The Experts below are selected from a list of 8847 Experts worldwide ranked by ideXlab platform
Michael R. Stratton - One of the best experts on this subject based on the ideXlab platform.
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1818 Evolution from primary breast Cancer to relapse and metastasis
European Journal of Cancer, 2015Co-Authors: Lucy R. Yates, David C. Wedge, Ludmil B. Alexandrov, Christine Desmedt, Stian Knappskog, Andrea L. Richardson, Christos Sotiriou, Anieta M. Sieuwerts, Andrew Tutt, Michael R. StrattonAbstract:L. Yates, D. Wedge, L. Alexandrov, C. Desmedt, S. Knappskog, A. Richardson, C. Sotiriou, A. Sieuwerts, A. Tutt, M. Stratton, P. Campbell. Wellcome Trust Sanger Institute, Cancer Genome Project, Cambridge, United Kingdom; Los Alamos National Laboratory, Theoretical Division, Los Alamos, USA; Institut Jules Bordet, Breast Cancer Translational Research Laboratory, Brussels, Belgium; University of Bergen, Section of Oncology, Department of Clinical Science, Bergen, Norway; Harvard Medical School, Brigham and Women’s Hospital, Boston, USA; ERASMUS MC Cancer Institute, Department of Pathology, Rotterdam, Netherlands; 7 Institute of Cancer Research, Breakthrough Breast Cancer, London, United Kingdom
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The driver landscape of angiosarcoma.
Journal of Clinical Oncology, 2014Co-Authors: Sam Behjati, Michael R. Stratton, Patrick S. Tarpey, Helen Sheldon, Adrienne M. Flanagan, Andrew Futreal, Adrian L. Harris, Peter J. CampbellAbstract:10511 Background: Angiosarcoma is an aggressive tumour of presumed vascular endothelial origin that is commonly thought to be driven by aberrant angiogenesis. The somatic alterations underpinning angiosarcoma are largely unknown and have not previously been investigated by unbiased next generation sequencing. Methods: Thirty nine primary and secondary angiosarcomas were screened by whole Genome (n=3), exome (n=8), or targeted re-sequencing of 33 angiogenesis genes (n=28). Somatic alterations were identified using the analysis pipeline of the Cancer Genome Project. Results: We identified novel driver mutations in two genes, PTPRB and PLCG1, both of which function to regulate angiogenesis signalling. PTPRB mutations were identified in 10/39 (26%) tumours, comprising 11 truncating and 3 missense variants. Four cases harboured 2 different PTPRB mutations each. A single recurrent missense variant, R707Q was identified in the PLCG1 gene in (3/34) 9% of tumours. The enrichment of PTPRB and PLCG1 mutations was hi...
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abstract 2206 genomics of drug sensitivity in Cancer gdsc a resource for therapeutic biomarker discovery in Cancer cells
Cancer Research, 2013Co-Authors: Wanjuan Yang, Elena J. Edelman, Howard Lightfoot, Simon A Forbes, Ramaswamy Sridhar, Andrew P Futreal, Daniel A. Haber, Patricia Greninger, Jorge Soares, Michael R. StrattonAbstract:The Genomic of Drug Sensitivity in Cancer (GDSC; www.CancerRxgene.org) resource facilitates development of targeted Cancer therapies through pre-clinical identification of therapeutic biomarkers. GDSC is the largest public resource for information on drug sensitivity in Cancer cells and links these data to extensive genomic information to identify molecular features that influence antiCancer drug response. There is compelling evidence that alterations in Cancer Genomes strongly influence clinical responses to antiCancer therapies. There are several examples where genomic changes are used as molecular biomarkers to stratify patients most likely to benefit from a treatment (e.g. BRAF in melanoma). Despite these successes, the majority of Cancer drugs have not been linked to specific molecular features that could be used to direct their clinical use to maximize patient benefit. We are using pharmacogenomic profiling in Cancer cell lines as a biomarker discovery platform by systematically linking pharmacological data with genomic information in Cancer cells. The GDSC database contains drug sensitivity data generated from high-throughput screening performed by the Cancer Genome Project at the Wellcome Trust Sanger Institute and the Center for Molecular Therapeutics at Massachusetts General Hospital using a collection of >1,200 Cancer cell lines. GDSC release v3 (November 2012) contains drug sensitivity data for almost 80,000 experiments, describing response to 142 antiCancer drugs across over 700 Cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from COSMIC (Catalogue of Somatic Mutations in Cancer), including information on somatic mutations in Cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal based on queries of specific antiCancer drugs or Cancer genes. Interactive graphical representations of the data are used throughout with links to related resources, and all datasets are freely available and downloadable. The GDSC database will undergo significant expansion in coming years as drug sensitivity and genomic datasets increase in size and complexity. GDSC provides a unique public resource incorporating large drug sensitivity and genomic datasets to facilitate discovery of new therapeutic biomarkers for Cancer therapies. Citation Format: Wanjuan Yang, Jorge Soares, Patricia Greninger, Elena Edelman, Howard Lightfoot, Simon Forbes, Ramaswamy Sridhar, P. Andrew Futreal, Daniel Haber, Michael Stratton, Cyril Benes, Ultan McDermott, Mathew Garnett. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in Cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2206. doi:10.1158/1538-7445.AM2013-2206
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cosmic the catalogue of somatic mutations in Cancer a resource to investigate acquired mutations in human Cancer
Nucleic Acids Research, 2010Co-Authors: Simon A Forbes, Nidhi Bindal, Sally Bamford, Andrew Menzies, Jon W. Teague, E. Dawson, Gurpreet Tang, Charlotte G Cole, Rebecca Ewing, Michael R. StrattonAbstract:The catalogue of Somatic Mutations in Cancer (COSMIC) (http://www.sanger.ac.uk/cosmic/) is the largest public resource for information on somatically acquired mutations in human Cancer and is available freely without restrictions. Currently (v43, August 2009), COSMIC contains details of 1.5-million experiments performed through 13 423 genes in almost 370 000 tumours, describing over 90 000 individual mutations. Data are gathered from two sources, publications in the scientific literature, (v43 contains 7797 curated articles) and the full output of the Genome-wide screens from the Cancer Genome Project (CGP) at the Sanger Institute, UK. Most of the world’s literature on point mutations in human Cancer has now been curated into COSMIC and while this is continually updated, a greater emphasis on curating fusion gene mutations is driving the expansion of this information; over 2700 fusion gene mutations are now described. Whole-Genome sequencing screens are now identifying large numbers of genomic rearrangements in Cancer and COSMIC is now displaying details of these analyses also. Examination of COSMIC’s data is primarily web-driven, focused on providing mutation range and frequency statistics based upon a choice of gene and/or Cancer phenotype. Graphical views provide easily interpretable summaries of large quantities of data, and export functions can provide precise details of user-selected data.
