1
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Hoyos D, Zappasodi R, Schulze I, Sethna Z, de Andrade KC, Bajorin DF, Bandlamudi C, Callahan MK, Funt SA, Hadrup SR, Holm JS, Rosenberg JE, Shah SP, Vázquez-García I, Weigelt B, Wu M, Zamarin D, Campitelli LF, Osborne EJ, Klinger M, Robins HS, Khincha PP, Savage SA, Balachandran VP, Wolchok JD, Hellmann MD, Merghoub T, Levine AJ, Łuksza M, Greenbaum BD. Fundamental immune-oncogenicity trade-offs define driver mutation fitness. Nature 2022; 606:172-179. [PMID: 35545680 PMCID: PMC9159948 DOI: 10.1038/s41586-022-04696-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/28/2022] [Indexed: 12/29/2022]
Abstract
Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3-6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical 'free fitness' framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.
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Affiliation(s)
- David Hoyos
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roberta Zappasodi
- Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA.
| | - Isabell Schulze
- Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zachary Sethna
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kelvin César de Andrade
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Dean F Bajorin
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chaitanya Bandlamudi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Margaret K Callahan
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel A Funt
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sine R Hadrup
- Experimental and Translational Immunology, Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jeppe S Holm
- Experimental and Translational Immunology, Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jonathan E Rosenberg
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michelle Wu
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitriy Zamarin
- Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | | | - Payal P Khincha
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Sharon A Savage
- Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Vinod P Balachandran
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jedd D Wolchok
- Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew D Hellmann
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Taha Merghoub
- Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
- Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Arnold J Levine
- Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA
| | - Marta Łuksza
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin D Greenbaum
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA.
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2
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Ullah MA, Alam S, Farzana M, Tayab Moin A, Binte Sayed Prapty CN, Zohora US, Rahman MS. Prognostic and therapeutic value of LSM5 gene in human brain cancer Glioma: An omics database exploration approach. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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3
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Guardia T, Eason M, Kontrogianni-Konstantopoulos A. Obscurin: A multitasking giant in the fight against cancer. Biochim Biophys Acta Rev Cancer 2021; 1876:188567. [PMID: 34015411 PMCID: PMC8349851 DOI: 10.1016/j.bbcan.2021.188567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/03/2021] [Accepted: 05/11/2021] [Indexed: 12/19/2022]
Abstract
Giant obscurins (720-870 kDa), encoded by OBSCN, were originally discovered in striated muscles as cytoskeletal proteins with scaffolding and regulatory roles. Recently though, they have risen to the spotlight as key players in cancer development and progression. Herein, we provide a timely prudent synopsis of the expanse of OBSCN mutations across 16 cancer types. Given the extensive work on OBSCN's role in breast epithelium, we summarize functional studies implicating obscurins as potent tumor suppressors in breast cancer and delve into an in silico analysis of its mutational profile and epigenetic (de)regulation using different dataset platforms and sophisticated computational tools. Lastly, we formally describe the OBSCN-Antisense-RNA-1 gene, which belongs to the long non-coding RNA family and discuss its potential role in modulating OBSCN expression in breast cancer. Collectively, we highlight the escalating involvement of obscurins in cancer biology and outline novel potential mechanisms of OBSCN (de)regulation that warrant further investigation.
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Affiliation(s)
- Talia Guardia
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Matthew Eason
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Aikaterini Kontrogianni-Konstantopoulos
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, USA.
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4
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Swanson DM, Lien T, Bergholtz H, Sørlie T, Frigessi A. A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort. Bioinformatics 2020; 35:4886-4897. [PMID: 31077301 DOI: 10.1093/bioinformatics/btz381] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/05/2019] [Accepted: 05/01/2019] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive. RESULTS We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results. AVAILABILITY AND IMPLEMENTATION The model is implemented in an R package called twl ('two-way latent'), available on CRAN. Data for analysis are available within the R package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David M Swanson
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Tonje Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
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5
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Khalyuzova MV, Litviakov NV, Takhauov RM, Isubakova DS, Usova TV, Bronikovskaya EV, Takhauova LR, Karpov AB. Delayed Changes in the Frequency of Unstable Chromosomal Aberrations and the CNA-Genetic Landscape of Blood Leukocytes in People Exposed to Long-Term Occupational Irradiation. BIOL BULL+ 2020. [DOI: 10.1134/s1062359019110049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Newton R, Wernisch L. A meta-analysis of multiple matched aCGH/expression cancer datasets reveals regulatory relationships and pathway enrichment of potential oncogenes. PLoS One 2019; 14:e0213221. [PMID: 31335867 PMCID: PMC6650054 DOI: 10.1371/journal.pone.0213221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets. Here we investigate genes in the database which are unusual in that their copy number exhibits consistent heterogeneous disruption in a high proportion of the cancer datasets. We assess the potential relevance of these genes to the pathology of the cancer samples, in light of their predicted regulatory relationships and enriched biological pathways. A network-based method was used to identify enriched pathways from the genes’ inferred targets. The analysis predicts both known and new regulator-target interactions and pathway memberships. We examine examples in detail, in particular the gene POGZ, which is disrupted in many of the cancer datasets and has an unusually large number of predicted targets, from which the network analysis predicts membership of cancer related pathways. The results suggest close involvement in known cancer pathways of genes exhibiting consistent heterogeneous copy number disruption. Further experimental work would clarify their relevance to tumor biology. The results of the analysis presented in the database METAMATCHED, and included here as an R archive file, constitute a large number of predicted regulatory relationships and pathway memberships which we anticipate will be useful in informing such experiments.
