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.
Highlights
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
We collected and investigated RNA-seq gene expression profiles of more than 11,000 single cells from three distinct primary human tissue sources: To characterise cells associated with the tumour microenvironment we accessed data from 19 melanoma patients[11], to characterise the baseline immune cell gene expression we accessed data from peripheral blood mononuclear cells (PBMCs) originating from four healthy subjects[12] and last, we generated immune and tumour cell gene expression profiles from four ovarian cancer ascites samples in-house
To investigate the extent to which gene expression profiles change as immune cells move from peripheral blood to the tumour microenvironment, we compared immune cell scRNA-seq profiles across three human data-sets: (1) data-set of 4000 single cells derived from peripheral blood of four healthy subjects[12]; (2) data-set of 4645 tumour-derived single cells from 19 melanoma patient samples[11] and an unpublished data-set of 3114 single cells from four ovarian cancer ascites samples
Summary
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. It is possible to infer the immune, tumour, and stroma cell content of a solid tumour from its bulk gene expression profile if reference gene expression profiles (RGEPs) can be established for each tumour-associated cell type. This class of inverse problems is known as deconvolution[7]. Deconvolution of bulk gene expression has been described and validated for haematological malignancies[8,9], where RGEPs can be established from peripheral blood mononuclear cells (PBMCs) This approach has been applied theoretically to solid tumours[10], but until recently it has been impossible to validate this extrapolation experimentally. Our work emphasises the importance of generating RGEPs specific to each indication of interest
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