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Current Protocols in Human Genetics - The Catalogue of Somatic Mutations in Cancer (COSMIC).
Current Protocols in Human Genetics, 2008Co-Authors: Simon A Forbes, Sally Bamford, Andrew Menzies, Jon W. Teague, G. Bhamra, E. Dawson, C Y Kok, Jody Clements, P A Futreal, Michael R. StrattonAbstract:COSMIC is currently the most comprehensive global resource for information on somatic mutations in human Cancer, combining curation of the scientific literature with tumor resequencing data from the Cancer Genome Project at the Sanger Institute, U.K. Almost 4800 genes and 250000 tumors have been examined, resulting in over 50000 mutations available for investigation. This information can be accessed in a number of ways, the most convenient being the Web-based system which allows detailed data mining, presenting the results in easily interpretable formats. This unit describes the graphical system in detail, elaborating an example walkthrough and the many ways that the resulting information can be thoroughly investigated by combining data, respecializing the query, or viewing the results in different ways. Alternate protocols overview the available precompiled data files available for download.
Andrew Menzies - One of the best experts on this subject based on the ideXlab platform.
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Abstract 3967: The Cancer Genome Project high throughput analysis pipeline
Molecular and Cellular Biology, 2012Co-Authors: Adam Butler, Andrew Menzies, Jon W. Teague, Keiran Raine, David T. Jones, John Marshall, Jon Hinton, Serge Dronov, John Gamble, Lucy StebbingsAbstract:The Cancer Genome Project (CGP) was set up in 2000 to use systematic mutation screening methods to increase our understanding of human Cancer. With the advent of next generation sequencing, and the large volumes of data that it generates, a new suite of software was required to rapidly and accurately screen these data for somatic changes. We have built an analysis pipeline to track and analyse large numbers of tumour samples, using in-house and externally available tools. The analysis pipeline is built around a ∼2,000 node compute farm and Lustre filesystem which outputs into our archive and data storage system, FileTrk. FileTrk holds the raw data files (BAM, CEL etc), the results of the analysis and any versioning information about the software used to generate these results. Sample lanes are aligned back to the Genome using Burrows-Wheeler Aligner (BWA) and lane-to-lane comparisons are made to ensure data integrity. Lanes from each sample are merged into a single sample BAM file and once 30 - 40x coverage is reached and the lanes have been quality assessed the sample is locked and ready for analysis. Mutation callers detect point mutations (Caveman, in-house software), small insertions/deletions (Pindel), breakpoints (BRASS, in-house software) and copy number changes (ASCAT & PICNIC, in-house software). The resulting mutations are post-processed to remove false positives, annotated to the RNA and protein level using standard nomenclature (Vagrent, in-house software) and uploaded to a database. Interfaces have been developed to enable the selection of random sets of mutations for validation, the outcome of the validations is recorded so specificity can be calculated for each sample in the system. IT systems are being developed to automatically export lists of somatic changes to COSMIC, the ICGC data portal and raw data to the European Genome-Phenome Archive (EGA). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3967. doi:1538-7445.AM2012-3967
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COSMIC: Mining complete Cancer Genomes in the catalogue of somatic mutations in Cancer
Nucleic Acids Research, 2011Co-Authors: Simon A Forbes, Charlotte Cole, Chai Yin Kok, Rebecca Shepherd, Nidhi Bindal, Mingming Jia, David Beare, Sally Bamford, Kenric Leung, Andrew MenziesAbstract:COSMIC (http://www.sanger.ac.uk/cosmic) curates comprehensive information on somatic mutations in human Cancer. Release v48 (July 2010) describes over 136,000 coding mutations in almost 542,000 tumour samples; of the 18,490 genes documented, 4803 (26%) have one or more mutations. Full scientific literature curations are available on 83 major Cancer genes and 49 fusion gene pairs (19 new Cancer genes and 30 new fusion pairs this year) and this number is continually increasing. Key amongst these is TP53, now available through a collaboration with the IARC p53 database. In addition to data from the Cancer Genome Project (CGP) at the Sanger Institute, UK, and The Cancer Genome Atlas Project (TCGA), large systematic screens are also now curated. Major website upgrades now make these data much more mineable, with many new selection filters and graphics. A Biomart is now available allowing more automated data mining and integration with other biological databases. Annotation of genomic features has become a significant focus; COSMIC has begun curating full-Genome resequencing experiments, developing new web pages, export formats and graphics styles. With all genomic information recently updated to GRCh37, COSMIC integrates many diverse types of mutation information and is making much closer links with Ensembl and other data resources.