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Affiliation(s)
- Richard Newton
- MRC Biostatistics Unit, Cambridge University, Cambridge, United Kingdom
- * E-mail:
| | - Lorenz Wernisch
- MRC Biostatistics Unit, Cambridge University, Cambridge, United Kingdom
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7
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Walter V, Du Y, Danilova L, Hayward MC, Hayes DN. MVisAGe Identifies Concordant and Discordant Genomic Alterations of Driver Genes in Squamous Tumors. Cancer Res 2018; 78:3375-3385. [PMID: 29700001 DOI: 10.1158/0008-5472.can-17-3464] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/10/2018] [Accepted: 04/16/2018] [Indexed: 02/06/2023]
Abstract
Integrated analyses of multiple genomic datatypes are now common in cancer profiling studies. Such data present opportunities for numerous computational experiments, yet analytic pipelines are limited. Tools such as the cBioPortal and Regulome Explorer, although useful, are not easy to access programmatically or to implement locally. Here, we introduce the MVisAGe R package, which allows users to quantify gene-level associations between two genomic datatypes to investigate the effect of genomic alterations (e.g., DNA copy number changes on gene expression). Visualizing Pearson/Spearman correlation coefficients according to the genomic positions of the underlying genes provides a powerful yet novel tool for conducting exploratory analyses. We demonstrate its utility by analyzing three publicly available cancer datasets. Our approach highlights canonical oncogenes in chr11q13 that displayed the strongest associations between expression and copy number, including CCND1 and CTTN, genes not identified by copy number analysis in the primary reports. We demonstrate highly concordant usage of shared oncogenes on chr3q, yet strikingly diverse oncogene usage on chr11q as a function of HPV infection status. Regions of chr19 that display remarkable associations between methylation and gene expression were identified, as were previously unreported miRNA-gene expression associations that may contribute to the epithelial-to-mesenchymal transition.Significance: This study presents an important bioinformatics tool that will enable integrated analyses of multiple genomic datatypes. Cancer Res; 78(12); 3375-85. ©2018 AACR.
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Affiliation(s)
- Vonn Walter
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania. .,Department of Biochemistry, Penn State College of Medicine, Hershey, Pennsylvania.,UNC Lineberger Comprehensive Cancer Center, School of Medicine, Chapel Hill, North Carolina
| | - Ying Du
- Center for Infectious Disease Research, Seattle, Washington
| | - Ludmila Danilova
- Johns Hopkins University School of Medicine and Bloomberg∼Kimmel Institute, Baltimore, Maryland.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Michele C Hayward
- UNC Lineberger Comprehensive Cancer Center, School of Medicine, Chapel Hill, North Carolina
| | - D Neil Hayes
- UNC Lineberger Comprehensive Cancer Center, School of Medicine, Chapel Hill, North Carolina.,Department of Internal Medicine, Division of Medical Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
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8
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Tokar T, Pastrello C, Ramnarine VR, Zhu CQ, Craddock KJ, Pikor LA, Vucic EA, Vary S, Shepherd FA, Tsao MS, Lam WL, Jurisica I. Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes. Oncotarget 2018; 9:9137-9155. [PMID: 29507679 PMCID: PMC5823624 DOI: 10.18632/oncotarget.24070] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 01/02/2018] [Indexed: 12/30/2022] Open
Abstract
In many cancers, significantly down- or upregulated genes are found within chromosomal regions with DNA copy number alteration opposite to the expression changes. Generally, this paradox has been overlooked as noise, but can potentially be a consequence of interference of epigenetic regulatory mechanisms, including microRNA-mediated control of mRNA levels. To explore potential associations between microRNAs and paradoxes in non-small-cell lung cancer (NSCLC) we curated and analyzed lung adenocarcinoma (LUAD) data, comprising gene expressions, copy number aberrations (CNAs) and microRNA expressions. We integrated data from 1,062 tumor samples and 241 normal lung samples, including newly-generated array comparative genomic hybridization (aCGH) data from 63 LUAD samples. We identified 85 “paradoxical” genes whose differential expression consistently contrasted with aberrations of their copy numbers. Paradoxical status of 70 out of 85 genes was validated on sample-wise basis using The Cancer Genome Atlas (TCGA) LUAD data. Of these, 41 genes are prognostic and form a clinically relevant signature, which we validated on three independent datasets. By meta-analysis of results from 9 LUAD microRNA expression studies we identified 24 consistently-deregulated microRNAs. Using TCGA-LUAD data we showed that deregulation of 19 of these microRNAs explains differential expression of the paradoxical genes. Our results show that deregulation of paradoxical genes is crucial in LUAD and their expression pattern is maintained epigenetically, defying gene copy number status.
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Affiliation(s)
- Tomas Tokar
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Chiara Pastrello
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Varune R Ramnarine
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,The Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, Canada
| | - Chang-Qi Zhu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Kenneth J Craddock
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Larrisa A Pikor
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, Canada
| | - Emily A Vucic
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, Canada
| | - Simon Vary
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Mathematical Institute, University of Oxford, Oxford, United Kingdom.,Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia
| | - Frances A Shepherd
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Ming-Sound Tsao
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Wan L Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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9
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Abstract
A variety of molecular techniques can be used in order to unravel the molecular composition of cells. In particular, the microarray technology has been used to identify novel biomarkers that may be useful in the diagnosis, prognosis, or treatment of cancer. The microarray technology is ideal for biomarker discovery as it allows for the screening of a large number of molecules at once. In this review, we focus on microRNAs (miRNAs) which are key molecules in cells and regulate gene expression post-transcriptionally. miRNAs are small, single-stranded RNA molecules that bind to complementary mRNAs. Binding of miRNAs to mRNAs leads either to degradation, or translational inhibition of the target mRNA. Roughly one third of all the mRNAs are postulated to be regulated by miRNAs. miRNAs are known to be deregulated in different types of cancer, including breast cancer, and it has been demonstrated that deregulation of several miRNAs can be used as biological markers in cancer. miRNA expression can for example discriminate between normal, benign and malignant breast tissue, and between different breast cancer subtypes.In the post-genomic era, an important task of molecular biology is to understand gene regulation in the context of biological networks. Because miRNAs have such a pronounced role in cells, it is pivotal to understand the mechanisms that underlie their control, and to identify how miRNAs influence cancer development and progression.
