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Ajore R, Niroula A, Pertesi M, Cafaro C, Thodberg M, Went M, Bao EL, Duran-Lozano L, Lopez de Lapuente Portilla A, Olafsdottir T, Ugidos-Damboriena N, Magnusson O, Samur M, Lareau CA, Halldorsson GH, Thorleifsson G, Norddahl GL, Gunnarsdottir K, Försti A, Goldschmidt H, Hemminki K, van Rhee F, Kimber S, Sperling AS, Kaiser M, Anderson K, Jonsdottir I, Munshi N, Rafnar T, Waage A, Weinhold N, Thorsteinsdottir U, Sankaran VG, Stefansson K, Houlston R, Nilsson B. Functional dissection of inherited non-coding variation influencing multiple myeloma risk. Nat Commun 2022; 13:151. [PMID: 35013207 PMCID: PMC8748989 DOI: 10.1038/s41467-021-27666-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/02/2021] [Indexed: 12/16/2022] Open
Abstract
Thousands of non-coding variants have been associated with increased risk of human diseases, yet the causal variants and their mechanisms-of-action remain obscure. In an integrative study combining massively parallel reporter assays (MPRA), expression analyses (eQTL, meQTL, PCHiC) and chromatin accessibility analyses in primary cells (caQTL), we investigate 1,039 variants associated with multiple myeloma (MM). We demonstrate that MM susceptibility is mediated by gene-regulatory changes in plasma cells and B-cells, and identify putative causal variants at six risk loci (SMARCD3, WAC, ELL2, CDCA7L, CEP120, and PREX1). Notably, three of these variants co-localize with significant plasma cell caQTLs, signaling the presence of causal activity at these precise genomic positions in an endogenous chromosomal context in vivo. Our results provide a systematic functional dissection of risk loci for a hematologic malignancy.
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Affiliation(s)
- Ram Ajore
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | - Abhishek Niroula
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
- Broad Institute of Massachusetts Institute of Technology and Harvard University, 415 Main Street, Boston, MA, 02142, USA
| | - Maroulio Pertesi
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | - Caterina Cafaro
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | - Malte Thodberg
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | - Molly Went
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, United Kingdom
| | - Erik L Bao
- Broad Institute of Massachusetts Institute of Technology and Harvard University, 415 Main Street, Boston, MA, 02142, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Laura Duran-Lozano
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | | | | | - Nerea Ugidos-Damboriena
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden
| | - Olafur Magnusson
- deCODE Genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
| | - Mehmet Samur
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Caleb A Lareau
- Broad Institute of Massachusetts Institute of Technology and Harvard University, 415 Main Street, Boston, MA, 02142, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Asta Försti
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120, Heidelberg, Germany
- Hopp Children's Cancer Center, Heidelberg, Germany
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, University Hospital of Heidelberg, 69120, Heidelberg, Germany
| | - Kari Hemminki
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120, Heidelberg, Germany
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, Prague, 30605, Czech Republic
| | | | - Scott Kimber
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, United Kingdom
| | - Adam S Sperling
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Martin Kaiser
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, United Kingdom
| | - Kenneth Anderson
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | - Nikhil Munshi
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Thorunn Rafnar
- deCODE Genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
| | - Anders Waage
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Box 8905, N-7491, Trondheim, Norway
| | - Niels Weinhold
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120, Heidelberg, Germany
- Department of Internal Medicine V, University Hospital of Heidelberg, 69120, Heidelberg, Germany
| | | | - Vijay G Sankaran
- Broad Institute of Massachusetts Institute of Technology and Harvard University, 415 Main Street, Boston, MA, 02142, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
| | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, United Kingdom
| | - Björn Nilsson
- Hematology and Transfusion Medicine, Department of Laboratory Medicine, BMC B13, 221 84, Lund, Sweden.
- Broad Institute of Massachusetts Institute of Technology and Harvard University, 415 Main Street, Boston, MA, 02142, USA.