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cosmic the catalogue of somatic mutations in Cancer a resource to investigate acquired mutations in human Cancer
Nucleic Acids Research, 2010Co-Authors: Simon A Forbes, Nidhi Bindal, Sally Bamford, Andrew Menzies, Jon W. Teague, E. Dawson, Gurpreet Tang, Charlotte G Cole, Rebecca Ewing, Michael R. StrattonAbstract:The catalogue of Somatic Mutations in Cancer (COSMIC) (http://www.sanger.ac.uk/cosmic/) is the largest public resource for information on somatically acquired mutations in human Cancer and is available freely without restrictions. Currently (v43, August 2009), COSMIC contains details of 1.5-million experiments performed through 13 423 genes in almost 370 000 tumours, describing over 90 000 individual mutations. Data are gathered from two sources, publications in the scientific literature, (v43 contains 7797 curated articles) and the full output of the Genome-wide screens from the Cancer Genome Project (CGP) at the Sanger Institute, UK. Most of the world’s literature on point mutations in human Cancer has now been curated into COSMIC and while this is continually updated, a greater emphasis on curating fusion gene mutations is driving the expansion of this information; over 2700 fusion gene mutations are now described. Whole-Genome sequencing screens are now identifying large numbers of genomic rearrangements in Cancer and COSMIC is now displaying details of these analyses also. Examination of COSMIC’s data is primarily web-driven, focused on providing mutation range and frequency statistics based upon a choice of gene and/or Cancer phenotype. Graphical views provide easily interpretable summaries of large quantities of data, and export functions can provide precise details of user-selected data.
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Current Protocols in Human Genetics - The Catalogue of Somatic Mutations in Cancer (COSMIC).
Current Protocols in Human Genetics, 2008Co-Authors: Simon A Forbes, Sally Bamford, Andrew Menzies, Jon W. Teague, G. Bhamra, E. Dawson, C Y Kok, Jody Clements, P A Futreal, Michael R. StrattonAbstract:COSMIC is currently the most comprehensive global resource for information on somatic mutations in human Cancer, combining curation of the scientific literature with tumor resequencing data from the Cancer Genome Project at the Sanger Institute, U.K. Almost 4800 genes and 250000 tumors have been examined, resulting in over 50000 mutations available for investigation. This information can be accessed in a number of ways, the most convenient being the Web-based system which allows detailed data mining, presenting the results in easily interpretable formats. This unit describes the graphical system in detail, elaborating an example walkthrough and the many ways that the resulting information can be thoroughly investigated by combining data, respecializing the query, or viewing the results in different ways. Alternate protocols overview the available precompiled data files available for download.
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The Catalogue of Somatic Mutations in Cancer (COSMIC).
Current protocols in human genetics, 2008Co-Authors: Simon A Forbes, Sally Bamford, Andrew Menzies, G. Bhamra, E. Dawson, Jody Clements, P A Futreal, C Kok, J W Teague, M R StrattonAbstract:COSMIC is currently the most comprehensive global resource for information on somatic mutations in human Cancer, combining curation of the scientific literature with tumor resequencing data from the Cancer Genome Project at the Sanger Institute, U.K. Almost 4800 genes and 250000 tumors have been examined, resulting in over 50000 mutations available for investigation. This information can be accessed in a number of ways, the most convenient being the Web-based system which allows detailed data mining, presenting the results in easily interpretable formats. This unit describes the graphical system in detail, elaborating an example walkthrough and the many ways that the resulting information can be thoroughly investigated by combining data, respecializing the query, or viewing the results in different ways. Alternate protocols overview the available precompiled data files available for download.
James R. Downing - One of the best experts on this subject based on the ideXlab platform.
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Abstract IA11: The St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project: Lessons learned
Pediatric Cancer Genomics and Epigenomics, 2014Co-Authors: James R. DowningAbstract:In January of 2010, St. Jude Children9s Research Hospital and The Genome Institute at the Washington University announced the launch of the Pediatric Cancer Genome Project (PCGP), a three year, $65 million privately funded initiative. The stated goal of this effort was to obtain 30-fold haploid coverage of the whole Genome of 600 pediatric tumors and matched non-tumor germ line DNA samples (1200 total Genomes), and to analyze these data in order to define the landscape of somatic mutations that underlie the major subtypes of pediatric leukemia, brain tumors, and solid malignancies. The initial phase of this Project was completed in February of this year with over 700 pediatric Cancers analyzed (1400 Genomes). This effort has produced fundamental insights into each of the pediatric Cancer subtypes sequenced. Not only has the data defined the spectrum of mutations that underlying specific pediatric Cancers, but for some of the diseases analyzed, we have identified new prognostic markers and/or therapeutic targets. The effort has led to the generation of an incredibly rich data source that should serve the scientific community for years to come. Based on the success of PCGP, we have begun a $30 million second phase of the Project (PCGP Phase II) that will be completed in a two year timeframe. In this second phase, we will focus not only on discovery science, but also on the establishing the technologies, analytical tools, and medical interface needed to move Genome-wide sequence analyses into the diagnostic work up of pediatric Cancer patients. In this talk, I will present an overview of this Project and highlight some of the important results that have emerged. Citation Format: James R. Downing. The St. Jude Children9s Research Hospital - Washington University Pediatric Cancer Genome Project: Lessons learned. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr IA11.
