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Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma

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Abstract

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

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Data availability

All data generated and methods described were in accordance with the relevant guidelines and are permitted by non-commercial organization and did not need access approval. The corresponding author can be contacted for reasonable data.

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Acknowledgements

We thank **ewan Chen for English language editing, and appreciate the data from the TCGA, CPTAC, and GEO datasets.

Funding

This study was supported by the National Natural Science Foundation of China (81773245, 81972858, 82202951 and 82172670), the Technology Innovation and Application Development Project of Chongqing (cstccxljrc201910), and the Cultivation Program for Clinical Research Talents of Army Medical University (2018XLC1010 and 2019XQN10).

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Authors and Affiliations

  1. Department of Basic Medicine, Army Medical University, Chongqing, 400038, People’s Republic of China

    Long** Zeng, Lincheng Zhang, Chenrui Yin & **ewan Chen

  2. Department of Medical Affairs, **nqiao Hospital, Army Medical University, Chongqing, 400037, People’s Republic of China

    Lingchen Li

  3. Department of Oncology, Affiliated Tumor Hospital, Chongqing, 400037, People’s Republic of China

    **ngyun Liao

  4. Cancer Institute, **nqiao Hospital, Army Medical University, Chongqing, 400037, People’s Republic of China

    Jianguo Sun

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JS designed the study. LZ and XL collected data. LZ and LL performed analyses. LZ, LZ, and CY wrote the text. All authors reviewed the manuscript.

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Correspondence to Jianguo Sun.

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Zeng, L., Li, L., Liao, X. et al. Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma. Med Oncol 41, 42 (2024). https://doi.org/10.1007/s12032-023-02214-3

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