1. Large-scale public data reuse to model immunotherapy response and resistance
- Author
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Karen Li, Jing Zhang, Wubing Zhang, X. Shirley Liu, Peng Jiang, Changxin Wan, and Jingxin Fu
- Subjects
lcsh:QH426-470 ,Computer science ,Systems biology ,medicine.medical_treatment ,lcsh:Medicine ,Computational biology ,computer.software_genre ,Database ,03 medical and health sciences ,0302 clinical medicine ,Neoplasms ,Databases, Genetic ,Genetics ,medicine ,Biomarkers, Tumor ,CRISPR ,Humans ,Molecular Biology ,Genetics (clinical) ,030304 developmental biology ,0303 health sciences ,Genes, Modifier ,Immune evasion ,lcsh:R ,Data reuse ,Immunotherapy ,Genomics ,Immune checkpoint ,Human genetics ,3. Good health ,lcsh:Genetics ,030220 oncology & carcinogenesis ,Molecular Medicine ,Biomarker (medicine) ,Data integration ,Web platform ,computer ,Software - Abstract
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.
- Published
- 2020