Integrated study of copy number states and genotype calls using high-density SNP arrays

We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls.     

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.     

基于SNP数据检测染色体拷贝数结果可信度分析

利用SNP数据检测肿瘤细胞染色体拷贝数变异是癌症相关研究的一个热点,目前已有多种方法可以通过分析SNP array数据检测染色体拷贝数。然而在某些情况下,这些检测方法检测结果与真实拷贝数具有一定错误率。目前并没有方法研究预测结果发生错误的规律。本文分别分析了GPHMM,ASCAT两种检测方法结果信息熵与检测正确率的关系,发现检测正确率与信息熵存在很强的相关性。通过对比不同肿瘤细胞比例下信息熵与正确率关系,本文发现随着肿瘤细胞比例的增大,检测结果信息熵平均值增大,方差减小;同时平均检测正确率也越来越大,方差显著减小。这些结果显示信息熵的大小可以反映出检测结果正确率的高低。最后,本文以高肿瘤细胞比例下拷贝数检测结果为例,研究了在变异类型单一,信息熵小的情况下,染色体倍性检测的正确率。结果表明信息熵可以作为衡量检测结果可信度的指标：即信息熵越高,检测结果越可信。     

TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples

Background

Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.

Results

We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.

Conclusions

TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.     

GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays

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.     

A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array

Background  

DNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required.     

Enhancing diagnosis, prognosis, and therapeutic outcome prediction of gliomas using genomics

Malignant gliomas are the most frequent type of primary brain tumors. Patients' outcome has not improved despite new therapeutics, thus underscoring the need for a better understanding of their genetics and a fresh approach to treatment. The lack of reproducibility in the classification of many gliomas presents an opportunity where genomics may be paramount for accurate diagnosis and therefore best for therapeutic decisions. The aim of this work is to identify large and focal copy number abnormalities (CNA) and loss of heterozygosity (LOH) events in a malignant glioma population. We hypothesized that these explorations will allow discovery of genetic markers that may improve diagnosis and predict outcome. DNA from glioma specimens were subjected to CNA and LOH analyses. Our studies revealed more than 4000 CNA and several LOH loci. Losses of chromosomes 1p and/or 19q, 10, 13, 14, and 22 and gains of 7, 19, and 20 were found. Several of these alterations correlated significantly with histology and grade. Further, LOH was detected at numerous chromosomes. Interestingly, several of these loci harbor genes with potential or reported tumor suppressor properties. These novel genetic signatures may lead to critical insights into diagnosis, classification, prognosis, and design of individualized therapies.     

Quantifying stability in gene list ranking across microarray derived clinical biomarkers

Background

Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.

Methods

To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.

Results

The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.

Conclusions

We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.     

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.     

Genome-Wide Identification of Somatic Aberrations from Paired Normal-Tumor Samples

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.     

SiDCoN: a tool to aid scoring of DNA copy number changes in SNP chip data

The recent application of genome-wide, single nucleotide polymorphism (SNP) microarrays to investigate DNA copy number aberrations in cancer has provided unparalleled sensitivity for identifying genomic changes. In some instances the complexity of these changes makes them difficult to interpret, particularly when tumour samples are contaminated with normal (stromal) tissue. Current automated scoring algorithms require considerable manual data checking and correction, especially when assessing uncultured tumour specimens. To address these limitations we have developed a visual tool to aid in the analysis of DNA copy number data. Simulated DNA Copy Number (SiDCoN) is a spreadsheet-based application designed to simulate the appearance of B-allele and logR plots for all known types of tumour DNA copy number changes, in the presence or absence of stromal contamination. The system allows the user to determine the level of stromal contamination, as well as specify up to 3 different DNA copy number aberrations for up to 5000 data points (representing individual SNPs). This allows users great flexibility to assess simple or complex DNA copy number combinations. We demonstrate how this utility can be used to estimate the level of stromal contamination within tumour samples and its application in deciphering the complex heterogeneous copy number changes we have observed in a series of tumours. We believe this tool will prove useful to others working in the area, both as a training tool, and to aid in the interpretation of complex copy number changes.     

Modeling recurrent DNA copy number alterations in array CGH data

MOTIVATION: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples. RESULTS: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions. AVAILABILITY: http://www.cs.ubc.ca/~sshah/acgh.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Secondary Radiation-Induced Bone Tumours Demonstrate a High Degree of Genomic Instability Predictive of a Poor Prognosis

Secondary bone tumours arising in the field of a preceding radiotherapy are a serious late effect, in particular considering the increasing survival times in patients treated for paediatric malignancies. In general, therapy associated tumours are known to show a more aggressive behaviour and a limited response to chemotherapy compared with their primary counterparts. It is not clear however whether this less favourable outcome is caused by inherent genetic factors of the tumour cells or by a general systemic condition of the patient. To elucidate this we analysed a series of bone sarcomas with a history of prior irradiation for the presence of genomic alterations and compared them with the alterations identified earlier in primary osteosarcomas. We analysed seven radiation induced bone sarcomas for genome-wide losses of heterozygosity (LOH) using Affymetrix 10K2 high-density single nucleotide polymorphism (SNP) arrays. Additionally, copy number changes were analysed at two distinct loci on 10q that were recently found to be of major prognostic significance in primary osteosarcomas. All the investigated tumours showed a LOH at 10q21.1 with 86% of cases (6/7) revealing a total genome-wide LOH score above 2400 and more than 24% of the genome being affected. Our results indicate similar genetic alterations in radiation induced sarcomas of bone and primary osteosarcomas with a poor prognosis. We speculate that the high degree of genomic instability found in these tumours causes the poor prognosis irrespective of the initiating event.     

