Pheo-type: a diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma

Flynn, Aidan, Dwight, Trisha, Harris, Jessica, Benn, Diana, Zhou, Li, Hogg, Annette, Catchpoole, Daniel, James, Paul, Duncan, Emma L., Trainer, Alison, Gill, Anthony J., Clifton-Bligh, Roderick, Hicks, Rodney J. and Tothill, Richard W. (2016) Pheo-type: a diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma. Journal of Clinical Endocrinology and Metabolism, 101 4: 1034-1043. doi:10.1210/jc.2015-3889

Author Flynn, Aidan
Dwight, Trisha
Harris, Jessica
Benn, Diana
Zhou, Li
Hogg, Annette
Catchpoole, Daniel
James, Paul
Duncan, Emma L.
Trainer, Alison
Gill, Anthony J.
Clifton-Bligh, Roderick
Hicks, Rodney J.
Tothill, Richard W.
Title Pheo-type: a diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma
Journal name Journal of Clinical Endocrinology and Metabolism   Check publisher's open access policy
ISSN 1945-7197
Publication date 2016-03-01
Year available 2016
Sub-type Article (original research)
DOI 10.1210/jc.2015-3889
Open Access Status Not Open Access
Volume 101
Issue 4
Start page 1034
End page 1043
Total pages 10
Place of publication Chevy Chase, MD United States
Publisher Endocrine Society
Language eng
Formatted abstract
Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers.

This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology.

A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential.

A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application.

The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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