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Affiliation(s)
- Andliena Tahiri
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Miriam R Aure
- Department of Cancer Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Ullernchausseen 70, 0379, Oslo, Norway
| | - Vessela N Kristensen
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway.
- Department of Cancer Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Ullernchausseen 70, 0379, Oslo, Norway.
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10
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Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data. Sci Rep 2017. [PMID: 29170526 DOI: 10.1038/s41598-017-16286-5]+[] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.
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11
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Lu X, Lu J, Liao B, Li X, Qian X, Li K. Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data. Sci Rep 2017. [PMID: 29170526 DOI: 10.1038/s41598-017-16286-5] [] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.
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Affiliation(s)
- Xinguo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China.
| | - Jibo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Bo Liao
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Xing Li
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Xin Qian
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Keqin Li
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China.,Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA
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12
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Lu X, Lu J, Liao B, Li X, Qian X, Li K. Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data. Sci Rep 2017; 7:16188. [PMID: 29170526 PMCID: PMC5700962 DOI: 10.1038/s41598-017-16286-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 11/09/2017] [Indexed: 01/08/2023] Open
Abstract
Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.
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Affiliation(s)
- Xinguo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China.
| | - Jibo Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Bo Liao
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Xing Li
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Xin Qian
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
| | - Keqin Li
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Nan Rd., Changsha, 410082, China
- Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA
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13
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Yan Z, Liu Y, Wei Y, Zhao N, Zhang Q, Wu C, Chang Z, Xu Y. The functional consequences and prognostic value of dosage sensitivity in ovarian cancer. MOLECULAR BIOSYSTEMS 2017; 13:380-391. [PMID: 28067383 DOI: 10.1039/c6mb00625f] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Copy number alteration (CNA) represents an important class of genetic variations that may contribute to tumorigenesis, tumor growth and metastatic spread. CNA can directly affect the expression of genes within the CNA regions; however, genes within the CNA regions exhibit heterogeneity in gene dosage sensitivity. In this study, a computational framework was built to identify 1170 dosage-sensitive genes (DSGs) and 1215 dosage-resistant genes (DRGs) that were related to ovarian serous cystadenocarcinoma (OV) through the association between CNA and gene expression. To analyze the different functions of the genes within the two groups, the functional annotation results indicated that DRGs were involved in cancer-related processes like immune response, cell death and apoptosis, while DSGs were enriched in essential processes like the cell cycle and the DNA metabolic process. Meanwhile, two three-dimensional regulatory networks for differentially expressed miRNAs, differentially expressed transcription factors (TFs) and DSGs or DRGs were constructed based on feed-forward loops. We identified key regulators (such as miR-16-5p, miR-98-5p, MYB and HOXA5) and cancer prognosis-related network motifs (such as miR-98-5p-HOXA5-TP53 and miR-16-5p-MYB-IGF1R) after the analysis of network topological features. Our results lead us to speculate that these genes and associated regulators may be potential mechanistic biomarkers for tumorigenesis and progression of cancer. Research on the network characteristics and the role of feed-forward loops in OV tumorigenesis and development could lead to feasible suggestions for the prevention and early diagnosis of OV, which will shed light on understanding the functional mechanism of CNA in cancer.
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Affiliation(s)
- Zichuang Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Yongjing Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Yunzhen Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Ning Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Qiang Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Cheng Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Zhiqiang Chang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Yan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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14
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Zhang XF, Ou-Yang L, Yan H. Incorporating prior information into differential network analysis using non-paranormal graphical models. Bioinformatics 2017; 33:2436-2445. [DOI: 10.1093/bioinformatics/btx208] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 04/05/2017] [Indexed: 02/02/2023] Open
Affiliation(s)
- Xiao-Fei Zhang
- Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Le Ou-Yang
- Department of Electronic Engineering, College of Information Engineering, Shenzhen University, Shenzhen, China
| | - Hong Yan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
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15
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Lai YP, Wang LB, Wang WA, Lai LC, Tsai MH, Lu TP, Chuang EY. iGC-an integrated analysis package of gene expression and copy number alteration. BMC Bioinformatics 2017; 18:35. [PMID: 28088185 PMCID: PMC5237550 DOI: 10.1186/s12859-016-1438-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 12/17/2016] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. RESULTS We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. CONCLUSION The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package's source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html .
