By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches.
Researchers from the Medical University of Innsbruck describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.
a Approaches based on gene set enrichment analysis rank the genes according to their expression in a sample and compute an enrichment score (ES) considering the position of a set of cell-type-specific marker genes (grey dots) in the ranked list. The ES is high when the marker genes are among the top highly expressed genes (magenta) and low otherwise (cyan). b Deconvolution algorithms model the expression of a gene in a mixture M as a linear combination of the expression of that gene in the different cell types, whose average expression profiles are summarized in a signature matrix S, weighted by the relative fractions F of the cell types in the mixture. c Cell types with higher amount of total mRNA contribute more to the cumulative expression of a heterogeneous sample and might be overestimated by deconvolution methods