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. 2020 Feb 26;12(1):21.
doi: 10.1186/s13073-020-0721-z.

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

Jingxin Fu  1   2   3 , Karen Li  4 , Wubing Zhang  1   2 , Changxin Wan  1   2 , Jing Zhang  5 , Peng Jiang  6   7 , X Shirley Liu  8
Affiliations

"VSports在线直播" Large-scale public data reuse to model immunotherapy response and resistance

Jingxin Fu et al. Genome Med. .

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 VSports手机版. 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. .

Keywords: Data integration; Immune evasion; Immunotherapy; Web platform. V体育安卓版.

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VSports注册入口 - Conflict of interest statement

X. S V体育ios版. L. is a co-founder, board member, and Scientific Advisor of GV20 Oncotherapy, Scientific Advisory Board of 3DMed Care. The remaining authors declare that they have no competing interests.

V体育平台登录 - Figures

Fig. 1
Fig. 1
TIDE web platform architecture. The TIDE web platform aims to facilitate the hypothesis generation, biomarker optimization, and patient stratification in immune-oncology research through a public data reuse approach. The platform functions are based on the integration of large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens. The web platform takes gene set or expression profiles as input and provides three interactive modules. A Gene prioritization for a user-input gene set. Every gene is ranked by their clinical relevance and CRISPR screen phenotype, including four types of metrics: 1, the association between gene expression and T cell dysfunction across cohorts, computed as the z-score in the Cox Proportional Hazard (PH) regression model; 2, the association between gene expression and ICB response across tumors, computed as the z-score in the Cox-PH regression; 3, the log-fold change in CRISPR screens probing the effect of gene knockout on lymphocyte-mediated tumor killing; 4, the gene expression in cell types driving T cell exclusion in tumors. Data cohorts are grouped by their metric types on the heatmap (columns). Genes (rows) can be interactively reordered by the gene values either on a single data set or any metric type groups. B Biomarker evaluation for a custom biomarker gene set. The predictive power of biomarkers in the public immunotherapy cohorts is quantified by two criteria, the area under the receiver operating characteristic curve (AUC) and the z-score in the Cox-PH regression. We visualize biomarkers’ AUC by bar plots (left panel) and Cox-PH z-scores by Kaplan-Meier curve (right panel). C Biomarker consensus to predict ICB response from gene expression profile. Every input transcriptomic profile is evaluated by TIDE, microsatellite instability (MSI) signature, interferon-gamma (IFNG) signature, and other biomarkers reported in the literature
Fig. 2
Fig. 2
Prioritization of genes with approved drugs. A total of 696 genes with launched drugs were collected from the OASIS database [9] (Additional file 5: Table S4). Among the gene set, top 20 hits were presented. Genes (row) are ranked by their weighted average value across four immunosuppressive indices (columns), including T cell dysfunction score, T cell exclusion score, association with ICB survival outcome, and log-fold change (logFC) in CRISPR screens. The T dysfunction score shows how a gene interacts with cytotoxic T cells to influence patient survival outcome, and the T cell exclusion score assesses the gene expression levels in immunosuppressive cell types that drive T cell exclusion. The association score of (z-score in the Cox-PH regression) ICB survival outcome evaluates genes whose activities are correlated with ICB benefit. The normalized logFC in CRISPR screens help identify regulators whose knockout can mediate the efficacy of lymphocyte-mediated tumor killing in cancer models
Fig. 3
Fig. 3
Comparison of biomarkers. The test biomarker is composed of genes with consistent evidence on cancer immune evasion (Additional file 3: Table S3). The area under receiver operating characteristic curve (AUC) is applied to evaluate the prediction performance of the test biomarker on the ICB response status
Fig. 4
Fig. 4
Comparison of biomarkers based on their association with overall survival. The right panel shows the association of the custom biomarker (Additional file 3: Table S3) with patients’ overall survival through Kaplan-Meier curves. In the left panel, the x-axis shows the z-score on Cox-PH regression and the y-axis indicates its significance level (two-sided Wald test)
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