Literature DB >> 18951438

Locus-specific databases and recommendations to strengthen their contribution to the classification of variants in cancer susceptibility genes.

Marc S Greenblatt1, Lawrence C Brody, William D Foulkes, Maurizio Genuardi, Robert M W Hofstra, Magali Olivier, Sharon E Plon, Rolf H Sijmons, Olga Sinilnikova, Amanda B Spurdle.   

Abstract

Locus-specific databases (LSDBs) are curated collections of sequence variants in genes associated with disease. LSDBs of cancer-related genes often serve as a critical resource to researchers, diagnostic laboratories, clinicians, and others in the cancer genetics community. LSDBs are poised to play an important role in disseminating clinical classification of variants. The IARC Working Group on Unclassified Genetic Variants has proposed a new system of five classes of variants in cancer susceptibility genes. However, standards are lacking for reporting and analyzing the multiple data types that assist in classifying variants. By adhering to standards of transparency and consistency in the curation and annotation of data, LSDBs can be critical for organizing our understanding of how genetic variation relates to disease. In this article we discuss how LSDBs can accomplish these goals, using existing databases for BRCA1, BRCA2, MSH2, MLH1, TP53, and CDKN2A to illustrate the progress and remaining challenges in this field. We recommend that: 1) LSDBs should only report a conclusion related to pathogenicity if a consensus has been reached by an expert panel. 2) The system used to classify variants should be standardized. The Working Group encourages use of the five class system described in this issue by Plon and colleagues. 3) Evidence that supports a conclusion should be reported in the database, including sources and criteria used for assignment. 4) Variants should only be classified as pathogenic if more than one type of evidence has been considered. 5) All instances of all variants should be recorded. Published 2008 Wiley-Liss, Inc.

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Year:  2008        PMID: 18951438      PMCID: PMC3446852          DOI: 10.1002/humu.20889

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  38 in total

1.  Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2.

Authors:  David E Goldgar; Douglas F Easton; Amie M Deffenbaugh; Alvaro N A Monteiro; Sean V Tavtigian; Fergus J Couch
Journal:  Am J Hum Genet       Date:  2004-08-02       Impact factor: 11.025

Review 2.  Locus-specific mutation databases: pitfalls and good practice based on the p53 experience.

Authors:  Thierry Soussi; Chikashi Ishioka; Mireille Claustres; Christophe Béroud
Journal:  Nat Rev Cancer       Date:  2006-01       Impact factor: 60.716

3.  Biallelic mutations in p16(INK4a) confer resistance to Ras- and Ets-induced senescence in human diploid fibroblasts.

Authors:  Thomas J Huot; Janice Rowe; Mark Harland; Sarah Drayton; Sharon Brookes; Chandra Gooptu; Patricia Purkis; Mike Fried; Veronique Bataille; Eiji Hara; Julia Newton-Bishop; Gordon Peters
Journal:  Mol Cell Biol       Date:  2002-12       Impact factor: 4.272

4.  Time for a unified system of mutation description and reporting: a review of locus-specific mutation databases.

Authors:  Mireille Claustres; Ourania Horaitis; Marijana Vanevski; Richard G H Cotton
Journal:  Genome Res       Date:  2002-05       Impact factor: 9.043

5.  The breast cancer information core: database design, structure, and scope.

Authors:  C Szabo; A Masiello; J F Ryan; L C Brody
Journal:  Hum Mutat       Date:  2000       Impact factor: 4.878

6.  Detailed computational study of p53 and p16: using evolutionary sequence analysis and disease-associated mutations to predict the functional consequences of allelic variants.

Authors:  M S Greenblatt; J G Beaudet; J R Gump; K S Godin; L Trombley; J Koh; J P Bond
Journal:  Oncogene       Date:  2003-02-27       Impact factor: 9.867

7.  Use of the American College of Radiology BI-RADS guidelines by community radiologists: concordance of assessments and recommendations assigned to screening mammograms.

