Flexible and accurate detection of genomic copy-number changes from aCGH

Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, "What is the probability that this gene/region has CNAs?" Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases.     

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.     

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.     

Determining Frequent Patterns of Copy Number Alterations in Cancer

Cancer progression is often driven by an accumulation of genetic changes but also accompanied by increasing genomic instability. These processes lead to a complicated landscape of copy number alterations (CNAs) within individual tumors and great diversity across tumor samples. High resolution array-based comparative genomic hybridization (aCGH) is being used to profile CNAs of ever larger tumor collections, and better computational methods for processing these data sets and identifying potential driver CNAs are needed. Typical studies of aCGH data sets take a pipeline approach, starting with segmentation of profiles, calls of gains and losses, and finally determination of frequent CNAs across samples. A drawback of pipelines is that choices at each step may produce different results, and biases are propagated forward. We present a mathematically robust new method that exploits probe-level correlations in aCGH data to discover subsets of samples that display common CNAs. Our algorithm is related to recent work on maximum-margin clustering. It does not require pre-segmentation of the data and also provides grouping of recurrent CNAs into clusters. We tested our approach on a large cohort of glioblastoma aCGH samples from The Cancer Genome Atlas and recovered almost all CNAs reported in the initial study. We also found additional significant CNAs missed by the original analysis but supported by earlier studies, and we identified significant correlations between CNAs.     

Crystallographic Studies of Chemically Modified Nucleic Acids: A Backward Glance

Chemically modified nucleic acids (CNAs) are widely explored as antisense oligonucleotide or small interfering RNA (siRNA) candidates for therapeutic applications. CNAs are also of interest in diagnostics, high‐throughput genomics and target validation, nanotechnology and as model systems in investigations directed at a better understanding of the etiology of nucleic acid structure, as well as the physicochemical and pairing properties of DNA and RNA, and for probing protein–nucleic acid interactions. In this article, we review research conducted in our laboratory over the past two decades with a focus on crystal‐structure analyses of CNAs and artificial pairing systems. We highlight key insights into issues ranging from conformational distortions as a consequence of modification to the modulation of pairing strength, and RNA affinity by stereoelectronic effects and hydration. Although crystal structures have only been determined for a subset of the large number of modifications that were synthesized and analyzed in the oligonucleotide context to date, they have yielded guiding principles for the design of new analogs with tailor‐made properties, including pairing specificity, nuclease resistance, and cellular uptake. And, perhaps less obviously, crystallographic studies of CNAs and synthetic pairing systems have shed light on fundamental aspects of DNA and RNA structure and function that would not have been disclosed by investigations solely focused on the natural nucleic acids.     

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.     

Copy number alterations among mammalian enzymes cluster in the metabolic network

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks.     

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.     

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.     

Cross-linked nanoassemblies from poly(ethylene glycol)-poly(aspartate) block copolymers as stable supramolecular templates for particulate drug delivery

Block copolymer cross-linked nanoassemblies (CNAs) were developed as stable supramolecular templates for particulate drug delivery. Poly(ethylene glycol)-poly(aspartate) [PEG-p(Asp)] block copolymers, consisting of PEG (5 or 12 kDa) and Asp (5, 14, 25, 33, and 37 repeating units), were used as scaffolds and grafts in combination to prepare a nanoassembly library of grafted nanoassemblies (GNAs) and CNAs. Four synthesis routes were tested to maximize the number of drug-binding Asp units per nanoassembly. Grafting-onto-scaffold and grafting-from-scaffold methods were used for GNA synthesis. Either partially or completely deprotected PEG-p(Asp) was cross-linked with diamine compounds to prepare CNAs. (1)H NMR and GPC measurements showed that GNAs and CNAs contained the maximum 183 and 253 Asp units, respectively. Initial screening of the nanoassemblies revealed that GNAs would be impractical for further development as drug carriers due to variable grafting efficiency and low product yields. CNAs were obtained in high yields and identified as a promising supramolecular template that can entrap and release ionizable drugs (doxorubicin), enhancing the particle stability of nanoassemblies in the pharmaceutically relevant pH ranges between 4 and 9. Light scattering measurements demonstrated that the particle size of CNAs remained uniform before and after drug entrapment, causing neither aggregation nor dissociation (<5 mg/mL).     

Lack of Genomic Heterogeneity at High-Resolution aCGH between Primary Breast Cancers and Their Paired Lymph Node Metastases

Lymph-node metastasis (LNM) predict high recurrence rates in breast cancer patients. Systemic treatment aims to eliminate (micro)metastatic cells. However decisions regarding systemic treatment depend largely on clinical and molecular characteristics of primary tumours. It remains, however, unclear to what extent metastases resemble the cognate primary breast tumours, especially on a genomic level, and as such will be eradicated by the systemic therapy chosen. In this study we used high-resolution aCGH to investigate DNA copy number differences between primary breast cancers and their paired LNMs. To date, no recurrent LNM-specific genomic aberrations have been identified using array comparative genomic hybridization (aCGH) analysis. In our study we employ a high-resolution platform and we stratify on different breast cancer subtypes, both aspects that might have underpowered previously performed studies.To test the possibility that genomic instability in triple-negative breast cancers (TNBCs) might cause increased random and potentially also recurrent copy number aberrations (CNAs) in their LNMs, we studied 10 primary TNBC–LNM pairs and 10 ER-positive (ER+) pairs and verified our findings adding additionally 5 TNBC-LNM and 22 ER+-LNM pairs. We found that all LNMs clustered nearest to their matched tumour except for two cases, of which one was due to the presence of two distinct histological components in one tumour. We found no significantly altered CNAs between tumour and their LNMs in the entire group or in the subgroups. Within the TNBC subgroup, no absolute increase in CNAs was found in the LNMs compared to their primary tumours, suggesting that increased genomic instability does not lead to more CNAs in LNMs. Our findings suggest a high clonal relationship between primary breast tumours and its LNMs, at least prior to treatment, and support the use of primary tumour characteristics to guide adjuvant systemic chemotherapy in breast cancer patients.     

