32. It is realistic to expect a result within 28 days. However, if the test is known to infl uence treatment decisions, the laboratory should have a policy for prioritization of samples. Reporting times should be adjusted with local clinicians, e.g., a urgent FISH result of childhood acute leukemia or a t(15; 1 7) should be expected within 4 8 h.
References
1. Heim S, Mittelman F (eds) (2015) Cancer cytogenetics: chromosomal and molecular genetic aberrations of tumor cells, 4th edn.
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J et al (2011) Canadian College of Medical Geneticists guidelines for the indications, anal- ysis, and reporting of cancer specimens. Curr Oncol 18:e250–e255
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7. Schoumans J, Suela J, Hastings R et al (2016) Guidelines for genomic array analysis in acquired haematological neoplastic disor- ders. Genes Chromosomes Cancer 55:480–
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8. Simons A, Sikkema-Raddatz B, de Leeuw N et al (2012) Genome-wide arrays in routine diagnos- tics of hematological malignancies. Hum Mutat 33:941–948. doi: 10.1038/ejhg.2015.35 9. Hanson CA, Steensma DP, Hodnefi eld JM
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Thomas S.K. Wan (ed.), Cancer Cytogenetics: Methods and Protocols, Methods in Molecular Biology, vol. 1541, DOI 10.1007/978-1-4939-6703-2_25, © Springer Science+Business Media LLC 2017
Chapter 25
Cytogenetic Resources and Information
Etienne De Braekeleer , Jean-Loup Huret , Hossain Mossafa , and Philippe Dessen
Abstract
The main databases devoted stricto sensu to cancer cytogenetics are the “Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer” ( http://cgap.nci.nih.gov/Chromosomes/Mitelman ), the
“Atlas of Genetics and Cytogenetics in Oncology and Haematology” ( http://atlasgeneticsoncology.org ), and COSMIC ( http://cancer.sanger.ac.uk/cosmic ).
However, being a complex multistep process, cancer cytogenetics are broadened to “cytogenomics,”
with complementary resources on: general databases (nucleic acid and protein sequences databases; car- tography browsers: GenBank, RefSeq, UCSC, Ensembl, UniProtKB, and Entrez Gene), cancer genomic portals associated with recent international integrated programs, such as TCGA or ICGC, other fusion genes databases, array CGH databases, copy number variation databases, and mutation databases. Other resources such as the International System for Human Cytogenomic Nomenclature (ISCN), the International Classifi cation of Diseases for Oncology (ICD-O), and the Human Gene Nomenclature Database (HGNC) allow a common language.
Data within the scientifi c/medical community should be freely available. However, most of the insti- tutional stakeholders are now gradually disengaging, and well-known databases are forced to beg or to disappear (which may happen!)
Key words Cytogenetic , Cancer , Database , Mitelman database , Atlas of Genetics and Cytogenetics in Oncology and Haematology , COSMIC , UCSC , Ensembl , ICD-O , HGNC
1 Introduction
A genetic event is present in each cancer case [ 1 ]. Cytogenetics has been a major player in the understanding of cancer genetics, and providing specifi c keys for diagnostic as well as prognostic assess- ments, enabling the subclassifi cation of otherwise seemingly iden- tical disease entities [ 2 ].
“Cancer Cytogenetics,” stricto sensu , deals with chromo- somes and cancer. “Cytogenomics,” as coined by Alain Bernheim [ 3 ], means the “genetics—as a whole—of the cell,” with complex interconnections and interactions between the various actors.
Therefore, the “Cancer Cytogenetics” fi eld should include
knowledge of the biology of normal and cancerous cells, gene fusions, mutations or copy number variations, epigenetics, pro- tein domains, signaling pathways, as well as gross and microscopic pathological presentation.
Presently, Internet gives access to a vast and complex network of knowledge that can make it a challenge for you to fi nd specifi c answer to your questions. Several databases are freely accessible.
We will briefl y describe the main ones in the following pages. In addition, there are several descriptions of databases (and particu- larly in cancer) in the special annual “Database issues” of Nucleic Acid Research.
In the 1970s, the introduction of chromosomal banding tech- niques invented by Caspersson and Zech [ 4 ] gave the possibility of identifying individual chromosomes, which were defi ned by a unique banding pattern. The description of chromosomal rear- rangements in leukemias immediately became clearer giving more gravity to the conclusions drawn. This was a new era for cancer cytogenetics with an increasing number of aberrant human malig- nant and benign karyotypes.
In the 1980s, the advent of molecular genetic techniques gave an opportunity to characterize the chromosomal breakpoints at the molecular level which has consequently highlighted two classes of genes implicated in these karyotypical rearrangements: the oncogenes and the tumor suppressor genes.
