Further advances in diagnosis of adrenal cancer: a high-throughput urinary steroid profiling method using liquid chromatography tandem mass spectrometry (LC-MS/MS)

Abstract

Context: Differentiating adrenocortical adenoma (ACA) from adrenocortical carcinoma (ACC) represents a continuous challenge in patients with (often incidentally discovered) adrenal masses, with unfavorable sensitivities and specificities provided by tumor size, imaging and even histology. We have previously developed urine steroid metabolomics as a tool for the detection of adrenal malignancy employing gas chromatography mass spectrometry (GC-MS) for the detection of 32 distinct steroid metabolites (JCEM2011; 96 (12): 3775–84). Using the most informative nine steroids, as determined by machine learning analysis, this method can diagnose ACC with superior sensitivity and specificity to currently used imaging modalities. However, GC-MS is a labor-intensive, relatively expensive and low throughput method. Methods: Here we developed a high-throughput liquid chromatography tandem mass spectrometry (LC-MS/MS) method capable of detecting 16 distinct steroid metabolites in a single 5 min run. This method was validated assessing linearity, sensitivity, specificity, reproducibility and matrix effects. We collected 24-h urine samples from 130 healthy controls, 294 ACA and 96 ACC patients and analysed steroid excretion both by GC-MS and the novel LC-MS/MS method. Results: Comparison of steroid analysis results showed very good correlation between the two methods. LC-MS/MS data revealed significant differences in steroid output between ACC and ACA, with 13 of the 16 measured steroids significantly increased in ACC. Steroid data were subjected to Matrix Relevance Learning Vector Quantization, which identified for both methods the same three steroids as most informative in detecting ACC. These were THS, 5-PD and 5-PT, the metabolites of 11-deoxycortisol, pregnenolone and 17OH-pregnenolone, respectively. Importantly, we could demonstrate very similar diagnostic performances of GC-MS and LC-MS/MS when using 16 steroids. Conclusion: This work represents an important step in the implementation of urine steroid metabolomics in the routine work-up of adrenal incidentalomas. We anticipate LC-MS/MS screening in all patients, followed by GC-MS confirmatory analysis of samples with a positive screening result.