Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer

Widespread clinical laboratory implementation of next-generation sequencing–based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing–based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings. Widespread clinical laboratory implementation of next-generation sequencing–based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing–based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings. The sequencing of human DNA for the human genome project has led to the emergence of technologies that identify genomic, transcriptional, proteomic, and epigenetic alterations in patients' tumors. Precision medicine uses concepts of the genetic and environmental basis of disease to individualize prevention, diagnosis, and treatment and integrates tumor molecular data into decision making in medical practice.1Garraway L.A. Verweij J. Ballman K.V. Precision oncology: an overview.J Clin Oncol. 2013; 31: 1803-1805Crossref PubMed Scopus (80) Google Scholar, 2Tsimberidou A.M. Targeted therapy in cancer.Cancer Chemother Pharmacol. 2015; 76: 1113-1132Crossref PubMed Google Scholar, 3Von Hoff D.D. Stephenson J.J. Rosen P. Loesch D.M. Borad M.J. Anthony S. Jameson G. Brown S. Cantafio N. Richards D.A. Fitch T.R. Wasserman E. Fernandez C. Green S. Sutherland W. Bittner M. Alarcon A. Mallery D. Penny R. Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers.J Clin Oncol. 2010; 28: 4877-4883Crossref PubMed Scopus (0) Google Scholar, 4Tsimberidou A.-M. Iskander N.G. Hong D.S. Wheler J.J. Falchook G.S. Fu S. Piha-Paul S. Naing A. Janku F. Luthra R. Ye Y. Wen S. Berry D. Kurzrock R. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative.Clin Cancer Res. 2012; 18: 6373-6383Crossref PubMed Scopus (0) Google Scholar Genomic information–based disease prognosis and the selective use of targeted therapy to target specific genotypic and biological biomarkers, combined with other therapeutic strategies based on the tumor biology of the individual patient, hold the promise of improving clinical outcomes and patient care. In recent years, assays for single-target detection have been replaced by next-generation sequencing (NGS) or massively parallel sequencing. This technology allows for the simultaneous evaluation of many genes and the generation of millions of short nucleic acid sequences in parallel.5Shendure J. Ji H. Next-generation DNA sequencing.Nat Biotechnol. 2008; 26: 1135-1145Crossref PubMed Scopus (0) Google Scholar, 6Bentley D.R. Balasubramanian S. Swerdlow H.P. Smith G.P. Milton J. Brown C.G. et al.Accurate whole human genome sequencing using reversible terminator chemistry.Nature. 2008; 456: 53-59Crossref PubMed Scopus (0) Google Scholar The NGS high-throughput platform is more efficient and less expensive and provides information that is not provided by single gene-by-gene Sanger DNA sequencing analysis or by gene-specific targeted hot spot mutation assays.7Sabatini L.M. Mathews C. Ptak D. Doshi S. Tynan K. Hegde M.R. Burke T.L. Bossler A.D. Genomic sequencing procedure microcosting analysis and health economic cost-impact analysis: a report of the Association for Molecular Pathology.J Mol Diagn. 2016; 18: 319-328Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar The vast number of variants identified by NGS in tumor tissue is attributed to the complexity of carcinogenesis, including the multistep process of genetic mutations and tumor heterogeneity (ie, multiple clones of cells with related but distinct molecular signatures within tumors).8Kandoth C. McLellan M.D. Vandin F. Ye K. Niu B. Lu C. Xie M. Zhang Q. McMichael J.F. Wyczalkowski M.A. Leiserson M.D. Miller C.A. Welch J.S. Walter M.J. Wendl M.C. Ley T.J. Wilson R.K. Raphael B.J. Ding L. Mutational landscape and significance across 12 major cancer types.Nature. 2013; 503: 333-339Crossref PubMed Scopus (736) Google Scholar, 9Ding L. Ley T.J. Larson D.E. Miller C.A. Koboldt D.C. Welch J.S. et al.Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing.Nature. 