The distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. T cells have many subsets with specific function and current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here, Immune Cell Abundance Identifier (ImmuCellAI), a gene set signature-based method, is introduced for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both the sequencing data with flow cytometry results and public expression data indicate that ImmuCellAI can estimate the abundance of immune cells with superior accuracy to other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals that the abundance of dendritic cells, cytotoxic T, and gamma delta T cells is significantly higher both in comparisons of on-treatment versus pre-treatment and responders versus non-responders. Meanwhile, an ImmuCellAI result-based model is built for predicting the immunotherapy response with high accuracy (area under curve 0.80-0.91). These results demonstrate the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction.
Keywords: T‐cell subsets; cancer; immune cells; immunotherapy.
© 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.