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

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Immune cell infiltration in solid tumours influences patient response to immunotherapy.
• Accurate characterization of tumour microenvironment cellular composition is crucial for personalized cancer treatment.

Purpose of the Study:

• To develop and validate a mathematical deconvolution method for determining solid tumour cellular composition from bulk gene expression data.
• To assess the necessity of tumour-derived reference gene expression profiles (RGEPs) for accurate deconvolution.

Main Methods:

• Mathematical deconvolution of bulk gene expression data.
• Utilizing indication-specific and cell type-specific RGEPs derived from single-cell RNA sequencing of tumour tissues.
• Comparison of tumour-derived RGEPs versus peripheral blood-derived RGEPs.

Main Results:

• Tumour-derived RGEPs are essential for accurate deconvolution; peripheral blood RGEPs are insufficient.
• Successfully distinguished nine major cell types and three T cell subtypes within solid tumours.
• Enabled estimation of various immune and stromal cell types, their ratios, and improved malignant cell gene expression profiles.

Conclusions:

• Accurate deconvolution of tumour immune cell composition requires tumour-specific reference gene expression profiles.
• This method enhances understanding of the tumour microenvironment and aids in predicting immunotherapy response.