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Translational Therapeutics

Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing

British Journal of Cancer volume 131, pages 387–402 (2024)Cite this article

Subjects

Abstract

Background

It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs).

Methods

By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells.

Results

In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients.

Conclusion

This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.

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Fig. 1: Feature importance analysis identified 26 genes robustly associated with Neo T cells.
Fig. 2: The 26-gene XGBoost-based model performed well on predicting Neo T cells.
Fig. 3: Transcriptomic landscape of CD8+ TIL from human gastrointestinal cancer.
Fig. 4: scRNA profiling of Neo T cells in human gastrointestinal cancer.
Fig. 5: Biological characteristics of Neo T cells in human gastrointestinal cancer.
Fig. 6: Intercellular communications of Neo T cells in human gastrointestinal cancer.
Fig. 7: The spatial transcriptome reveals the expression patterns of LTA and its ligands within the tumour microenvironment.
Fig. 8: The fraction of Neo T cells could be used as a potential biomarker for neoadjuvant treatment with anti-PD-1therapy.
Fig. 9: Characterization of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers.

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Data availability

The single cell datasets generated during this investigation are accessible in the Code Ocean database (https://codeocean.com/capsule/0506291/tree). Source of the original data are provided with this paper. The ML model and all analysis process codes have been uploaded to the GitHub webside (https://github.com/shizhiwen1990/Neo).

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Acknowledgements

We thank Chunhong Zheng and Eric Tran for assistance in setting up the scRNA-seq data of Neo T cells. We also thank Lei Zheng and Elana J. Fertig for assistance in setting up the bulk RNA-seq data of CD 8 + TIL cells from the neoadjuvant Immunotherapy cohort.

Funding

This study was supported by the Wenzhou Medical University Talent Research Startup Project; the Key Projects Jointly Constructed by Department of Science and Technology of National Administration of Traditional Chinese Medicine & Administration of Traditional Chinese Medicine of Zhejiang Province (NO.GZY-ZJ-KJ-24088), the Basic scientific research Project of Wenzhou Medical University (NO. KYYW202107); Wenzhou Key Laboratory of Cancer Pathogenesis and Translation, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University.

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Author notes

  1. These authors contributed equally: Hongwei Sun, Xiao Han.

Authors and Affiliations

  1. Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Hongwei Sun, Zhengliang Du, Geer Chen & Zhiwen Shi

  2. KangChen Bio-tech., Ltd, ShangHai, China

    Xiao Han

  3. Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China

    Tonglei Guo, Fei Xie, Weiyue Gu & Zhiwen Shi

  4. Chineo Medical Technology Co., Ltd, Beijing, 100101, China

    Weiyue Gu & Zhiwen Shi

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  1. Hongwei Sun
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Contributions

Designing research studies: Zhiwen Shi, Hongwei Sun. Clinical sample collection: Zhengliang Du and Geer Chen. Collecting data: Zhiwen Shi, Xiao Han Zhengliang Du and Hongwei Sun. Analysing data: Zhiwen Shi, Xiao Han, Tonglei Guo, Fei xie and Hongwei Sun. Multiplex immunohistochemistry: Geer Chen and Zhengliang Du. Preparing the manuscript: Zhiwen Shi, Hongwei Sun. Grammar Check: Tonglei Guo and Xiao Han. Supervision: Zhiwen Shi. Funding Acquisition: Zhiwen Shi and Hongwei Sun; Weiyue Gu provided the venue and hardware for the relevant experiments. The authors read and approved the final manuscript.

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Correspondence to Zhiwen Shi.

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Sun, H., Han, X., Du, Z. et al. Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing. Br J Cancer 131, 387–402 (2024). https://doi.org/10.1038/s41416-024-02737-0

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