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. 2023 Jun 16;108(7):1768-1775.
doi: 10.1210/clinem/dgad002.

Genomic Profiling Reveals the Variant Landscape of Sporadic Parathyroid Adenomas in Chinese Population

Xiaohui Tao  1 , Tian Xu  1 , Xiaoyun Lin  1 , Shuqin Xu  1 , Youben Fan  2 , Bomin Guo  2 , Xianzhao Deng  2 , Qiong Jiao  3 , Lihui Chen  1 , Zhe Wei  1 , Chengkun Chen  2 , Wendi Yang  1 , Zhenlin Zhang  1 , Xiangtian Yu  4 , Hua Yue  1
Affiliations

Genomic Profiling Reveals the Variant Landscape of Sporadic Parathyroid Adenomas in Chinese Population

Xiaohui Tao et al. J Clin Endocrinol Metab. .

Abstract

Objective: To define somatic variants of parathyroid adenoma (PA) and to provide novel insights into the underlying molecular mechanism of sporadic PA.

Methods: Basic clinical characteristics and biochemical indices of 73 patients with PA were collected. Whole-exome sequencing was performed on matched tumor-constitutional DNA pairs to detect somatic alterations. Functional annotation was carried out by ingenuity pathway analysis afterward. The protein expression of the variant gene was confirmed by immunohistochemistry, and the relationship between genotype and phenotype was analyzed.

Results: Somatic variants were identified in 1549 genes, with an average of 69 variants per tumor (range, 13-2109; total, 9083). Several novel recurrent somatic variants were detected, such as KMT2D (15/73), MUC4 (14/73), POTEH (13/73), CD22 (12/73), HSPA2 (12/73), HCFC1 (11/73), MAGEA1 (11/73), and SLC4A3 (11/73), besides the previously reported PA-related genes, including MEN1 (11/73), CASR (6/73), MTOR (4/73), ASXL3 (3/73), FAT1 (3/73), ZFX (5/73), EZH1 (2/73), POT1 (2/73), and EZH2 (1/73). Among them, KMT2D might be the candidate driver gene of PA. Crucially, 5 patients carried somatic mutations in CDC73, showed an aggressive phenotype similar to that of parathyroid carcinoma (PC), and had a decreased expression of parafibromin. Pathway analysis of recurrent potential PA-associated driver variant genes revealed functional enrichments in the signaling pathway of Notch.

Conclusion: Our study expanded the pathogenic variant spectrum of PA and indicated that KMT2D might be a novel candidate driver gene and be considered as a diagnostic biomarker for PA. Meanwhile, CDC73 mutations might be an early developmental event from PA to PC. The results provided insights into elucidating the pathogenesis of parathyroid tumorigenesis and a certain basis for clinical diagnosis and treatment.

Keywords: molecular mechanism; parafibromin; primary hyperparathyroidism (PHPT); somatic variations; whole-exome sequencing (WES).

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Figures

Figure 1.
Figure 1.
Landscape of high-frequency somatic variant genes. (A) Landscape of high-frequency somatic variant genes in 73 patients with PA; (B) landscape of high-frequency somatic variant cancer-related genes in 73 patients with PA. Different colors represented different mutation types. The X-axis and Y-axis represent samples and terms of somatic mutant genes. The numbers on the right side indicate the variant frequency.
Figure 2.
Figure 2.
Immunohistochemical staining of paraffin-embedded PA tissue slide using anti-CDC73 at dilution of 1:400. (A, B) Representative images of the IHC-positive adenoma from T17 and T29; (C, D) representative images of the IHC-positive adenoma from T24 and T49 (magnification 10; bar 100 μm). The cell nuclei of CDC73 mutation-negative adenoma are positive for parafibromin staining (A, B), whereas CDC73-mutated samples are immunonegative for parafibromin (C, D).
Figure 3.
Figure 3.
Landscape, Oncotree, and functional analysis of CDC73 and high-frequency somatic variant genes in 73 patients with PA. (A) Landscape of specific mutations carrying CDC73 somatic mutation samples; (B) Oncotree constructed from CDC73 and high-frequency variant genes (variant frequency ≥10%); (C) top 6 significantly enriched biological processes of CDC73 and high-frequency variant genes (variant frequency ≥10%); (D) protein–protein interaction network of CDC73 and high-frequency somatic variant genes (variant frequency ≥10%), and reveal that KMT2D, SMAD1, POTEH, HSPA2, and ASPM are hub genes of the network.
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