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erratum the pediatric Cancer Genome Project
Nature Genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Nat. Genet. 44, 619–622 (2012); published online 29 May 2012; corrected after print 9 July 2012 In the version of this article initially published, there were errors in the labeling of Figure 1b. Specifically, hyperdiploidy was mislabeled twice as hypodiploidy, and hypodiploidy was defined incorrectly as >44 instead of <44 chromosomes.
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Erratum: The Pediatric Cancer Genome Project
Nature Genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Nat. Genet. 44, 619–622 (2012); published online 29 May 2012; corrected after print 9 July 2012 In the version of this article initially published, there were errors in the labeling of Figure 1b. Specifically, hyperdiploidy was mislabeled twice as hypodiploidy, and hypodiploidy was defined incorrectly as >44 instead of
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The Pediatric Cancer Genome Project
Nature genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Subject terms: Cancer genomics• Paediatric Cancer• Sequencing At a glance Figures View all figures Figure 1: Frequency of Cancer diagnoses and leukemia subtypes in children and adults. (a) The frequency of Cancer types in children (left) and adults (right) on the basis of 2012 Surveillance, Epidemiology and End Results (SEER) data. Each chart is organized with Cancers listed from the most common to the least common in a clockwise fashion. (b) The frequency of T-cell lineage (blue text) and B-cell lineage (black text) subtypes of acute lymphoblastic leukemia (ALL) in children (left) and adults (right). Each chart is organized with ALL subtypes listed from the most common to the least common in a clockwise fashion. iAMP21, intrachromosomal amplification of chromosome 21. Full size image View in article Figure 2: Genetic landscape of 15 different types of pediatric Cancers determined from whole-Genome sequencing of 260 tumors and matching germline samples. The number of somatic mutations in each sample, including single-nucleotide variations (SNVs), insertion and/or deletion events (indels) and structural variations, is shown as the height in the three-dimensional graph. Only high-quality variations or validated somatic mutations are included in the summary. CDS, protein-coding regions; tier 1, mutations in annotated genes; tier 2, mutations in non-coding conserved or regulatory regions; tier 3, mutations in non-repetitive, non-coding and non-conserved regions; tier 4, mutations in repetitive regions. Tier 2 and tier 3/tier 4 mutations were rescaled to 1/10 and 1/100 of the original counts to maintain a consistent scale with the results for other somatic lesions. INF, infant ALL; CBF, core-binding-factor acute myeloid leukemia; TALL, T-cell ALL; AMLM7, acute megakaryoblastic leukemia; HYPO, hypodiploid ALL; PHALL, Philadelphia chromosome–positive BCR-ABL1 ALL; RB, retinoblastoma; RHB, rhabdomyosarcoma; NBL, neuroblastoma; OS, osteosarcoma; ACT, adrenocortical carcinoma; HGG, high-grade glioblastoma; LGG, low-grade glioma; EPD, ependymoma; MB, medulloblastoma. Full size image View in article
Jinghui Zhang - One of the best experts on this subject based on the ideXlab platform.
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Genomic Resource Projects
Cancer Genomics, 2014Co-Authors: Matthew Parker, Erin Hedlund, Jinghui ZhangAbstract:The vast amount of genomic data being produced by the research community is becoming readily accessible to biomedical researchers and clinicians to apply to their Cancer(s) of interest. The major Cancer Genome Projects, among others, The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC) and the Pediatric Cancer Genome Project (PCGP) are contributing to this genomic data goldmine by sequencing hundreds to thousands of Cancer Genomes and supplementing these data with analyses such as gene expression and methylation. In addition to the raw data that are being made available through large data warehouses, “Data Portals” are becoming the norm for accessing and analyzing these data by third parties. We describe key features of some of these portals and other tools for the analysis of next-generation sequencing and other genomic data.
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erratum the pediatric Cancer Genome Project
Nature Genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Nat. Genet. 44, 619–622 (2012); published online 29 May 2012; corrected after print 9 July 2012 In the version of this article initially published, there were errors in the labeling of Figure 1b. Specifically, hyperdiploidy was mislabeled twice as hypodiploidy, and hypodiploidy was defined incorrectly as >44 instead of <44 chromosomes.
