Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma

Vasileios Chortis, Irina Bancos, Thomas Nijman, Lorna C Gilligan, Angela E Taylor, Cristina L Ronchi, O Michael W, Jochen Schreiner, Miriam Asia, Anna Riester, Paola Perotti, Lib Rosella, Marcus Quinkler, Letizia Canu, Isabel Paiva, Maria J Bugalho, Darko Kastelan, M Conall Dennedy, Mark Sherlock, Urszula AmbroziakDimitra Vassiliadi, Jerome Bertherat, Felix Beuschlein, Martin Fassnacht, Jonathan J Deeks, Michael Biehl, Wiebke Arlt*

*Corresponding author for this work

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Context: Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). Objective, Design, Setting: This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. Patients and Methods: 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography-mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. Results: Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre-and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine "steroid fingerprint" at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). Conclusion: Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence.

Original languageEnglish
Article numberdgz141
Pages (from-to)1-12
Number of pages12
JournalJournal of Clinical Endocrinology and Metabolism
Issue number3
Early online date29-Oct-2019
Publication statusPublished - Mar-2020

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