Quantifying tumor-infiltrating immune cells from transcriptomics data

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Abstract

By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.

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Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

Pornpimol Charoentong, Francesca Finotello, Mihaela Angelova … (2017)

The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

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Computational genomics tools for dissecting tumour-immune cell interactions.

Hubert Hackl, Pornpimol Charoentong, Francesca Finotello … (2016)

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour-immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines.

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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

Max Schelker, Sonia Feau, Jinyan Du … (2017)

As interactions between the immune system and tumour cells are governed by a complex network of cell–cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.

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Author and article information

Contributors

Francesca Finotello:

ORCID: http://orcid.org/0000-0003-0712-4658

francesca.finotello@i-med.ac.at

Zlatko Trajanoski:

ORCID: http://orcid.org/0000-0002-0636-7351

zlatko.trajanoski@i-med.ac.at

Journal

Journal ID (nlm-ta): Cancer Immunol Immunother

Journal ID (iso-abbrev): Cancer Immunol. Immunother

Title: Cancer Immunology, Immunotherapy

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0340-7004

ISSN (Electronic): 1432-0851

Publication date (Electronic): 14 March 2018

Publication date PMC-release: 14 March 2018

Publication date (Print): 2018

Volume: 67

Issue: 7

Pages: 1031-1040

Affiliations

ISNI 0000 0000 8853 2677, GRID grid.5361.1, Biocenter, Division for Bioinformatics, , Medical University of Innsbruck, ; Innrain 80, 6020 Innsbruck, Austria

Author information

Francesca Finotello http://orcid.org/0000-0003-0712-4658

Zlatko Trajanoski http://orcid.org/0000-0002-0636-7351

Article

Publisher ID: 2150

DOI: 10.1007/s00262-018-2150-z

PMC ID: 6006237

PubMed ID: 29541787

SO-VID: e3b83cd6-1704-4f58-a98d-a8ddb6e5574b

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 11 December 2017

Date accepted : 9 March 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100010676, H2020 Societal Challenges;

Award ID: 633592

Award Recipient : Zlatko Trajanoski

Funded by: Tiroler Krebsforschungsinstitut

Award ID: 17003

Award Recipient : Francesca Finotello

Custom metadata

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: tils,rna-seq,next-generation sequencing,ngs,deconvolution,gene expression

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: tils, rna-seq, next-generation sequencing, ngs, deconvolution, gene expression

Quantifying tumor-infiltrating immune cells from transcriptomics data

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Karger: Oncology

Most cited references 30

Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

Computational genomics tools for dissecting tumour-immune cell interactions.

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

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