Focal chromosomal copy number aberrations in cancer—Needles in a genome haystack

The extent of focal chromosomal copy number aberrations (CNAs) in cancer has been uncovered through technical innovations, and this discovery has been critical for the identification of new cancer driver genes in genomics projects such as TCGA and ICGC. Unlike constitutive copy number variations (CNVs), focal CNAs are the result of many selection events during the evolution of cancer genomes. Therefore, it is possible that a single gene in a focal CNA gives the tumor a selective growth advantage. This concept has been instrumental in the discovery of new cancer driver genes. However, focal CNAs lack a consensus definition; therefore, we propose one based on pragmatic considerations. We also describe different strategies to identify focal CNAs and procedures to distinguish them from large CNAs and CNVs.     

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

SUMMARY: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. AVAILABILITY: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.     

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan‐cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis‐defined CNA signatures are predictive of glycolytic phenotypes, including ¹⁸F‐fluorodeoxy‐glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer‐linked metabolic enzymes. A pan‐cancer and cross‐species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer‐driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution.     

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.     

Identification of Novel Genomic Aberrations in AML-M5 in a Level of Array CGH

To assess the possible existence of unbalanced chromosomal abnormalities and delineate the characterization of copy number alterations (CNAs) of acute myeloid leukemia-M5 (AML-M5), R-banding karyotype, oligonucelotide array CGH and FISH were performed in 24 patients with AML-M5. A total of 117 CNAs with size ranging from 0.004 to 146.263 Mb was recognized in 12 of 24 cases, involving all chromosomes other than chromosome 1, 4, X and Y. Cryptic CNAs with size less than 5 Mb accounted for 59.8% of all the CNAs. 12 recurrent chromosomal alterations were mapped. Seven out of them were described in the previous AML studies and five were new candidate AML-M5 associated CNAs, including gains of 3q26.2-qter and 13q31.3 as well as losses of 2q24.2, 8p12 and 14q32. Amplication of 3q26.2-qter was the sole large recurrent chromosomal anomaly and the pathogenic mechanism in AML-M5 was possibly different from the classical recurrent 3q21q26 abnormality in AML. As a tumor suppressor gene, FOXN3, was singled out from the small recurrent CNA of 14q32, however, it is proved that deletion of FOXN3 is a common marker of myeloid leukemia rather than a specific marker for AML-M5 subtype. Moreover, the concurrent amplication of MLL and deletion of CDKN2A were noted and it might be associated with AML-M5. The number of CNA did not show a significant association with clinico-biological parameters and CR number of the 22 patients received chemotherapy. This study provided the evidence that array CGH served as a complementary platform for routine cytogenetic analysis to identify those cryptic alterations in the patients with AML-M5. As a subtype of AML, AML-M5 carries both common recurrent CNAs and unique CNAs, which may harbor novel oncogenes or tumor suppressor genes. Clarifying the role of these genes will contribute to the understanding of leukemogenic network of AML-M5.     

Methylation microarray-based detection of clinical copy-number aberrations in CLL benchmarked to standard FISH analysis

Copy-number aberrations (CNAs) are assessed using FISH analysis in diagnostics of chronic lymphocytic leukemia (CLL), but CNAs can also be extrapolated from Illumina BeadChips developed for genome-wide methylation microarray screening. Increasing numbers of microarray data-sets are available from diagnostic samples, making it useful to assess the potential in CNA diagnostics.We benchmarked the limitations of CNA testing from two Illumina BeadChips (EPIC and 450k) and using two common packages for analysis (conumee and ChAMP) to FISH-based assessment of 11q, 13q, and 17p deletions in 202 CLL samples.Overall, the two packages predicted CNAs with similar accuracy regardless of the microarray type, but lower than FISH-based assessment. We showed that the bioinformatics analysis needs to be adjusted to the specific CNA, as no general settings were identified. Altogether, we were able to predict CNAs using methylation microarray data, however, with limited accuracy, making FISH-based assessment of deletions the superior diagnostic choice.     

