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标题：Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy
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作者：Yuqi Kang ; Siddharth Vijay ; Taranjit S. Gujral 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：5
页码：1-18
DOI：10.1016/j.isci.2022.104228
语种：English
出版社：Elsevier
摘要：SummaryImmunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%–30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed DeepGeneX, a computational framework that uses advanced deep neural network modeling and feature elimination to reduce single-cell RNA-seq data on ∼26,000 genes to six of the most important genes (CCR7,SELL,GZMB,WARS,GZMH, andLGALS1), that accurately predict response to immunotherapy. We also discovered that the high LGALS1 and WARS-expressing macrophage population represent a biomarker for ICB therapy nonresponders, suggesting that these macrophages may be a target for improving ICB response. Taken together, DeepGeneX enables biomarker discovery and provides an understanding of the molecular basis for the model’s predictions.Graphical abstractDisplay OmittedHighlights•Predicting biomarkers for immunotherapy response remains a challenge•DeepGeneX combines neural networks with single-cell RNAseq to predict responders•LGALS1 and WARS-expressing macrophages in nonresponders impact T cell activation•DeepGeneX enables biomarker discovery and elucidates underlying molecular mechanismGene network; Immune response; Neural networks; Cancer; Artificial intelligence