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

Max Schelker, Sonia Feau, Jinyan Du, Nav Ranu, Edda Klipp, Gavin MacBeath, Birgit Schoeberl and Andreas Raue ()
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Max Schelker: Merrimack Pharmaceuticals, Inc.
Sonia Feau: Merrimack Pharmaceuticals, Inc.
Jinyan Du: Merrimack Pharmaceuticals, Inc.
Nav Ranu: Merrimack Pharmaceuticals, Inc.
Edda Klipp: Humboldt-Universität zu Berlin, Unter den Linden 6
Gavin MacBeath: Merrimack Pharmaceuticals, Inc.
Birgit Schoeberl: Merrimack Pharmaceuticals, Inc.
Andreas Raue: Merrimack Pharmaceuticals, Inc.

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: Abstract 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02289-3

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DOI: 10.1038/s41467-017-02289-3

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