Details

Autor(en) / Beteiligte

• Yeung, Jacky T.
• Vesely, Matthew D.
• Miyagishima, Danielle F.

Titel

In silico analysis of the immunological landscape of pituitary adenomas

Ist Teil von

• Journal of neuro-oncology, 2020-05, Vol.147 (3), p.595-598

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.