Centre for Endocrinology, Diabetes, and Metabolism, School of Clinical and Experimental Medicine, School of Mathematics, and School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom.
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands; Endocrine and Diabetes Unit, Department of Medicine I, University Hospital, University of Würzburg, 97080 Würzburg, Germany; Department of Endocrinology, INCa-COMETE, Cochin Hospital, Institut Cochin, Institut National de la Santé et de la Recherche Médicale Unité 1016, René Descartes University, 75006 Paris, France; Familial Cancer Clinic and Division of Endocrinology, Veneto Institute of Oncology Instituto di Ricovero e Cura a Carattere Scientifico and Department of Medical and Surgical Sciences, University of Padua, 35100 Padua, Italy; Department of Clinical and Biological Sciences, Internal Medicine I, University of Turin, 10124 Turin, Italy; and Wellcome Trust Clinical Research Facility, University Hospital Birmingham National Health Service Foundation Trust, Birmingham B15 2TH, United Kingdom.
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.
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.
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.
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.
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.
Written by:
Arlt W, Biehl M, Taylor AE, Hahner S, Libé R, Hughes BA, Schneider P, Smith DJ, Stiekema H, Krone N, Porfiri E, Opocher G, Bertherat J, Mantero F, Allolio B, Terzolo M, Nightingale P, Shackleton CH, Bertagna X, Fassnacht M, Stewart PM. Are you the author?
Reference: J Clin Endocrinol Metab. 2011 Sep 14. Epub ahead of print.
doi: 10.1210/jc.2011-1565
PubMed Abstract
PMID: 21917861
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