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Affiliation(s)
- Yi-Pin Lai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan
| | - Liang-Bo Wang
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Wei-An Wang
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan
| | - Liang-Chuan Lai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Physiology, National Taiwan University, Taipei, Taiwan
| | - Mong-Hsun Tsai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan.,Institute of Biotechnology, National Taiwan University, Taipei, Taiwan
| | - Tzu-Pin Lu
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
| | - Eric Y Chuang
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan. .,Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
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16
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Litviakov NV, Cherdyntseva NV, Tsyganov MM, Slonimskaya EM, Ibragimova MK, Kazantseva PV, Kzhyshkowska J, Choinzonov EL. Deletions of multidrug resistance gene loci in breast cancer leads to the down-regulation of its expression and predict tumor response to neoadjuvant chemotherapy. Oncotarget 2016; 7:7829-41. [PMID: 26799285 PMCID: PMC4884957 DOI: 10.18632/oncotarget.6953] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 12/05/2015] [Indexed: 01/10/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is intensively used for the treatment of primary breast cancer. In our previous studies, we reported that clinical tumor response to NAC is associated with the change of multidrug resistance (MDR) gene expression in tumors after chemotherapy. In this study we performed a combined analysis of MDR gene locus deletions in tumor DNA, MDR gene expression and clinical response to NAC in 73 BC patients. Copy number variations (CNVs) in biopsy specimens were tested using high-density microarray platform CytoScanTM HD Array (Affymetrix, USA). 75%–100% persons having deletions of MDR gene loci demonstrated the down-regulation of MDR gene expression. Expression of MDR genes was 2–8 times lower in patients with deletion than in patients having no deletion only in post-NAC tumors samples but not in tumor tissue before chemotherapy. All patients with deletions of ABCB1 ABCB 3 ABCC5 gene loci – 7q21.1, 6p21.32, 3q27 correspondingly, and most patients having deletions in ABCC1 (16p13.1), ABCC2 (10q24), ABCG1 (21q22.3), ABCG2 (4q22.1), responded favorably to NAC. The analysis of all CNVs, including both amplification and deletion showed that the frequency of 13q14.2 deletion was 85% among patients bearing tumor with the deletion at least in one MDR gene locus versus 9% in patients with no deletions. Differences in the frequency of 13q14.2 deletions between the two groups were statistically significant (p = 2.03 ×10−11, Fisher test, Bonferroni-adjusted p = 1.73 × 10−8). In conclusion, our study for the first time demonstrates that deletion MDR gene loci can be used as predictive marker for tumor response to NAC.
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Affiliation(s)
- Nikolai V Litviakov
- Laboratory of Oncovirology, Tomsk Cancer Research Institute, Tomsk, Russian Federation.,Laboratory of Translational Cell and Molecular Biomedicine, National Research Tomsk State University, Tomsk, Russian Federation
| | - Nadezhda V Cherdyntseva
- Laboratory of Translational Cell and Molecular Biomedicine, National Research Tomsk State University, Tomsk, Russian Federation.,Laboratory of Molecular Oncology and Immunology, Tomsk Cancer Research Institute, Tomsk, Russian Federation
| | - Matvey M Tsyganov
- Laboratory of Oncovirology, Tomsk Cancer Research Institute, Tomsk, Russian Federation.,Laboratory of Translational Cell and Molecular Biomedicine, National Research Tomsk State University, Tomsk, Russian Federation
| | - Elena M Slonimskaya
- Department of General Oncology, Tomsk Cancer Research Institute, Tomsk, Russian Federation
| | - Marina K Ibragimova
- Laboratory of Oncovirology, Tomsk Cancer Research Institute, Tomsk, Russian Federation
| | - Polina V Kazantseva
- Department of General Oncology, Tomsk Cancer Research Institute, Tomsk, Russian Federation
| | - Julia Kzhyshkowska
- Laboratory of Translational Cell and Molecular Biomedicine, National Research Tomsk State University, Tomsk, Russian Federation.,Department of Innate Immunity and Tolerance, Institute of Transfusion Medicine and Immunology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Eugeniy L Choinzonov
- Department of Head and Neck Cancer, Tomsk Cancer Research Institute, Tomsk, Russian Federation
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17
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Karlsson J, Larsson E. FocalScan: Scanning for altered genes in cancer based on coordinated DNA and RNA change. Nucleic Acids Res 2016; 44:e150. [PMID: 27474725 PMCID: PMC5100559 DOI: 10.1093/nar/gkw674] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 06/22/2016] [Accepted: 07/17/2016] [Indexed: 01/23/2023] Open
Abstract
Somatic genomic copy-number alterations can lead to transcriptional activation or inactivation of tumor driver or suppressor genes, contributing to the malignant properties of cancer cells. Selection for such events may manifest as recurrent amplifications or deletions of size-limited (focal) regions. While methods have been developed to identify such focal regions, finding the exact targeted genes remains a challenge. Algorithms are also available that integrate copy number and RNA expression data, to aid in identifying individual targeted genes, but specificity is lacking. Here, we describe FocalScan, a tool designed to simultaneously uncover patterns of focal copy number alteration and coordinated expression change, thus combining both principles. The method outputs a ranking of tentative cancer drivers or suppressors. FocalScan works with RNA-seq data, and unlike other tools it can scan the genome unaided by a gene annotation, enabling identification of novel putatively functional elements including lncRNAs. Application on a breast cancer data set suggests considerably better performance than other DNA/RNA integration tools.
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Affiliation(s)
- Joakim Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden
| | - Erik Larsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden
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18
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Liu Y, Zhang R, Zhao N, Zhang Q, Yan Z, Chang Z, Wei Y, Wu C, Xu J, Xu Y. A comparative analysis reveals the dosage sensitivity and regulatory patterns of lncRNA in prostate cancer. MOLECULAR BIOSYSTEMS 2016; 12:3176-85. [PMID: 27507663 DOI: 10.1039/c6mb00359a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although the key roles of long non-coding RNAs (lncRNAs) in multiple diseases are well documented, the relationship between the lncRNA copy number and expression is unknown. Here, we present a comprehensive study that demonstrates the impact of miRNA-TF co-regulatory motifs on the dosage effects of protein-coding genes (PCGs) and lncRNAs in prostate cancer. By integrating copy number profiles, expression profiles and regulatory relationships with miRNAs and transcription factors, we reveal that lncRNAs and PCGs correlate with distinct dosage sensitivity and regulatory pattern characteristics. We also show that lncRNAs from different genomic regions have different features. Using a custom-built framework, we identified 24 candidate prostate cancer-related lncRNAs based on the known properties of established prostate-related lncRNAs. Our method will enable the identification of cancer-related lncRNAs, which will provide new insights into cancer lncRNA biology.