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Song M, Zhong H. Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers. Bioinformatics 2021; 36:5027-5036. [PMID: 32619008 PMCID: PMC7755420 DOI: 10.1093/bioinformatics/btaa613] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 05/24/2020] [Accepted: 06/26/2020] [Indexed: 12/14/2022] Open
Abstract
Motivation Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility. Results We present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity—dominance by either up- or downregulated genes in a zone—along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond. Availability and implementation Weighted univariate clustering algorithms are implemented within the R package ‘Ckmeans.1d.dp’ (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingzhou Song
- Department of Computer Science.,Molecular Biology Graduate Program, New Mexico State University, Las Cruces, NM 88003, USA
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3
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Głodzik D, Purdie C, Rye IH, Simpson PT, Staaf J, Span PN, Russnes HG, Nik-Zainal S. Mutational mechanisms of amplifications revealed by analysis of clustered rearrangements in breast cancers. Ann Oncol 2018; 29:2223-2231. [PMID: 30252041 PMCID: PMC6290883 DOI: 10.1093/annonc/mdy404] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background Complex clusters of rearrangements are a challenge in interpretation of cancer genomes. Some clusters of rearrangements demarcate clear amplifications of driver oncogenes but others are less well understood. A detailed analysis of rearrangements within these complex clusters could reveal new insights into selection and underlying mutational mechanisms. Patients and methods Here, we systematically investigate rearrangements that are densely clustered in individual tumours in a cohort of 560 breast cancers. Applying an agnostic approach, we identify 21 hotspots where clustered rearrangements recur across cancers. Results Some hotspots coincide with known oncogene loci including CCND1, ERBB2, ZNF217, chr8:ZNF703/FGFR1, IGF1R, and MYC. Others contain cancer genes not typically associated with breast cancer: MCL1, PTP4A1, and MYB. Intriguingly, we identify clustered rearrangements that physically connect distant hotspots. In particular, we observe simultaneous amplification of chr8:ZNF703/FGFR1 and chr11:CCND1 where deep analysis reveals that a chr8-chr11 translocation is likely to be an early, critical, initiating event. Conclusions We present an overview of complex rearrangements in breast cancer, highlighting a potential new way for detecting drivers and revealing novel mechanistic insights into the formation of two common amplicons.
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Affiliation(s)
- D Głodzik
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Wellcome Trust Sanger Institute, Hinxton, Cambridge
| | - C Purdie
- Department of Pathology, Ninewells Hospital & Medical School, Dundee, UK
| | - I H Rye
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - P T Simpson
- Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - J Staaf
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - P N Span
- Department of Radiation Oncology, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - H G Russnes
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - S Nik-Zainal
- Wellcome Trust Sanger Institute, Hinxton, Cambridge; Academic Department of Medical Genetics, The Clinical School University of Cambridge, Cambridge, UK.
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Delatola EI, Lebarbier E, Mary-Huard T, Radvanyi F, Robin S, Wong J. SegCorr a statistical procedure for the detection of genomic regions of correlated expression. BMC Bioinformatics 2017; 18:333. [PMID: 28697800 PMCID: PMC5504623 DOI: 10.1186/s12859-017-1742-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 06/26/2017] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation. CONCLUSIONS SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.
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Affiliation(s)
- Eleni Ioanna Delatola
- AgroParisTech UMR518, Paris, 75005, France.
- INRA UMR518, Paris, 75005, France.
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France.