Comparative Analysis of Methods for Identifying Recurrent Copy Number Alterations in Cancer

Recurrent copy number alterations (CNAs) play an important role in cancer genesis. While a number of computational methods have been proposed for identifying such CNAs, their relative merits remain largely unknown in practice since very few efforts have been focused on comparative analysis of the methods. To facilitate studies of recurrent CNA identification in cancer genome, it is imperative to conduct a comprehensive comparison of performance and limitations among existing methods. In this paper, six representative methods proposed in the latest six years are compared. These include one-stage and two-stage approaches, working with raw intensity ratio data and discretized data respectively. They are based on various techniques such as kernel regression, correlation matrix diagonal segmentation, semi-parametric permutation and cyclic permutation schemes. We explore multiple criteria including type I error rate, detection power, Receiver Operating Characteristics (ROC) curve and the area under curve (AUC), and computational complexity, to evaluate performance of the methods under multiple simulation scenarios. We also characterize their abilities on applications to two real datasets obtained from cancers with lung adenocarcinoma and glioblastoma. This comparison study reveals general characteristics of the existing methods for identifying recurrent CNAs, and further provides new insights into their strengths and weaknesses. It is believed helpful to accelerate the development of novel and improved methods.     

Detection of DNA copy number abnormality by microarray expression analysis

Gene copy-number abnormalities (CNAs) are characteristic of solid tumors and are found in association with developmental abnormalities and/or mental retardation. The ultimate impact of CNAs is exerted by the altered expression of encoded genes. We have utilized high-density oligonucleotide arrays from Affymetrix to identify DNA CNAs via their impact on mRNA expression levels. In these studies, we have used three different trisomic cell lines (trisomy 9, trisomy 18, trisomy 21) as models of CNAs and have compared mRNA expression in those trisomic cells with that observed in diploid cell lines of matched tissue origin. Our data clearly show that genes from CNA chromosome regions are substantially over-represented (P<0.000001 by chi-square analysis) in the differentially expressed subset from comparisons of all three trisomic cell lines with normal matching cells. In addition, we have been able to detect the origin of the duplication by a statistical scan for over-expressed genes. These data show that microarray detection of differential mRNA expression can be used to identify significant DNA CNAs.     

A comprehensive characterization of genome-wide copy number aberrations in colorectal cancer reveals novel oncogenes and patterns of alterations

To develop a comprehensive overview of copy number aberrations (CNAs) in stage-II/III colorectal cancer (CRC), we characterized 302 tumors from the PETACC-3 clinical trial. Microsatellite-stable (MSS) samples (n = 269) had 66 minimal common CNA regions, with frequent gains on 20 q (72.5%), 7 (41.8%), 8 q (33.1%) and 13 q (51.0%) and losses on 18 (58.6%), 4 q (26%) and 21 q (21.6%). MSS tumors have significantly more CNAs than microsatellite-instable (MSI) tumors: within the MSI tumors a novel deletion of the tumor suppressor WWOX at 16 q23.1 was identified (p<0.01). Focal aberrations identified by the GISTIC method confirmed amplifications of oncogenes including EGFR, ERBB2, CCND1, MET, and MYC, and deletions of tumor suppressors including TP53, APC, and SMAD4, and gene expression was highly concordant with copy number aberration for these genes. Novel amplicons included putative oncogenes such as WNK1 and HNF4A, which also showed high concordance between copy number and expression. Survival analysis associated a specific patient segment featured by chromosome 20 q gains to an improved overall survival, which might be due to higher expression of genes such as EEF1B2 and PTK6. The CNA clustering also grouped tumors characterized by a poor prognosis BRAF-mutant-like signature derived from mRNA data from this cohort. We further revealed non-random correlation between CNAs among unlinked loci, including positive correlation between 20 q gain and 8 q gain, and 20 q gain and chromosome 18 loss, consistent with co-selection of these CNAs. These results reinforce the non-random nature of somatic CNAs in stage-II/III CRC and highlight loci and genes that may play an important role in driving the development and outcome of this disease.     

