PT - JOURNAL ARTICLE AU - Max Schelker AU - Sonia Feau AU - Jinyan Du AU - Nav Ranu AU - Edda Klipp AU - Gavin MacBeath AU - Birgit Schoeberl AU - Andreas Raue TI - Estimation of immune cell content in tumour tissue using single-cell RNA-seq data AID - 10.1101/127001 DP - 2017 Jan 01 TA - bioRxiv PG - 127001 4099 - http://biorxiv.org/content/early/2017/04/12/127001.short 4100 - http://biorxiv.org/content/early/2017/04/12/127001.full AB - 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- 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.