2012; 481: 506-510Crossref PubMed Scopus (725) Google Scholar Herein, tumor refers to tissue deriving from either a benign or malignant neoplasm. NGS results obtained using DNA or RNA extracted from tumor tissue frequently demonstrate a complex molecular signature that is different from that of normal tissue for any given patient. The significance of the change relative to tumorigenesis depends on the type of genetic aberration, the location of the variant, and the normal function of the protein. Genetic variants can be germline or somatic. A germline variant is defined as a genetic alteration that occurs within the germ cells (egg or sperm), such that the alteration can be passed to subsequent generations. A somatic variant is defined as a genetic alteration that occurs in any of the cells of the body, except the germ cells, and therefore is not passed on to subsequent generations. Genetic variations may be activating, resulting in a gain of function of the protein, such as a missense mutation in the functional or kinase domain of the protein, allowing for autophosphorylation of the protein, the loss of regulation for downstream signaling, and uncontrolled cell growth and proliferation. Conversely, the genetic alteration may be inactivating, such as nonsense, splice-site, and frameshift insertion/deletion mutations, thereby causing a loss of function of a tumor-suppressor gene. The types of variants observed include single-nucleotide variants (SNVs) that cause a missense, silent, or nonsense amino acid substitution, or a splice site alteration affecting normal splicing of the mRNA transcript. Alternatively, one or more nucleotides may be involved in duplications, deletions, insertions, or even a more complex pattern with a nucleotide(s) deletion coupled with a nucleotide(s) insertion (indels) at a particular location. Also common in the pathogenesis of cancer is a change in copy number of cancer-related genes. Generically identified as copy number variants (CNVs), these include copy number alterations of various types. Examples of CNVs include the common loss (deletion) of the tumor-suppressor RB1 gene in retinoblastoma or the gain (amplification) of the oncogene ERBB2 in invasive breast carcinoma. In addition, structural rearrangements, including chromosome translocations, deletions, duplication, or inversions, are frequently identified in tumor DNA and result in gene fusions and associated fusion proteins with unique cancer-promoting properties, such as the EML4-ALK recurrent inversion mutation that is seen in non-small cell lung cancer. Molecular profiles obtained on tumor DNA and RNA can guide the clinical management of cancer patients. This information can provide diagnostic or prognostic information, identify a potential treatment regimen or targeted therapy, and determine eligibility for the following: i) a Food and Drug Administration (FDA)–approved medication for that tumor type, ii) a medication available as off-label treatment for the specific molecular alteration in a nonapproved tumor type, or iii) a targeted therapy available in clinical trials with investigational agents based on an identified molecular alteration. In the United States, the Clinical Laboratory Improvement Amendments of 1988 provide regulatory oversight to laboratories performing tumor genomics characterization (US Government Publishing Office, Electronic Code of Federal Regulations, Title 42, §Part 493.1, http://www.ecfr.gov/cgi-bin/text-idx?SID=1248e3189da5e5f936e55315402bc38b&node=pt42.5.493&rgn=div5#se42.5.493_11, last accessed July 6, 2016). Clinical Laboratory Improvement Amendment certification of laboratories, ongoing quality assurance/improvement, and appropriate proficiency testing are required to ensure accurate and reproducible molecular profiling. Implementaion of NGS identifies large numbers of genetic variations in tumor DNA, which are crucial for optimal patient care, and treatment guidelines are developed based on specific molecular findings; therefore, it is imperative to unify the interpretation and reporting of molecular results among laboratiores performing these tests.10Schilsky R.L. Implementing personalized cancer care.Nat Rev Clin Oncol. 2014; 11: 432-438Crossref PubMed Scopus (0) Google Scholar, 11Teutsch S.M. Bradley L.A. Palomaki G.E. Haddow J.E. Piper M. Calonge N. David Dotson W. Douglas M.P. Berg A.O. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group.Genet Med. 2009; 11: 3-14Crossref PubMed Scopus (0) Google Scholar, 12Lindeman N.I. Cagle P.T. Beasley M.B. Chitale D.A. Dacic S. Giaccone G. Jenkins R.B. Kwiatkowski D.J. Saldivar J.