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France.
| | - Emilie Lebarbier
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
| | - Tristan Mary-Huard
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
- INRA, UMR 0320 - UMR 8120 Genetique Quantitative et Evolution-Le Moulon, Gif-sur-Yvette, F-91190, France
| | - François Radvanyi
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France
| | - Stéphane Robin
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
| | - Jennifer Wong
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France
- Molecular Oncology Unit, Department of Biochemistry, Hospital Saint Louis, AP-HP, Cedex 10, Paris, 75475, France
- Université Paris Diderot, Sorbonne Paris Cité, CNRS UMR7212/INSERM U944, Cedex 10, Paris, 75475, France
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5
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Glodzik D, Morganella S, Davies H, Simpson PT, Li Y, Zou X, Diez-Perez J, Staaf J, Alexandrov LB, Smid M, Brinkman AB, Rye IH, Russnes H, Raine K, Purdie CA, Lakhani SR, Thompson AM, Birney E, Stunnenberg HG, van de Vijver MJ, Martens JWM, Børresen-Dale AL, Richardson AL, Kong G, Viari A, Easton D, Evan G, Campbell PJ, Stratton MR, Nik-Zainal S. A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers. Nat Genet 2017; 49:341-348. [PMID: 28112740 PMCID: PMC5988034 DOI: 10.1038/ng.3771] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 12/16/2016] [Indexed: 12/18/2022]
Abstract
Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may be sites of selective susceptibility to double-strand-break damage due to high transcriptional activity or, through incrementally increasing copy number, may be sites of secondary selective pressure. The transcriptomic consequences ranged from strong individual oncogene effects to weak but quantifiable multigene expression effects. We thus present a somatic-rearrangement mutational process affecting coding sequences and noncoding regulatory elements and contributing a continuum of driver consequences, from modest to strong effects, thereby supporting a polygenic model of cancer development.
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Affiliation(s)
| | | | | | - Peter T Simpson
- The University of Queensland: UQ Centre for Clinical Research and School of Medicine, Brisbane, Queensland, Australia
| | - Yilong Li
- Wellcome Trust Sanger Institute, Cambridge, UK
| | - Xueqing Zou
- Wellcome Trust Sanger Institute, Cambridge, UK
| | | | - Johan Staaf
- Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Ludmil B Alexandrov
- Wellcome Trust Sanger Institute, Cambridge, UK
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Marcel Smid
- Department of Medical Oncology, Erasmus MC Cancer Institute and Cancer Genomics Netherlands, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Arie B Brinkman
- Department of Molecular Biology, Faculties of Science and Medicine, Radboud University, Nijmegen, the Netherlands
| | - Inga Hansine Rye
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norwegian Radiumhospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hege Russnes
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norwegian Radiumhospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Colin A Purdie
- Department of Pathology, Ninewells Hospital &Medical School, Dundee, UK
| | - Sunil R Lakhani
- The University of Queensland: UQ Centre for Clinical Research and School of Medicine, Brisbane, Queensland, Australia
- Pathology Queensland, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Alastair M Thompson
- Department of Pathology, Ninewells Hospital &Medical School, Dundee, UK
- Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridgeshire, UK
| | - Hendrik G Stunnenberg
- Department of Medical Oncology, Erasmus MC Cancer Institute and Cancer Genomics Netherlands, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - John W M Martens
- Department of Medical Oncology, Erasmus MC Cancer Institute and Cancer Genomics Netherlands, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norwegian Radiumhospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Andrea L Richardson
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Gu Kong
- Department of Pathology, College of Medicine, Hanyang University, Seoul, South Korea
| | - Alain Viari
- Equipe Erable, INRIA Grenoble-Rhône-Alpes, Montbonnot-Saint Martin, France
- Synergie Lyon Cancer, Centre Léon Bérard, Lyon, France
| | - Douglas Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Gerard Evan
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | | | | | - Serena Nik-Zainal
- Wellcome Trust Sanger Institute, Cambridge, UK
- East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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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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Jörnsten R, Abenius T, Kling T, Schmidt L, Johansson E, Nordling TEM, Nordlander B, Sander C, Gennemark P, Funa K, Nilsson B, Lindahl L, Nelander S. Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Mol Syst Biol 2011; 7:486. [PMID: 21525872 PMCID: PMC3101951 DOI: 10.1038/msb.2011.17] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2010] [Accepted: 03/21/2011] [Indexed: 12/25/2022] Open
