GeneCount: genome-wide calculation of absolute tumor DNA copy numbers from array comparative genomic hybridization data

Absolute tumor DNA copy numbers can currently be achieved only on a single gene basis by using fluorescence in situ hybridization (FISH). We present GeneCount, a method for genome-wide calculation of absolute copy numbers from clinical array comparative genomic hybridization data. The tumor cell fraction is reliably estimated in the model. Data consistent with FISH results are achieved. We demonstrate significant improvements over existing methods for exploring gene dosages and intratumor copy number heterogeneity in cancers.     

Intratumour Diversity of Chromosome Copy Numbers in Neuroblastoma Mediated by On-Going Chromosome Loss from a Polyploid State

Neuroblastomas (NBs) are tumours of the sympathetic nervous system accounting for 8–10% of paediatric cancers. NBs exhibit extensive intertumour genetic heterogeneity, but their extent of intratumour genetic diversity has remained unexplored. We aimed to assess intratumour genetic variation in NBs with a focus on whole chromosome changes and their underlying mechanism. Allelic ratios obtained by SNP-array data from 30 aneuploid primary NBs and NB cell lines were used to quantify the size of clones harbouring specific genomic imbalances. In 13 cases, this was supplemented by fluorescence in situ hybridisation to assess copy number diversity in detail. Computer simulations of different mitotic segregation errors, single cell cloning, analysis of mitotic figures, and time lapse imaging of dividing NB cells were used to infer the most likely mechanism behind intratumour variation in chromosome number. Combined SNP array and FISH analyses showed that all cases exhibited higher inter-cellular copy number variation than non-neoplastic control tissue, with up to 75% of tumour cells showing non-modal chromosome copy numbers. Comparisons of copy number profiles, resulting from simulations of different segregation errors to genomic profiles of 120 NBs indicated that loss of chromosomes from a tetraploid state was more likely than other mechanisms to explain numerical aberrations in NB. This was supported by a high frequency of lagging chromosomes at anaphase and polyploidisation events in growing NB cells. The dynamic nature of numerical aberrations was corroborated further by detecting substantial copy number diversity in cell populations grown from single NB cells. We conclude that aneuploid NBs typically show extensive intratumour chromosome copy number diversity, and that this phenomenon is most likely explained by continuous loss of chromosomes from a polyploid state.     

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

Background

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.

Results

The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.

Conclusions

We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.     

Single-cell paired-end genome sequencing reveals structural variation per cell cycle

The nature and pace of genome mutation is largely unknown. Because standard methods sequence DNA from populations of cells, the genetic composition of individual cells is lost, de novo mutations in cells are concealed within the bulk signal and per cell cycle mutation rates and mechanisms remain elusive. Although single-cell genome analyses could resolve these problems, such analyses are error-prone because of whole-genome amplification (WGA) artefacts and are limited in the types of DNA mutation that can be discerned. We developed methods for paired-end sequence analysis of single-cell WGA products that enable (i) detecting multiple classes of DNA mutation, (ii) distinguishing DNA copy number changes from allelic WGA-amplification artefacts by the discovery of matching aberrantly mapping read pairs among the surfeit of paired-end WGA and mapping artefacts and (iii) delineating the break points and architecture of structural variants. By applying the methods, we capture DNA copy number changes acquired over one cell cycle in breast cancer cells and in blastomeres derived from a human zygote after in vitro fertilization. Furthermore, we were able to discover and fine-map a heritable inter-chromosomal rearrangement t(1;16)(p36;p12) by sequencing a single blastomere. The methods will expedite applications in basic genome research and provide a stepping stone to novel approaches for clinical genetic diagnosis.     

Reliable Single Cell Array CGH for Clinical Samples

Background

Disseminated cancer cells (DCCs) and circulating tumor cells (CTCs) are extremely rare, but comprise the precursors cells of distant metastases or therapy resistant cells. The detailed molecular analysis of these cells may help to identify key events of cancer cell dissemination, metastatic colony formation and systemic therapy escape.

Methodology/Principal Findings

Using the Ampli1™ whole genome amplification (WGA) technology and high-resolution oligonucleotide aCGH microarrays we optimized conditions for the analysis of structural copy number changes. The protocol presented here enables reliable detection of numerical genomic alterations as small as 0.1 Mb in a single cell. Analysis of single cells from well-characterized cell lines and single normal cells confirmed the stringent quantitative nature of the amplification and hybridization protocol. Importantly, fixation and staining procedures used to detect DCCs showed no significant impact on the outcome of the analysis, proving the clinical usability of our method. In a proof-of-principle study we tracked the chromosomal changes of single DCCs over a full course of high-dose chemotherapy treatment by isolating and analyzing DCCs of an individual breast cancer patient at four different time points.

Conclusions/Significance

The protocol enables detailed genome analysis of DCCs and thereby assessment of the clonal evolution during the natural course of the disease and under selection pressures. The results from an exemplary patient provide evidence that DCCs surviving selective therapeutic conditions may be recruited from a pool of genomically less advanced cells, which display a stable subset of specific genomic alterations.     

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.     

