Juliane Lippert1, Silke Appenzeller2, Raimunde Liang3, Silviu Sbiera3, Stefan Kircher4, Barbara Altieri3,5, Indrajit Nanda1, Isabel Weigand3, Andrea Gehrig1, Sonja Steinhauer3, Renzo J M Riemens1,6, Andreas Rosenwald4,7, Clemens R Müller1, Matthias Kroiss3,7, Simone Rost1, Martin Fassnacht3,7,8, Cristina L Ronchi3,9,10. 1. Institute of Human Genetics, University of Würzburg, Würzburg, Germany. 2. Core Unit Bioinformatics, Comprehensive Cancer Center Mainfranken, University Hospital of Würzburg, Würzburg, Germany. 3. Department of Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany. 4. Institute for Pathology, University of Würzburg, Würzburg, Germany. 5. Division of Endocrinology and Metabolic Diseases, Catholic University of the Sacred Heart, Rome, Italy. 6. Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, LK Maastricht, Netherlands. 7. Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany. 8. Central Labor, University Hospital of Würzburg, Würzburg, Germany. 9. Institute of Metabolism and System Research, University of Birmingham, Birmingham, England. 10. Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, England.
Abstract
Context: Adrenocortical carcinoma (ACC) has a heterogeneous prognosis, and current medical therapies have limited efficacy in its advanced stages. Genome-wide multiomics studies identified molecular patterns associated with clinical outcome. Objective: Here, we aimed at identifying a molecular signature useful for both personalized prognostic stratification and druggable targets, using methods applicable in clinical routine. Design: In total, 117 tumor samples from 107 patients with ACC were analyzed. Targeted next-generation sequencing of 160 genes and pyrosequencing of 4 genes were applied to formalin-fixed, paraffin-embedded (FFPE) specimens to detect point mutations, copy number alterations, and promoter region methylation. Molecular results were combined with clinical/histopathological parameters (tumor stage, age, symptoms, resection status, and Ki-67) to predict progression-free survival (PFS). Results: In addition to known driver mutations, we detected recurrent alterations in genes not previously associated with ACC (e.g., NOTCH1, CIC, KDM6A, BRCA1, BRCA2). Best prediction of PFS was obtained integrating molecular results (more than one somatic mutation, alterations in Wnt/β-catenin and p53 pathways, high methylation pattern) and clinical/histopathological parameters into a combined score (P < 0.0001, χ2 = 68.6). Accuracy of prediction for early disease progress was 83.3% (area under the receiver operating characteristic curve: 0.872, 95% confidence interval 0.80 to 0.94). Furthermore, 17 potentially targetable alterations were found in 64 patients (e.g., in CDK4, NOTCH1, NF1, MDM2, and EGFR and in DNA repair system). Conclusions: This study demonstrates that molecular profiling of FFPE tumor samples improves prognostication of ACC beyond clinical/histopathological parameters and identifies new potential drug targets. These findings pave the way to precision medicine in this rare disease.
Context:Adrenocortical carcinoma (ACC) has a heterogeneous prognosis, and current medical therapies have limited efficacy in its advanced stages. Genome-wide multiomics studies identified molecular patterns associated with clinical outcome. Objective: Here, we aimed at identifying a molecular signature useful for both personalized prognostic stratification and druggable targets, using methods applicable in clinical routine. Design: In total, 117 tumor samples from 107 patients with ACC were analyzed. Targeted next-generation sequencing of 160 genes and pyrosequencing of 4 genes were applied to formalin-fixed, paraffin-embedded (FFPE) specimens to detect point mutations, copy number alterations, and promoter region methylation. Molecular results were combined with clinical/histopathological parameters (tumor stage, age, symptoms, resection status, and Ki-67) to predict progression-free survival (PFS). Results: In addition to known driver mutations, we detected recurrent alterations in genes not previously associated with ACC (e.g., NOTCH1, CIC, KDM6A, BRCA1, BRCA2). Best prediction of PFS was obtained integrating molecular results (more than one somatic mutation, alterations in Wnt/β-catenin and p53 pathways, high methylation pattern) and clinical/histopathological parameters into a combined score (P < 0.0001, χ2 = 68.6). Accuracy of prediction for early disease progress was 83.3% (area under the receiver operating characteristic curve: 0.872, 95% confidence interval 0.80 to 0.94). Furthermore, 17 potentially targetable alterations were found in 64 patients (e.g., in CDK4, NOTCH1, NF1, MDM2, and EGFR and in DNA repair system). Conclusions: This study demonstrates that molecular profiling of FFPE tumor samples improves prognostication of ACC beyond clinical/histopathological parameters and identifies new potential drug targets. These findings pave the way to precision medicine in this rare disease.
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