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Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction

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Abstract

There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy.

Key messages

  • We creatively developed a prognostic signature (57-MPS) based on clonal diversity.

  • The high-risk samples displayed an underlying immunosuppressive mechanism.

  • 57-MPS improved the predictive performance of PD-L1 for immunotherapy.

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Data availability

All the data used in this study are publicly available.

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Funding

This study was supported by grants from the National Natural Science Foundation of China (81872396 to LQ, 32270710 to YG), HMU Marshal Initiative Funding (No. HMUMIF-21023 to LQ) and the Postdoctoral Scientific Research Developmental Fund (grant number LBH-Q16166 to YG).

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Author notes

  1. Sainan Zhang and Mengyue Li contributed equally.

Authors and Affiliations

  1. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China

    Sainan Zhang, Mengyue Li, Yilong Tan, Juxuan Zhang, Wenbin Jiang, Xin Li, Haitao Qi, Lefan Tang, Wenyuan Zhao, Yunyan Gu & Lishuang Qi

  2. Basic Medicine College, Harbin Medical University, Harbin, China

    Yixin Liu

  3. Department of Systems Engineering and Operations Research, George Mason University, Fairfax, USA

    Ran Ji

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  1. Sainan Zhang
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  2. Mengyue Li
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Contributions

Lishuang Qi and Yunyan Gu contributed to the study concept and design. Sainan Zhang and Mengyue Li contributed to the data collection and analysed the data. Yilong Tan, Juxuan Zhang, Yixin Liu and Wenbin Jiang provided the methods of statistical analyses. Lishuang Qi and Sainan Zhang drafted the paper. Xin Li, Haitao Qi, Lefan Tang, Ran Ji and Wenyuan Zhao provided comments and improvements to the paper. All authors read and approved the final manuscript.

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Correspondence to Yunyan Gu or Lishuang Qi.

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Zhang, S., Li, M., Tan, Y. et al. Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction. J Mol Med 100, 1755–1769 (2022). https://doi.org/10.1007/s00109-022-02266-4

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