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Affiliation(s)
- Yongjing Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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19
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Nabavi S. Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data. BMC Genomics 2016; 17:638. [PMID: 27526849 PMCID: PMC4986197 DOI: 10.1186/s12864-016-2942-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 07/15/2016] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. RESULTS To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. CONCLUSIONS The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.
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Affiliation(s)
- Sheida Nabavi
- Computer Science and Engineering Department, Institute for Systems Genomics, University of Connecticut, 371 Fairfield Way, Unit 4155, Storrs, CT, 06268, USA.
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20
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Hekman JP, Johnson JL, Kukekova AV. Transcriptome Analysis in Domesticated Species: Challenges and Strategies. Bioinform Biol Insights 2016; 9:21-31. [PMID: 26917953 PMCID: PMC4756862 DOI: 10.4137/bbi.s29334] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 12/21/2015] [Accepted: 12/26/2015] [Indexed: 12/13/2022] Open
Abstract
Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.
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Affiliation(s)
- Jessica P. Hekman
- Department of Animal Sciences, College of ACES, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Jennifer L. Johnson
- Department of Animal Sciences, College of ACES, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Anna V. Kukekova
- Department of Animal Sciences, College of ACES, University of Illinois at Urbana-Champaign, Urbana, USA
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21
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Menezes RX, Mohammadi L, Goeman JJ, Boer JM. Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack. BMC Bioinformatics 2016; 17:77. [PMID: 26860128 PMCID: PMC4746904 DOI: 10.1186/s12859-016-0926-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 01/29/2016] [Indexed: 11/23/2022] Open
Abstract
Background It has been shown that a random-effects framework can be used to test the association between a gene’s expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Methods and results Here, we extend the model introduced by Menezes et al. 2009 to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations and yields a test to help separate true associations from noise. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Conclusions Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0926-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Renée X Menezes
- Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1089a, HV Amsterdam, 1081, The Netherlands.
| | | | - Jelle J Goeman
- Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands. .,Medical Statistics and Bioinformatics, Leiden University Medical Center, Nijmegen, The Netherlands.
| | - Judith M Boer
- Department of Pediatric Oncology and Hematology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands. .,Netherlands Bioinformatics Centre, Nijmegen, The Netherlands.
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22
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Srihari S, Kalimutho M, Lal S, Singla J, Patel D, Simpson PT, Khanna KK, Ragan MA. Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach. MOLECULAR BIOSYSTEMS 2016; 12:963-72. [DOI: 10.1039/c5mb00655d] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
We apply a network approach to identify genes associated incisor intranswith copy-number alterations in breast cancer pathogenesis.
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Affiliation(s)
- Sriganesh Srihari
- Institute for Molecular Bioscience
- The University of Queensland
- St. Lucia
- Australia
| | | | - Samir Lal
- The University of Queensland
- UQ Centre for Clinical Research
- Brisbane
- Australia
| | - Jitin Singla
- Indian Institute of Technology Roorkee
- Roorkee
- India
| | - Dhaval Patel
- Indian Institute of Technology Roorkee
- Roorkee
- India
| | - Peter T. Simpson
- The University of Queensland
- UQ Centre for Clinical Research
- Brisbane
- Australia
- The University of Queensland
| | - Kum Kum Khanna
- QIMR-Berghofer Medical Research Institute
- Brisbane
- Australia
| | - Mark A. Ragan
- Institute for Molecular Bioscience
- The University of Queensland
- St. Lucia
- Australia
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23
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Liu X, Zou J, Su J, Lu Y, Zhang J, Li L, Yin F. Downregulation of transient receptor potential cation channel, subfamily C, member 1 contributes to drug resistance and high histological grade in ovarian cancer. Int J Oncol 2015; 48:243-52. [PMID: 26647723 DOI: 10.3892/ijo.2015.3254] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 10/15/2015] [Indexed: 11/05/2022] Open
Abstract
Transient receptor potential cation channel, subfamily C, member 1 (TRPC1) participates in many physiological functions but has also been implicated in cancer development. However, little is known about the role of TRPC1 in ovarian cancer (OC), including the drug resistance of these tumors. In the present study, a significant and consistent downregulation of TRPC1 in drug-resistant OC tissues/cells was determined using real-time quantitative polymerase chain reaction assays and the microarrays deposited in Oncomine and Gene Expression Omnibus (GEO) profiles. Protein/gene-protein/gene and protein-chemical interactions indicated that TRPC1 interacts with 14 proteins/genes and 6 chemicals, all of which are involved in the regulation of drug resistance in OC. Biological process annotation of TRPC1, OC, and drug resistance indicated a role for TRPC1 in drug-resistance-related functions in OC, mainly via the cell cycle, gene expression and cell growth and cell death. Analysis of mRNA-microRNA interactions showed that 8 out of 11 major pathways enriched from 38 predominant microRNAs targeting TRPC1 were involved in the regulation of drug resistance in OC, and 8 out of these top 10 microRNAs were implicated in the drug resistance in ovarian and other cancers. In a clinical analysis using data obtained from The Cancer Genome Atlas project (TCGA) cohort on 341 OC patients, TRPC1 expression was found to differ significantly between grade 2 and grade 3 tumors, with low-level expression correlating with higher tumor grade. This is the first report to show a potential association between the downregulation of TRPC1 and both drug resistance and high histological tumor grade in OC. Our results provide the basis for further investigations of the drug-resistance-related functions of TRPC1 in OC and other forms of cancer.
