Clinical Predictors and Algorithm for the Genetic Diagnosis of Pheochromocytoma Patients

Zoran Erlic(Cleveland Clinic), Lisa Rybicki(Cleveland Clinic), Mariola Pęczkowska(Cleveland Clinic), Henriette Golcher(Cleveland Clinic), Peter Herbert Kann(Cleveland Clinic), Michael Brauckhoff(Cleveland Clinic), Karsten Müssig(Cleveland Clinic), Michaela Muresan(Cleveland Clinic), Andréas Schäffler(Cleveland Clinic), Nicole Reisch(Cleveland Clinic), M. Schott(Cleveland Clinic), Martin Faßnacht(Cleveland Clinic), Giuseppe Opocher(Cleveland Clinic), Silke Klose(Cleveland Clinic), Christian Fottner(Cleveland Clinic), Flavio Forrer(Cleveland Clinic), Ursula Plöckinger(Cleveland Clinic), Stephan Petersenn(Cleveland Clinic), Dimitry Zabolotny(Cleveland Clinic), Oleg Kollukch(Cleveland Clinic), Svetlana Yaremchuk(Cleveland Clinic), Andrzej Januszewicz(Cleveland Clinic), Martin K. Walz(Cleveland Clinic), Charis Eng(Cleveland Clinic), Hartmut P.H. Neumann(Cleveland Clinic), for the European-American Pheochromocytoma Study Group
Clinical Cancer Research
October 14, 2009
Cited by 173

Abstract

PURPOSE: Six pheochromocytoma susceptibility genes causing distinct syndromes have been identified; approximately one of three of all pheochromocytoma patients carry a predisposing germline mutation. When four major genes (VHL, RET, SDHB, SDHD) are analyzed in a clinical laboratory, costs are approximately $3,400 per patient. The aim of the study is to systematically obtain a robust algorithm to identify who should be genetically tested, and to determine the order in which genes should be tested. EXPERIMENTAL DESIGN: DNA from 989 apparently nonsyndromic patients were scanned for germline mutations in the genes VHL, RET, SDHB, SDHC, and SDHD. Clinical parameters were analyzed as potential predictors for finding mutations by multiple logistic regression, validated by bootstrapping. Cost reduction was calculated between prioritized gene testing compared with that for all genes. RESULTS: Of 989 apparently nonsyndromic pheochromocytoma cases, 187 (19%) harbored germline mutations. Predictors for presence of mutation are age <45 years, multiple pheochromocytoma, extra-adrenal location, and previous head and neck paraganglioma. If we used the presence of any one predictor as indicative of proceeding with gene testing, then 342 (34.6%) patients would be excluded, and only 8 carriers (4.3%) would be missed. We were also able to statistically model the priority of genes to be tested given certain clinical features. E.g., for patients with prior head and neck paraganglioma, the priority would be SDHD>SDHB>RET>VHL. Using the clinical predictor algorithm to prioritize gene testing and order, a 44.7% cost reduction in diagnostic process can be achieved. CONCLUSIONS: Clinical parameters can predict for mutation carriers and help prioritize gene testing to reduce costs in nonsyndromic pheochromocytoma presentations.


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