Mutation Profile of Aggressive Pheochromocytoma and Paraganglioma with Comparison of TCGA Data

Abstract

In pheochromocytoma and paraganglioma (PPGL), germline or somatic mutations in one of the known susceptibility genes are identified in up to 60% patients. However, the peculiar genetic events that drive the aggressive behavior including metastasis in PPGL are poorly understood. We performed targeted next-generation sequencing analysis to characterize the mutation profile in fifteen aggressive PPGL patients and compared accessible data of aggressive PPGLs from The Cancer Genome Atlas (TCGA) with findings of our cohort. A total of 115 germline and 34 somatic variants were identified with a median 0.58 per megabase tumor mutation burden in our cohort. The most frequent mutation was SDHB germline mutation (27%) and the second frequent mutations were somatic mutations for SETD2, NF1, and HRAS (13%, respectively). Patients were subtyped into three categories based on the kind of mutated genes: pseudohypoxia (n = 5), kinase (n = 5), and unknown (n = 5) group. In copy number variation analysis, deletion of chromosome arm 1p harboring SDHB gene was the most frequently observed. In our cohort, SDHB mutation and pseudohypoxia subtype were significantly associated with poor overall survival. In conclusion, subtyping of mutation profile can be helpful in aggressive PPGL patients with heterogeneous prognosis to make relevant follow-up plan and achieve proper treatment.

Keywords: DNA mutation analysis; aggressive pheochromocytoma; high-throughput nucleotide sequencing; paraganglioma; prognosis.

Figure 1

Top 26 altered genes in Asan Medical Center (AMC) pheochromocytomas and paragangliomas (PPGLs) with germline or somatic mutation (A) Mutations detected by targeted massive parallel sequencing were depicted. Tumor samples are arranged from left to right. The type of mutation is annotated for each sample by the color. Germline mutation is marked as a dot in a square. The mutation frequency is presented in the right of the panel. The mutation number per sample is presented on the top of the panel. (B) Most frequently altered germline and somatic mutations.

Figure 2

(A) Classified mutation subtypes of AMC PPGLs, (B) proportion of mutation subtypes in both AMC and TCGA cohorts.

Figure 3

Next-Generation Sequencing (NGS)-based copy number variant (CNV) detection. (A) CNV heatmap using log2 counts. (B) CNV analysis of specific genes.

Figure 4

The tumor mutation burden (TMB) of metastatic pheochromocytoma and paraganglioma (PPGL) of AMC was 0.58. That of TCGA non-aggressive and aggressive tumor was 0.18 and 0.32 respectively. Data source: https://gdc.cancer.gov/about-data/publications/mc3-2017, accessed on 28 July 2020. Abbreviations for tumors and median TMB are summarized in Supplementary Table S3.

Figure 5

Survival of aggressive pheochromocytoma and paraganglioma (PPGL). (A) Overall survival (OS) of AMC and TCGA cohort, (B) Disease free survival (DFS) of AMC and TCGA cohort, (C) OS difference of AMC subjects according to SDHB mutation, (D) DFS difference of AMC subjects according to SDHB mutation, (E) OS difference of AMC subjects according to mutation subtypes, and (F) DFS difference of AMC subjects according to mutation subtypes.