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Affiliation(s)
- Xia Liu
- Center for Translational Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Jing Zou
- Medical Scientific Research Centre, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Jie Su
- Key Laboratory of High-Incidence-Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Nanning, Guangxi 530021, P.R. China
| | - Yi Lu
- Center for Translational Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Jian Zhang
- Center for Translational Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Li Li
- Key Laboratory of High-Incidence-Tumor Prevention and Treatment (Guangxi Medical University), Ministry of Education, Nanning, Guangxi 530021, P.R. China
| | - Fuqiang Yin
- Medical Scientific Research Centre, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
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24
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Investigating inter-chromosomal regulatory relationships through a comprehensive meta-analysis of matched copy number and transcriptomics data sets. BMC Genomics 2015; 16:967. [PMID: 26581858 PMCID: PMC4650296 DOI: 10.1186/s12864-015-2100-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 10/15/2015] [Indexed: 12/16/2022] Open
Abstract
Background Gene regulatory relationships can be inferred using matched array comparative genomics and transcriptomics data sets from cancer samples. The way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment. There are now a large number of such data sets publicly available making a meta-analysis of the data possible. Results We infer inter-chromosomal acting gene regulatory relationships from a meta-analysis of 31 publicly available matched array comparative genomics and transcriptomics data sets in humans. We obtained statistically significant predictions of target genes for 1430 potential regulatory genes. The regulatory relationships being inferred are either direct relationships, of a transcription factor on its target, or indirect ones, through pathways containing intermediate steps. We analyse the predictions in terms of cocitations, both publications which cite a regulator with any of its inferred targets and cocitations of any genes in a target list. Conclusions The most striking observation from the results is the greater number of inter-chromosomal regulatory relationships involving repression compared to those involving activation. The complete results of the meta-analysis are presented in the database METAMATCHED. We anticipate that the predictions contained in the database will be useful in informing experiments and in helping to construct networks of regulatory relationships. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2100-5) contains supplementary material, which is available to authorized users.
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25
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Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat Commun 2015; 6:8554. [PMID: 26436532 PMCID: PMC4600750 DOI: 10.1038/ncomms9554] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 09/04/2015] [Indexed: 12/27/2022] Open
Abstract
We present a novel hierarchical Bayes statistical model, xseq, to systematically quantify the impact of somatic mutations on expression profiles. We establish the theoretical framework and robust inference characteristics of the method using computational benchmarking. We then use xseq to analyse thousands of tumour data sets available through The Cancer Genome Atlas, to systematically quantify somatic mutations impacting expression profiles. We identify 30 novel cis-effect tumour suppressor gene candidates, enriched in loss-of-function mutations and biallelic inactivation. Analysis of trans-effects of mutations and copy number alterations with xseq identifies mutations in 150 genes impacting expression networks, with 89 novel predictions. We reveal two important novel characteristics of mutation impact on expression: (1) patients harbouring known driver mutations exhibit different downstream gene expression consequences; (2) expression patterns for some mutations are stable across tumour types. These results have critical implications for identification and interpretation of mutations with consequent impact on transcription in cancer. Assessing functional impact of mutations in cancer on gene expression can improve our understanding of cancer biology and may identify potential therapeutic targets. Here, Ding et al. describe a novel statistical model named xseq for a systematic survey of how mutations impact transcriptome landscapes across 12 different tumour types.
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Cava C, Bertoli G, Castiglioni I. Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential. BMC SYSTEMS BIOLOGY 2015; 9:62. [PMID: 26391647 PMCID: PMC4578257 DOI: 10.1186/s12918-015-0211-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 09/15/2015] [Indexed: 12/11/2022]
Abstract
BACKGROUND Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics. RESULTS These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses. CONCLUSION This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy.
| | - Gloria Bertoli
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy.
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Milan, Italy.
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Li K, Liu Y, Zhou Y, Zhang R, Zhao N, Yan Z, Zhang Q, Zhang S, Qiu F, Xu Y. An integrated approach to reveal miRNAs' impacts on the functional consequence of copy number alterations in cancer. Sci Rep 2015; 5:11567. [PMID: 26099552 PMCID: PMC4477324 DOI: 10.1038/srep11567] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 05/29/2015] [Indexed: 12/21/2022] Open
Abstract
Copy number alteration (CNA) is known to induce gene expression changes mainly through dosage effect, and therefore affect the initiation and progression of tumor. However, tumor samples exhibit heterogeneity in gene dosage sensitivity due to the complicated mechanisms of transcriptional regulation. Currently, no high-throughput method has been available for identifying the regulatory factors affecting the functional consequences of CNA, and determining their effects on cancer. In view of the important regulatory role of miRNA, we investigated the influence of miRNAs on the dosage sensitivities of genes within the CNA regions. By integrating copy number, mRNA expression, miRNA expression profiles of three kinds of cancer, we observed a tendency for high dosage-sensitivity genes to be more targeted by miRNAs in cancer, and identified the miRNAs regulating the dosage sensitivity of amplified/deleted target genes. The results show that miRNAs can modulate oncogenic biological functions by regulating the genes within the CNA regions, and thus play a role as a trigger or balancer in cancer, affecting cancer processes, even survival. This work provided a framework for analyzing the regulation of dosage effect, which will shed a light on understanding the oncogenic and tumor suppressive mechanisms of CNA. Besides, new cancer-related miRNAs were identified.
