Evolution of large chromosomal copy number aberrations in breast cancer through genetic network rewiring
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ABSTRACT:
PROVIDER: PRJEB62760 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR11518505_1.fastq.gz | Fastqsanger.gz | |||
| ERR11518505_2.fastq.gz | Fastqsanger.gz | |||
| ERR11518506_1.fastq.gz | Fastqsanger.gz | |||
| ERR11518506_2.fastq.gz | Fastqsanger.gz | |||
| ERR11518507_1.fastq.gz | Fastqsanger.gz |
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Project description:MotivationCopy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, because different CNAs may overlap with one another on the genome. A recent popular model for copy number evolution is the copy number distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. In the CND, all events contribute equally; however, it is well known that rates of CNAs vary by length, genomic position and type (amplification versus deletion).ResultsWe introduce a weighted CND that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted CND as well as the associated transformation. This algorithm is based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We show that the weighted CND improves phylogenetic reconstruction on simulated data where CNAs occur with varying probabilities, aids in the derivation of phylogenies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large pan-cancer dataset.Availability and implementationCode is available at https://github.com/raphael-group/WCND.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:UnlabelledHuman prostate cancer is known to harbor recurrent genomic aberrations consisting of chromosomal losses, gains, rearrangements, and mutations that involve oncogenes and tumor suppressors. Genetically engineered mouse (GEM) models have been constructed to assess the causal role of these putative oncogenic events and provide molecular insight into disease pathogenesis. While GEM models generally initiate neoplasia by manipulating a single gene, expression profiles of GEM tumors typically comprise hundreds of transcript alterations. It is unclear whether these transcriptional changes represent the pleiotropic effects of single oncogenes, and/or cooperating genomic or epigenomic events. Therefore, it was determined whether structural chromosomal alterations occur in GEM models of prostate cancer and whether the changes are concordant with human carcinomas. Whole genome array-based comparative genomic hybridization (CGH) was used to identify somatic chromosomal copy number aberrations (SCNA) in the widely used TRAMP, Hi-Myc, Pten-null, and LADY GEM models. Interestingly, very few SCNAs were identified and the genomic architecture of Hi-Myc, Pten-null, and LADY tumors were essentially identical to the germline. TRAMP neuroendocrine carcinomas contained SCNAs, which comprised three recurrent aberrations including a single copy loss of chromosome 19 (encoding Pten). In contrast, cell lines derived from the TRAMP, Hi-Myc, and Pten-null tumors were notable for numerous SCNAs that included copy gains of chromosome 15 (encoding Myc) and losses of chromosome 11 (encoding p53).ImplicationsChromosomal alterations are not a prerequisite for tumor formation in GEM prostate cancer models and cooperating events do not naturally occur by mechanisms that recapitulate changes in genomic integrity as observed in human prostate cancer.
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Project description:Background and aimsInflammatory bowel diseases [IBD] have a complex polygenic aetiology. Rare genetic variants can cause monogenic intestinal inflammation. The impact of chromosomal aberrations and large structural abnormalities on IBD susceptibility is not clear. We aimed to comprehensively characterise the phenotype and prevalence of patients with IBD who possess rare numerical and structural chromosomal abnormalities.MethodsWe performed a systematic literature search of databases PubMed and Embase; and analysed gnomAD, Clinvar, the 100 000 Genomes Project, and DECIPHER databases. Further, we analysed international paediatric IBD cohorts to investigate the role of IL2RA duplications in IBD susceptibility.ResultsA meta-analysis suggests that monosomy X [Turner syndrome] is associated with increased expressivity of IBD that exceeds the population baseline (1.86%, 95% confidence interval [CI] 1.48 to 2.34%) and causes a younger age of IBD onset. There is little evidence that Klinefelter syndrome, Trisomy 21, Trisomy 18, mosaic Trisomy 9 and 16, or partial trisomies contribute to IBD susceptibility. Copy number analysis studies suggest inconsistent results. Monoallelic loss of X-linked or haploinsufficient genes is associated with IBD by hemizygous or heterozygous deletions, respectively. However, haploinsufficient gene deletions are detected in healthy reference populations, suggesting that the expressivity of IBD might be overestimated. One duplication that has previously been identified as potentially contributing to IBD risk involves the IL2RA/IL15R loci. Here we provide additional evidence that a microduplication of this locus may predispose to very-early-onset IBD by identifying a second case in a distinct kindred. However, the penetrance of intestinal inflammation in this genetic aberration is low [<2.6%].ConclusionsTurner syndrome is associated with increased susceptibility to intestinal inflammation. Duplication of the IL2RA/IL15R loci may contribute to disease risk.
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Project description:DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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Project description:Canine Diffuse Large B-cell Lymphoma (cDLBCL) is an aggressive cancer with variable clinical response. Despite recent attempts by gene expression profiling to identify the dog as a potential animal model for human DLBCL, this tumor remains biologically heterogeneous with no prognostic biomarkers to predict prognosis. The aim of this work was to identify copy number aberrations (CNAs) by high-resolution array comparative genomic hybridization (aCGH) in 12 dogs with newly diagnosed DLBCL. In a subset of these dogs, the genetic profiles at the end of therapy and at relapse were also assessed. In primary DLBCLs, 90 different genomic imbalances were counted, consisting of 46 gains and 44 losses. Two gains in chr13 were significantly correlated with clinical stage. In addition, specific regions of gains and losses were significantly associated to duration of remission. In primary DLBCLs, individual variability was found, however 14 recurrent CNAs (>30%) were identified. Losses involving IGK, IGL and IGH were always found, and gains along the length of chr13 and chr31 were often observed (>41%). In these segments, MYC, LDHB, HSF1, KIT and PDGFRα are annotated. At the end of therapy, dogs in remission showed four new CNAs, whereas three new CNAs were observed in dogs at relapse compared with the previous profiles. One ex novo CNA, involving TCR, was present in dogs in remission after therapy, possibly induced by the autologous vaccine. Overall, aCGH identified small CNAs associated with outcome, which, along with future expression studies, may reveal target genes relevant to cDLBCL.
