Single-cell transcriptome and genome analyses of pituitary neuroendocrine tumors. Issue 11 (28th April 2021)

• Record Type: Journal Article
• Authors: Cui, Yueli; Li, Chao; Jiang, Zhenhuan; Zhang, Shu; Li, Qingqing; Liu, Xixi; Zhou, Yuan; Li, Runting; Wei, Liudong; Li, Lianwang; Zhang, Qi; Wen, Lu; Tang, Fuchou; Zhou, Dabiao
• Is Part Of: Neuro-oncology. Volume 23:Issue 11(2021)
• Page Start: 1859
• Page End: 1871
• Publication Date: 2021-04-28
• Subjects: intra-tumoral heterogeneity -- pituitary neuroendocrine tumors -- single-cell sequencing; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab102
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Pituitary neuroendocrine tumors (PitNETs) are the second most common intracranial tumor. We lacked a comprehensive understanding of the pathogenesis and heterogeneity of these tumors. Methods: We performed high-precision single-cell RNA sequencing for 2679 individual cells obtained from 23 surgically resected samples of the major subtypes of PitNETs from 21 patients. We also performed single-cell multi-omics sequencing for 238 cells from 5 patients. Results: Unsupervised clustering analysis distinguished all tumor subtypes, which was in accordance with the classification based on immunohistochemistry and provided additional information. We identified 3 normal endocrine cell types: somatotrophs, lactotrophs, and gonadotrophs. Comparisons of tumor and matched normal cells showed that differentially expressed genes of gonadotroph tumors were predominantly downregulated, while those of somatotroph and lactotroph tumors were mainly upregulated. We identified novel tumor-related genes, such as AMIGO2, ZFP36, BTG1, and DLG5 . Tumors expressing multiple hormone genes showed little transcriptomic heterogeneity. Furthermore, single-cell multi-omics analysis demonstrated that the tumor had a relatively uniform pattern of genome with slight heterogeneity in copy number variations. Conclusions: Our single-cell transcriptome and single-cell multi-omics analyses provide novel insights into the characteristics and heterogeneity of these complex neoplasms for the identification of biomarkers and therapeutic targets.