Authors:  Constance Lehman; Sarah Holt; Susan Peacock; Emily White; Nicole Urban
Journal:  AJR Am J Roentgenol       Date:  2002-07       Impact factor: 3.959

8.  Cancer risk assessment at the atomic level.

Authors:  Alvaro N A Monteiro; Fergus J Couch
Journal:  Cancer Res       Date:  2006-02-15       Impact factor: 12.701

9.  Detection of protein folding defects caused by BRCA1-BRCT truncation and missense mutations.

Authors:  R Scott Williams; Daniel I Chasman; D Duong Hau; Benjamin Hui; Albert Y Lau; J N Mark Glover
Journal:  J Biol Chem       Date:  2003-10-08       Impact factor: 5.157

Review 10.  Mutations associated with HNPCC predisposition -- Update of ICG-HNPCC/INSiGHT mutation database.

Authors:  Päivi Peltomäki; Hans Vasen
Journal:  Dis Markers       Date:  2004       Impact factor: 3.434

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  18 in total

1.  Characterization of germline mutations of MLH1 and MSH2 in unrelated south American suspected Lynch syndrome individuals.

Authors:  Mev Dominguez Valentin; Felipe Carneiro da Silva; Erika Maria Monteiro dos Santos; Bianca Garcia Lisboa; Ligia Petrolini de Oliveira; Fabio de Oliveira Ferreira; Israel Gomy; Wilson Toshihiko Nakagawa; Samuel Aguiar Junior; Mariana Redal; Carlos Vaccaro; Adriana Della Valle; Carlos Sarroca; Dirce Maria Carraro; Benedito Mauro Rossi
Journal:  Fam Cancer       Date:  2011-12       Impact factor: 2.375

2.  Clinical Variant Classification: A Comparison of Public Databases and a Commercial Testing Laboratory.

Authors:  William Gradishar; KariAnne Johnson; Krystal Brown; Erin Mundt; Susan Manley
Journal:  Oncologist       Date:  2017-04-13

3.  Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed.

Authors:  Stephanie Hicks; David A Wheeler; Sharon E Plon; Marek Kimmel
Journal:  Hum Mutat       Date:  2011-04-07       Impact factor: 4.878

Review 4.  Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation.

Authors:  Haiming Tang; Paul D Thomas
Journal:  Genetics       Date:  2016-06       Impact factor: 4.562

5.  Tumor characteristics as an analytic tool for classifying genetic variants of uncertain clinical significance.

Authors:  Robert M W Hofstra; Amanda B Spurdle; Diana Eccles; William D Foulkes; Niels de Wind; Nicoline Hoogerbrugge; Frans B L Hogervorst
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

6.  Assessing pathogenicity: overview of results from the IARC Unclassified Genetic Variants Working Group.

Authors:  Sean V Tavtigian; Marc S Greenblatt; David E Goldgar; Paolo Boffetta
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

7.  Prediction and assessment of splicing alterations: implications for clinical testing.

Authors:  Amanda B Spurdle; Fergus J Couch; Frans B L Hogervorst; Paolo Radice; Olga M Sinilnikova
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

8.  Assessment of functional effects of unclassified genetic variants.

Authors:  Fergus J Couch; Lene Juel Rasmussen; Robert Hofstra; Alvaro N A Monteiro; Marc S Greenblatt; Niels de Wind
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

9.  Practical guidelines addressing ethical issues pertaining to the curation of human locus-specific variation databases (LSDBs).

Authors:  Sue Povey; Aida I Al Aqeel; Anne Cambon-Thomsen; Raymond Dalgleish; Johan T den Dunnen; Helen V Firth; Marc S Greenblatt; Carol Isaacson Barash; Michael Parker; George P Patrinos; Judith Savige; Maria-Jesus Sobrido; Ingrid Winship; Richard G H Cotton
Journal:  Hum Mutat       Date:  2010-11       Impact factor: 4.878

10.  Using exome data to identify malignant hyperthermia susceptibility mutations.

Authors:  Stephen G Gonsalves; David Ng; Jennifer J Johnston; Jamie K Teer; Peter D Stenson; David N Cooper; James C Mullikin; Leslie G Biesecker
Journal:  Anesthesiology       Date:  2013-11       Impact factor: 7.892

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