Abstract
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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Affiliation(s)
- Rebecka Jörnsten
- Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden
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8
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Geng H, Iqbal J, Chan WC, Ali HH. Virtual CGH: an integrative approach to predict genetic abnormalities from gene expression microarray data applied in lymphoma. BMC Med Genomics 2011; 4:32. [PMID: 21486456 PMCID: PMC3086850 DOI: 10.1186/1755-8794-4-32] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2010] [Accepted: 04/12/2011] [Indexed: 01/27/2023] Open
Abstract
Background Comparative Genomic Hybridization (CGH) is a molecular approach for detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key causes of tumorigenesis. However in the post-genomic era, most studies in cancer biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as a result, an enormous amount of GEP data had been accumulated in public databases for a wide variety of tumor types. We exploited this resource of GEP data to define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP would be more functionally relevant CNAs in the disease pathogenesis since the functional effects of CNAs can be reflected by altered gene expression. Methods We proposed a novel computational approach, coined virtual CGH (vCGH), which employs hidden Markov models (HMMs) to predict DNA CNAs from their corresponding GEP data. vCGH was first trained on the paired GEP and CGH data generated from a sufficient number of tumor samples, and then applied to the GEP data of a new tumor sample to predict its CNAs. Results Using cross-validation on 190 Diffuse Large B-Cell Lymphomas (DLBCL), vCGH achieved 80% sensitivity, 90% specificity and 90% accuracy for CNA prediction. The majority of the recurrent regions defined by vCGH are concordant with the experimental CGH, including gains of 1q, 2p16-p14, 3q27-q29, 6p25-p21, 7, 11q, 12 and 18q21, and losses of 6q, 8p23-p21, 9p24-p21 and 17p13 in DLBCL. In addition, vCGH predicted some recurrent functional abnormalities which were not observed in CGH, including gains of 1p, 2q and 6q and losses of 1q, 6p and 8q. Among those novel loci, 1q, 6q and 8q were significantly associated with the clinical outcomes in the DLBCL patients (p < 0.05). Conclusions We developed a novel computational approach, vCGH, to predict genome-wide genetic abnormalities from GEP data in lymphomas. vCGH can be generally applied to other types of tumors and may significantly enhance the detection of functionally important genetic abnormalities in cancer research.
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Affiliation(s)
- Huimin Geng
- Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA
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9
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Morganella S, Cerulo L, Viglietto G, Ceccarelli M. VEGA: variational segmentation for copy number detection. ACTA ACUST UNITED AC 2010; 26:3020-7. [PMID: 20959380 DOI: 10.1093/bioinformatics/btq586] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Genomic copy number (CN) information is useful to study genetic traits of many diseases. Using array comparative genomic hybridization (aCGH), researchers are able to measure the copy number of thousands of DNA loci at the same time. Therefore, a current challenge in bioinformatics is the development of efficient algorithms to detect the map of aberrant chromosomal regions. METHODS We describe an approach for the segmentation of copy number aCGH data. Variational estimator for genomic aberrations (VEGA) adopt a variational model used in image segmentation. The optimal segmentation is modeled as the minimum of an energy functional encompassing both the quality of interpolation of the data and the complexity of the solution measured by the length of the boundaries between segmented regions. This solution is obtained by a region growing process where the stop condition is completely data driven. RESULTS VEGA is compared with three algorithms that represent the state of the art in CN segmentation. Performance assessment is made both on synthetic and real data. Synthetic data simulate different noise conditions. Results on these data show the robustness with respect to noise of variational models and the accuracy of VEGA in terms of recall and precision. Eight mantle cell lymphoma cell lines and two samples of glioblastoma multiforme are used to evaluate the behavior of VEGA on real biological data. Comparison between results and current biological knowledge shows the ability of the proposed method in detecting known chromosomal aberrations. AVAILABILITY VEGA has been implemented in R and is available at the address http://www.dsba.unisannio.it/Members/ceccarelli/vega in the section Download.
