{"title":"28.在 CIViC 数据模型中添加非基因特征","authors":"Arpad Danos , Kilannin Krysiak , Adam Coffman , Joshua McMichael , Mariam Khanfar , Cameron Grisdale , Alex Wagner , Malachi Griffith , Obi Griffith","doi":"10.1016/j.cancergen.2024.08.030","DOIUrl":null,"url":null,"abstract":"<div><div>CIViC (<span><span>www.civicdb.org</span><svg><path></path></svg></span>) is a free and open knowledgebase that leverages public curation together with expert moderation to address the bottleneck created with the need to interpret large numbers of variants from next generation sequencing of tumor DNA. Curation from literature and meeting abstracts is utilized to create Evidence Items (EIDs), and collections of EIDs are summarized into Assertions which reflect the state of the field for a variant. Assertions incorporate variant classification standards such as those from AMP/ASCO/CAP for clinical actionability, or ClinGen/CGC/VICC guidelines for oncogenicity. The CIViC data model has consistently developed to better capture the considerable variation of cancer. CIViC employs a flexible model for gene variants which may be combined into multi-gene Molecular Profiles. While gene mutations play the largest role in personalized medicine, other entities such as tumor mutation burden (TMB), microsatellite instability (MSI) or homologous recombination repair deficiency (HRD) are increasingly used as diagnostic, prognostic or therapeutic markers. To capture entities such as these, CIViC has introduced a new feature type, which models tumorbiomarkers not directly associated with genes or specific regions of the genome. This new type of biomarker accompanies Genes as a formal Feature entity in the CIViC data model. Like Genes, each of these biomarkers will have a page in CIViC, and be associated with an NCI thesaurus entry whenever possible. CIViC is developing a generalized Feature-Variant data model, enabling the addition of new Feature types in future updates, such as large genomic regions.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S9"},"PeriodicalIF":1.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"28. Addition of non-gene features to the CIViC data model\",\"authors\":\"Arpad Danos , Kilannin Krysiak , Adam Coffman , Joshua McMichael , Mariam Khanfar , Cameron Grisdale , Alex Wagner , Malachi Griffith , Obi Griffith\",\"doi\":\"10.1016/j.cancergen.2024.08.030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>CIViC (<span><span>www.civicdb.org</span><svg><path></path></svg></span>) is a free and open knowledgebase that leverages public curation together with expert moderation to address the bottleneck created with the need to interpret large numbers of variants from next generation sequencing of tumor DNA. Curation from literature and meeting abstracts is utilized to create Evidence Items (EIDs), and collections of EIDs are summarized into Assertions which reflect the state of the field for a variant. Assertions incorporate variant classification standards such as those from AMP/ASCO/CAP for clinical actionability, or ClinGen/CGC/VICC guidelines for oncogenicity. The CIViC data model has consistently developed to better capture the considerable variation of cancer. CIViC employs a flexible model for gene variants which may be combined into multi-gene Molecular Profiles. While gene mutations play the largest role in personalized medicine, other entities such as tumor mutation burden (TMB), microsatellite instability (MSI) or homologous recombination repair deficiency (HRD) are increasingly used as diagnostic, prognostic or therapeutic markers. To capture entities such as these, CIViC has introduced a new feature type, which models tumorbiomarkers not directly associated with genes or specific regions of the genome. This new type of biomarker accompanies Genes as a formal Feature entity in the CIViC data model. Like Genes, each of these biomarkers will have a page in CIViC, and be associated with an NCI thesaurus entry whenever possible. CIViC is developing a generalized Feature-Variant data model, enabling the addition of new Feature types in future updates, such as large genomic regions.</div></div>\",\"PeriodicalId\":49225,\"journal\":{\"name\":\"Cancer Genetics\",\"volume\":\"286 \",\"pages\":\"Page S9\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Genetics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210776224000681\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Genetics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210776224000681","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
28. Addition of non-gene features to the CIViC data model
CIViC (www.civicdb.org) is a free and open knowledgebase that leverages public curation together with expert moderation to address the bottleneck created with the need to interpret large numbers of variants from next generation sequencing of tumor DNA. Curation from literature and meeting abstracts is utilized to create Evidence Items (EIDs), and collections of EIDs are summarized into Assertions which reflect the state of the field for a variant. Assertions incorporate variant classification standards such as those from AMP/ASCO/CAP for clinical actionability, or ClinGen/CGC/VICC guidelines for oncogenicity. The CIViC data model has consistently developed to better capture the considerable variation of cancer. CIViC employs a flexible model for gene variants which may be combined into multi-gene Molecular Profiles. While gene mutations play the largest role in personalized medicine, other entities such as tumor mutation burden (TMB), microsatellite instability (MSI) or homologous recombination repair deficiency (HRD) are increasingly used as diagnostic, prognostic or therapeutic markers. To capture entities such as these, CIViC has introduced a new feature type, which models tumorbiomarkers not directly associated with genes or specific regions of the genome. This new type of biomarker accompanies Genes as a formal Feature entity in the CIViC data model. Like Genes, each of these biomarkers will have a page in CIViC, and be associated with an NCI thesaurus entry whenever possible. CIViC is developing a generalized Feature-Variant data model, enabling the addition of new Feature types in future updates, such as large genomic regions.
期刊介绍:
The aim of Cancer Genetics is to publish high quality scientific papers on the cellular, genetic and molecular aspects of cancer, including cancer predisposition and clinical diagnostic applications. Specific areas of interest include descriptions of new chromosomal, molecular or epigenetic alterations in benign and malignant diseases; novel laboratory approaches for identification and characterization of chromosomal rearrangements or genomic alterations in cancer cells; correlation of genetic changes with pathology and clinical presentation; and the molecular genetics of cancer predisposition. To reach a basic science and clinical multidisciplinary audience, we welcome original full-length articles, reviews, meeting summaries, brief reports, and letters to the editor.