Chromosome-Specific Painting in Cucumis Species Using Bulked Oligonucleotides

Chromosome-specific painting is a powerful technique in molecular cytogenetic and genome research. We developed an oligonucleotide (oligo)-based chromosome painting technique in cucumber (Cucumis sativus) that will be applicable in any plant species with a sequenced genome. Oligos specific to a single chromosome of cucumber were identified using a newly developed bioinformatic pipeline and then massively synthesized de novo in parallel. The synthesized oligos were amplified and labeled with biotin or digoxigenin for use in fluorescence in situ hybridization (FISH). We developed three different probes with each containing 23,000–27,000 oligos. These probes spanned 8.3–17 Mb of DNA on targeted cucumber chromosomes and had the densities of 1.5–3.2 oligos per kilobases. These probes produced FISH signals on a single cucumber chromosome and were used to paint homeologous chromosomes in other Cucumis species diverged from cucumber for up to 12 million years. The bulked oligo probes allowed us to track a single chromosome in early stages during meiosis. We were able to precisely map the pairing between cucumber chromosome 7 and chromosome 1 of Cucumis hystrix in a F₁ hybrid. These two homeologous chromosomes paired in 71% of prophase I cells but only 25% of metaphase I cells, which may provide an explanation of the higher recombination rates compared to the chiasma frequencies between homeologous chromosomes reported in plant hybrids.     

Relationships between Chromosome 7 Gain,MET Gene Copy Number Increase and MET Protein Overexpression in Chinese Papillary Renal Cell Carcinoma Patients

To investigate the relationships between Chromosome 7 gain, mesenchymal-epithelial transition factor (MET) gene copy number increase and MET protein overexpression in Chinese patients with papillary renal cell carcinoma (PRCC), immunohistochemistry (IHC), immunofluorescence (IF) and fluorescence in situ hybridization (FISH) were performed on 98 formalin-fixed, paraffin-embedded (FFPE) PRCC samples. Correlations between MET gene copy number increase, Chromosome 7 gain and MET protein overexpression were analyzed statistically. A highly significant correlation was observed between the percentage of tumor cells with MET gene copy number ≥3 and CEP7 copy number ≥3 (R² = 0.90, p<0.001) across two subtypes of PRCC. In addition, the percentage of tumor cells with MET gene copy number ≥3 was found to increase along with increases in MET IHC score. This correlation was further confirmed in those PRCC tumor cells with average MET gene copy number >5 using combined IF and FISH methodology. Overall, this study provides evidence that Chromosome 7 gain drives MET gene copy number increase in PRCC tumors, and appears to subsequently lead to an increase in MET protein overexpression in these tumor cells. This supports MET activation as a potential therapeutic target in sporadic PRCC.     

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.     

High resolution array-CGH analysis of single cells

Heterogeneity in the genome copy number of tissues is of particular importance in solid tumor biology. Furthermore, many clinical applications such as pre-implantation and non-invasive prenatal diagnosis would benefit from the ability to characterize individual single cells. As the amount of DNA from single cells is so small, several PCR protocols have been developed in an attempt to achieve unbiased amplification. Many of these approaches are suitable for subsequent cytogenetic analyses using conventional methodologies such as comparative genomic hybridization (CGH) to metaphase spreads. However, attempts to harness array-CGH for single-cell analysis to provide improved resolution have been disappointing. Here we describe a strategy that combines single-cell amplification using GenomePlex library technology (GenomePlex^® Single Cell Whole Genome Amplification Kit, Sigma-Aldrich, UK) and detailed analysis of genomic copy number changes by high-resolution array-CGH. We show that single copy changes as small as 8.3 Mb in single cells are detected reliably with single cells derived from various tumor cell lines as well as patients presenting with trisomy 21 and Prader–Willi syndrome. Our results demonstrate the potential of this technology for studies of tumor biology and for clinical diagnostics.     

Modeling DNA methylation in a population of cancer cells

Little is known about how human cancers grow because direct observations are impractical. Cancers are clonal populations and the billions of cancer cells present in a visible tumor are progeny of a single transformed cell. Therefore, human cancers can be represented by somatic cell ancestral trees that start from a single transformed cell and end with billions of present day cancer cells. We use a genealogical approach to infer tumor growth from somatic trees, employing haplotype DNA methylation pattern variation, or differences between specific CpG sites or "tags," in the cancer genome. DNA methylation is an epigenetic mark that is copied, with error, during genome replication. At our tags, neutral copy errors in DNA methylation appear to occur at random, and much more frequently than sequence copy errors. To reconstruct a cancer tree, we sample and compare human colorectal genomes within small geographic regions (a cancer fragment), between fragments on the same side of the tumor, and between fragments from opposite tumor halves. The combined information on both physical distance and epigenetic distance informs our model for tumor ancestry. We use approximate Bayesian computation, a simulation-based method, to model tumor growth under a variety of evolutionary scenarios, estimating parameters that fit observed DNA methylation patterns. We conclude that methylation patterns sampled from human cancers are consistent with replication errors and certain simple cancer growth models. The inferred cancer trees are consistent with Gompertzian growth, a well-known cancer growth pattern.     