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Affiliation(s)
- Sandro Morganella
- Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy
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10
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Hu W, Chan CS, Wu R, Zhang C, Sun Y, Song JS, Tang LH, Levine AJ, Feng Z. Negative regulation of tumor suppressor p53 by microRNA miR-504. Mol Cell 2010; 38:689-99. [PMID: 20542001 DOI: 10.1016/j.molcel.2010.05.027] [Citation(s) in RCA: 238] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2009] [Revised: 01/26/2010] [Accepted: 04/02/2010] [Indexed: 12/19/2022]
Abstract
Tumor suppressor p53 plays a central role in tumor prevention. p53 protein levels and activity are under a tight and complex regulation in cells to maintain the proper function of p53. MicroRNAs play a key role in the regulation of gene expression. Here we report the regulation of p53 through miR-504. miR-504 acts as a negative regulator of human p53 through its direct binding to two sites in the p53 3' untranslated region. Overexpression of miR-504 decreases p53 protein levels and functions in cells, including p53 transcriptional activity, p53-mediated apoptosis, and cell-cycle arrest in response to stress, and furthermore promotes tumorigenecity of cells in vivo. These results demonstrate the direct negative regulation of p53 by miR-504 as a mechanism for p53 regulation in cells, which highlights the importance of microRNAs in tumorigenesis.
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Affiliation(s)
- Wenwei Hu
- Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey, New Brunswick, NJ 08903, USA
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Chang CW, Mycek MA. Precise fluorophore lifetime mapping in live-cell, multi-photon excitation microscopy. OPTICS EXPRESS 2010; 18:8688-96. [PMID: 20588712 PMCID: PMC3410727 DOI: 10.1364/oe.18.008688] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Fluorophore excited state lifetime is a useful indicator of micro-environment in cellular optical molecular imaging. For quantitative sensing, precise lifetime determination is important, yet is often difficult to accomplish when using the experimental conditions favored by live cells. Here we report the first application of temporal optimization and spatial denoising methods to two-photon time-correlated single photon counting (TCSPC) fluorescence lifetime imaging microscopy (FLIM) to improve lifetime precision in live-cell images. The results demonstrated a greater than five-fold improvement in lifetime precision. This approach minimizes the adverse effects of excitation light on live cells and should benefit FLIM applications to high content analysis and bioimage informatics.
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Affiliation(s)
- Ching-Wei Chang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2099,
USA
| | - Mary-Ann Mycek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2099,
USA
- Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan 48109-2099,
USA
- Applied Physics Program, University of Michigan, Ann Arbor, Michigan 48109-2099,
USA
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12
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Rancoita PMV, Hutter M, Bertoni F, Kwee I. Bayesian DNA copy number analysis. BMC Bioinformatics 2009; 10:10. [PMID: 19133123 PMCID: PMC2674052 DOI: 10.1186/1471-2105-10-10] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2008] [Accepted: 01/08/2009] [Indexed: 11/18/2022] Open
Abstract
Background Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. Results We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. Conclusion We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.
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Affiliation(s)
- Paola M V Rancoita
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland.
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Oron AP, Jiang Z, Gentleman R. Gene set enrichment analysis using linear models and diagnostics. ACTA ACUST UNITED AC 2008; 24:2586-91. [PMID: 18790795 DOI: 10.1093/bioinformatics/btn465] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion. RESULTS We demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of genes. Individual residuals, grouped or aggregated by chromosomal loci, indicate problematic samples and potential data-entry errors, and help identify hyperdiploidy as a factor playing a key role in expression for this dataset. Subsequent analysis pinpoints suspected DNA copy number abnormalities of specific samples and chromosomes (most prevalent are chromosomes X, 21 and 14), and also reveals significant expression differences between the hyperdiploid and diploid groups on other chromosomes (most prominently 19, 22, 3 and 13)--differences which are apparently not associated with copy number. AVAILABILITY Software for the statistical tools demonstrated in this article is available as Bioconductor package GSEAlm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Assaf P Oron
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA.
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