-S. Squire J. Thunnissen E. Ladanyi M. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors.J Mol Diagn. 2013; 15: 415-453Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 13McShane L.M. Cavenagh M.M. Lively T.G. Eberhard D.A. Bigbee W.L. Williams P.M. Mesirov J.P. Polley M.-Y.C. Kim K.Y. Tricoli J.V. Taylor J.M.G. Shuman D.J. Simon R.M. Doroshow J.H. Conley B.A. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration.BMC Med. 2013; 11: 220Crossref PubMed Scopus (0) Google Scholar In the spring of 2015, a clinical laboratory–focused working group was formed to establish recommendations for the interpretation and reporting of sequence variants identified in tumor tissue analogous to the recently published standards and guidelines for the interpretation of sequence variants in genes associated with mendelian disorders.14Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. Voelkerding K. Rehm H.L. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet Med. 2015; 17: 405-423Abstract Full Text Full Text PDF PubMed Scopus (381) Google Scholar The multidisciplinary working group, convened by the Association for Molecular Pathology (AMP), included investigators with expertise in molecular pathology, medical genetics, and clinical oncology and included liaison representation from the American College of Medical Genetics and Genomics (ACMG), American Society of Clinical Oncology, and College of American Pathologists. To broadly assess the current practice of NGS testing for tumor tissue, an NGS technical survey and an NGS reporting survey were prepared by the working group and made available online to the AMP membership community for approximately 4 weeks (Figure 1). A representative from each laboratory performing NGS testing was encouraged to participate; 67 responses were received for the technical survey, while 44 participants completed the reporting survey. Participants reported performing NGS testing on both solid tumors and hematological neoplasms, with the number of genes contained within the testing panels ranging from 1 to 10 genes to >100 genes. A minority of respondents reported performing exome (12%) or genome (5%) analysis on tumor tissue. All participants noted the ability to detect SNVs; 95% of participants stated that small indels could be identified by their testing methods, whereas CNVs and gene fusions were interrogated by only 35% and 37% of participating laboratories, respectively. In addition to differences in NGS techniques reported among the participating laboratories, this survey also highlighted significant differences in annotation and reporting of variants (Figure 1). Preliminary findings from this survey and additional public open comment designed to further inform this project were gathered during a workshop held at the 2015 AMP Annual Meeting (Austin, TX). Classification and reporting of variants to health care providers is critical for patient care, including the following: accurate reporting of tumor response to targeted therapy; establishment of national guidelines for patient care; and collaborative institutional clinical trials, thereby supporting the need for standardization among laboratories performing these tests. The goal of these guidelines is to establish standardized classification, annotation, interpretation, and reporting of sequence variants associated with cancer. The guidelines presented herein are based on literature review, empirical data, and the professional judgment of the working group members. With the publication of an increasing number of large-scale genome sequencing projects for a variety of tumor types, a wealth of genomic information is being generated and consolidated into many public databases globally15McLaren W. Pritchard B. Rios D. Chen Y. Flicek P. Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.Bioinformatics. 