Статья

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

Jingxin FuClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, ChinaKaren LiWubing ZhangClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, ChinaChangxin WanClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, ChinaJing ZhangTongji Hospital, School of life Science and Technology, Tongji University, Shanghai, 200065, People's Republic of China. [email protected]Peng JiangNational Cancer InstituteX. Shirley LiuDepartment of Data Sciences, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA. [email protected]

2020en

ABI

Аннотация

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 (http://tide.dfci.harvard.edu). 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.

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Идентификаторы

DOI: 10.1186/s13073-020-0721-z

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar