Abstract
RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.
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Data availability
The dataset used for testing the pipeline running time in Fig. 2 and in the RIMA online tutorial was obtained from Sequence Read Archive PRJNA482620 via ref. 109.
Code availability
The RIMA source code is available at https://github.com/liulab-dfci/RIMA_pipeline and as Supplementary Software 1. The online tutorial is available at https://liulab-dfci.github.io/RIMA/.
Change history
17 July 2023
In the verzion of this article initially published, the alternating color groups in the Table 1 "Tasks" rows were offset and are now corrected in the HTML and PDF versions of the article.
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Acknowledgements
We acknowledge members of the Shirley Liu laboratory, the Center for Functional Cancer Epigenetics and the Cancer Immune Data Commons for their helpful suggestions and support during pipeline development. Support for this project is provided by the PACT and made possible through funding provided to the FNIH by AbbVie Inc., Amgen Inc., Boehringer-Ingelheim Pharma GmbH & Co. KG, Bristol-Myers Squibb, Celgene Corporation, Genentech Inc., Gilead, GlaxoSmithKline plc, Janssen Pharmaceutical Companies of Johnson & Johnson, Novartis Institutes for Biomedical Research, Pfizer Inc. and Sanofi.
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L.Y., J.W., A.J., S.B., C.J.W. and Y.L. developed and optimized the RIMA pipeline. Y.L., J.W., L.Y. and J.A. drafted the manuscript. Y.L., J.A. and L.Y. drafted the online tutorial. L.S. provided suggestions for immune repertoire analysis. J.F. provided suggestions for immune response analysis. L.T., A.S., C.T., Y.Z., Z.Z., G.B., M.T., X.Q. and H.W.L. participated in conceptualization and project discussion. Y.L., F.M. and X.S.L. supervised the project. All authors read and approved the final manuscript.
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X.S.L. conducted the work while being on the faculty at the Dana-Farber Cancer Institute and is currently a board member and CEO of GV20 Therapeutics. F.M. is a cofounder of and has equity in Harbinger Health, has equity in Zephyr AI and serves as a consultant for Harbinger Health, Zephyr AI and Red Cell Partners. F.M. declares that none of these relationships are directly or indirectly related to the content of this manuscript. All other authors do not have any conflicts.
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Yang, L., Wang, J., Altreuter, J. et al. Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA. Nat Protoc 18, 2404–2414 (2023). https://doi.org/10.1038/s41596-023-00841-8
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DOI: https://doi.org/10.1038/s41596-023-00841-8
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