TY  - JOUR
A1  - Arlt, Wiebke
A1  - Biehl, Michael
A1  - Taylor, Angela E.
A1  - Hahner, Stefanie
A1  - Libé, Rossella
A1  - Hughes, Beverly A.
A1  - Schneider, Petra
A1  - Smith, David J.
A1  - Stiekema, Han
A1  - Krone, Nils
A1  - Porfiri, Emilio
A1  - Opocher, Giuseppe
A1  - Bertherat, Jerôme
A1  - Mantero, Franco
A1  - Allolio, Bruno
A1  - Terzolo, Massimo
A1  - Nightingale, Peter
A1  - Shackleton, Cedric H. L.
A1  - Bertagna, Xavier
A1  - Fassnacht, Martin
A1  - Stewart, Paul M.
T1  - Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors
JF  - The Journal of Clinical Endocrinology & Metabolism
N2  - 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.
KW  - adrenal cortex hormones
KW  - urine
KW  - adrenal cortex neoplasms
KW  - mass spectrometry
KW  - metabolomics
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-154682
VL  - 96
IS  - 12
SP  - 3775
EP  - 3784
ER  -