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Erratum: The Pediatric Cancer Genome Project
Nature Genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Nat. Genet. 44, 619–622 (2012); published online 29 May 2012; corrected after print 9 July 2012 In the version of this article initially published, there were errors in the labeling of Figure 1b. Specifically, hyperdiploidy was mislabeled twice as hypodiploidy, and hypodiploidy was defined incorrectly as >44 instead of
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The Pediatric Cancer Genome Project
Nature genetics, 2012Co-Authors: James R. Downing, Li Ding, Jinghui Zhang, Richard K. Wilson, Elaine R. Mardis, Ching-hon Pui, Timothy J. Ley, William E. EvansAbstract:Subject terms: Cancer genomics• Paediatric Cancer• Sequencing At a glance Figures View all figures Figure 1: Frequency of Cancer diagnoses and leukemia subtypes in children and adults. (a) The frequency of Cancer types in children (left) and adults (right) on the basis of 2012 Surveillance, Epidemiology and End Results (SEER) data. Each chart is organized with Cancers listed from the most common to the least common in a clockwise fashion. (b) The frequency of T-cell lineage (blue text) and B-cell lineage (black text) subtypes of acute lymphoblastic leukemia (ALL) in children (left) and adults (right). Each chart is organized with ALL subtypes listed from the most common to the least common in a clockwise fashion. iAMP21, intrachromosomal amplification of chromosome 21. Full size image View in article Figure 2: Genetic landscape of 15 different types of pediatric Cancers determined from whole-Genome sequencing of 260 tumors and matching germline samples. The number of somatic mutations in each sample, including single-nucleotide variations (SNVs), insertion and/or deletion events (indels) and structural variations, is shown as the height in the three-dimensional graph. Only high-quality variations or validated somatic mutations are included in the summary. CDS, protein-coding regions; tier 1, mutations in annotated genes; tier 2, mutations in non-coding conserved or regulatory regions; tier 3, mutations in non-repetitive, non-coding and non-conserved regions; tier 4, mutations in repetitive regions. Tier 2 and tier 3/tier 4 mutations were rescaled to 1/10 and 1/100 of the original counts to maintain a consistent scale with the results for other somatic lesions. INF, infant ALL; CBF, core-binding-factor acute myeloid leukemia; TALL, T-cell ALL; AMLM7, acute megakaryoblastic leukemia; HYPO, hypodiploid ALL; PHALL, Philadelphia chromosome–positive BCR-ABL1 ALL; RB, retinoblastoma; RHB, rhabdomyosarcoma; NBL, neuroblastoma; OS, osteosarcoma; ACT, adrenocortical carcinoma; HGG, high-grade glioblastoma; LGG, low-grade glioma; EPD, ependymoma; MB, medulloblastoma. Full size image View in article
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Abstract 4867: Identification of an inv(16)-encodedCBFA2T3-GLIS2fusion protein in 34% of non-infant acute megkaryoblastic leukemias: A report from the Pediatric Cancer Genome Project
Tumor Biology, 2012Co-Authors: Tanja A. Gruber, Jinghui Zhang, Amanda Larson Gedman, Cary Koss, Suresh Marada, Shann-ching Chen, Stacey K. Ogden, Vedant GuptaAbstract:Acute Megakaryoblastic Leukemia (AMKL) accounts for ∼10% of childhood acute myeloid leukemia (AML). Although AMKL patients with Down syndrome (DS-AMKL) have an excellent 5 year event-free survival (EFS), non-DS-AMKL patients have an extremely poor outcome with a 3 year EFS Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4867. doi:1538-7445.AM2012-4867
Xiang Chen - One of the best experts on this subject based on the ideXlab platform.
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Incidence of Germline Mutations in Cancer-Predisposition Genes in Children with Hematologic Malignancies: a Report from the Pediatric Cancer Genome Project
Blood, 2014Co-Authors: Michael F. Walsh, John Easton, Xiang Chen, Michael Edmonson, Tanja A. Gruber, Donald Yergeau, Bhavin Vadodaria, Rose B. McgeeAbstract:![Graphic][1] Pathologic germ line mutations that predispose patients to Cancer are estimated to occur in 4-30% of all pediatric oncology cases. In addition to leukemia specific familial predisposition syndromes, children with rare constitutional syndromes, heterogeneous dysmorphic syndromes, and multiple-Cancer hereditary predisposition syndromes are all at an increased risk for hematologic malignancies. However, to date no Genome-wide analysis has been done to define the range of germ line mutations that occur in pediatric patients with hematological malignancies. To determine the frequency of pediatric Cancer patients that have germ line variants of pathological significance in genes that predisposed to Cancer, we analyzed the germ line and tumor DNA from 1120 pediatric Cancer patients that were enrolled in the St. Jude – Washington University Pediatric Cancer Genome Project (PCGP). Samples were analyzed by whole-Genome sequencing (n = 595), whole-exome sequencing (n = 456), or both (n = 69). Single nucleotide variants (SNVs), insertions/deletions (indels), structural variations (SV) and copy number alterations (CNAs) were detected using our analytical pipeline and all single nucleotide polymorphisms (SNPs) previously identified in non-Cancer populations were filtered out. Our analysis then focused on the 23 Cancer predisposition genes recently recommended for germ line analysis by the American College of Genetics and Genomics, along with an additional 8 genes that have been previously shown to predispose to pediatric Cancer at a high penetrance. All variants in these 31 genes were classified as pathologic, likely pathologic, uncertain significance, likely benign, and benign based on literature review and in-silico predictions on the effect of novel mutations. An expanded analysis including a total of 565 genes known to play a role in oncogenesis was also evaluated. Pathologic or likely pathologic germ line variants in one of the 31 genes were detected in 8% (90/1120) of patients, including: 16% (46/287) of patients with solid tumors, 8.6% (21/245) with brain tumors, and 3.9% (23/588) with leukemia. Expanding this