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.     

Latent Factor Analysis to Discover Pathway-Associated Putative Segmental Aneuploidies in Human Cancers

Tumor microenvironmental stresses, such as hypoxia and lactic acidosis, play important roles in tumor progression. Although gene signatures reflecting the influence of these stresses are powerful approaches to link expression with phenotypes, they do not fully reflect the complexity of human cancers. Here, we describe the use of latent factor models to further dissect the stress gene signatures in a breast cancer expression dataset. The genes in these latent factors are coordinately expressed in tumors and depict distinct, interacting components of the biological processes. The genes in several latent factors are highly enriched in chromosomal locations. When these factors are analyzed in independent datasets with gene expression and array CGH data, the expression values of these factors are highly correlated with copy number alterations (CNAs) of the corresponding BAC clones in both the cell lines and tumors. Therefore, variation in the expression of these pathway-associated factors is at least partially caused by variation in gene dosage and CNAs among breast cancers. We have also found the expression of two latent factors without any chromosomal enrichment is highly associated with 12q CNA, likely an instance of “trans”-variations in which CNA leads to the variations in gene expression outside of the CNA region. In addition, we have found that factor 26 (1q CNA) is negatively correlated with HIF-1α protein and hypoxia pathways in breast tumors and cell lines. This agrees with, and for the first time links, known good prognosis associated with both a low hypoxia signature and the presence of CNA in this region. Taken together, these results suggest the possibility that tumor segmental aneuploidy makes significant contributions to variation in the lactic acidosis/hypoxia gene signatures in human cancers and demonstrate that latent factor analysis is a powerful means to uncover such a linkage.     

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.     

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.     

Comparative Oncogenomic Analysis of Copy Number Alterations in Human and Zebrafish Tumors Enables Cancer Driver Discovery

The identification of cancer drivers is a major goal of current cancer research. Finding driver genes within large chromosomal events is especially challenging because such alterations encompass many genes. Previously, we demonstrated that zebrafish malignant peripheral nerve sheath tumors (MPNSTs) are highly aneuploid, much like human tumors. In this study, we examined 147 zebrafish MPNSTs by massively parallel sequencing and identified both large and focal copy number alterations (CNAs). Given the low degree of conserved synteny between fish and mammals, we reasoned that comparative analyses of CNAs from fish versus human MPNSTs would enable elimination of a large proportion of passenger mutations, especially on large CNAs. We established a list of orthologous genes between human and zebrafish, which includes approximately two-thirds of human protein-coding genes. For the subset of these genes found in human MPNST CNAs, only one quarter of their orthologues were co-gained or co-lost in zebrafish, dramatically narrowing the list of candidate cancer drivers for both focal and large CNAs. We conclude that zebrafish-human comparative analysis represents a powerful, and broadly applicable, tool to enrich for evolutionarily conserved cancer drivers.     

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.     

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.     

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/.     

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.     

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.     

Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data

Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes.     

Genomic profiling of submucosal-invasive gastric cancer by array-based comparative genomic hybridization

Genomic copy number aberrations (CNAs) in gastric cancer have already been extensively characterized by array comparative genomic hybridization (array CGH) analysis. However, involvement of genomic CNAs in the process of submucosal invasion and lymph node metastasis in early gastric cancer is still poorly understood. In this study, to address this issue, we collected a total of 59 tumor samples from 27 patients with submucosal-invasive gastric cancers (SMGC), analyzed their genomic profiles by array CGH, and compared them between paired samples of mucosal (MU) and submucosal (SM) invasion (23 pairs), and SM invasion and lymph node (LN) metastasis (9 pairs). Initially, we hypothesized that acquisition of specific CNA(s) is important for these processes. However, we observed no significant difference in the number of genomic CNAs between paired MU and SM, and between paired SM and LN. Furthermore, we were unable to find any CNAs specifically associated with SM invasion or LN metastasis. Among the 23 cases analyzed, 15 had some similar pattern of genomic profiling between SM and MU. Interestingly, 13 of the 15 cases also showed some differences in genomic profiles. These results suggest that the majority of SMGCs are composed of heterogeneous subpopulations derived from the same clonal origin. Comparison of genomic CNAs between SMGCs with and without LN metastasis revealed that gain of 11q13, 11q14, 11q22, 14q32 and amplification of 17q21 were more frequent in metastatic SMGCs, suggesting that these CNAs are related to LN metastasis of early gastric cancer. In conclusion, our data suggest that generation of genetically distinct subclones, rather than acquisition of specific CNA at MU, is integral to the process of submucosal invasion, and that subclones that acquire gain of 11q13, 11q14, 11q22, 14q32 or amplification of 17q21 are likely to become metastatic.     

TAGCNA: a method to identify significant consensus events of copy number alterations in cancer

Somatic copy number alteration (CNA) is a common phenomenon in cancer genome. Distinguishing significant consensus events (SCEs) from random background CNAs in a set of subjects has been proven to be a valuable tool to study cancer. In order to identify SCEs with an acceptable type I error rate, better computational approaches should be developed based on reasonable statistics and null distributions. In this article, we propose a new approach named TAGCNA for identifying SCEs in somatic CNAs that may encompass cancer driver genes. TAGCNA employs a peel-off permutation scheme to generate a reasonable null distribution based on a prior step of selecting tag CNA markers from the genome being considered. We demonstrate the statistical power of TAGCNA on simulated ground truth data, and validate its applicability using two publicly available cancer datasets: lung and prostate adenocarcinoma. TAGCNA identifies SCEs that are known to be involved with proto-oncogenes (e.g. EGFR, CDK4) and tumor suppressor genes (e.g. CDKN2A, CDKN2B), and provides many additional SCEs with potential biological relevance in these data. TAGCNA can be used to analyze the significance of CNAs in various cancers. It is implemented in R and is freely available at http://tagcna.sourceforge.net/.     

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.     

Identification of Constrained Cancer Driver Genes Based on Mutation Timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.     

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.     

Specific Genomic Regions Are Differentially Affected by Copy Number Alterations across Distinct Cancer Types, in Aggregated Cytogenetic Data

Background

Regional genomic copy number alterations (CNA) are observed in the vast majority of cancers. Besides specifically targeting well-known, canonical oncogenes, CNAs may also play more subtle roles in terms of modulating genetic potential and broad gene expression patterns of developing tumors. Any significant differences in the overall CNA patterns between different cancer types may thus point towards specific biological mechanisms acting in those cancers. In addition, differences among CNA profiles may prove valuable for cancer classifications beyond existing annotation systems.

Principal Findings

We have analyzed molecular-cytogenetic data from 25579 tumors samples, which were classified into 160 cancer types according to the International Classification of Disease (ICD) coding system. When correcting for differences in the overall CNA frequencies between cancer types, related cancers were often found to cluster together according to similarities in their CNA profiles. Based on a randomization approach, distance measures from the cluster dendrograms were used to identify those specific genomic regions that contributed significantly to this signal. This approach identified 43 non-neutral genomic regions whose propensity for the occurrence of copy number alterations varied with the type of cancer at hand. Only a subset of these identified loci overlapped with previously implied, highly recurrent (hot-spot) cytogenetic imbalance regions.

Conclusions

Thus, for many genomic regions, a simple null-hypothesis of independence between cancer type and relative copy number alteration frequency can be rejected. Since a subset of these regions display relatively low overall CNA frequencies, they may point towards second-tier genomic targets that are adaptively relevant but not necessarily essential for cancer development.