Multiple genes and their variants that lend susceptibility to many diseases will play a major role in clinical routine. Genetics-based cost reduction strategies in diagnostic processes are important in the setting of multiple susceptibility genes for a single disease. Head and neck paraganglioma (HNP) is caused by germline mutations of at least three succinate dehydrogenase subunit genes (SDHx). Mutation analysis for all 3 costs ∼US$2,700 per patient. Genetic classification is essential for downstream management of the patient and preemptive management of family members. Utilizing HNP as a model, we wanted to determine predictors to prioritize the most heritable clinical presentations and which gene to begin testing in HNP presentations, to reduce costs of genetic screening. Patients were tested for SDHB, SDHC, and SDHD intragenic mutations and large deletions. Clinical parameters were analyzed as potential predictors for finding germline mutations. Cost reduction was calculated between prioritized gene testing compared with that for all genes. Of 598 patients, 30.6% had SDHx germline mutations: 34.4% in SDHB, 14.2% SDHC, and 51.4% SDHD. Predictors for an SDHx mutation are family history [odds ratio (OR), 37.9], previous pheochromocytoma (OR, 10.9), multiple HNP (OR, 10.6), age ≤40 years (OR, 4.0), and male gender (OR, 3.5). By screening only preselected cases and a stepwise approach, 60% cost reduction can be achieved, with 91.8% sensitivity and 94.5% negative predictive value. Our data give evidence that clinical parameters can predict for mutation and help prioritize gene testing to reduce costs in HNP. Such strategy is cost-saving in the practice of genetics-based personalized health care. [Cancer Res 2009;69(8):3650–6]

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 ∼$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.

Background: Head and neck paragangliomas (HNPs) occur as sporadic or familial entities, the latter mostly in association with germline mutations of the SDHB, SDHC, or SDHD (SDHx) genes. Heritable non-SDHx HNP might occur in von Hippel-Lindau disease (VHL, VHL gene), multiple endocrine neoplasia type 2 (MEN2, RET gene), and neurofibromatosis type 1 (NF1, NF1 gene). Reports of non-SDHx HNP presentations are scarce and guidance for genetic testing nonexistent. Patients and Methods: An international consortium registered patients with HNPs and performed mutation analyses of the SDHx, VHL, and RET genes. Those with SDHx germline mutations were excluded for purposes of this study. Personal and family histories were evaluated for paraganglial tumors, for the major tumor manifestations, and for family history of VHL, MEN2, or NF1. Results: Twelve patients were found to have hereditary non-SDHx HNPs of a total of 809 HNP and 2084 VHL registrants, 11 in the setting of germline VHL mutations and one of a RET mutation. The prevalence of hereditary HNP is five in 1000 VHL patients and nine in 1000 non-SDHx HNP patients. Comprehensive literature review revealed previous reports of HNPs in five VHL, two MEN2, and one NF1 patient. Overall, 11 here presented HNP cases, and four previously reported VHL-HNPs had lesions characteristic for VHL and/or a positive family history for VHL. Conclusions: Our observations provide evidence that molecular genetic testing for VHL or RET germline mutations in patients with HNP should be done only if personal and/or family history shows evidence for one of these syndromes.

Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).