Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors.

Wiebke Arlt, M Biehl, Angela Taylor, S Hahner, R Libé, Beverly Hughes, Petra Schneider, DJ Smith, H Stiekema, Nils Krone, Emilio Porfiri, G Opocher, J Bertherat, F Mantero, B Allolio, M Terzolo, Peter Nightingale, Cedric Shackleton, X Bertagna, M FassnachtPaul Stewart

Research output: Contribution to journalArticle

262 Citations (Scopus)


Context:Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.Objective:Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.Design:Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.Results:Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.Conclusions:Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.
Original languageEnglish
Pages (from-to)3775-3784
Number of pages10
JournalThe Journal of clinical endocrinology and metabolism
Issue number12
Publication statusPublished - 14 Sept 2011


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