2010; 26: 2069-2070Crossref PubMed Scopus (584) Google Scholar, 16Auton A. Abecasis G.R. Altshuler D.M. Durbin R.M. Bentley D.R. Chakravarti A. et al.1000 Genomes Project ConsortiumA global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (262) Google Scholar, 17Sherry S.T. Ward M.H. Kholodov M. Baker J. Phan L. Smigielski E.M. Sirotkin K. dbSNP: the NCBI database of genetic variation.Nucleic Acids Res. 2001; 29: 308-311Crossref PubMed Google Scholar, 18Lappalainen I. Lopez J. Skipper L. Hefferon T. Spalding J.D. Garner J. Chen C. Maguire M. Corbett M. Zhou G. Paschall J. Ananiev V. Flicek P. Church D.M. DbVar and DGVa: public archives for genomic structural variation.Nucleic Acids Res. 2013; 41: D936-D941Crossref PubMed Scopus (0) Google Scholar, 19Forbes S.A. Beare D. Gunasekaran P. Leung K. Bindal N. Boutselakis H. Ding M. Bamford S. Cole C. Ward S. Kok C.Y. Jia M. De T. Teague J.W. Stratton M.R. McDermott U. Campbell P.J. COSMIC: exploring the world's knowledge of somatic mutations in human cancer.Nucleic Acids Res. 2015; 43: D805-D811Crossref PubMed Scopus (440) Google Scholar, 20Gao J. Aksoy B.A. Dogrusoz U. Dresdner G. Gross B. Sumer S.O. Sun Y. Jacobsen A. Sinha R. Larsson E. Cerami E. Sander C. Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.Sci Signal. 2013; 6: pl1Crossref PubMed Scopus (904) Google Scholar, 21Gundem G. Perez-Llamas C. Jene-Sanz A. Kedzierska A. Islam A. Deu-Pons J. Furney S.J. Lopez-Bigas N. IntOGen: integration and data mining of multidimensional oncogenomic data.Nat Methods. 2010; 7: 92-93Crossref PubMed Scopus (0) Google Scholar, 22Petitjean A. Mathe E. Kato S. Ishioka C. Tavtigian S.V. Hainaut P. Olivier M. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database.Hum Mutat. 2007; 28: 622-629Crossref PubMed Scopus (0) Google Scholar, 23Zhang J. Baran J. Cros A. Guberman J.M. Haider S. Hsu J. Liang Y. Rivkin E. Wang J. Whitty B. Wong-Erasmus M. Yao L. Kasprzyk A. International cancer genome consortium data portal: a one-stop shop for cancer genomics data.Database. 2011; 2011: bar026Google Scholar, 24Pruitt K.D. Brown G.R. Hiatt S.M. Thibaud-Nissen F. Astashyn A. Ermolaeva O. Farrell C.M. Hart J. Landrum M.J. McGarvey K.M. Murphy M.R. O'Leary N.A. Pujar S. Rajput B. Rangwala S.H. Riddick L.D. Shkeda A. Sun H. Tamez P. Tully R.E. Wallin C. Webb D. Weber J. Wu W. Dicuccio M. Kitts P. Maglott D.R. Murphy T.D. Ostell J.M. RefSeq: an update on mammalian reference sequences.Nucleic Acids Res. 2014; 42: D756-D763Crossref PubMed Scopus (312) Google Scholar, 25Dalgleish R. Flicek P. Cunningham F. Astashyn A. Tully R.E. Proctor G. Chen Y. McLaren W.M. Larsson P. Vaughan B.W. Béroud C. Dobson G. Lehväslaiho H. Taschner P.E. den Dunnen J.T. Devereau A. Birney E. Brookes A.J. Maglott D.R. Locus Reference Genomic sequences: an improved basis for describing human DNA variants.Genome Med. 2010; 2: 24Crossref PubMed Scopus (0) Google Scholar, 26Karolchik D. Hinrichs A.S. Furey T.S. Roskin K.M. Sugnet C.W. Haussler D. Kent W.J. The UCSC Table Browser data retrieval tool.Nucleic Acids Res. 2004; 32: D493-D496Crossref PubMed Google Scholar, 27Smedley D. Haider S. Durinck S. Pandini L. Provero P. Allen J. Arnaiz O. Awedh M. Baldock R. The BioMart community portal: an innovative alternative to large, centralized data repositories.Nucleic Acids Res. 2015; 43: W589-W598Crossref PubMed Scopus (0) Google Scholar, 28Landrum M.J. Lee J.M. Riley G.R. Jang W. Rubinstein W.S. Church D.M. Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype.Nucleic Acids Res. 2014; 42: D980-D985Crossref PubMed Scopus (353) Google Scholar, 29Stenson P.D. Ball E.V. Mort M. Phillips A.D. Shaw K. Cooper D.N. The human gene mutation database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution.Curr Protoc Bioinformatics. 2012; Chapter 1: Unit1.13Google Scholar, 30Fokkema I.F.A.C. Taschner P.E.M. Schaafsma G.C.P. Celli J. Laros J.F.J. den Dunnen J.T. LOVD v.2.0: the next generation in gene variant databases.Hum Mutat. 2011; 32: 557-563Crossref PubMed Scopus (0) Google Scholar, 31Liu X. Wu C. Li C. Boerwinkle E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs.Hum Mutat. 