MixHMM: inferring copy number variation and allelic imbalance using SNP arrays and tumor samples mixed with stromal cells

Detection of DNA copy number alterations by matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis of single nucleotide polymorphisms

OBJECTIVES

Copy number alterations (CNAs) are frequently found in malignant tissues. Different approaches have been used for CNA detection. However, it is not easy to detect a large panel of CNA targets in heterogenous tumors.

METHODS

We have developed a CNAs detection approach through quantitatively analyzed allelic imbalance by allelotyping single nucleotide polymorphisms (SNPs) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Furthermore, the copy number changes were quantified by real-competitive PCR (rcPCR) to distinguish loss of heterozygosity (LOH) and genomic amplification. The approach was used to validate the CNA regions detected by next generation sequencing (NGS) in early-stage lung carcinoma.

RESULTS

CNAs were detected in heterogeneous DNA samples where tumor DNA is present at only 10% through the SNP based allelotyping. In addition, two different types of CNAs (loss of heterozygosity and chromosome amplification) were able to be distinguished quantitatively by rcPCR. Validation on a total of 41 SNPs from the selected CNA regions showed that copy number changes did occur, and the tissues from early-stage lung carcinoma were distinguished from normal.

CONCLUSIONS

CNA detection by MALDI-TOF MS can be used for validating potentially interesting genomic regions identified from next generation sequencing, and for detecting CNAs in tumor tissues consisting of a mixture of neoplastic and normal cells.

A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome are important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of subclones. In this article, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region and region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotype and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state variable is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to study renal cell carcinoma datasets in The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach. The R source code is available online at https://dceg.cancer.gov/tools/analysis/subhmm. Expectation-Maximization algorithm; Forward-backward algorithm; Somatic copy number alteration; Tumor subclones.

hsegHMM: hidden Markov model-based allele-specific copy number alteration analysis accounting for hypersegmentation

Background

Somatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and prognosis. The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity. Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA.

Results

We propose hsegHMM, a hidden Markov model approach that accounts for hypersegmentation for allele-specific SCNA analysis. hsegHMM provides statistical inference of copy number profiles by using an efficient E-M algorithm procedure. Through simulation and application studies, we found that hsegHMM handles hypersegmentation effectively with a t-distribution as a part of the emission probability distribution structure and a carefully defined state space. We also compared hsegHMM with FACETS which is a current method for allele-specific SCNA analysis. For the application, we use a renal cell carcinoma sample from The Cancer Genome Atlas (TCGA) study.

Conclusions

We demonstrate the robustness of hsegHMM to hypersegmentation. Furthermore, hsegHMM provides the quantification of uncertainty in identifying allele-specific SCNAs over the entire chromosomes. hsegHMM performs better than FACETS when read depth (coverage) is uneven across the genome.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2412-y) contains supplementary material, which is available to authorized users.

Detection of clinically relevant copy number alterations in oral cancer progression using multiplexed droplet digital PCR

Copy number alterations (CNAs), a common genomic event during carcinogenesis, are known to affect a large fraction of the genome. Common recurrent gains or losses of specific chromosomal regions occur at frequencies that they may be considered distinctive features of tumoral cells. Here we introduce a novel multiplexed droplet digital PCR (ddPCR) assay capable of detecting recurrent CNAs that drive tumorigenesis of oral squamous cell carcinoma. Applied to DNA extracted from oral cell lines and clinical samples of various disease stages, we found good agreement between CNAs detected by our ddPCR assay with those previously reported using comparative genomic hybridization or single nucleotide polymorphism arrays. Furthermore, we demonstrate that the ability to target specific locations of the genome permits detection of clinically relevant oncogenic events such as small, submicroscopic homozygous deletions. Additional capabilities of the multiplexed ddPCR assay include the ability to infer ploidy level, quantify the change in copy number of target loci with high-level gains, and simultaneously assess the status and viral load for high-risk human papillomavirus types 16 and 18. This novel multiplexed ddPCR assay therefore may have clinical value in differentiating between benign oral lesions from those that are at risk of progressing to oral cancer.

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.

Somatic mosaicism in the human genome

Somatic mosaicism refers to the occurrence of two genetically distinct populations of cells within an individual, derived from a postzygotic mutation. In contrast to inherited mutations, somatic mosaic mutations may affect only a portion of the body and are not transmitted to progeny. These mutations affect varying genomic sizes ranging from single nucleotides to entire chromosomes and have been implicated in disease, most prominently cancer. The phenotypic consequences of somatic mosaicism are dependent upon many factors including the developmental time at which the mutation occurs, the areas of the body that are affected, and the pathophysiological effect(s) of the mutation. The advent of second-generation sequencing technologies has augmented existing array-based and cytogenetic approaches for the identification of somatic mutations. We outline the strengths and weaknesses of these techniques and highlight recent insights into the role of somatic mosaicism in causing cancer, neurodegenerative, monogenic, and complex disease.

