Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays.
Ontology highlight
ABSTRACT:
SUBMITTER: Staaf J
PROVIDER: S-EPMC2592714 | biostudies-literature | 2008
REPOSITORIES: biostudies-literature
Publications
Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays.
Staaf Johan J Lindgren David D Vallon-Christersson Johan J Isaksson Anders A Göransson Hanna H Juliusson Gunnar G Rosenquist Richard R Höglund Mattias M Borg Ake A Ringnér Markus M
Genome biology 20080916 9
We present a strategy for detection of loss-of-heterozygosity and allelic imbalance in cancer cells from whole genome single nucleotide polymorphism genotyping data. Using a dilution series of a tumor cell line mixed with its paired normal cell line and data generated on Affymetrix and Illumina platforms, including paired tumor-normal samples and tumors characterized by fluorescent in situ hybridization, we demonstrate a high sensitivity and specificity of the strategy for detecting both minute ...[more]
Similar Datasets
Project description:High-resolution microarray-based whole genome genotyping (WGG) techniques based on SNP analysis have successfully been applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Problems in data interpretation arise when WGG is applied on tumor tissue specimens, in which normal cell components and tumor subpopulations frequently exist. Such heterogeneity may lead to reduced detection of cancer cell specific genomic alterations. To circumvent problems with sample heterogeneity, we propose using a segmentation strategy derived from DNA copy number analysis for detection of LOH and allelic imbalance. We generated an experimental dilution series of a tumor cell line mixed with its paired normal cell line and simulated data for such dilutions to test the strategy. We also used data sets generated on both Affymetrix and Illumina WGG platforms, including paired tumor-normal samples and tumors previously characterized by FISH. We tested the segmentation strategy against several reported algorithms. We demonstrate high sensitivity and specificity of the segmentation strategy for detecting both minute and gross allelic imbalances originating from DNA copy number gain, loss, and neutral events in tumor specimens. For example, hemizygous copy number loss can be detected in samples containing only 20-25% tumor cells. Furthermore, the strategy can identify cell subpopulation specific events and accurately estimate the fraction of cells affected by an allelic imbalance. Thus, the segmentation strategy extends the usefulness of WGG platforms for investigation of allelic imbalances in heterogeneous tumor genomes.
2008-09-18 | GSE11976 | GEO
Project description:Due to limitations of surgical dissection and tumor heterogeneity, tumor samples collected for cancer genomics studies are often heavily diluted with normal tissue or contain subpopulations of cells harboring important aberrations. Methods for profiling tumor-associated allelic imbalance in such scenarios break down at aberrant cell proportions of 10%-15% and below. Here, we present an approach that offers a vast improvement for detection of subtle allelic imbalance, or low proportions of cells harboring aberrant allelic ratio among nonaberrant cells, in unpaired tumor samples using SNP microarrays. We leverage the expected pattern of allele-specific intensity ratios determined by an individual's germline haplotypes, information that has been ignored in existing approaches. We demonstrate our method on real and simulated data from the CRL-2324 breast cancer cell line genotyped on the Illumina 370K array. Assuming a 5 million SNP array, we can detect the presence of aberrant cells in proportions lower than 0.25% in the breast cancer sample, approaching the sensitivity of some minimal residual disease assays. Further, we apply a hidden Markov model to identify copy-neutral LOH (loss of heterozygosity) events as short as 11 Mb in mixtures of only 4% tumor using 370K data. We anticipate our approach will offer a new paradigm for genomic profiling of heterogeneous samples.
| S-EPMC3530675 | biostudies-literature
Project description: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.
| S-EPMC1458964 | biostudies-literature
Project description:There is an increasing interest in using single nucleotide polymorphism (SNP) genotyping arrays for profiling chromosomal rearrangements in tumors, as they allow simultaneous detection of copy number and loss of heterozygosity with high resolution. Critical issues such as signal baseline shift due to aneuploidy, normal cell contamination, and the presence of GC content bias have been reported to dramatically alter SNP array signals and complicate accurate identification of aberrations in cancer genomes. To address these issues, we propose a novel Global Parameter Hidden Markov Model (GPHMM) to unravel tangled genotyping data generated from tumor samples. In contrast to other HMM methods, a distinct feature of GPHMM is that the issues mentioned above are quantitatively modeled by global parameters and integrated within the statistical framework. We developed an efficient EM algorithm for parameter estimation. We evaluated performance on three data sets and show that GPHMM can correctly identify chromosomal aberrations in tumor samples containing as few as 10% cancer cells. Furthermore, we demonstrated that the estimation of global parameters in GPHMM provides information about the biological characteristics of tumor samples and the quality of genotyping signal from SNP array experiments, which is helpful for data quality control and outlier detection in cohort studies.