2016; 37: 235-241Crossref PubMed Scopus (11) Google Scholar (Table 1). For example, the National Cancer Institute's Genome Data Commons contains National Cancer Institute–generated data from some of the largest and most comprehensive cancer genomic data sets, including The Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Therapies, and the Cancer Genome Characterization Initiative (https://gdc.cancer.gov, last accessed September 25, 2016). Another public somatic variant database is the Catalog of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk/cosmic, last accessed September 30, 2016), which contains millions of somatic alterations across numerous tumor types. Several other data repositories, such as reference sequence information, population databases, and germline variant databases, that are frequently used in somatic variant analysis are also constantly increasing and improving. The genomic databases provide information that is necessary for accurate annotation and prioritization of somatic variants. As a general rule, clinical laboratories should exercise the following cautionary steps on the use of public databases:1.Understand the content of the database and how the data are aggregated. The clinical laboratory should review the documentation or published literature relating to a given database to ascertain the source, type, and intent of the database.2.Pay specific attention to the limitation of each database to avoid overinterpretation of annotation results.3.Confirm the versions of the human genome assembly as well as mRNA transcript references to ensure appropriate Human Genome Variation Society (HGVS) annotation.4.Whenever possible, use genomic coordinates, instead of HGVS nomenclature, to unambiguously query genomic databases.5.Assess the quality of the provided genomic data based on the source, from publications or another database, the number of a specific entry, single or multiple, the depth of the study, the use of appropriate controls, confirmation of a variant's somatic origin, and functional and potential drug response studies.6.Verify data quality of the pathological diagnosis provided (eg, site, diagnosis, and subtype).Table 1Databases Relevant to Interpretation of Somatic Sequence VariantsUtility/functionDatabaseLocation (web address)Population databases to exclude polymorphisms1000 Genomes Project16Auton A. Abecasis G.R. Altshuler D.M. Durbin R.M. Bentley D.R. Chakravarti A. et al.1000 Genomes Project ConsortiumA global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (262) Google Scholarhttp://browser.1000genomes.orgExome Variant Serverhttp://evs.gs.washington.edu/EVSdbSNP17Sherry S.T. Ward M.H. Kholodov M. Baker J. Phan L. Smigielski E.M. Sirotkin K. dbSNP: the NCBI database of genetic variation.Nucleic Acids Res. 2001; 29: 308-311Crossref PubMed Google Scholarhttp://www.ncbi.nlm.nih.gov/snpdbVar18Lappalainen I. Lopez J. Skipper L. Hefferon T. Spalding J.D. Garner J. Chen C. Maguire M. Corbett M. Zhou G. Paschall J. Ananiev V. Flicek P. Church D.M. DbVar and DGVa: public archives for genomic structural variation.Nucleic Acids Res. 2013; 41: D936-D941Crossref PubMed Scopus (0) Google Scholarhttp://www.ncbi.nlm.nih.gov/dbvarExAChttp://exac.broadinstitute.orgCancer-specific variant databasesCatalog of Somatic Mutations in Cancer19Forbes S.A. Beare D. Gunasekaran P. Leung K. Bindal N. Boutselakis H. Ding M. Bamford S. Cole C. Ward S. Kok C.Y. Jia M. De T. Teague J.W. Stratton M.R. McDermott U. Campbell P.J. COSMIC: exploring the world's knowledge of somatic mutations in human cancer.Nucleic Acids Res. 2015; 43: D805-D811Crossref PubMed Scopus (440) Google Scholarhttp://cancer.sanger.ac.uk/cosmicMy Cancer Genomehttp://www.mycancergenome.orgPersonalized cancer therapy, MD Anderson Cancer Centerhttps://pct.mdanderson.orgcBioPortal, Memorial Sloan Kettering Cancer Center20Gao J. Aksoy B.A. Dogrusoz U. Dresdner G. Gross B. Sumer S.O. Sun Y. Jacobsen A. Sinha R. Larsson E. Cerami E. Sander C. Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.Sci Signal. 2013; 6: pl1Crossref PubMed Scopus (904) Google Scholarhttp://www.cbioportal.orgIntogen21Gundem G. Perez-Llamas C. Jene-Sanz A. Kedzierska A. Islam A. Deu-Pons J. Furney S.J. Lopez-Bigas N. IntOGen: integration and data mining of multidimensional oncogenomic data.Nat Methods. 2010; 7: 92-93Crossref PubMed Scopus (0) Google Scholarhttps://www.intogen.org/searchClinicalTrials.govhttps://clinicaltrials.govIARC (WHO) TP53 mutation database22Petitjean A. Mathe E. Kato S. Ishioka C. Tavtigian S.V. Hainaut P. Olivier M. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database.Hum Mutat. 2007; 28: 622-629Crossref PubMed Scopus (0) Google Scholarhttp://p53.iarc.frPediatric Cancer Genome Project (St. Jude Children's Research Hospital–Washington University)http://explorepcgp.orgInternational Cancer Genome Consortium23Zhang J. Baran J. Cros A. Guberman J.M. Haider S. Hsu J. Liang Y. Rivkin E. Wang J. Whitty B. Wong-Erasmus M. Yao L. Kasprzyk A. International cancer genome consortium data portal: a one-stop shop for cancer genomics data.Database. 