Cytogenetically balanced translocations are associated with focal copy number alterations

Current cytogenetic methods (e.g., G-banding and multicolor chromosomal painting) allow detection of translocation events but lack the resolution to (a) locate the breakpoints precisely at the chromosome band level or (b) discriminate balanced translocations from translocations with copy number alterations not previously reported, or imperfectly balanced translocations. In this study, we demonstrate that cytogenetically balanced translocations are in fact frequently associated with segmental gain or loss of DNA. The recent development of a whole genome tiling path BAC array has enabled tiling resolution analysis of genomic segmental copy number status. Combining tiling resolution BAC array comparative genomic hybridization (array CGH) with G-Banding analysis and multicolor chromosomal painting approaches such as spectral karyotyping (SKY) facilitates high-resolution mapping of genomic alterations associated with imperfectly balanced translocations. Using a refined version of our CGH array we have deduced the copy number status throughout the genomes of three cytogenetically well-characterized prostate cancer cell lines (PC3, DU145, LNCaP) to determine whether translocations are associated with focal gains and losses of DNA. At 78 kb tiling resolution we identified the boundaries of 170, 80, and 34 known and novel copy number alterations (CNA) in these cell line genomes, respectively. Thirty-three of the 36 known translocations (92%, P < 0.001) in DU145 were associated with segmental CNA. Likewise, 80% (P < 0.001) of the known translocations showed association in LNCaP. Although many translocation breakpoints exhibit segmental alteration in PC3, the pattern of chromosomal rearrangements is too complex for use in comprehensive association with CNA boundaries. Our results reveal that imperfectly balanced translocations in tumor genomes are a phenomenon that occurs at frequencies much higher than previously demonstrated. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

CEQer: A Graphical Tool for Copy Number and Allelic Imbalance Detection from Whole-Exome Sequencing Data

Copy number alterations (CNA) are common events occurring in leukaemias and solid tumors. Comparative Genome Hybridization (CGH) is actually the gold standard technique to analyze CNAs; however, CGH analysis requires dedicated instruments and is able to perform only low resolution Loss of Heterozygosity (LOH) analyses. Here we present CEQer (Comparative Exome Quantification analyzer), a new graphical, event-driven tool for CNA/allelic-imbalance (AI) coupled analysis of exome sequencing data. By using case-control matched exome data, CEQer performs a comparative digital exonic quantification to generate CNA data and couples this information with exome-wide LOH and allelic imbalance detection. This data is used to build mixed statistical/heuristic models allowing the identification of CNA/AI events. To test our tool, we initially used in silico generated data, then we performed whole-exome sequencing from 20 leukemic specimens and corresponding matched controls and we analyzed the results using CEQer. Taken globally, these analyses showed that the combined use of comparative digital exon quantification and LOH/AI allows generating very accurate CNA data. Therefore, we propose CEQer as an efficient, robust and user-friendly graphical tool for the identification of CNA/AI in the context of whole-exome sequencing data.     

Rseg--an R package to optimize segmentation of SNP array data

The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. RESULTS: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts. AVAILABILITY: The R package, Rseg, is available at: http://www.cs.au.dk/~plamy/Rseg/ and runs on Linux and MS-Windows.     

Minimum error calibration and normalization for genomic copy number analysis

BackgroundCopy number variations (CNV) are regional deviations from the normal autosomal bi-allelic DNA content. While germline CNVs are a major contributor to genomic syndromes and inherited diseases, the majority of cancers accumulate extensive “somatic” CNV (sCNV or CNA) during the process of oncogenetic transformation and progression. While specific sCNV have closely been associated with tumorigenesis, intriguingly many neoplasias exhibit recurrent sCNV patterns beyond the involvement of a few cancer driver genes. Currently, CNV profiles of tumor samples are generated using genomic micro-arrays or high-throughput DNA sequencing. Regardless of the underlying technology, genomic copy number data is derived from the relative assessment and integration of multiple signals, with the data generation process being prone to contamination from several sources. Estimated copy number values have no absolute or strictly linear correlation to their corresponding DNA levels, and the extent of deviation differs between sample profiles, which poses a great challenge for data integration and comparison in large scale genome analysis.ResultsIn this study, we present a novel method named “Minimum Error Calibration and Normalization for Copy Numbers Analysis” (Mecan4CNA). It only requires CNV segmentation files as input, is platform independent, and has a high performance with limited hardware requirements. For a given multi-sample copy number dataset, Mecan4CNA can batch-normalize all samples to the corresponding true copy number levels of the main tumor clones. Experiments of Mecan4CNA on simulated data showed an overall accuracy of 93% and 91% in determining the normal level and single copy alteration (i.e. duplication or loss of one allele), respectively. Comparison of estimated normal levels and single copy alternations with existing methods and karyotyping data on the NCI-60 tumor cell line produced coherent results. To estimate the method's impact on downstream analyses, we performed GISTIC analyses on the original and Mecan4CNA normalized data from the Cancer Genome Atlas (TCGA) where the normalized data showed prominent improvements of both sensitivity and specificity in detecting focal regions.ConclusionsMecan4CNA provides an advanced method for CNA data normalization, especially in meta-analyses involving large profile numbers and heterogeneous source data quality. With its informative output and visualization options, Mecan4CNA also can improve the interpretation of individual CNA profiles. Mecan4CNA is freely available as a Python package and through its code repository on Github.     

qpure: A Tool to Estimate Tumor Cellularity from Genome-Wide Single-Nucleotide Polymorphism Profiles

Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 (-value = 0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 (-value 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 (-value = 0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/.