Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors

W Arlt, M Biehl, AE Taylor, S Hahner… - The Journal of …, 2011 - academic.oup.com
W Arlt, M Biehl, AE Taylor, S Hahner, R Libe, BA Hughes, P Schneider, DJ Smith
The Journal of Clinical Endocrinology & Metabolism, 2011academic.oup.com
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 …
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.
Oxford University Press