Comprehensive study of tumour single nucleotide polymorphism array data reveals significant driver aberrations and disrupted signalling pathways in human hepatocellular cancer

The authors describe an integrated method for analysing cancer driver aberrations and disrupted pathways by using tumour single nucleotide polymorphism (SNP) arrays. The authors new method adopts a novel statistical model to explicitly quantify the SNP signals, and therefore infers the genomic aberrations, including copy number alteration and loss of heterozygosity. Examination on the dilution series dataset shows that this method can correctly identify the genomic aberrations even with the existence of severe normal cell contamination in tumour sample. Furthermore, with the results of the aberration identification obtained from multiple tumour samples, a permutation-based approach is proposed for identifying the statistically significant driver aberrations, which are further incorporated with the known signalling pathways for pathway enrichment analysis. By applying the approach to 286 hepatocellular tumour samples, they successfully uncover numerous driver aberration regions across the cancer genome, for example, chromosomes 4p and 5q, which harbour many known hepatocellular cancer related genes such as alpha-fetoprotein (AFP) and ectodermal-neural cortex (ENC1). In addition, they identify nine disrupted pathways that are highly enriched by the driver aberrations, including the systemic lupus erythematosus pathway, the vascular endothelial growth factor (VEGF) signalling pathway and so on. These results support the feasibility and the utility of the proposed method on the characterisation of the cancer genome and the downstream analysis of the driver aberrations and the disrupted signalling pathways.

High-definition reconstruction of clonal composition in cancer

The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.

Chromosomal instability associated with global DNA hypomethylation is associated with the initiation and progression of esophageal squamous cell carcinoma

BACKGROUND

Global DNA hypomethylation is associated with increased chromosomal instability and plays an important role in tumorigenesis. The methylation status of the long interspersed nuclear element-1 (LINE-1) element is a useful surrogate marker for global DNA methylation. Although LINE-1 hypomethylation is recognized as a poor prognostic marker, the correlation of LINE-1 methylation level with tumor suppressor gene mutation, chromosomal instability, and clinical significance in esophageal squamous cell carcinoma (ESCC) remains unclear.

METHODS

Using resected tumor tissues and the corresponding normal esophageal mucosa from 105 patients with ESCC, bisulfite pyrosequencing analysis was performed to quantify the LINE-1 methylation levels. p53 mutations in exons two to ten were detected by polymerase chain reaction direct sequencing. Chromosomal instability was assessed by single nucleotide polymorphism array comparative genomic hybridization analysis.

RESULTS

The LINE-1 methylation level of ESCC was significantly lower than matched normal mucosa. LINE-1 methylation levels of normal mucosa from the esophagus had a significant inverse correlation with both cigarette smoking and alcohol consumption of the study subjects. LINE-1 hypomethylation of ESCC was significantly associated with lymph node metastasis, lymphovascular invasion, the frequency of p53 mutation and poor survivability. The LINE-1 methylation levels in ESCC had a significant inverse association with the percentage of copy number alterations in the whole genome, mirroring chromosomal instability.

CONCLUSIONS

Our results suggested that whole genome hypomethylation caused by chronic inflammation could initiate carcinogenesis of esophageal squamous cells through chromosomal instability. In addition, chromosomal instability associated with the global hypomethylation might correlate highly with the progression of ESCC.

Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts

The goal of this paper is to review recent research on copy number variations (CNVs) and their association with complex and rare diseases. In the latter part of this paper, we focus on how large biorepositories such as the electronic medical record and genomics (eMERGE) consortium may be best leveraged to systematically mine for potentially pathogenic CNVs, and we end with a discussion of how such variants might be reported back for inclusion in electronic medical records as part of medical history.

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.

Sensitive and specific detection of mosaic chromosomal abnormalities using the Parent-of-Origin-based Detection (POD) method

Background

Mosaic somatic alterations are present in all multi-cellular organisms, but the physiological effects of low-level mosaicism are largely unknown. Most mosaic alterations remain undetectable with current analytical approaches, although the presence of such alterations is increasingly implicated as causative for disease.

Results

Here, we present the Parent-of-Origin-based Detection (POD) method for chromosomal abnormality detection in trio-based SNP microarray data. Our software implementation, triPOD, was benchmarked using a simulated dataset, outperformed comparable software for sensitivity of abnormality detection, and displayed substantial improvement in the detection of low-level mosaicism while maintaining comparable specificity. Examples of low-level mosaic abnormalities from a large autism dataset demonstrate the benefits of the increased sensitivity provided by triPOD. The triPOD analyses showed robustness across multiple types of Illumina microarray chips. Two large, clinically-relevant datasets were characterized and compared.

Conclusions

Our method and software provide a significant advancement in the ability to detect low-level mosaic abnormalities, thereby opening new avenues for research into the implications of mosaicism in pathogenic and non-pathogenic processes.

Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples

Background

SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.

Results

In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.

Conclusions

On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

HDAC inhibitors induce transcriptional repression of high copy number genes in breast cancer through elongation blockade

Treatment with histone deacetylase inhibitors (HDACI) results in potent cytotoxicity of a variety of cancer cell types, and these drugs are used clinically to treat hematological tumors. They are known to repress the transcription of ERBB2 and many other oncogenes, but little is known about this mechanism. Using global run-on sequencing (GRO-seq) to measure nascent transcription, we find that HDACI cause transcriptional repression by blocking RNA polymerase II elongation. Our data show that HDACI preferentially repress the transcription of highly expressed genes as well as high copy number genes in HER2+ breast cancer genomes. In contrast, genes that are activated by HDACI are moderately expressed. We analyzed gene copy number in combination with microarray and GRO-seq analysis of expression level, in normal and breast cancer cells to show that high copy number genes are more likely to be repressed by HDACI than non-amplified genes. The inhibition of transcription of amplified oncogenes, which promote survival and proliferation of cancer cells, might explain the cancer-specific lethality of HDACI, and may represent a general therapeutic strategy for cancer.

Current analysis platforms and methods for detecting copy number variation

Copy number variation (CNV), generated through duplication or deletion events that affect one or more loci, is widespread in the human genomes and is often associated with functional consequences that may include changes in gene expression levels or fusion of genes. Genome-wide association studies indicate that some disease phenotypes and physiological pathways might be impacted by CNV in a small number of characterized genomic regions. However, the pervasiveness and full impact of such variation remains unclear. Suitable analytic methods are needed to thoroughly mine human genomes for genomic structural variation, and to explore the interplay between observed CNV and disease phenotypes, but many medical researchers are unfamiliar with the features and nuances of recently developed technologies for detecting CNV. In this article, we evaluate a suite of commonly used and recently developed approaches to uncovering genome-wide CNVs and discuss the relative merits of each.

Change-point analysis of paired allele-specific copy number variation data

The recent genome-wide allele-specific copy number variation data enable us to explore two types of genomic information including chromosomal genotype variations as well as DNA copy number variations. For a cancer study, it is common to collect data for paired normal and tumor samples. Then, two types of paired data can be obtained to study a disease subject. However, there is a lack of methods for a simultaneous analysis of these four sequences of data. In this study, we propose a statistical framework based on the change-point analysis approach. The validity and usefulness of our proposed statistical framework are demonstrated through the simulation studies and applications based on an experimental data set.

Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data

Background

The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.

Results

We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.

Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based.

Conclusions

We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.

Visualization of genomic changes by segmented smoothing using an L0 penalty

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the L(0) norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA.

Statistical inference of allelic imbalance from transcriptome data

Next-generation sequencing and the availability of high-density genotyping arrays have facilitated an analysis of somatic and meiotic mutations at unprecedented level, but drawing sensible conclusions about the functional relevance of the detected variants still remains a formidable challenge. In this context, the study of allelic imbalance in intermediate RNA phenotypes may prove a useful means to elucidate the likely effects of DNA variants of unknown significance. We developed a statistical framework for the assessment of allelic imbalance in next-generation transcriptome sequencing (RNA-seq) data that requires neither an expression reference nor the underlying nuclear genotype(s), and that allows for allele miscalls. Using extensive simulation as well as publicly available whole-transcriptome data from European-descent individuals in HapMap, we explored the power of our approach in terms of both genotype inference and allelic imbalance assessment under a wide range of practically relevant scenarios. In so doing, we verified a superior performance of our methodology, particularly at low sequencing coverage, compared to the more simplistic approach of completely ignoring allele miscalls. Because the proposed framework can be used to assess somatic mutations and allelic imbalance in one and the same set of RNA-seq data, it will be particularly useful for the analysis of somatic genetic variation in cancer studies.

Loss of heterozygosity analysis in malignant gliomas

Despite recent advances in the diagnosis and treatment of glioblastomas, patient outcomes for these highly malignant tumors remain poor. Research into the molecular pathology of glioblastoma has uncovered various genetic changes that contribute to malignancy. Some of the identified molecular markers--such as loss of heterozygosity (LOH) on chromosome 1p/19q and chromosome 10, O6-methylguanine methyltransferase promoter hypermethylation, and mutation of isocitrate dehydrogenase-1--may help to predict patient outcomes. Indeed, LOH analysis is an effective approach to classify malignant gliomas. Genome-wide analyses have revealed that the extent and pattern of LOH regions may have important implications for the clinical course of the disease. As the genetic underpinnings of malignant gliomas are complex and varied, careful selection of the methods for genetic analysis in the clinic is important. The fundamental principles of each assay need to be understood to allow careful selection of practically useful methods. This review summarizes recent developments in the molecular analysis of malignant glioma.

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.