DNA Methylation Profiling Reveals Prognostically Significant Groups in Pediatric Adrenocortical Tumors: A Report From the International Pediatric Adrenocortical Tumor Registry

Abstract

PURPOSE Pediatric adrenocortical carcinomas (ACCs) are aggressive; the overall survival of patients with ACCs is 40%-50%. Appropriate staging and histologic classification are crucial because children with incomplete resections, metastases, or relapsed disease have a dismal prognosis. The clinical course of pediatric adrenocortical tumors (ACTs) is difficult to predict using the current classification schemas, which rely on subjective microscopic and gross macroscopic variables. Recent advances in adult ACT studies have revealed distinct DNA methylation patterns with prognostic significance that have not been systematically interrogated in the pediatric population. PATIENTS AND METHODS We performed DNA methylation analyses on 48 newly diagnosed ACTs from the International Pediatric Adrenocortical Tumor Registry and 12 pediatric adrenal controls to evaluate for distinct methylation groups. Pediatric methylation data were also compared systematically with the adult ACC cohort from The Cancer Genome Atlas (TCGA). RESULTS Two pediatric ACT methylation groups were identified and showed differences in selected clinicopathologic and outcome characteristics. The A1 group was enriched for CTNNB1 variants and unfavorable outcome. The A2 group was enriched for TP53 germline variants, younger age at onset, and favorable outcome. Pediatric ACT methylation groups were maintained when International Pediatric Adrenocortical Tumor Registry cohort data were combined with TCGA cohort data. The CpG-island hypermethylator phenotype characterizing the TCGA cohort was not identified in the pediatric patients. When methylome findings were combined with independent histopathologic review using the Wieneke criteria, a high-risk population was identified with uniform fatal outcome. CONCLUSION Our results indicate DNA methylation analysis can enhance current diagnostic algorithms. A combination of methylation and histologic classification produced the strongest prediction model and may prove useful in future risk-adapted therapeutic trials.