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Affiliation(s)
- Kening Li
- 1] College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China [2] School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yongjing Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuanshuai Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Rui Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ning Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Zichuang Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qiang Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shujuan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Fujun Qiu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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Peng C, Shen Y, Ge M, Wang M, Li A. Discovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data. MOLECULAR BIOSYSTEMS 2015; 11:2345-53. [PMID: 26091184 DOI: 10.1039/c5mb00264h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Glioblastoma (GBM) is the most common malignant brain cancer in adults. Investigating the regulatory mechanisms underlying GBM is effective for the in-depth study of GBM. The Cancer Genome Atlas (TCGA) project is producing large-scale data and makes the comprehensive study of the diverse regulatory mechanisms underlying GBM possible. Although there have been research studies on GBM with large-scale data, distinguishing different regulatory mechanisms and identifying the key regulation types remain challenging. In this study, we integrated multi-dimensional data of differentially expressed genes in GBM: copy number variation (CNV), gene expression, miRNA expression and methylation, by performing partial correlation analysis with the Lasso technique. Our results showed that there were single-factor and multi-factor regulatory mechanisms in GBM. In further analysis of the regulation subtypes, we discovered that single-factor and multi-factor regulations are potentially distinct in functionality. Moreover, multi-factor regulations especially the key regulation subtypes may be more relevant to GBM and affect many GBM-related genes such as ERBB2 and MAPK1. This study not only verifies the utility of multi-dimensional data integration into GBM research but also distinguishes the key multi-factor regulatory subtypes that may drive pathogenesis of GBM from various regulatory mechanisms.
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Affiliation(s)
- Chen Peng
- School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, People's Republic of China
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ICan: an integrated co-alteration network to identify ovarian cancer-related genes. PLoS One 2015; 10:e0116095. [PMID: 25803614 PMCID: PMC4372216 DOI: 10.1371/journal.pone.0116095] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 12/04/2014] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. RESULTS We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). CONCLUSION In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.
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Thomas LE, Winston J, Rad E, Mort M, Dodd KM, Tee AR, McDyer F, Moore S, Cooper DN, Upadhyaya M. Evaluation of copy number variation and gene expression in neurofibromatosis type-1-associated malignant peripheral nerve sheath tumours. Hum Genomics 2015; 9:3. [PMID: 25884485 PMCID: PMC4367978 DOI: 10.1186/s40246-015-0025-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 01/18/2015] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Neurofibromatosis type-1 (NF1) is a complex neurogenetic disorder characterised by the development of benign and malignant tumours of the peripheral nerve sheath (MPNSTs). Whilst biallelic NF1 gene inactivation contributes to benign tumour formation, additional cellular changes in gene structure and/or expression are required to induce malignant transformation. Although few molecular profiling studies have been performed on the process of progression of pre-existing plexiform neurofibromas to MPNSTs, the integrated analysis of copy number alterations (CNAs) and gene expression is likely to be key to understanding the molecular mechanisms underlying NF1-MPNST tumorigenesis. In a pilot study, we employed this approach to identify genes differentially expressed between benign and malignant NF1 tumours. RESULTS SPP1 (osteopontin) was the most differentially expressed gene (85-fold increase in expression), compared to benign plexiform neurofibromas. Short hairpin RNA (shRNA) knockdown of SPP1 in NF1-MPNST cells reduced tumour spheroid size, wound healing and invasion in four different MPNST cell lines. Seventy-six genes were found to exhibit concordance between CNA and gene expression level. CONCLUSIONS Pathway analysis of these genes suggested that glutathione metabolism and Wnt signalling may be specifically involved in NF1-MPNST development. SPP1 is associated with malignant transformation in NF1-associated MPNSTs and could prove to be an important target for therapeutic intervention.
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Affiliation(s)
- Laura E Thomas
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Jincy Winston
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Ellie Rad
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Kayleigh M Dodd
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Andrew R Tee
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Fionnuala McDyer
- Almac Diagnostics, 19 Seagoe Industrial Estate, Craigavon, Northern Ireland, BT63 5QD, UK.
| | - Stephen Moore
- Almac Diagnostics, 19 Seagoe Industrial Estate, Craigavon, Northern Ireland, BT63 5QD, UK.
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Meena Upadhyaya
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.
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Newton R, Wernisch L. A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory relationships. PLoS One 2014; 9:e105522. [PMID: 25148247 PMCID: PMC4141782 DOI: 10.1371/journal.pone.0105522] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 07/21/2014] [Indexed: 12/25/2022] Open
Abstract
Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.
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Affiliation(s)
- Richard Newton
- Biostatistics Unit, Medical Research Council, Cambridge, United Kingdom
- * E-mail:
| | - Lorenz Wernisch
- Biostatistics Unit, Medical Research Council, Cambridge, United Kingdom
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Li X, Peng S. Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis. Chin J Cancer Res 2014; 25:623-36. [PMID: 24385689 DOI: 10.3978/j.issn.1000-9604.2013.11.01] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 11/05/2013] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Identification of colorectal cancer (CRC) metastasis genes is one of the most important issues in CRC research. For the purpose of mining CRC metastasis-associated genes, an integrated analysis of microarray data was presented, by combined with evidence acquired from comparative genomic hybridization (CGH) data. METHODS Gene expression profile data of CRC samples were obtained at Gene Expression Omnibus (GEO) website. The 15 important chromosomal aberration sites detected by using CGH technology were used for integrated genomic and transcriptomic analysis. Significant Analysis of Microarray (SAM) was used to detect significantly differentially expressed genes across the whole genome. The overlapping genes were selected in their corresponding chromosomal aberration regions, and analyzed by using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, SVM-T-RFE gene selection algorithm was applied to identify metastasis-associated genes in CRC. RESULTS A minimum gene set was obtained with the minimum number [14] of genes, and the highest classification accuracy (100%) in both PRI and META datasets. A fraction of selected genes are associated with CRC or its metastasis. CONCLUSIONS Our results demonstrated that integration analysis is an effective strategy for mining cancer-associated genes.