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.     

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model—spaceMap—to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations(CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast(n=77) and ovarian(n=174) cancer studies. Each cancer exhibits disruption of ‘ion transmembrane transport' and‘regulation from RNA polymerase Ⅱ promoter' by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap—including vignettes and documentation—is hosted on https://topherconley.github.io/spacemap.     

A transcriptomic computational analysis of mastic oil-treated Lewis lung carcinomas reveals molecular mechanisms targeting tumor cell growth and survival

Background

DNA copy number alterations are frequently observed in ovarian cancer, but it remains a challenge to identify the most relevant alterations and the specific causal genes in those regions.

Methods

We obtained high-resolution 500K SNP array data for 52 ovarian tumors and identified the most statistically significant minimal genomic regions with the most prevalent and highest-level copy number alterations (recurrent CNAs). Within a region of recurrent CNA, comparison of expression levels in tumors with a given CNA to tumors lacking that CNA and to whole normal ovary samples was used to select genes with CNA-specific expression patterns. A public expression array data set of laser capture micro-dissected (LCM) non-malignant fallopian tube epithelia and LCM ovarian serous adenocarcinoma was used to evaluate the effect of cell-type mixture biases.

Results

Fourteen recurrent deletions were detected on chromosomes 4, 6, 9, 12, 13, 15, 16, 17, 18, 22 and most prevalently on X and 8. Copy number and expression data suggest several apoptosis mediators as candidate drivers of the 8p deletions. Sixteen recurrent gains were identified on chromosomes 1, 2, 3, 5, 8, 10, 12, 15, 17, 19, and 20, with the most prevalent gains localized to 8q and 3q. Within the 8q amplicon, PVT1, but not MYC, was strongly over-expressed relative to tumors lacking this CNA and showed over-expression relative to normal ovary. Likewise, the cell polarity regulators PRKCI and ECT2 were identified as putative drivers of two distinct amplicons on 3q. Co-occurrence analyses suggested potential synergistic or antagonistic relationships between recurrent CNAs. Genes within regions of recurrent CNA showed an enrichment of Cancer Census genes, particularly when filtered for CNA-specific expression.

Conclusion

These analyses provide detailed views of ovarian cancer genomic changes and highlight the benefits of using multiple reference sample types for the evaluation of CNA-specific expression changes.     

Identification of genomic functional hotspots with copy number alteration in liver cancer

Copy number alterations (CNAs) can be observed in most of cancer patients. Several oncogenes and tumor suppressor genes with CNAs have been identified in different kinds of tumor. However, the systematic survey of CNA-affected functions is still lack. By employing systems biology approaches, instead of examining individual genes, we directly identified the functional hotspots on human genome. A total of 838 hotspots on human genome with 540 enriched Gene Ontology functions were identified. Seventy-six aCGH array data of hepatocellular carcinoma (HCC) tumors were employed in this study. A total of 150 regions which putatively affected by CNAs and the encoded functions were identified. Our results indicate that two immune related hotspots had copy number alterations in most of patients. In addition, our data implied that these immune-related regions might be involved in HCC oncogenesis. Also, we identified 39 hotspots of which copy number status were associated with patient survival. Our data implied that copy number alterations of the regions may contribute in the dysregulation of the encoded functions. These results further demonstrated that our method enables researchers to survey biological functions of CNAs and to construct regulation hypothesis at pathway and functional levels.     

SWAN pathway-network identification of common aneuploidy-based oncogenic drivers

Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.     

Voting-Based Cancer Module Identification by Combining Topological and Data-Driven Properties

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights.     

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.     

Impact on disease development, genomic location and biological function of copy number alterations in non-small cell lung cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells.