Large-scale public data reuse to model immunotherapy response and resistance

被引:591
|
作者
Fu, Jingxin [1 ,2 ,3 ]
Li, Karen [4 ]
Zhang, Wubing [1 ,2 ]
Wan, Changxin [1 ,2 ]
Zhang, Jing [3 ]
Jiang, Peng [2 ,5 ]
Liu, X. Shirley [2 ]
机构
[1] Tongji Univ, Sch Life Sci & Technol, Shanghai Pulm Hosp, Clin Translat Res Ctr, Shanghai 200433, Peoples R China
[2] Harvard TH Chan Sch Publ Hlth, Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02215 USA
[3] Tongji Univ, Tongji Hosp, Sch Life Sci & Technol, Shanghai 200065, Peoples R China
[4] Winsor Sch, Boston, MA 02215 USA
[5] NCI, Canc Data Sci Lab, NIH, Bethesda, MD 20892 USA
关键词
Immunotherapy; Immune evasion; Data integration; Web platform; CANCER; CELLS; GENES;
D O I
10.1186/s13073-020-0721-z
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页数:8
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