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In silico analysis of the immunological landscape of pituitary adenomas

  • Laboratory Investigation
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Purpose

Immunotherapy has gained traction in the treatment of solid tumors but the immunological landscape of pituitary adenomas is not well defined. We sought to investigate the immunological composition in pituitary adenomas using RNA deconvolution (CIBERSORTx) on an existing gene expression dataset for pituitary adenomas.

Methods

We applied an established computational approach (CIBERSORTx) on 134 pituitary adenomas from a previously published gene expression dataset to infer the proportions of 22 subsets of immune cells. We investigated associations between each immune cell type and tumor subtype.

Results

We found that the majority of infiltrating immune cells within pituitary adenomas were comprised of M2 macrophages followed by resting CD4+ memory T cells and mast cells. Silent pituitary tumors have higher M2 macrophage fractions when compared to other subtypes. In contrast, Cushing pituitary tumors, both overt and subclinical cases, had higher CD8+ T cells fractions than GH tumors, prolactinomas, hyperthyroid tumors, and silent tumors.

Conclusions

RNA deconvolution of the immune infiltrates of pituitary adenomas using CIBERSORTx suggests that most pituitary adenomas comprise of M2 macrophages, but each adenoma subtype has a unique immune landscape. This may have implications in targeting each adenoma subtype with different immunotherapies.

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Authors and Affiliations

  1. Department of Neurological Surgery, Yale University School of Medicine, P.O. Box 208082, New Haven, CT, 06520-8082, USA

    Jacky T. Yeung & Danielle F. Miyagishima

  2. Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA

    Matthew D. Vesely

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  1. Jacky T. Yeung
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Correspondence to Jacky T. Yeung.

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Yeung, J.T., Vesely, M.D. & Miyagishima, D.F. In silico analysis of the immunological landscape of pituitary adenomas. J Neurooncol 147, 595–598 (2020). https://doi.org/10.1007/s11060-020-03476-x

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  • DOI: https://doi.org/10.1007/s11060-020-03476-x

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