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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
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Journal Article
Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
2017
Overview
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.
Mathematical approaches can be used to assess immune cell composition from the tumour's bulk expression data. Here the authors optimise the CYBERSORT-based deconvolution algorithm by including cell type-specific reference gene expression profiles generated from tumour-derived single-cell RNA sequencing data.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