The study of fusion genes led to the development of specifi c drugs targeting chimeric proteins. The tyrosine kinase inhibitor Imatinib, approved in 2001, was the fi rst drug that was specifi cally designed to target the chimeric protein BCR-ABL1 in chronic myelogenous leukemia (CML) [ 5 , 6 ] by blocking its kinase activ- ity. This drug dramatically improved the lifespan and quality of life of patients bearing CML.
In 1983, Felix Mitelman published a colossal catalogue of all the known chromosomal rearrangements. In 2000, the catalogue became accessible for the public under the name of “Mitelman Database of Chromosome Aberrations in Cancer” on the Internet, making it freely accessible.
How did the idea of the Atlas appear? Prognosis of a leukemia depends on the genes involved and treatment s depends on the severity of the disease. However, thousands of genes were found to be implicated in cancer. The conclusion was that huge databases were needed to collect and summarize data to produce meta- analyses. The Atlas was established in 1997 to answer that call to contribute to “meta-medicine,” meaning the mediation between the knowledge and the knowledge users in medicine.
1.1 Brief History
1.2 Catalog of Chromosome Aberrations in Cancer
1.3 Atlas of Genetics and Cytogenetics in Oncology and Haematology
2 General Resources
The HUGO Gene Nomenclature Committee (HGNC) is the authority that assigns standardized nomenclature to human genes.
HGNC is responsible for approving unique symbols and names for human loci, including protein coding genes, ncRNA genes, and pseudogenes to allow unambiguous scientifi c communication. The database contains 39,000 approved symbols [ 7 ].
ISCN is the language used to describe abnormal karyotypes.
Periodic revisions and updates occurred and ISCN has become ever more complicated [ 8 ]. A new version is being released by the end of 2016 [ 9 ] but will not be freely available on the web.
A common language must be found for reasons of interoperability of different databases. The ICD-O code (International Classifi cation of Diseases—Oncology) has been established by the World Health Organization) WHO/OMS. It contains an International Classifi cation of Diseases for Oncology, Third Edition (ICD-O-3) for coding the site (topography) and the histology (morphology) providing a topographical (organ) identifi er, and the basic and detailed pathology.
However, such classifi cation of tumors is not used by all data- bases (e.g., the Mitelman database or the Catalogue of Somatic Mutations in Cancer (COSMIC) database have their own classifi - cations, with no apparent matching). This is a real obstacle for the integration of data by new resources.
The fi rst DNA sequence database gave way to the creation of the public GenBank ( http://www.ncbi.nlm.nih.gov/genbank/ ) [ 10 ] in 1982. As of February 2016, GenBank has 190,250,235 loci, 207,018,196,067 bases, from 190,250,235 reported sequences.
The need to have (in parallel to the genome projects) the best representation of genomic and transcript sequences has instigated the development of consensus databases (as Reference Sequences (RefSeq), UC Santa Cruz Genomics Institute (UCSC), Ensemb l).
Several databases of consensus nucleic sequences provide detailed structures of genes and isoforms. All this information can easily be visualized in different browsers (UCSC, Ensembl) or described in detail on the Entrez Gene ( see Subheading 2.2.1 ).
RefSeq ( http://www.ncbi.nlm.nih.gov/refseq/ ) maintains and curates a database of annotated genomic, transcript, and protein consensus sequence records. RefSeq represents sequences of more than 55,000 organisms. Ensembl ( http://www.ensembl.org/ ) produces automatic annotation on selected eukaryotic genomes [ 7 ]. The UCSC Genome Browser database ( see Subheading 2.3.1 ) is a large collection containing 160 genome assemblies represent- ing 91 species [ 11 ].
2.1 General Databases 2.1.1 Gene Nomenclature: HGNC ( http://www.genenames.
org/ )
2.1.2 An International System for Human Cytogenomic Nomenclature (ISCN) 2.1.3 International Classifi cation of Diseases for Oncology (ICD-O) ( http://www.who.int/
classifi cations/icd/
adaptations/oncology/en/ )
2.1.4 Nucleic Acid Databases
In parallel to the nucleic databases, a curated protein database, SwissProt, was developed by Amos Bairoch. This was extended by the UniProt Knowledgebase (UniProtKB). In addition to the amino acid sequence, protein name, and description with domains, it provides brief annotation information (Fig. 1 ). UniProtKB ( http://www.uniprot.org/ ) consists of two sections: “TrEMBL,”
computationally analyzed, and “Swiss-Prot,” manually annotated, with information extracted from the literature and 2.1.5 Protein Sequence
Databases
Fig. 1 RAP1GDS1 at UniProtKB ( http://www.uniprot.org/uniprot/P52306 )
curator- evaluated computational analysis. The number of proteins entered in UniProtKB/Swiss-Prot has risen to 550,960 for the SwissProt part and 63,686,057 for the nonreviewed part for TrEMBL [ 12 ].