Comprehensive Genomic Characterization of Parathyroid Cancer Identifies Novel Candidate Driver Mutations and Core Pathways

Abstract

Context: Elucidating the genomic landscape of sporadic parathyroid carcinoma (PC) has been limited by low tumor incidence.

Objective: Identify driver mutations of sporadic PC and potential actionable pathways.

Methods: Patients undergoing surgical resection for sporadic PC between 1980 and 2016 at MD Anderson Cancer Center were identified. Patients with sporadic PC according to World Health Organization diagnostic criteria and with available formalin-fixed, paraffin-embedded (FFPE) PC tumor tissue were included and their clinical data analyzed to assess extent of disease. Patients with parathyroid tumors of uncertain malignancy or atypical parathyroid neoplasms were excluded. Thirty-one patients meeting diagnostic criteria had available tissue for analysis. FFPE PC tumors were subjected to DNA extraction and next-generation whole-exome sequencing. All variant calls are single-algorithm only. Twenty-nine samples passed quality assurance after DNA extraction.

Main outcome measures: Somatic or private germline mutations present in sporadic PC and identification of pathways involved in tumorigenesis.

Results: We identified 35 genes with considerable mutational load; only eight genes were previously identified in other PC cohorts. These genes mediate critical processes, including chromosome organization, DNA repair, and cell cycle regulations. Gene mutations involved in MAPK signaling and immune response are also heavily implicated. These findings are limited by inherent molecular artifacts in FFPE tissue analysis and the absence of matched germline DNA. Additionally, variant calls are only single algorithm and may include false-positive/negative calls.

Conclusion: We identified 33 candidate driver genes of sporadic PC, in addition to previously known driver genes CDC73 and MEN1.

Keywords: genome; mutation; parathyroid carcinoma; whole exome sequencing.

Figure 1.

Somatic variant calls. (A) The average number of somatic missense mutations per sample for the reported PC cohort (red), the previously published PC cohorts (brown), and 14 cohorts of other cancer types (black) obtained from the TCGA portal. (B) The selection constraints index λ for somatic (red) and germline (blue) calls of the reported PC cohort (solid bars), compared with somatic variants of TCGA and germline variants of the 1000 Genomes Project, respectively (dotted bars).

Figure 2.

Protein interactions according to the String v10.5 database. The strength of the links indicates the confidence of association. Links with confidence ≥0.15 are shown. (A) Thirty-eight of the 39 genes with K-S P value ≤0.5 were able to map on the String network. (B) Fifty-seven of 59 genes with bias to high or intermediate EA scores were able to map on the String network.

Figure 3.

Gene pathways under positive selection in PC. (A) Formation of the β-catenin, (B) meiosis, and (C) DNA repair gene pathways of the Reactome database were found to be nonrandomly mutated in patients with PC. Nonsynonymous mutations are binned in deciles by their EA score. Higher scores indicate loss of function, whereas intermediate scores indicate gain of function or function separation. Absolute counts of mutations in each bin are represented by the height of the bin. Each component gene is represented by a color, and plots are labeled with the Reactome pathway associated with the gene set.

Figure 4.

Somatic mutations of known cancer drivers show selection patterns in parathyroid tumor variants. (A) The ratios of missense-to-silent and nonsense-to-silent somatic parathyroid tumor variants for 213 cancer-associated genes (blue) and for the rest of the genes (orange). (B) The distribution of EA fitness scores for missense variants normalized by the number of silent mutations for the 213 cancer-associated genes (blue bars) and for the rest of the genes (orange line). (C) Gene-interaction links (String v10.5 database; link confidence >0.4) among TP53, CDC73, and MEN1 with genes that have impactful variants in parathyroid tumors but they were not listed in TCGC.

Figure 5.

Genes with different germline mutation burden between patients with parathyroid cancer and general human population. (A) Genes that harbor germline variants with higher impact in patients with PC than in individuals of the ExAC database. (B) Genes that harbor germline variants with lower impact in patients with parathyroid cancer than in individuals of the ExAC database.