2011; 2011: bar026Google Scholarhttps://dcc.icgc.orgSequence repositories and data hostsNCBI Genomehttp://www.ncbi.nlm.nih.gov/genomeRefSeqGene24Pruitt K.D. Brown G.R. Hiatt S.M. Thibaud-Nissen F. Astashyn A. Ermolaeva O. Farrell C.M. Hart J. Landrum M.J. McGarvey K.M. Murphy M.R. O'Leary N.A. Pujar S. Rajput B. Rangwala S.H. Riddick L.D. Shkeda A. Sun H. Tamez P. Tully R.E. Wallin C. Webb D. Weber J. Wu W. Dicuccio M. Kitts P. Maglott D.R. Murphy T.D. Ostell J.M. RefSeq: an update on mammalian reference sequences.Nucleic Acids Res. 2014; 42: D756-D763Crossref PubMed Scopus (312) Google Scholarhttp://www.ncbi.nlm.nih.gov/refseq/rsgLocus Reference Genomic25Dalgleish R. Flicek P. Cunningham F. Astashyn A. Tully R.E. Proctor G. Chen Y. McLaren W.M. Larsson P. Vaughan B.W. Béroud C. Dobson G. Lehväslaiho H. Taschner P.E. den Dunnen J.T. Devereau A. Birney E. Brookes A.J. Maglott D.R. Locus Reference Genomic sequences: an improved basis for describing human DNA variants.Genome Med. 2010; 2: 24Crossref PubMed Scopus (0) Google Scholarhttp://www.lrg-sequence.orgUCSC table browser26Karolchik D. Hinrichs A.S. Furey T.S. Roskin K.M. Sugnet C.W. Haussler D. Kent W.J. The UCSC Table Browser data retrieval tool.Nucleic Acids Res. 2004; 32: D493-D496Crossref PubMed Google Scholarhttps://genome.ucsc.edu/cgi-bin/hgTablesEnsemble BioMart27Smedley D. Haider S. Durinck S. Pandini L. Provero P. Allen J. Arnaiz O. Awedh M. Baldock R. The BioMart community portal: an innovative alternative to large, centralized data repositories.Nucleic Acids Res. 2015; 43: W589-W598Crossref PubMed Scopus (0) Google Scholarhttp://useast.ensembl.org/biomart/martviewOther disease/mutation databases useful in the context of variant interpretation for cancer genomicsClinVar28Landrum M.J. Lee J.M. Riley G.R. Jang W. Rubinstein W.S. Church D.M. Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype.Nucleic Acids Res. 2014; 42: D980-D985Crossref PubMed Scopus (353) Google Scholarhttp://www.ncbi.nlm.nih.gov/clinvarHuman Gene Mutation Database29Stenson P.D. Ball E.V. Mort M. Phillips A.D. Shaw K. Cooper D.N. The human gene mutation database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution.Curr Protoc Bioinformatics. 2012; Chapter 1: Unit1.13Google Scholarhttp://www.hgmd.orgLeiden Open Variation Database30Fokkema I.F.A.C. Taschner P.E.M. Schaafsma G.C.P. Celli J. Laros J.F.J. den Dunnen J.T. LOVD v.2.0: the next generation in gene variant databases.Hum Mutat. 2011; 32: 557-563Crossref PubMed Scopus (0) Google Scholarhttp://www.lovd.nldbNSFP (compiled database of precomputed in silico prediction scores for nonsynonymous SNVs)31Liu X. Wu C. Li C. Boerwinkle E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs.Hum Mutat. 2016; 37: 235-241Crossref PubMed Scopus (11) Google Scholarhttps://sites.google.com/site/jpopgen/dbNSFPEnsemble Variant Effect Predictor15McLaren W. Pritchard B. Rios D. Chen Y. Flicek P. Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.Bioinformatics. 2010; 26: 2069-2070Crossref PubMed Scopus (584) Google Scholarhttp://www.ensembl.org/info/docs/tools/vep/index.htmlThese are not comprehensive lists, and inclusion does not represent an organizational endorsement of any individual database or product. All websites last accessed June 7, 2016.dbSNP, The Database of Short Genetic Variation; ExAC, Exome Aggregation Consortium; IARC, International Agency for Research on Cancer; NCBI, National Center for Biotechnology Information; SNV, single-nucleotide variant; UCSC, University of California, Santa Cruz; WHO, World Health Organization. Open table in a new tab These are not comprehensive lists, and inclusion does not represent an organizational endorsement of any individual database or product. All websites last accessed June 7, 2016. dbSNP, The Database of Short Genetic Variation; ExAC, Exome Aggregation Consortium; IARC, International Agency for Research on Cancer; NCBI, National Center for Biotechnology Information; SNV, single-nucleotide variant; UCSC, University of California, Santa Cruz; WHO, World Health Organization. Reference sequence databases provide information on the version of the human genome