Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0

• Published in: Cell reports methods 2024-12, Vol.4 (12), p.100910, Article 100910
• Main Authors: Zeng, Dongqiang, Fang, Yiran, Qiu, Wenjun, Luo, Peng, Wang, Shixiang, Shen, Rongfang, Gu, Wenchao, Huang, Xiatong, Mao, Qianqian, Wang, Gaofeng, Lai, Yonghong, Rong, Guangda, Xu, Xi, Shi, Min, Wu, Zuqiang, Yu, Guangchuang, Liao, Wangjun
• Format: Article
• Language: English
• Subjects: Computational Biology - methods; Gene Expression Profiling - methods; gene signatures; Humans; immunotherapy; Immunotherapy - methods; multi-omics; Neoplasms - genetics; Neoplasms - immunology; Neoplasms - pathology; Sequence Analysis, RNA; Single-Cell Analysis - methods; single-cell data; Software; Transcriptome; tumor microenvironment; Tumor Microenvironment - immunology; tumor-immune interaction; tumor-metabolism
• ISSN: 2667-2375; 2667-2375
• DOI: 10.1016/j.crmeth.2024.100910

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module. [Display omitted] •IOBR 2.0 offers a pipeline for TME analysis and biomarker identification•Includes six modules for RNA data preprocessing, TME profiling, genome-TME interactions•Integrates 10 deconvolution methods and 322 TME-related gene signatures•Supports phenotypic analysis in bulk RNA-seq data using single-cell features The growing use of immunotherapy has highlighted the critical role of the TME in influencing treatment outcomes. Advances in sequencing technologies have enabled researchers to analyze the TME from multiple perspectives, leading to new insights. However, the complexity and volume of multi-omics data present challenges for analysis and interpretation. To address these, we present IOBR 2.0, an upgraded version of IOBR 1.0 that works as an integrated tool that simplifies TME analysis and visualization using multi-omics data. IOBR 2.0 enables researchers to systematically investigate TME characteristics and identify biomarkers for immunotherapy outcomes. Zeng et al. present IOBR 2.0, a comprehensive and user-friendly toolkit for TME profiling and biomarker discovery in multi-omics studies, updated from IOBR 1.0. Featuring six integrated analysis modules, IOBR 2.0 facilitates the exploration of TME patterns, genome-TME interactions, and single-cell characteristics within bulk RNA-seq data, off