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.

FIG 1.

Unsupervised analysis and copy number profiling of DNA methylation data from pediatric adrenocortical tumors. (A) Unsupervised analysis of DNA methylation data visualized by t-distributed stochastic neighbor embedding (t-SNE) plot identified 2 methylation groups, A1 and A2. (B) Heat map showing the methylation profiles in each group (A1 and A2) identified by hierarchical clustering along with age distribution. (C) Copy number variation (CNV) frequency plots by DNA methylation group. (D) Examples of CNV profiles of individual patients from each methylation group. (E) Significant CNVs within and between methylation groups. (*) Excluding sex chromosomes.

FIG 2.

Mutational and immunohistochemical profiles of methylation groups. (A) Oncoprint of diagnostic, immunophenotypic, and mutational profiles by DNA methylation group. (B) TP53-domain plot with methylation group (A1 and A2)–specific distribution of variants. (C) CTNNB1-domain plot with methylation group–specific distribution of variants. IHC, immunohistochemistry; UMP, uncertain malignant potential.

FIG 3.

Comparison of DNA methylation data between the International Pediatric Adrenocortical Tumor Registry (IPACTR) and The Cancer Genome Atlas (TCGA) cohorts. (A) Unsupervised analysis of IPACTR and TCGA cohorts combined and visualized by t-distributed stochastic neighbor embedding (t-SNE) plot. (B) Methylation level of IPACTR and TCGA cohorts by methylation of CpG islands, non-CpG islands, and global methylation using 20,000 probes. CpG-island hypermethylator phenotypes (CIMPs) are noted as high (CIMP-H), intermediate (CIMP-I), or low (CIMP-L). P values are based on computed means for each patient.

FIG 4.

Kaplan-Meier plots of overall survival from disease based on (A) initial clinical diagnosis, (B) diagnosis rendered at the time of central review, (C) DNA methylation group, and (D) combined risk stratification using central review and methylation-classification group. UMP, uncertain malignant potential.

FIG A1.

The relationship between tumor volume and tumor weight.

FIG A2.

Age distribution of the International Pediatric Adrenocortical Tumor Registry (IPACTR) cohort. (A) Bimodal age distribution of the IPACTR cohort. (B) Age distribution by methylation group.

FIG A3.

Gene set enrichment analysis results. (A) Gene set enrichment analysis of the A1 vs. A2 methylation groups. (B) Positive enrichment plot for cyclin D1 gene set in the A1 group. (C) Positive enrichment plot for embryonic organ developmental gene set in the A1 group. (D) Negative enrichment plot for the olfactory receptor gene set in the A1 group. ID, identification.

FIG A4.

Copy number frequency plots for The Cancer Genome Atlas cohort by CpG-island methylator phenotype (CIMP) classification.

FIG A5.

Expanded copy number and Manhattan plots of IPACTR cohort by methylation group. (A) Expanded copy number plot ordered by methylation group. (B) Manhattan plot showing prevalence of gains and losses by methylation group. (C) Confirmatory fluorescence in situ hybridization highlighting selected examples of copy number changes. Prev, prevalence.

FIG A6.

Relapse-free survival models by Bayesian information criterion (BIC) evidence weights.