analysis to 565 Cancer gene resulted in only a slight increase, with a pathologic or likely pathologic variant being detected in 8.6% (97/1120) of patients. The most frequently effected genes included TP53 (n=48), APC (n=7) and BRCA2 (n=6). Importantly, in >50% of these patients, analysis of their tumor DNA revealed the absence of a wild type allele for the Cancer predisposition gene that was altered in the germ line. The 588 pediatric patients with leukemia included 116 acute myeloid leukemias (AMLs: FAB M7 n=20; Core Binding Factor leukemias n=86; MLL- R n=10) and 472 acute lymphoblastic leukemias (ALLs: E2A-PBX 1 n=53; ERG-R n=39; TEL-AML1 n=53; Hyperdiploid n=69; Hypodiploid n=47; BCR-ABL1 n=40; T-ALL n=32; MLL-R n=40; BCR-ABL -like n=31; and Other n=68). Across this cohort, 3.9% (23/588) of leukemia patients harbored a pathologic germ line mutations in one of the 31 Cancer pre-disposing genes. This number increased to 4.6% (27/588; 28 mutations) when the expanded gene list was evaluated. TP53 (n=10) was the most frequently altered germ line gene in pediatric leukemia patients and was found predominantly in low-hypodiploid ALL, as previously reported. Germ line pathologic variants were also identified in KRAS , RUNX1 , APC , BRCA2 , and RET (2 cases each), and NRAS , SH2B3 , BRCA1 , MUTYH , PTCH1 , SDHA, VHL , and NF2 (1 case each). Although germ line mutations in RUNX1 and SH2B3 are typically associated with myeloid neoplasms, we identified these lesions in 3 cases of B lineage ALL suggesting an association with a wider spectrum of leukemia. In conclusion, a small but significant proportion of pediatric patients with leukemia carry a germ line variant of pathologic significance in a Cancer predisposition gene. These results suggest that these germ line lesions likely play a direct role in the pathogenesis of the patient’s presenting leukemia. Moreover, our results suggest that these patients would benefit from future clinical surveillance for the development of a second Cancer. Lastly, these data demonstrate the power of comprehensive next generation DNA/RNA sequencing for the identification of pediatric patients who carry a germ line pathologic variant in a Cancer predisposition gene. Disclosures No relevant conflicts of interest to declare. [1]: /embed/inline-graphic-2.gif
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rb1 gene inactivation by chromothripsis in human retinoblastoma
Oncotarget, 2014Co-Authors: Justina Dolorita Mcevoy, Panduka Nagahawatte, Jennifer Richardsyutz, Charles G Mullighan, Matthew W Wilson, Marcus B. Valentine, Guangchun Song, David Finkelstein, Xiang Chen, Rachel C. BrennanAbstract:// Justina McEvoy 1,* , Panduka Nagahawatte 2,* , David Finkelstein 2 , Jennifer Richards-Yutz 6 , Marcus Valentine 13 , Jing Ma 14 , Charles Mullighan 14 , Guangchun Song 14 , Xiang Chen 2 , Matthew Wilson 4 , Rachel Brennan 12 , Stanley Pounds 3 , Jared Becksfort 2 , Robert Huether 2 , Charles Lu 7 , Robert S. Fulton 7,8 , Lucinda L. Fulton 7,8 , Xin Hong 7,8 , David J. Dooling 7,8 , Kerri Ochoa 7,8 , Elaine R. Mardis 7,8,9 , Richard K.Wilson 7,8,10 , John Easton 2 , Jinghui Zhang 2 , James R. Downing 14 , Arupa Ganguly 5,6,* and Michael A. Dyer 1,4,11 for the St. Jude Children’s Research Hospital – Washington University Pediatric Cancer Genome Project 1 Departments of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA. 2 Computational Biology and Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA. 3 Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA. 4 Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN 5 Department of Genetics, School of Medicine, Philadelphia, PA, USA 6 Genetic Diagnostic Laboratory at University of Pennsylvania, School of Medicine, Philadelphia, PA, USA. 7 The Genome Institute, Washington University School of Medicine in St Louis, St Louis, Missouri, USA. 8 Department of Genetics, Washington University School of Medicine in St Louis, St Louis, Missouri, USA. 9 Siteman Cancer Center, Washington University School of Medicine in St Louis, St Louis, Missouri, USA. 10 Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri, USA 11 Howard Hughes Medical Institute, Chevy Chase, MD 12 Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA. 13 Cytogenetics, St. Jude Children’s Research Hospital, Memphis, TN, USA. 14 Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA. * These authors contributed equally to this work. Correspondence: Michael A. Dyer , email: // Keywords : chromothripsis, retinoblastoma, RB1, MYCN Received : December 13, 2013 Accepted : January 7, 2014 Published : January 11, 2014 Abstract Retinoblastoma is a rare childhood Cancer of the developing retina. Most retinoblastomas initiate with biallelic inactivation of the RB1 gene through diverse mechanisms including point mutations, nucleotide insertions, deletions, loss of heterozygosity and promoter hypermethylation. Recently, a novel mechanism of retinoblastoma initiation was proposed. Gallie and colleagues discovered that a small proportion of retinoblastomas lack RB1 mutations and had MYCN amplification [ 1 ]. In this study, we identified recurrent chromosomal, regional and focal genomic lesions in 94 primary retinoblastomas with their matched normal DNA using SNP 6.0 chips. We also analyzed the RB1 gene mutations and compared the mechanism of RB1 inactivation to the recurrent copy number variations in the retinoblastoma Genome. In addition to the previously described focal amplification of MYCN and deletions in RB1 and BCOR , we also identified recurrent focal amplification of OTX2 , a transcription factor required for retinal photoreceptor development. We identified 10 retinoblastomas in our cohort that lacked RB1 point mutations or indels. We performed whole Genome sequencing on those 10 tumors and their corresponding germline DNA. In one of the tumors, the RB1 gene was unaltered, the MYCN gene was amplified and RB1 protein was expressed in the nuclei of the tumor cells. In addition, several tumors had complex patterns of structural variations and we identified 3 tumors with chromothripsis at the RB1 locus. This is the first report of chromothripsis as a mechanism for RB1 gene inactivation in Cancer.