A Robust Method to Analyze Copy Number Alterations of Less than 100 kb in Single Cells Using Oligonucleotide Array CGH

Comprehensive genome wide analyses of single cells became increasingly important in cancer research, but remain to be a technically challenging task. Here, we provide a protocol for array comparative genomic hybridization (aCGH) of single cells. The protocol is based on an established adapter-linker PCR (WGAM) and allowed us to detect copy number alterations as small as 56 kb in single cells. In addition we report on factors influencing the success of single cell aCGH downstream of the amplification method, including the characteristics of the reference DNA, the labeling technique, the amount of input DNA, reamplification, the aCGH resolution, and data analysis. In comparison with two other commercially available non-linear single cell amplification methods, WGAM showed a very good performance in aCGH experiments. Finally, we demonstrate that cancer cells that were processed and identified by the CellSearch® System and that were subsequently isolated from the CellSearch® cartridge as single cells by fluorescence activated cell sorting (FACS) could be successfully analyzed using our WGAM-aCGH protocol. We believe that even in the era of next-generation sequencing, our single cell aCGH protocol will be a useful and (cost-) effective approach to study copy number alterations in single cells at resolution comparable to those reported currently for single cell digital karyotyping based on next generation sequencing data.     

Noise reduction from genotyping microarrays using probe level information

Genomic copy number change is one of the important phenomenon observed in cancer and other genetic disorders. Recently oligonucleotide microarrays have been used to analyze changes in the copy number. Although high density microarrays provide genome wide useful data on copy number, they are often associated with substantial amount of experimental noise that could affect the performance of the analyses. We used the high density oligonucleotide genotyping microarrays in our experiments that uses redundant probe tiling approach for individual SNPs. We found that the noise in the genotyping microarray data is associated with several experimental steps during target preparation and devised an algorithm that takes into account those experimental parameters. Additionally, defective probes that do not hybridize well to the target and therefore could not be modified inherently were detected and omitted automatically by using the algorithm. When we applied the algorithm to actual datasets, we could reduce the noise substantially without compressing the dynamic range. Additionally, combinatorial use of our noise reduction algorithm and conventional breakpoint detection algorithm successfully detected a microamplification of c-myc which was overlooked in the raw data. The algorithm described here is freely available with the software upon request to all non-profit researchers.     

Comprehensive FISH Probe Design Tool Applied to Imaging Human Immunoglobulin Class Switch Recombination

We present a web engine boosted fluorescence in-situ hybridization (webFISH) algorithm using a genome-wide sequence similarity search to design target-specific single-copy and repetitive DNA FISH probes. The webFISH algorithm featuring a user-friendly interface (http://www.webfish2.org/) maximizes the coverage of the examined sequences with FISH probes by considering locally repetitive sequences absent from the remainder of the genome. The highly repetitive human immunoglobulin heavy chain sequence was analyzed using webFISH to design three sets of FISH probes. These allowed direct simultaneous detection of class switch recombination in both immunoglobulin-heavy chain alleles in single cells from a population of cultured primary B cells. It directly demonstrated asynchrony of the class switch recombination in the two alleles in structurally preserved nuclei while permitting parallel readout of protein expression by immunofluorescence staining. This novel technique offers the possibility of gaining unprecedented insight into the molecular mechanisms involved in class switch recombination.     

CGHsweep: an algorithm for analyzing chromosomal aberrations in genome using aCGH profiles

DNA copy number aberrations along the genome are vital markers for studying pathogenesis of various diseases including cancers. Array-based Comparative Genome Hybridization (aCGH), which is a high-throughput cytogenetic method, helps in identifying genome-wide copy number aberrations, both gains and losses. Here, we propose a computational technique to analyze aCGH data and to identify potential DNA copy number alterations along the genome. Our technique detects the possible breakpoints by comparing contiguous probe log ratios, reports the aberrant segments and handles outliers to minimize false discovery rate. Empirically, we tested our algorithm on both prokaryotic (Brucella ovis) and eukaryotic (glioblastoma and colorectal cancer datasets from human) genomes. Our findings complement previous studies; our performance is competitive, sometimes superior, against other popular methods.     

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.     

Integrative Analysis of Genome, 3D Genome,and Transcriptome Alterations of Clinical Lung Cancer Samples

Genomic studies of cancer cell alterations, such as mutations, copy number variations (CNVs), and translocations, greatly promote our understanding of the genesis and development of cancers. However, the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties. To explore the 3D genome structure in clinical lung cancer, we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer, combining with RNA sequenceing analysis. We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells (1 × 10⁴), compared the genome architecture between clinical samples and cell lines of lung cancer, and identified conserved and changed spatial chromatin structures between normal and cancer samples. We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer. By integrating those different types of cancer alterations, we showed significant associations between CNVs, 3D genome, and gene expression. We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures. Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.     

Allele-specific amplification in cancer revealed by SNP array analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.     

Mutation Discovery in Regions of Segmental Cancer Genome Amplifications with CoNAn-SNV: A Mixture Model for Next Generation Sequencing of Tumors

Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.