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.     

Genomic Profiling of Oral Squamous Cell Carcinoma by Array-Based Comparative Genomic Hybridization

We designed a study to investigate genetic relationships between primary tumors of oral squamous cell carcinoma (OSCC) and their lymph node metastases, and to identify genomic copy number aberrations (CNAs) related to lymph node metastasis. For this purpose, we collected a total of 42 tumor samples from 25 patients and analyzed their genomic profiles by array-based comparative genomic hybridization. We then compared the genetic profiles of metastatic primary tumors (MPTs) with their paired lymph node metastases (LNMs), and also those of LNMs with non-metastatic primary tumors (NMPTs). Firstly, we found that although there were some distinctive differences in the patterns of genomic profiles between MPTs and their paired LNMs, the paired samples shared similar genomic aberration patterns in each case. Unsupervised hierarchical clustering analysis grouped together 12 of the 15 MPT-LNM pairs. Furthermore, similarity scores between paired samples were significantly higher than those between non-paired samples. These results suggested that MPTs and their paired LNMs are composed predominantly of genetically clonal tumor cells, while minor populations with different CNAs may also exist in metastatic OSCCs. Secondly, to identify CNAs related to lymph node metastasis, we compared CNAs between grouped samples of MPTs and LNMs, but were unable to find any CNAs that were more common in LNMs. Finally, we hypothesized that subpopulations carrying metastasis-related CNAs might be present in both the MPT and LNM. Accordingly, we compared CNAs between NMPTs and LNMs, and found that gains of 7p, 8q and 17q were more common in the latter than in the former, suggesting that these CNAs may be involved in lymph node metastasis of OSCC. In conclusion, our data suggest that in OSCCs showing metastasis, the primary and metastatic tumors share similar genomic profiles, and that cells in the primary tumor may tend to metastasize after acquiring metastasis-associated CNAs.     

Methods for monitoring autophagy

Autophagy is an intracellular bulk degradation system that is found ubiquitously in eukaryotes. Autophagy is responsible for the degradation of most long-lived proteins and some organelles. Cytoplasmic constituents, including organelles, are sequestered into double-membraned autophagosomes, which subsequently fuse with lysosomes where their contents are degraded. This system has been implicated in various physiological processes including protein and organelle turnover, the starvation response, cellular differentiation, cell death, and pathogenesis. However, methods for monitoring autophagy have been very limited and unsatisfactory. The most standard method is conventional electron microscopy. In addition, some biochemical methods have been utilized to measure autophagic activity. Recently, the molecular basis of autophagosome formation has been extensively studied using yeast cells; these studies have provided useful marker proteins for autophagosomes. Importantly, most of these proteins are conserved in mammals. Using these probes, we can now specifically monitor autophagic activity.     

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.     

Germline DNA Copy Number Aberrations Identified as Potential Prognostic Factors for Breast Cancer Recurrence

Breast cancer recurrence (BCR) is a common treatment outcome despite curative-intent primary treatment of non-metastatic breast cancer. Currently used prognostic and predictive factors utilize tumor-based markers, and are not optimal determinants of risk of BCR. Germline-based copy number aberrations (CNAs) have not been evaluated as determinants of predisposition to experience BCR. In this study, we accessed germline DNA from 369 female breast cancer subjects who received curative-intent primary treatment following diagnosis. Of these, 155 experienced BCR and 214 did not, after a median duration of follow up after breast cancer diagnosis of 6.35 years (range = 0.60–21.78) and 8.60 years (range = 3.08–13.57), respectively. Whole genome CNA genotyping was performed on the Affymetrix SNP array 6.0 platform. CNAs were identified using the SNP-Fast Adaptive States Segmentation Technique 2 algorithm implemented in Nexus Copy Number 6.0. Six samples were removed due to poor quality scores, leaving 363 samples for further analysis. We identified 18,561 CNAs with ≥1 kb as a predefined cut-off for observed aberrations. Univariate survival analyses (log-rank tests) identified seven CNAs (two copy number gains and five copy neutral-loss of heterozygosities, CN-LOHs) showing significant differences (P<2.01×10⁻⁵) in recurrence-free survival (RFS) probabilities with and without CNAs.We also observed three additional but distinct CN-LOHs showing significant differences in RFS probabilities (P<2.86×10⁻⁵) when analyses were restricted to stratified cases (luminal A, n = 208) only. After adjusting for tumor stage and grade in multivariate analyses (Cox proportional hazards models), all the CNAs remained strongly associated with the phenotype of BCR. Of these, we confirmed three CNAs at 17q11.2, 11q13.1 and 6q24.1 in representative samples using independent genotyping platforms. Our results suggest further investigations on the potential use of germline DNA variations as prognostic markers in cancer-associated phenotypes.