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abstract 4869 whole Genome sequence analysis of mll rearranged infant acute lymphoblastic leukemias reveals remarkably few somatic mutations a report from the st jude children s research hospital washington university pediatric Cancer Genome Project
Cancer Research, 2012Co-Authors: Anna Andersson, Linda Holmfeldt, Michael Rusch, John Easton, Jianmin Wang, Xiang Chen, Matthew Parker, Susana C Raimondi, Jared Becksfort, Pankaj GuptaAbstract:Infant acute lymphoblastic leukemia (ALL) is characterized by MLL rearrangements (MLLr) and poor prognosis. To determine the complement of somatic mutations in this high risk leukemia, we performed whole Genome sequencing (WGS) on 22 infants with MLL rearranged ALL. An analysis of the structure of the MLLr revealed that over half had complex rearrangements involving either three or more chromosomes, carried cryptic rearrangements, or contained at the breakpoints deletions, amplifications, insertions, or inversion of sequences. In three cases, genetic rearrangements were predicted to generate in addition to the MLL-partner gene fusion, novel in-frame fusions including KRAS-MLL; RAD51B-MLL / AFF1-RAD51B, AFF1-RAD51B-MLL; MLLT10-ATP5L-YPEL4 / ATP5L-YPEL4. An analysis of the number of non-silent mutations revealed infant ALL to have the lowest frequency of somatic mutations of any Cancer sequenced to date. After removal of SVs and CNAs associated with the MLLr, a mean of only 3.5 SVs and 2.2 SNVs affecting the coding region of annotated genes or regulatory RNAs were detected per case. Despite the paucity of mutations several pathways were recurrently targeted including PI3K/RAS pathway in 45% (KRAS (n=4), NRAS (n=2), and non-recurrent mutations in NF1, PTPN11, PIK3R1, and ARHGAP32 (p200Rho/GAP)), B cell differentiation in 23% as a result of mono-allelic deletion or gains of PAX5, 14% with deletions of the CDKN2A/B, and 2 cases with focal deletions of the non-coding RNA genes DLEU1/2. WGS of two infant ALL relapse samples and comparison with their matched diagnostic samples revealed a marked increase in the number of mutations at relapse. Moreover, an analysis of the allelic ratios of mutated genes revealed clonal heterogeneity at diagnosis with relapse appearing to arise from a minor diagnostic clone. Because of the exceedingly low number of mutations detected in infant ALL, we used exome sequencing to determine the frequency of non-silent SNVs in 20 MLLr leukemias (9 ALLs, 10 AMLs and 1 AUL) in older children (7-19 yrs). This analysis revealed that non-infant pediatric MLL leukemias harbor a significantly higher number of non-silent somatic SNVs than infant ALL (mean 7.4/case in older patients vs 2.2/case in infants, p Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4869. doi:1538-7445.AM2012-4869
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Discovery of Novel Recurrent Mutations in Childhood Early T-Cell Precursor Acute Lymphoblastic Leukemia by Whole Genome Sequencing - a Report From the St Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project
Blood, 2011Co-Authors: Jinghui Zhang, Linda Holmfeldt, Susan L. Heatley, Debbie Payne-turner, Li Ding, Gang Wu, John Easton, Jianmin Wang, Xiang Chen, Michael RuschAbstract:Abstract 68 Early T-cell precursor acute lymphoblastic leukemia (ETP ALL) is characterized by an immature T-lineage immunophenotype (cCD3+, CD1a-, CD8- and CD5dim) aberrant expression of myeloid and stem cell markers, a distinct gene expression profile and very poor outcome. The underlying genetic basis of this form of leukemia is unknown. Here we report results of whole Genome sequencing (WGS) of tumor and normal DNA from 12 children with ETP ALL. Genomes were sequenced to 30-fold haploid coverage using the Illumina GAIIx platform, and all putative somatic sequence and structural variants were validated. The frequency of mutations in 43 genes was assessed in a recurrence cohort of 52 ETP and 42 non-ETP T-ALL samples from patients enrolled in St Jude, Children9s Oncology Group and AEIOP trials. Transcriptomic resequencing was performed for two WGS cases, and whole exome sequencing for three ETP ALL cases in the recurrence cohort. We identified 44 interchromosomal translocations (mean 4 per patient, range 0–12), 32 intrachromosomal translocations (mean 3, 0–7), 53 deletions (mean 4, 0–10) and 16 insertions (mean 1, 0–5). Three cases exhibited a pattern of complex rearrangements suggestive of a single cellular catastrophe (“chromothripsis”), two of which had mutations targeting mismatch and DNA repair ( MLH3 and DCLRE1C ). While no single chromosomal alteration was present in all cases, 10 of 12 ETP ALLs harbored chromosomal rearrangements, several of which involved complex multichromosomal translocations and resulted in the expression of chimeric in-frame novel fusion genes disrupting hematopoietic regulators, including ETV6-INO80D, NAP1L1-MLLT10, RUNX1-EVX1 and NUP214-SQSTM1 , each occurring in a single case . An additional ETP case with the ETV6-INO80D fusion was identified in the recurrence cohort. Additionally, 51% of structural variants had breakpoints in genes, including those with roles in hematopoiesis and leukemogenesis, and genes also targeted by mutation in other cases ( MLH3, SUZ12, RUNX1 ). We identified a high frequency of activating mutations in genes regulating cytokine receptor and Ras signalling in ETP ALL (67.2% of ETP compared to 19% of non-ETP T-ALL) including NRAS (17%) , FLT3 (14%) , JAK3 (9%) , SH2B3 (or LNK; 9%), IL7R (8%) , JAK1 (8%) , KRAS (3%) , and BRAF (2%) . Seven cases (5 ETP, 2 non-ETP) harbored in frame insertion mutations in the transmembrane domain of IL7R, which were transforming when expressed in the murine cell lines, and resulted in enhanced