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Affiliation(s)
- Xiaobo Li
- Department of Computer Science and Technology, College of Engineering, Lishui University, Lishui 323000, China; ; School of Science and Technology, Zhejiang International Studies University, Hangzhou 310012, China
| | - Sihua Peng
- Department of Biological Technology, School of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China
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Samur MK, Shah PK, Wang X, Minvielle S, Magrangeas F, Avet-Loiseau H, Munshi NC, Li C. The shaping and functional consequences of the dosage effect landscape in multiple myeloma. BMC Genomics 2013; 14:672. [PMID: 24088394 PMCID: PMC3907079 DOI: 10.1186/1471-2164-14-672] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2013] [Accepted: 09/30/2013] [Indexed: 02/06/2023] Open
Abstract
Background Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. Results We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Conclusions Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.
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Affiliation(s)
- Mehmet K Samur
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA.
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Samur MK, Yan Z, Wang X, Cao Q, Munshi NC, Li C, Shah PK. canEvolve: a web portal for integrative oncogenomics. PLoS One 2013; 8:e56228. [PMID: 23418540 PMCID: PMC3572035 DOI: 10.1371/journal.pone.0056228] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2012] [Accepted: 01/07/2013] [Indexed: 12/24/2022] Open
Abstract
Background & Objective Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need. Results canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis. Conclusion At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets. Availability The canEvolve web portal is available at http://www.canevolve.org/.
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Affiliation(s)
- Mehmet Kemal Samur
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics and Medical Informatics, Akdeniz University, Antalya, Turkey
| | - Zhenyu Yan
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Xujun Wang
- Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China
| | - Qingyi Cao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Nikhil C. Munshi
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Cheng Li
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
- * E-mail: (PKS); (CL)
| | - Parantu K. Shah
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, United States of America
- * E-mail: (PKS); (CL)
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Thieme S, Groth P. Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data. ACTA ACUST UNITED AC 2013; 29:671-7. [PMID: 23341502 PMCID: PMC3597144 DOI: 10.1093/bioinformatics/btt028] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches. RESULTS Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls.
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Affiliation(s)
- Sebastian Thieme
- Department of Theoretical Biophysics, Humboldt-University of Berlin, 10115 Berlin, Germany and Therapeutic Research Group Oncology, Bayer Pharma AG, 13353 Berlin, Germany
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Tong P, Coombes KR. integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using item response theory. ACTA ACUST UNITED AC 2012; 28:2861-9. [PMID: 23014630 DOI: 10.1093/bioinformatics/bts561] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
MOTIVATION Identifying genes altered in cancer plays a crucial role in both understanding the mechanism of carcinogenesis and developing novel therapeutics. It is known that there are various mechanisms of regulation that can lead to gene dysfunction, including copy number change, methylation, abnormal expression, mutation and so on. Nowadays, all these types of alterations can be simultaneously interrogated by different types of assays. Although many methods have been proposed to identify altered genes from a single assay, there is no method that can deal with multiple assays accounting for different alteration types systematically. RESULTS In this article, we propose a novel method, integration using item response theory (integIRTy), to identify altered genes by using item response theory that allows integrated analysis of multiple high-throughput assays. When applied to a single assay, the proposed method is more robust and reliable than conventional methods such as Student's t-test or the Wilcoxon rank-sum test. When used to integrate multiple assays, integIRTy can identify novel-altered genes that cannot be found by looking at individual assay separately. We applied integIRTy to three public cancer datasets (ovarian carcinoma, breast cancer, glioblastoma) for cross-assay type integration which all show encouraging results. AVAILABILITY AND IMPLEMENTATION The R package integIRTy is available at the web site http://bioinformatics.mdanderson.org/main/OOMPA:Overview. CONTACT kcoombes@mdanderson.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pan Tong
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Stepanenko AA, Kavsan VM. Evolutionary karyotypic theory of cancer versus conventional cancer gene mutation theory. ACTA ACUST UNITED AC 2012. [DOI: 10.7124/bc.000059] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- A. A. Stepanenko
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine
| | - V. M. Kavsan
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine
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Skylaki S, Tomlinson SR. Recurrent transcriptional clusters in the genome of mouse pluripotent stem cells. Nucleic Acids Res 2012; 40:e153. [PMID: 22798478 PMCID: PMC3479167 DOI: 10.1093/nar/gks663] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
A number of studies have shown that transcriptome analysis in terms of chromosomal location can reveal regions of non-random transcriptional activity within the genome. Genomic clusters of differentially expressed genes can identify genomic patterns of structural organization, underlying copy number variations or long-range epigenetic regulation such as X-chromosome inactivation. Here we apply an integrative bioinformatics analysis to a collection of 315 freely available mouse pluripotent stem cell samples to discover transcriptional clusters in the genome. We show that over half of the analysed samples (56.83%) carry whole or partial-chromosome spanning clusters which recur in genomic regions previously implicated in chromosomal imbalances. Strikingly, we found that the presence of such large-clusters is linked to the differential expression of a limited number of genes, common to all samples carrying clusters irrespectively of the chromosome where the cluster is found. We have used these genes to train and test classification models that can predict samples that carry large-scale clusters on any chromosome with over 90% accuracy. Our findings suggest that there is a common downstream activation in these cells that affects a limited number of nodes. We propose that this effect is linked to selective advantage and identify potential driver genes.
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Affiliation(s)
- Stavroula Skylaki
- MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK
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Lahti L, Schäfer M, Klein HU, Bicciato S, Dugas M. Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review. Brief Bioinform 2012; 14:27-35. [PMID: 22441573 PMCID: PMC3548603 DOI: 10.1093/bib/bbs005] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.
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Affiliation(s)
- Leo Lahti
- Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands.
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