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

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.
Competing Interests: The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments). Emilia M. PintoPatents, Royalties, Other Intellectual Property: Genotyping assays to identify mutations in xaf1 pending to St Jude Children's Research HospitalGerard P. ZambettiResearch Funding: Johnson & Johnson Patents, Royalties, Other Intellectual Property: Mcl1 antibody license (Rockland Labs) to St Jude Children's Research Hospital. I receive small royalty on an annual basis patent pending for genotyping assays to identify mutations in XAF1 Provisional application #62/659,427; foreign filing April 18, 2019 No other potential conflicts of interest were reported.
(© 2019 by American Society of Clinical Oncology.)