scDrugAtlas:用于揭示肿瘤疗效异质性的综合性单细胞药物反应图谱

Wei Huang, Xinda Ren, Yinpu Bai, Hui Liu
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摘要

肿瘤的异质性往往导致对同种药物治疗的反应存在巨大差异。肿瘤内原有或获得性耐药细胞亚群的存活和增殖,最终导致肿瘤复发和转移。耐药性是临床肿瘤治疗失败的主要原因。因此,准确识别耐药肿瘤细胞亚群可大大促进精准医疗和新药研发。然而,单细胞药物反应数据的稀缺极大地阻碍了肿瘤细胞耐药机制的探索和计算预测方法的开发。本文提出的 scDrugAtlas 是一个致力于整合单细胞水平药物反应数据的综合性数据库。我们从各种公共资源中手动编译了 100 多个包含单细胞药物反应的数据集。目前的版本包括来自 1000 多个样本(细胞系、小鼠、PDX 模型、患者和细菌)的大规模单细胞转录谱和药物反应标签,涉及 66 种药物和 13 种主要癌症类型。特别是,我们根据组织来源(原发或复发/转移)、药物暴露时间和药物诱导的细胞表型,为每个反应标签指定了置信度。我们相信,scDrugAtlas 能极大地帮助生物信息学社区开发计算模型,也能帮助生物学家识别耐药肿瘤细胞及其潜在的分子机制。scDrugAtlas 数据库的网址是:http://drug.hliulab.tech/scDrugAtlas/。
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scDrugAtlas: an integrative single-cell drug response atlas for unraveling tumor heterogeneity in therapeutic efficacy
Tumor heterogeneity often leads to substantial differences in responses to same drug treatment. The presence of pre-existing or acquired drug-resistant cell subpopulations within a tumor survive and proliferate, ultimately resulting in tumor relapse and metastasis. The drug resistance is the leading cause of failure in clinical tumor therapy. Therefore, accurate identification of drug-resistant tumor cell subpopulations could greatly facilitate the precision medicine and novel drug development. However, the scarcity of single-cell drug response data significantly hinders the exploration of tumor cell resistance mechanisms and the development of computational predictive methods. In this paper, we propose scDrugAtlas, a comprehensive database devoted to integrating the drug response data at single-cell level. We manually compiled more than 100 datasets containing single-cell drug responses from various public resources. The current version comprises large-scale single-cell transcriptional profiles and drug response labels from more than 1,000 samples (cell line, mouse, PDX models, patients and bacterium), across 66 unique drugs and 13 major cancer types. Particularly, we assigned a confidence level to each response label based on the tissue source (primary or relapse/metastasis), drug exposure time and drug-induced cell phenotype. We believe scDrugAtlas could greatly facilitate the Bioinformatics community for developing computational models and biologists for identifying drug-resistant tumor cells and underlying molecular mechanism. The scDrugAtlas database is available at: http://drug.hliulab.tech/scDrugAtlas/.
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