colony formation when expressed in primary murine hematopoietic cells. The IL7R mutations resulted in constitutive Jak-Stat activation in these cell lines and primary leukemic cells expressing these mutations. Fifty-eight percent of ETP cases (compared to 17% of non-ETP cases) harbored mutations known or predicted to disrupt hematopoietic and lymphoid development, including ETV6 (33%) , RUNX1 (16%), IKZF1 (14%) , GATA3 (10%) , EP300 (5%) and GATA2 (2%). GATA3 regulates early T cell development, and mutations in this gene were observed exclusively in ETP ALL. The mutations were commonly biallelic, and were clustered at R276, a residue critical for binding of GATA3 to DNA. Strikingly, mutations disrupting chromatin modifying genes were also highly enriched in ETP ALL. Genes encoding the the polycomb repressor complex 2 ( EZH2, SUZ12 and EED ), that mediates histone 3 lysine 27 (H3K27) trimethylation were deleted or mutated in 42% of ETP ALL compared to 12% of non-ETP T-ALL. In addition, alterations of the H3K36 trimethylase SETD2 were observed in 5 ETP cases, but not in non-ETP ALL. We also identified recurrent mutations in genes that have not previously been implicated in hematopoietic malignancies including RELN, DNM2, ECT2L, HNRNPA1 and HNRNPR . Using gene set enrichment analysis we demonstrate that the gene expression profile of ETP ALL shares features not only with normal human hematopoietic stem cells, but also with leukemic initiating cells (LIC) purified from patients with acute myeloid leukemia (AML). These results indicate that mutations that drive proliferation, impair differentiation and disrupt histone modification cooperate to induce an aggressive leukemia with an aberrant immature phenotype. The similarity of the gene expression pattern with that observed in the LIC of AML raises the possibility that myeloid-directed therapies might improve the outcome of ETP ALL. Disclosures: Evans: St. Jude Children9s research Hospital: Employment, Patents & Royalties; NIH & NCI: Research Funding; Aldagen: Membership on an entity9s Board of Directors or advisory committees.
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whole Genome sequence analysis of 22 mll rearranged infant acute lymphoblastic leukemias reveals remarkably few somatic mutations a report from the st jude children s research hospital washington university pediatric Cancer Genome Project
Blood, 2011Co-Authors: Anna Andersson, Linda Holmfeldt, Michael Rusch, John Easton, Jianmin Wang, Xiang Chen, Matthew Parker, Amanda Larson Gedman, Susana C Raimondi, Guanchun SongAbstract:Abstract 69 Infant ( Analysis of the structure of MLL rearrangements at the base pair level revealed that over half had complex rearrangements that involved either three or more chromosomes, or contained at the breakpoints deletions, amplifications, insertions, or inversion of sequences. In five of the complex cases, chromosomal rearrangements were predicted to generate not only a MLL-partner gene fusion, but also novel in-frame fusions including KRAS-MLL; RAD51B-MLL / AFF1-RAD51B; MLLT10-CTNNAP3B; MLLT10-ATP5L / ATP5L-YPEL4; and CRTAM-GNL3. An analysis of the sequence surrounding the breakpoints of MLL and its partner genes suggest that the predominant mechanism of rearrangement involved non-homologous end joining. An analysis of the total number of non-silent mutations revealed infant ALL to have the lowest frequency of non-silent somatic mutations of any Cancer sequenced to date. After removal of SVs and CNAs associated with the MLL rearrangements, a mean of only 2 somatic SVs and 2 SNVs affecting the coding region of annotated genes or regulatory RNAs were detected per case, with a range of non-silent mutation of between 0 and 11 per case (0–7 SV and 0–5 SNV). Despite the paucity of mutations several pathways were recurrently targeted. Mutations leading to activation of signaling through the PI3K/RAS pathway was observed in 45% of the cases with mutation of individual components including KRAS (n=4), NRAS (n=2), and non-recurrent mutations in NF1, PTPN11, PIK3R1, and the GTPase activating protein ARHGAP32 (p200Rho/GAP), which mediates cross-talk between RAS and Rho signaling. Other pathways altered include B cell differentiation, with 23% of cases containing mono-allelic deletion or gains of PAX5, 14% with deletions of the CDKN2A/B, and 2 cases with focal deletions of the non-coding RNA genes DLEU1/2. WGS of two infant ALL relapse samples and comparison with the data from their matched diagnostic samples revealed a marked increase in the number of mutations at relapse with additional SVs, SNVs, and CNAs identified. Moreover, an analysis of the allelic ratios of mutated genes revealed clonal heterogeneity at diagnosis with relapse appearing to arise from a minor diagnostic clone. Because of the exceedingly low frequency of mutations detected in infant ALL, we decided to define the frequency of non-silent SNVs in MLL rearranged leukemia occurring in older children (7–19 years of age). Exome sequencing was performed on 13 MLL leukemias (8 ALLs and 5 AMLs). This analysis revealed that non-infant pediatric MLL rearranged leukemias harbor a significantly higher number of non-silent somatic SNVs than infant ALL (mean 8/case in older patients versus 2/case in infants, p In summary our analysis demonstrated an exceedingly small number of mutations required to generate infant MLL rearranged leukemia. The number of detected somatic mutations may represent the lower limit of mutations required to transform a normal human cell into Cancer. Disclosures: Fioretos:Cantargia AB: Equity Ownership, Membership on an entity9s Board of Directors or advisory committees; Qlucore AB: Equity Ownership, Membership on an entity9s Board of Directors or advisory committees.
