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Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA

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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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Fig. 1: Flowchart of immune analysis of bulk RNA-seq data using RNA-seq IMmune Analysis (RIMA).
Fig. 2: Running time of RIMA pipeline.

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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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Author notes

  1. These authors contributed equally: Lin Yang, Jin Wang, Jennifer Altreuter, Aashna Jhaveri.

Authors and Affiliations

  1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Lin Yang, Jin Wang, Jennifer Altreuter, Aashna Jhaveri, Cheryl J. Wong, Li Song, Jingxin Fu, Sudheshna Bodapati, Avinash Sahu, Collin Tokheim, Yi Zhang, Zexian Zeng, Gali Bai, Ming Tang, Franziska Michor, Yang Liu & X. Shirley Liu

  2. School of Life Science and Technology, Tongji University, Shanghai, China

    Jin Wang & Jingxin Fu

  3. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Cheryl J. Wong

  4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA

    Li Song, Avinash Sahu, Collin Tokheim, Yi Zhang, Zexian Zeng, Franziska Michor & X. Shirley Liu

  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Jingxin Fu, Len Taing, Xintao Qiu & Henry W. Long

  6. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    Jingxin Fu, Len Taing, Xintao Qiu, Henry W. Long & X. Shirley Liu

  7. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Franziska Michor

  8. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Franziska Michor

  9. Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA

    Franziska Michor

  10. The Ludwig Center at Harvard, Boston, MA, USA

    Franziska Michor

Authors
  1. Lin Yang
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  2. Jin Wang
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  3. Jennifer Altreuter
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  4. Aashna Jhaveri
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  5. Cheryl J. Wong
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  6. Li Song
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  7. Jingxin Fu
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  8. Len Taing
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  9. Sudheshna Bodapati
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  10. Avinash Sahu
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  11. Collin Tokheim
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  12. Yi Zhang
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  13. Zexian Zeng
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  14. Gali Bai
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  15. Ming Tang
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  16. Xintao Qiu
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  17. Henry W. Long
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  18. Franziska Michor
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  19. Yang Liu
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  20. X. Shirley Liu
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Contributions

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.

Corresponding authors

Correspondence to Yang Liu or X. Shirley Liu.

Ethics declarations

Competing interests

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.

Peer review

Peer review information

Nature Protocols thanks Zlatko Trajanoski, Zemin Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Software 1

Source code of the RIMA pipeline

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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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