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Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC

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Abstract

Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.

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Availability of data and material

All the data used in this study can be found in TCGA and GEO database. All presented data in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We greatly thank the China Scholarship Council (CSC) for supporting the research and work of Xuanwen Bao (No. 201608210186), Tianyu Zhao (No. 201708120056) and Run Shi (No. 201708320347). We would like to thank Haowen Deng for helpful discussions and suggestions in building the neural network framework.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Xuanwen Bao and Run Shi contributed equally to the manuscript.

Authors and Affiliations

  1. Department of Medical Oncology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China

    Xuanwen Bao & Weijia Fang

  2. Technical University Munich (TUM), 80333, Munich, Germany

    Xuanwen Bao

  3. Department of Radiation Oncology, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany

    Run Shi

  4. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany

    Tianyu Zhao

  5. Comprehensive Pneumology Center (CPC), Member DZL, German Center for Lung Research, Munich, Germany

    Tianyu Zhao

  6. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Tianyu Zhao

  7. Ludwig-Maximilians-Universität München (LMU), 80539, Munich, Germany

    Yanfang Wang

  8. Institute of Radiation Biology, Helmholtz Center Munich, German Research Center for Environmental Health, 85764, Neuherberg, Germany

    Natasa Anastasov & Michael Rosemann

Authors
  1. Xuanwen Bao
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Contributions

WJF and Michael Rosemann conceived and designed the experiments. XWB and RS performed the analysis. XWB, MR and YFW wrote the paper. NA, TYZ, Michael Rosemann, WJ.F and YFW reviewed the draft. All authors read and approved the final manuscript.

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Correspondence to Michael Rosemann or Weijia Fang.

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Bao, X., Shi, R., Zhao, T. et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC. Cancer Immunol Immunother 70, 189–202 (2021). https://doi.org/10.1007/s00262-020-02669-7

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  • DOI: https://doi.org/10.1007/s00262-020-02669-7

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