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Pheo-Type: A Diagnostic Gene-expression Assay for the Classification of Pheochromocytoma and Paraganglioma
Aidan Flynn1, Trisha Dwight1, Jessica Harris1
1The Peter MacCallum Cancer Centre (A.F., A.H., P.J., A.T., R.J.H., R.W.T.), East Melbourne, Victoria, 3002 Australia; The Department of Pathology (R.W.T., A.F.), University of Melbourne, Parkville, Victoria 3010, Australia; Cancer Genetics (T.D., D.B., R.C.-B.), Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, New South Wales 2065, Australia; University of Sydney (T.D., D.B., A.J.G., R.C.-B.), Sydney, New South Wales 2006, Australia; The University of Queensland Diamantina Institute, Translational Research Institute (J.H., E.L.D.), Princess Alexandra Hospital, Woolloongabba, Queensland 4102, Australia; The Tumor Bank (L.Z., D.C.), Children's Cancer Research Unit, The Children's Hospital at Westmead, St Westmead, New South Wales 2145, Australia; The Sir Peter MacCallum Department of Oncology (P.J., A.T., R.J.H.), University of Melbourne, Parkville, Victoria 3010, Australia; Department of Endocrinology (E.L.D.), Royal Brisbane and Women's Hospital, Brisbane, Queensland 4029, Australia; Royal Melbourne Hospital and Department of Medicine (A.T.), University of Melbourne, Parkville, Victoria 3010, Australia; and Cancer Diagnosis and Pathology Group (A.J.G.), Kolling Institute of Medical Research and the Department of Anatomical Pathology, Royal North Shore Hospital, Sydney, New South Wales 2065, Australia.
Context:
Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers.
Objective:
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.
Design:
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.
Results:
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.
Conclusions:
The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.
