Large-scale public data reuse to model immunotherapy response and resistance
Large-scale public data reuse to model immunotherapy response and resistance
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DOI：
10.1186/s13073-020-0721-z
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发表时间：
2020-02-26
期刊：
影响因子：
12.3
通讯作者：
Liu, X. Shirley
中科院分区：
文献类型：
--
作者：
Fu, Jingxin;Li, Karen;Liu, X. Shirley
尽管越来越多的免疫检查点阻断（ICB）试验具有可用的组学数据，但全面评估ICB应答和免疫逃避机制的稳健性仍然具有挑战性。为了应对这些挑战，我们在网络平台TIDE（）上整合了已发表的ICB试验、非免疫治疗肿瘤概况和CRISPR筛选的大规模组学数据和生物标志物。我们处理了来自公共数据库的188个肿瘤队列中超过33000个样本的组学数据，来自12项ICB临床研究的998个肿瘤，以及8个识别抗癌免疫反应基因调节剂的CRISPR筛选。将这些数据与TIDE网络平台上的三个交互式分析模块相结合，我们展示了公共数据重用在假设生成、生物标志物优化和患者分层中的实用性。
Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.