴海马硬化的颞叶癫痫患者海马组织内BDNF表达变化

目的：研究伴海马硬化的难治性颞叶癫痫（TLE）患者海马组织内脑源性神经营养因子（brain derivedneurotrophic factor,BDNF）的表达变化,探讨其在难治性颞叶癫痫发病机制中的作用。方法：采集5例伴海马硬化的难治性TLE患者手术中切除的海马组织,用逆转录-聚合酶链反应（RT—PCR）法检测BDNFmRNA表达,并与3例非海马硬化TLE患者对照。结果：与非海马硬化组比较,伴海马硬化的难治性TLE患者海马组织中的BDNFmRNA表达明显增加（P〈0．01）。结论：伴海马硬化的难治性TLE患者海马组织中BDNFmRNA表达表达增高,可能在海马硬化和难治性颞叶癫痫发生、发展中具有重要作用。     

Genome-wide detection of LOH in prostate cancer using human SNP microarray technology

Loss of heterozygosity (LOH) of chromosomal regions is crucial in tumor progression. In this study we assessed the potential of the Affymetrix GeneChip HuSNP mapping assay for detecting genome-wide LOH in prostate tumors. We analyzed two human prostate cell lines, P69SV40Tag (P69) and its tumorigenic subline, M12, and 11 prostate cancer cases. The M12 cells showed LOH in chromosomes 3p12.1-p22.1, 11q22.1-q24.2, 19p13.12, and 19q13.42. All of the prostate cases with informative single-nucleotide polymorphism (SNP) markers showed LOH in 1p31.2, 10q11.21, 12p13.1, 16q23.1-q23.2, 17p13.3, 17q21.31, and 21q21.2. Additionally, a high percentage of cases showed LOH at 6p25.1-p25.3 (75%), 8p22-p23.2, and 10q22.1 (70%). Several tumor suppressor genes (TSGs) have been mapped in these loci. These results demonstrate that the HuSNP mapping assay can serve as an alternative to comparative genomic hybridization for assessing genome-wide LOH and can identify chromosomal regions harboring candidate TSGs implicated in prostate cancer.     

A single‐nucleotide polymorphism‐based approach for rapid and cost‐effective genetic wolf monitoring in Europe based on noninvasively collected samples

Noninvasive genetics based on microsatellite markers has become an indispensable tool for wildlife monitoring and conservation research over the past decades. However, microsatellites have several drawbacks, such as the lack of standardisation between laboratories and high error rates. Here, we propose an alternative single‐nucleotide polymorphism (SNP)‐based marker system for noninvasively collected samples, which promises to solve these problems. Using nanofluidic SNP genotyping technology (Fluidigm), we genotyped 158 wolf samples (tissue, scats, hairs, urine) for 192 SNP loci selected from the Affymetrix v2 Canine SNP Array. We carefully selected an optimised final set of 96 SNPs (and discarded the worse half), based on assay performance and reliability. We found rates of missing data in this SNP set of <10% and genotyping error of ~1%, which improves genotyping accuracy by nearly an order of magnitude when compared to published data for other marker types. Our approach provides a tool for rapid and cost‐effective genotyping of noninvasively collected wildlife samples. The ability to standardise genotype scoring combined with low error rates promises to constitute a major technological advancement and could establish SNPs as a standard marker for future wildlife monitoring.     

Concurrent analysis of loss of heterozygosity (LOH) and copy number abnormality (CNA) for oral premalignancy progression using the Affymetrix 10K SNP mapping array

Like most human cancers, oral squamous cell carcinoma (SCC) is characterized by genetic instabilities. In this study, a single platform (Affymetrix 10K SNP mapping array) was used to generate both loss of heterozygosity (LOH) and DNA copy number abnormality (CNA) read-outs for precise and high-resolution genetic alteration profiles. As a proof of principle, we performed this concordant analysis on a panel of deletion and trisomy cell lines with known chromosomal alterations and the precise LOH and CNA regions were detected as expected. Using a previously described oral SCC progression model system, we identified a set of genomic regions that may be associated with the malignancy progression, including chromosome regions 3pter–3p35.3, 3p14.1–3p13, 11p, 11q14.3–11q22.2, and 11q13.5–11q14.1. These data show that it is feasible to utilize high-density SNP arrays to generate concordant LOH and CNA profiles at high resolution.     

