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Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types

Oncogene volume 40, pages 4686–4694 (2021)Cite this article

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Abstract

Cancer-associated fibroblasts (CAFs) constitute a prominent component of the tumor microenvironment and play critical roles in cancer progression and drug resistance. Although recent studies indicate CAFs may consist of several CAF subtypes, the breadth of CAF heterogeneity and functional roles of CAF subtypes in cancer progression remain unclear. In this study, we implemented a cell-type deconvolutional approach to comprehensively characterize cell-type alternations across 18 cancer types from The Cancer Genome Atlas (TCGA). Pan-cancer survival analysis using deconvoluted CAF subtypes revealed myofibroblastic CAF (myCAF) composition as a poor prognostic factor in nine cancer types. Patients with higher myCAF compositions tend to have worse response to six antineoplastic drugs predicted by a lncRNA-based Elastic Net prediction model (LENP). In addition, integrative mutational analysis identified 14 and 413 genes associated with the differentiation degree of myCAF and inflammatory CAF (iCAF), respectively, with significant enrichment of genes involved in fibroblast and extracellular matrix (ECM)-related pathways. In summary, our findings systematically illustrated the complex roles of CAF subtypes in patient prognosis and drug response, and identified putative driver genes in CAF-subtype differentiation. These results provided novel therapeutic perspectives for targeting CAF subtypes in tumor microenvironment and arranging treatment scheme based on the CAF compositions in different cancer types.

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Fig. 1: Analysis workflow.
Fig. 2: Application of cellular deconvolution to discover CAFs in TCGA data set.
Fig. 3: Kaplan–Meier plots for the compositions of myCAFs associated with patient survival probability.
Fig. 4: Drug response sensitivity associated with the compositions of myCAFs.
Fig. 5: Volcano plot of fibroblasts driver genes causing a significant change in compositions of myCAFs and iCAFs.
Fig. 6: Functional enrichment analysis.

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Data and code availability

All data are publicly available. R scripts are available upon request.

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Acknowledgements

We thank the members of Bioinformatics and Systems Medicine Laboratory (BSML) for valuable discussion.

Funding

This work was partially supported by National Institutes of Health grant (R01LM012806) and the Cancer Prevention and Research Institute of Texas grant (CPRIT RP180734 and RP170668). Publication charges for this article have been funded by CPRIT RP180734.

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Authors and Affiliations

  1. Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    Bingrui Li, Guangsheng Pei, Peilin Jia & Zhongming Zhao

  2. Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Jun Yao

  3. Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Qingqing Ding

  4. Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA

    Zhongming Zhao

  5. MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA

    Zhongming Zhao

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Contributions

PJ, ZZ, and BL conceived the study. PJ and ZZ collected the data. BL performed data analysis. QD examined H&E image data. BL, GP, JY, PJ, and ZZ wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Peilin Jia or Zhongming Zhao.

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Li, B., Pei, G., Yao, J. et al. Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types. Oncogene 40, 4686–4694 (2021). https://doi.org/10.1038/s41388-021-01870-x

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