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.
Highlights
Despite growing numbers of published immune checkpoint blockade (ICB) trials in different cancer types with available omics data and clinical outcomes, ICB response prediction remains an open question
We present a data-driven approach integrating large-scale omics data and biomarkers on published ICB trials, nonimmunotherapy tumor profiles, and Clustered regularly interspaced short palindromic repeats (CRISPR) screens on a web platform Tumor Immune Dysfunction and Evolution (TIDE)
The statistical model of TIDE was trained on clinical tumor profiles without ICB treatments since the immune evasion mechanisms in treatmentnaïve tumors are likely to influence patient response to
Summary
Despite growing numbers of published immune checkpoint blockade (ICB) trials in different cancer types with available omics data and clinical outcomes, ICB response prediction remains an open question. With the limited data size in each clinical study, it is challenging to comprehensively evaluate the complexity of ICB response and immune evasion mechanisms. To address these challenges, we present a data-driven approach integrating large-scale omics data and biomarkers on published ICB trials, nonimmunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (http://tide.dfci.harvard.edu). The clinical study data from ICB naïve cohorts includes 33K samples in 188 tumor cohorts from well-curated databases, including TCGA [2], METABRIC [3], and PRECOG [4] We integrated these data on the TIDE web platform using the MySQL database. We provided three interactive modules for hypothesis generation, biomarker optimization, and patient stratification (Fig. 1)
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