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CancerTracer: a curated database for intrapatient tumor heterogeneity
Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatme...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145559/ https://www.ncbi.nlm.nih.gov/pubmed/31701131 http://dx.doi.org/10.1093/nar/gkz1061 |
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author | Wang, Chen Yang, Jian Luo, Hong Wang, Kun Wang, Yu Xiao, Zhi-Xiong Tao, Xiang Jiang, Hao Cai, Haoyang |
author_facet | Wang, Chen Yang, Jian Luo, Hong Wang, Kun Wang, Yu Xiao, Zhi-Xiong Tao, Xiang Jiang, Hao Cai, Haoyang |
author_sort | Wang, Chen |
collection | PubMed |
description | Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies. |
format | Online Article Text |
id | pubmed-7145559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71455592020-04-13 CancerTracer: a curated database for intrapatient tumor heterogeneity Wang, Chen Yang, Jian Luo, Hong Wang, Kun Wang, Yu Xiao, Zhi-Xiong Tao, Xiang Jiang, Hao Cai, Haoyang Nucleic Acids Res Database Issue Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies. Oxford University Press 2020-01-08 2019-11-08 /pmc/articles/PMC7145559/ /pubmed/31701131 http://dx.doi.org/10.1093/nar/gkz1061 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Wang, Chen Yang, Jian Luo, Hong Wang, Kun Wang, Yu Xiao, Zhi-Xiong Tao, Xiang Jiang, Hao Cai, Haoyang CancerTracer: a curated database for intrapatient tumor heterogeneity |
title | CancerTracer: a curated database for intrapatient tumor heterogeneity |
title_full | CancerTracer: a curated database for intrapatient tumor heterogeneity |
title_fullStr | CancerTracer: a curated database for intrapatient tumor heterogeneity |
title_full_unstemmed | CancerTracer: a curated database for intrapatient tumor heterogeneity |
title_short | CancerTracer: a curated database for intrapatient tumor heterogeneity |
title_sort | cancertracer: a curated database for intrapatient tumor heterogeneity |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145559/ https://www.ncbi.nlm.nih.gov/pubmed/31701131 http://dx.doi.org/10.1093/nar/gkz1061 |
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