RNA-Sequencing Analysis of Adrenocortical Carcinoma, Pheochromocytoma and Paraganglioma from a Pan-Cancer Perspective

• Main Authors: Joakim Crona, Samuel Backman, Staffan Welin, David Taïeb, Per Hellman, Peter Stålberg, Britt Skogseid, Karel Pacak
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Cancers
• Subjects: pheochromocytoma; paraganglioma; adrenocortical carcinoma; adrenal tumor; pan-cancer analysis; neural crest; neuroendocrine
• Online Access: https://www.mdpi.com/2072-6694/10/12/518

Adrenocortical carcinoma (ACC) and pheochromocytoma and paraganglioma (PPGL) are defined by clinicopathological criteria and can be further sub-divided based on different molecular features. Whether differences between these molecular subgroups are significant enough to re-challenge their current clinicopathological classification is currently unknown. It is also not fully understood to which other cancers ACC and PPGL show similarity to. To address these questions, we included recent RNA-Seq data from the Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets. Two bioinformatics pipelines were used for unsupervised clustering and principal components analysis. Results were validated using consensus clustering model and interpreted according to previous pan-cancer experiments. Two datasets consisting of 3319 tumors from 35 disease categories were studied. Consistent with the current classification, ACCs clustered as a homogenous group in a pan-cancer context. It also clustered close to neural crest derived tumors, including gliomas, neuroblastomas, pancreatic neuroendocrine tumors, and PPGLs. Contrary, some PPGLs mixed with pancreatic neuroendocrine tumors or neuroblastomas. Thus, our unbiased gene-expression analysis of PPGL did not overlap with their current clinicopathological classification. These results emphasize some importances of the shared embryological origin of these tumors, all either related or close to neural crest tumors, and opens for investigation of a complementary categorization based on gene-expression features.