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bioRxiv · 10.1101/127001

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

Schelker, M.·Feau, S.·Du, J.·Ranu, N.·Klipp, E.·MacBeath, G.·Schoeberl, B.·Raue, A.
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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 patients 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- 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. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

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Schelker, M., Feau, S., Du, J., Ranu, N., Klipp, E., MacBeath, G., Schoeberl, B., Raue, A.. 2017-04-12. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. https://doi.org/10.1101/127001

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