CancerTracer: a curated database for intrapatient tumor heterogeneity
CancerTracer: a curated database for intrapatient tumor heterogeneity
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CancerTracer：一个针对患者内肿瘤异质性的精选数据库。
DOI：
10.1093/nar/gkz1061
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发表时间：
2020-01-08
影响因子：
14.9
通讯作者：
Cai, Haoyang
中科院分区：
文献类型：
--
作者：
Wang, Chen;Yang, Jian;Cai, Haoyang
对癌症的全面基因组分析揭示了大量患者内的分子异质性，这可能解释了一些耐药性和治疗失败的情况。检查单个肿瘤的克隆组成及其在疾病进展和治疗中的演变，可以为药物设计确定精确的治疗靶点。多区域和单细胞测序是可用于捕获肿瘤内异质性的强大工具。在这里，我们提出了一个名为CancerTracer的数据库（http：//cailab.labshare.cn/cancertracer）：一个手动管理的数据库，旨在跟踪和表征个体患者肿瘤生长的演变轨迹。我们从1548名患者中收集了6000多个肿瘤样本，对应于45种不同类型的癌症。基于在多次活检中鉴定的体细胞突变或拷贝数改变构建患者特异性肿瘤系统发育树。使用结构化异质性数据，研究人员可以识别所有肿瘤区域共享的共同驱动事件，以及感兴趣的肿瘤不同区域中存在的异质体细胞事件。该数据库还可用于研究原发性和转移性肿瘤之间的系统发育关系。我们希望CancerTracer将显著提高我们对肿瘤进化史的理解，并可能有助于识别个性化癌症治疗的预测性生物标志物。
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.