DeepTI: A deep learning-based framework decoding tumor-immune interactions for precision immunotherapy in oncology

Jianfei Ma; Yan Jin; Yuanyuan Tang; Lijun Li

doi:10.1016/j.slasd.2021.12.003

DeepTI: A deep learning-based framework decoding tumor-immune interactions for precision immunotherapy in oncology

SLAS Discov. 2022 Mar;27(2):121-127. doi: 10.1016/j.slasd.2021.12.003. Epub 2021 Dec 17.

Authors

Jianfei Ma¹, Yan Jin², Yuanyuan Tang³, Lijun Li⁴

Affiliations

¹ Key Laboratory of Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan 430074, China. Electronic address: D201880703@hust.edu.cn.
² Department of Human Anatomy & Histoembryology, School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453003, Henan Province, China; State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China.
³ Department of Human Anatomy & Histoembryology, School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453003, Henan Province, China; Key Laboratory for Molecular Neurology of Xinxiang, Xinxiang 453003, Henan Province, China.
⁴ Department of Orthopedics, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, PR China; Orthopedics Research Institute of Zhejiang University, Hangzhou, 310000, PR China; Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou City, Zhejiang Province, PR China. Electronic address: lilijun@zju.edu.cn.

PMID: 35058187
DOI: 10.1016/j.slasd.2021.12.003

Abstract

Background: Increasing evidence suggests the immunomodulatory potential of genes in oncology. But the identification of immune attributes of genes is costly and time-consuming, which leads to an urgent demand to develop a prediction model.

Method: We developed a deep learning-based model to predict the immune properties of genes. This model is trained in 70% of samples and evaluated in 30% of samples. Furthermore, it uncovers 60 new immune-related genes. We analyzed the expression perturbation and prognostic value of these genes in gastric cancer. Finally, we validated these genes in immunotherapy-related datasets to check the predictive potential of immunotherapeutic sensitivity.

Result: This model classifies genes as immune-promoted or immune-inhibited based on the human PPI network and it achieves an accuracy of 0.68 on the test set. It uncovers 60 new immune-related genes, most of which are validated in the published literature. These genes are found to be downregulated in gastric cancer and significantly associated with the immune microenvironment in gastric cancer. Analysis of immunotherapy shows that these genes can discriminate between responder and non-responder.

Conclusion: This model can facilitate the identification of immune properties of genes, decoding tumor-immune interactions for precision immunotherapy in oncology.

Keywords: Deep learning; Immunity; Immunotherapy; Oncology; Precision medicine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Humans
Immunologic Factors
Immunotherapy
Stomach Neoplasms* / genetics
Stomach Neoplasms* / therapy
Tumor Microenvironment / genetics

Substances

Immunologic Factors