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

Dongqiang Zeng; Yiran Fang; Wenjun Qiu; Peng Luo; Shixiang Wang; Rongfang Shen; Wenchao Gu; Xiatong Huang; Qianqian Mao; Gaofeng Wang; Yonghong Lai; Guangda Rong; Xi Xu; Min Shi; Zuqiang Wu; Guangchuang Yu; Wangjun Liao

doi:10.1016/j.crmeth.2024.100910

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

Cell Rep Methods. 2024 Dec 16;4(12):100910. doi: 10.1016/j.crmeth.2024.100910. Epub 2024 Dec 2.

Authors

Dongqiang Zeng¹, Yiran Fang², Wenjun Qiu², Peng Luo³, Shixiang Wang⁴, Rongfang Shen⁵, Wenchao Gu⁶, Xiatong Huang², Qianqian Mao², Gaofeng Wang⁷, Yonghong Lai², Guangda Rong², Xi Xu⁸, Min Shi², Zuqiang Wu⁹, Guangchuang Yu¹⁰, Wangjun Liao¹¹

Affiliations

¹ Cancer Center, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China; Foshan Key Laboratory of Translational Medicine in Oncology, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China; Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, P.R. China.
² Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, P.R. China.
³ Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, P.R. China.
⁴ Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, P.R. China.
⁵ Department of Thyroid and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, P.R. China.
⁶ Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
⁷ Department of Plastic and Aesthetic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, Guangdong, P.R. China; Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁸ The First School of Clinical Medical, Southern Medical University, Guangzhou, Guangdong, P.R. China.
⁹ Cancer Center, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China; Foshan Key Laboratory of Translational Medicine in Oncology, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China. Electronic address: [email protected].
¹⁰ Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, P.R. China. Electronic address: [email protected].
¹¹ Cancer Center, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China; Foshan Key Laboratory of Translational Medicine in Oncology, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, P.R. China; Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, P.R. China. Electronic address: [email protected].

Abstract

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.

Keywords: CP: cancer biology; gene signatures; immunotherapy; multi-omics; single-cell data; tumor microenvironment; tumor-immune interaction; tumor-metabolism.

MeSH terms

Computational Biology / methods
Gene Expression Profiling / methods
Humans
Immunotherapy / methods
Neoplasms* / genetics
Neoplasms* / immunology
Neoplasms* / pathology
Sequence Analysis, RNA
Single-Cell Analysis / methods
Software
Transcriptome
Tumor Microenvironment* / immunology