Bugalho, Maria João

Abstract Hereditary tumor predisposition syndromes pose a challenge for early detection and timely treatment of tumors. In von Hippel–Lindau disease, desirable personalized surveillance programs are lacking due to insufficient data on genotype-specific risk profiles of individual mutations. To describe neoplastic risk profiles for carriers of pathogenic and likely pathogenic VHL germline mutations, our observational study recruited 1,350 participants from 40 centers worldwide. 432 different VHL germline mutations were observed, with p.Asn78Ser, p.Arg161Ter, p.Arg161Gln, p.Arg167Gln, p.Arg167Trp and p.Tyr98His being the six most frequent, occurring in a total of 493 carriers (36.5%) and in ≥30 patients each. Age-related penetrance risks for retinal hemangioblastoma, central nervous system hemangioblastoma, renal cell carcinoma, pancreatic neuroendocrine tumors and pheochromocytoma/paraganglioma in carriers of the most frequent VHL mutations were assessed. In addition, the number of organs affected, the frequency of surgery and the outcome are reported. Pairwise comparisons of the age-dependent tumor penetrance of these six mutations showed that 47 out of 90 pairs were significantly different. The most significant associations were found in p.Tyr98His (n = 19), followed by p.Arg161Ter (n = 10). All pairwise comparisons of mutations affecting different codons showed at least one significant (P < 0.05) difference, except for p.Asn78Ser vs p.Arg161Ter. Thus, tumor risk varied by VHL mutation type and location, but did not differ between the truncating mutation p.Arg161Ter and the missense mutation p.Asn78Ser. Our study demonstrates the importance of mutation-specific phenotype prediction. With appropriate validation, the data have important implications for risk assessment and decision making in tumor prevention for carriers of the respective VHL mutations.

Adrenocortical carcinoma (ACC) is an orphan disease lacking effective systemic treatment options. The low incidence of the disease and high cost of clinical trials are major obstacles in the search for improved treatment strategies. As a novel approach, registry-based clinical trials have been introduced in clinical research, so allowing for significant cost reduction, but without compromising scientific benefit. Herein, we describe how the European Network for the Study of Adrenal Tumours (ENSAT) could transform its current registry into one fit for a clinical trial infrastructure. The rationale to perform randomized registry-based trials in ACC is outlined including an analysis of relevant limitations and challenges. We summarize a survey on this concept among ENSAT members who expressed a strong interest in the concept and rated its scientific potential as high. Legal aspects, including ethical approval of registry-based randomization were identified as potential obstacles. Finally, we describe three potential randomized registry-based clinical trials in an adjuvant setting and for advanced disease with a high potential to be executed within the framework of an advanced ENSAT registry. Thus we, therefore, provide the basis for future registry-based trials for ACC patients. This could ultimately provide proof-of-principle of how to perform more effective randomized trials for an orphan disease.

Abstract 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.