EYES2019 7th ESE Young Endocrinologists and Scientists (EYES) Meeting Oral Presentations (67 abstracts)
Urine steroid metabolomics as a novel diagnostic tool for recurrent adrenocortical carcinoma detection
Vasileios Chortis 1, , Irina Bancos 1, , Thomas Nijman 5 , Lorna C Gilligan 1 , Angela E Taylor 1 , Cristina L Ronchi 1, , Michael W O’Reilly 1, , Jochen Schreiner 6 , Miriam Asia 2, , Anna Riester 7 , Massimo Terzolo 8 , Rosella Libé 9 , Marcus Quinkler 10 , Letizia Canu 11 , Isabel Paiva 12 , Maria J Bugalho 13 , Darko Kastelan 14 , M Conall Dennedy 15 , Mark Sherlock 16 , Urszula Ambroziak 17 , Dimitra Vassiliadi 18 , Jerome Bertherat 9 , Felix Beuschlein 7, , Martin Fassnacht 6, , Jonathan J Deeks 22, , Michael Biehl 5 & Wiebke Arlt 1,
1Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; 2Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK; 3Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; 4Divison of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA; 5Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands; 6Division of Endocrinology and Diabetes, Department of Internal Medicine I, University Hospital, University of Würzburg, Germany; 7Medizinische Klinik and Poliklinik IV, Ludwig-Maximilians-Universität München, Munich, Germany; 8University of Turin, Turin, Italy; 9INCa- COMETE, Cochin Hospital, Institut Cochin, Institut National de la Sante et de la Recherche Medicale Unite 1016, Rene Descartes University, Paris, France; 10Endocrinology in Charlottenburg, Berlin, Germany; 11Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy; 12University Hospital of Coimbra, Portugal; 13Serviço de Endocrinologia Diabetes e Metabolismo, Hospital de Santa Maria, Lisbon, Portugal; 14Department of Endocrinology, University Hospital Centre Zagreb, Zagreb, Croatia; 15National University of Ireland Galway (NUIG), Galway, Republic of Ireland; 16Department of Endocrinology, Beaumont Hospital, Dublin and the Royal College of Surgeons, Republic of Ireland; 17Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland; 18Evangelismos Hospital, Athens, Greece; 19Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitäts-Spital Zürich, Zürich, Switzerland; 20Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany; 21Central Laboratory, University Hospital of Würzburg, Würzburg, Germany; 22Institute of Applied Health Research, University of Birmingham, Birmingham, UK; 23NIHR Birmingham Biomedical Research Centre, University Birmingham NHS Hospital Trusts and University of Birmingham, Birmingham, UK.
Objective: 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). This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for post-operative recurrence detection after microscopically complete (R0) resection of ACC.
Methods: 135 patients from 14 clinical centers provided post-operative 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. Conversely, 39 patients remained disease-free for 33 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 three blinded experts detected recurrence by the time of radiological diagnosis in 50–72% of cases, improving to 69–92%, if a pre-operative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22–39% of patients. Specificities varied considerably, ranging from 61 to 96%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity=specificity=81%).
Conclusion: Urine steroid metabolomics is a promising tool for post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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