| S-EPMC3130254 | biostudies-literature
Project description:The requirement for large amounts of good quality DNA for whole-genome applications prohibits their use for small, laser capture micro-dissected (LCM), and/or rare clinical samples, which are also often formalin-fixed and paraffin-embedded (FFPE). Whole-genome amplification of DNA from these samples could, potentially, overcome these limitations. However, little is known about the artefacts introduced by amplification of FFPE-derived DNA with regard to genotyping, and subsequent copy number and loss of heterozygosity (LOH) analyses. Using a ligation adaptor amplification method, we present data from a total of 22 Affymetrix SNP 6.0 experiments, using matched paired amplified and non-amplified DNA from 10 LCM FFPE normal and dysplastic oral epithelial tissues, and an internal method control. An average of 76.5% of SNPs were called in both matched amplified and non-amplified DNA samples, and concordance was a promising 82.4%. Paired analysis for copy number, LOH, and both combined, showed that copy number changes were reduced in amplified DNA, but were 99.5% concordant when detected, amplifications were the changes most likely to be 'missed', only 30% of non-amplified LOH changes were identified in amplified pairs, and when copy number and LOH are combined ?50% of gene changes detected in the unamplified DNA were also detected in the amplified DNA and within these changes, 86.5% were concordant for both copy number and LOH status. However, there are also changes introduced as ?20% of changes in the amplified DNA are not detected in the non-amplified DNA. An integrative network biology approach revealed that changes in amplified DNA of dysplastic oral epithelium localize to topologically critical regions of the human protein-protein interaction network, suggesting their functional implication in the pathobiology of this disease. Taken together, our results support the use of amplification of FFPE-derived DNA, provided sufficient samples are used to increase power and compensate for increased error rates.
| S-EPMC3180289 | biostudies-literature
Project description:BackgroundGenotyping platforms such as single nucleotide polymorphism (SNP) arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV) and allelic imbalance including loss-of-heterozygosity (LOH) beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH) platforms. Several algorithms based on hidden Markov models (HMMs) have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data.MethodsWe have developed MixHMM, a novel hidden Markov model using hidden states based on chromosomal structural aberrations. MixHMM allows CNV detection for copy numbers up to 7 and allows more complete and accurate description of other forms of allelic imbalance, such as increased copy number LOH or imbalanced amplifications. MixHMM also incorporates a novel sample mixing model that allows detection of tumor CNV events in heterogeneous tumor samples, where cancer cells are mixed with a proportion of stromal cells.ConclusionsWe validate MixHMM and demonstrate its advantages with simulated samples, clinical tumor samples and a dilution series of mixed samples. We have shown that the CNVs of cancer cells in a tumor sample contaminated with up to 80% of stromal cells can be detected accurately using Illumina BeadChip and MixHMM.AvailabilityThe MixHMM is available as a Python package provided with some other useful tools at http://genecube.med.yale.edu:8080/MixHMM.
| S-EPMC2879364 | biostudies-literature
Project description:SummaryWhole-exome sequencing (WES) has extensively been used in cancer genome studies; however, the use of WES data in the study of loss of heterozygosity or more generally allelic imbalance (AI) has so far been very limited, which highlights the need for user-friendly and flexible software that can handle low-quality datasets. We have developed a statistical approach, ExomeAI, for the detection of recurrent AI events using WES datasets, specifically where matched normal samples are not available.AvailabilityExomeAI is a web-based application, publicly available at: http://genomequebec.mcgill.ca/exomeai.ContactJavadNadaf@gmail.com or somayyeh.fahiminiya@mcgill.caSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4308664 | biostudies-literature
Project description:To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level.
| S-EPMC2432039 | biostudies-literature
Project description:Prostate cancer (PCa) is the most commonly diagnosed non-cutaneous cancer in male subjects in Western countries. The widespread use of prostate-specific antigen (PSA) has increased the detection of this cancer form in earlier stages. Moreover, it has increased the need for new diagnostic procedures to be developed for patient stratification based on risk of progression. We analysed laser-microdissected prostate tumour tissue from 43 patients with histologically verified PCa, using the new high-resolution Affymetrix Mapping 50K single-nucleotide polymorphism array. The results showed six major loss of heterozygosity regions at chromosomes 6q14-16, 8p23-11, 10q23, 13q13-21 and 16q21-24 and a novel region at chromosome 21q22.2, all of which reveal concomitant copy number loss. Tumour development was further characterised by numerous novel genomic regions almost exclusively showing copy number loss. However, tumour progression towards a metastatic stage, as well as poor differentiation, was identified by specific patterns of copy number gains of genomic regions located at chromosomes 8q, 1q, 3q and 7q. Androgen ablation therapy was further characterised by copy gain at chromosomes 2p and 10q. In conclusion, patterns of allelic imbalance were discovered in PCa, consisting allelic loss as an early event in tumour development, and distinct patterns of allelic amplification related to tumour progression and poor differentiation.
| S-EPMC2360016 | biostudies-literature
Project description:MOTIVATION:Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. RESULTS:We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies. AVAILABILITY AND IMPLEMENTATION:Code available on R-Forge: https://r-forge.r-project.org/projects/CloneSeeker/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6736450 | biostudies-literature