Single nucleotide polymorphism (SNP)-based loss of heterozygosity (LOH) testing by real time PCR in patients suspect of myeloproliferative disease

During tumor development, loss of heterozygosity (LOH) often occurs. When LOH is preceded by an oncogene activating mutation, the mutant allele may be further potentiated if the wild-type allele is lost or inactivated. In myeloproliferative neoplasms (MPN) somatic acquisition of JAK2V617F may be followed by LOH resulting in loss of the wild type allele. The occurrence of LOH in MPN and other proliferative diseases may lead to a further potentiating the mutant allele and thereby increasing morbidity. A real time PCR based SNP profiling assay was developed and validated for LOH detection of the JAK2 region (JAK2LOH). Blood of a cohort of 12 JAK2V617F-positive patients (n=6 25-50% and n=6>50% JAK2V617F) and a cohort of 81 patients suspected of MPN was stored with EDTA and subsequently used for validation. To generate germ-line profiles, non-neoplastic formalin-fixed paraffin-embedded tissue from each patient was analyzed. Results of the SNP assay were compared to those of an established Short Tandem Repeat (STR) assay. Both assays revealed JAK2LOH in 1/6 patients with 25-50% JAK2V617F. In patients with >50% JAK2V617F, JAK2LOH was detected in 6/6 by the SNP assay and 5/6 patients by the STR assay. Of the 81 patients suspected of MPN, 18 patients carried JAK2V617F. Both the SNP and STR assay demonstrated the occurrence of JAK2LOH in 5 of them. In the 63 JAK2V617F-negative patients, no JAK2LOH was observed by SNP and STR analyses. The presented SNP assay reliably detects JAK2LOH and is a fast and easy to perform alternative for STR analyses. We therefore anticipate the SNP approach as a proof of principle for the development of LOH SNP-assays for other clinically relevant LOH loci.     

SNP rs10248565 in HDAC9 as a novel genomic aberration biomarker of lung adenocarcinoma in non-smoking women

Background

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Although cigarette smoking is the primary risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. Since cancer results from progressive accumulation of genetic aberrations, genomic rearrangements may be early events in carcinogenesis.

Results

In order to identify biomarkers of early-stage adenocarcinoma, the genome-wide DNA aberrations of 60 pairs of lung adenocarcinoma and adjacent normal lung tissue in non-smoking women were examined using Affymetrix Genome-Wide Human SNP 6.0 arrays. Common copy number variation (CNV) regions were identified by ≥30% of patients with copy number beyond 2 ± 0.5 of copy numbers for each single nucleotide polymorphism (SNP) and at least 100 continuous SNP variant loci. SNPs associated with lung adenocarcinoma were identified by McNemar’s test. Loss of heterozygosity (LOH) SNPs were identified in ≥18% of patients with LOH in the locus. Aberration of SNP rs10248565 at HDAC9 in chromosome 7p21.1 was identified from concurrent analyses of CNVs, SNPs, and LOH.

Conclusion

The results elucidate the genetic etiology of lung adenocarcinoma by demonstrating that SNP rs10248565 may be a potential biomarker of cancer susceptibility.     

Genotyping performance between saliva and blood-derived genomic DNAs on the DMET array: a comparison

The Affymetrix Drug Metabolism Enzymes and Transporters (DMET) microarray is the first assay to offer a large representation of SNPs conferring genetic diversity across known pharmacokinetic markers. As a convenient and painless alternative to blood, saliva samples have been reported to work well for genotyping on the high density SNP arrays, but no reports to date have examined this application for saliva-derived DNA on the DMET platform. Genomic DNA extractions from saliva samples produced an ample quantity of genomic DNA for DMET arrays, however when human amplifiable DNA was measured, it was determined that a large percentage of this DNA was from bacteria or fungi. A mean of 37.3% human amplifiable DNA was determined for saliva-derived DNAs, which results in a significant decrease in the genotyping call rate (88.8%) when compared with blood-derived DNAs (99.1%). More interestingly, the percentage of human amplifiable DNA correlated with a higher genotyping call rate, and almost all samples with more than 31.3% human DNA produced a genotyping call rate of at least 96%. SNP genotyping results for saliva derived DNA (n = 39) illustrated a 98.7% concordance when compared with blood DNA. In conclusion, when compared with blood DNA and tested on the DMET array, saliva-derived DNA provided adequate genotyping quality with a significant lower number of SNP calls. Saliva-derived DNA does perform very well if it contains greater than 31.3% human amplifiable DNA.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.