ImmuCellAI: A Unique Method for Comprehensive T‐Cell Subsets Abundance Prediction and its Application in Cancer Immunotherapy

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 urgentl...

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Published in:Advanced science Vol. 7; no. 7; pp. 1902880 - n/a
Main Authors: Miao, Ya‐Ru, Zhang, Qiong, Lei, Qian, Luo, Mei, Xie, Gui‐Yan, Wang, Hongxiang, Guo, An‐Yuan
Format: Journal Article
Language:English
Published: Germany John Wiley & Sons, Inc 01.04.2020
John Wiley and Sons Inc
Wiley
Subjects:
Algorithms
Cancer
Datasets
Flow cytometry
Gene expression
immune cells
Immune system
Immunotherapy
Lymphocytes
Melanoma
Methods
Neutrophils
Performance evaluation
T‐cell subsets
immune cells
cancer
T‐cell subsets
immunotherapy
ISSN:2198-3844, 2198-3844
Online Access:Get full text
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Summary: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. Immune Cell Abundance Identifier (ImmuCellAI) is a gene set signature‐based method for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals the dynamic change of immune cell abundance. An ImmuCellAI result‐based model for predicting the immunotherapy response achieves high accuracy with area under curve 0.80–0.91.
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ISSN:2198-3844
2198-3844
DOI:10.1002/advs.201902880