image: ImmunoCheckDB consists of three main functional modules: meta-analysis, network meta-analysis, and multiomics association analysis. The data collection, data overview, and specific applications of the analysis modules of ImmunoCheckDB are presented. Abbreviations: OS(m), Median Overall Survival; PFS(m), Median Progression - Free Survival; HRs, Hazard Ratios; ORs, Odds Ratios; MsigDB, Molecular Signatures Database; TCGA, The Cancer Genome Atlas; TISIDB, Tumor - Immune System Interaction Database.
Credit: Dr. Peng Luo
This study is led by Dr. Quan Cheng (Department of Neurosurgery, Xiangya Hospital, Central South University), Dr. Anqi Lin (Department of Oncology, Zhujiang Hospital, Southern Medical University), Dr. Shixiang Wang (Department of Biomedical Informatics, Central South University), and Dr. Peng Luo (Department of Oncology, Zhujiang Hospital, Southern Medical University).
In a significant advancement for cancer immunotherapy, researchers present ImmunoCheckDB, a web platform integrating meta-analysis and multiomic data to identify biomarkers for immune checkpoint inhibitor (ICI) therapies. The platform bridges a critical gap in existing resources by enabling pan-cancer exploration of ICI efficacy through combined meta-analytical and multiomic approaches.
ImmunoCheckDB curates 173 studies involving 93,234 individuals across 18 cancer types and 30 ICI regimens. It integrates survival outcomes (mOS, mPFS, ORR) for traditional/network meta-analysis with multiomic datasets (transcriptomics, epigenomics, genomics, immune infiltration), supporting pan-cancer biomarker discovery.
Key features include online tools for meta-analysis, network meta-analysis, and multiomic association analysis. Users can generate real-time visualizations (forest plots, funnel plots, network diagrams) to compare ICI efficacies, while multiomic analysis links molecular features (e.g., gene expression, mutations, pathways) to clinical outcomes. For example, the platform revealed that high NCAPG2 expression correlates with poor mOS in multiple cancers, while LRP1B mutations and IFNG/PDCD1/LAG3 expression associate with favorable ICI responses.
Compared to existing tools, ImmunoCheckDB stands out for its pan-cancer scope, large dataset (160 studies, 81,930 individuals), and integrated analytical methods. While acknowledging limitations (e.g., lack of safety data), the team plans updates to include safety meta-analysis, expanded subgroup criteria, and Bayesian modeling.
ImmunoCheckDB is freely accessible at https://smuonco.shinyapps.io/ImmunoCheckDB/, empowering researchers to drive data-driven insights in personalized cancer treatment.
See the article:
ImmunoCheckDB: A Comprehensive Platform for Evaluating Cancer Immunotherapy Biomarkers Through Meta-Analyses and Multiomic Profiling
https://doi.org/10.1002/mef2.70025
Journal
MedComm – Future Medicine
Article Title
ImmunoCheckDB: A Comprehensive Platform for Evaluating Cancer Immunotherapy Biomarkers Through Meta-Analyses and Multiomic Profiling
Article Publication Date
19-Jun-2025