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Project description:Osteosarcoma is an aggressive bone tumor that preferentially develops in adolescents. The tumor is characterized by an abundance of genomic aberrations, which hampers the identification of the driver genes involved in osteosarcoma tumorigenesis. Our study aims to identify these genes by the investigation of focal copy number aberrations (CNAs, <3 Mb). For this purpose, we subjected 26 primary tumors of osteosarcoma patients to high-resolution single nucleotide polymorphism array analyses and identified 139 somatic focal CNAs. Of these, 72 had at least one gene located within or overlapping the focal CNA, with a total of 94 genes. For 84 of these genes, the expression status in 31 osteosarcoma samples was determined by expression microarray analysis. This enabled us to identify the genes of which the over- or underexpression was in more than 35% of cases in accordance to their copy number status (gain or loss). These candidate genes were subsequently validated in an independent set and furthermore corroborated as driver genes by verifying their role in other tumor types. We identified CMTM8 as a new candidate tumor suppressor gene and GPR177 as a new candidate oncogene in osteosarcoma. In osteosarcoma, CMTM8 has been shown to suppress EGFR signaling. In other tumor types, CMTM8 is known to suppress the activity of the oncogenic protein c-Met and GPR177 is known as an overexpressed upstream regulator of the Wnt-pathway. Further studies are needed to determine whether these proteins also exert the latter functions in osteosarcoma tumorigenesis.
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Project description:The metastatic process is complex and remains a major obstacle in the management of colorectal cancer. To gain a better insight into the pathology of metastasis, we investigated genomic aberrations in a large cohort of matched colorectal cancer primaries and distant metastases from various sites by high resolution array comparative genomic hybridization. In total, 62 primary colorectal cancers, and 68 matched metastases (22 liver, 11 lung, 12 ovary, 12 omentum, and 11 distant lymph nodes) were analyzed. Public datasets were used for validation purposes. Metastases resemble their matched primary tumors in the majority of the patients. This validates the significant overlap in chromosomal aberrations between primary tumors and corresponding metastases observed previously. We observed 15 statistically significant different regions between the primary tumors and their matched metastases, of which only one recurrent event in metastases was observed. We conclude, based on detailed analysis and large independent datasets, that chromosomal copy number aberrations in colorectal metastases resemble their primary counterparts, and differences are typically non-recurrent.
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Project description:Although rare, mucoepidermoid carcinoma (MEC) is one of the most common malignant salivary gland tumors. The presence of the t(11;19)(q21;p13) translocation in a subset of MECs has raised interest in genomic aberrations in MEC. In the present study we conducted genome-wide copy-number-aberration analysis by micro-array comparative-genomic-hybridization on 27 MEC samples. Low/intermediate-grade MECs had significantly fewer copy-number-aberrations compared to high-grade MECs (low vs high: 3.48 vs 30; p = 0.0025; intermediate vs high: 5.7 vs 34.5; p = 0.036). The translocation-negative MECs contained more copy-number-aberrations than translocation-positive MECs (average amount of aberrations 15.9 vs 2.41; p =0.04). Within all 27 MEC samples, 16p11.2 and several regions on 8q were the most frequently gained regions , while 1q23.3 was the most frequently detected loss. Low/intermediate-grade MEC samples had copy-number-aberrations in chromosomes 1, 12 and 16, while high-grade MECs had a copy-number-aberration in 8p. The most commonly observed copy-number-aberration was the deletion of 3p14.1, which was observed in 4 of the translocation-negative MEC samples. No recurrent copy-number-aberrations were found in translocation-positive MEC samples. Based on these results, we conclude that MECs may be classified as follows: (i) t(11;19)(q21;p13) translocation-positive tumors with no or few chromosomal aberrations and (ii) translocation-negative tumors with multiple chromosomal aberrations.
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Project description:MotivationDNA copy number (CN) data are a fast-growing source of information used in basic and translational cancer research. Most CN segmentation data are presented without regard to the relationship between chromosomal regions. We offer both a toolkit to help scientists without programming experience visually explore the CN interactome and a package that constructs CN interactomes from publicly available data sets.ResultsThe CNVScope visualization, based on a publicly available neuroblastoma CN data set, clearly displays a distinct CN interaction in the region of the MYCN, a canonical frequent amplicon target in this cancer. Exploration of the data rapidly identified cis and trans events, including a strong anticorrelation between 11q loss and17q gain with the region of 11q loss bounded by the cell cycle regulator CCND1.AvailabilityThe shiny application is readily available for use at http://cnvscope.nci.nih.gov/, and the package can be downloaded from CRAN (https://cran.r-project.org/package=CNVScope), where help pages and vignettes are located. A newer version is available on the GitHub site (https://github.com/jamesdalg/CNVScope/), which features an animated tutorial. The CNVScope package can be locally installed using instructions on the GitHub site for Windows and Macintosh systems. This CN analysis package also runs on a linux high-performance computing cluster, with options for multinode and multiprocessor analysis of CN variant data. The shiny application can be started using a single command (which will automatically install the public data package).
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