Neuroblastoma is a tumor with great clinical heterogeneity. Patients in North America are risk-stratified using a number of features including age at diagnosis, disease stage, tumor histology, MYCN status (amplified versus nonamplified), and tumor cell ploidy. In this paper, we review the evidence for utilizing these features in the risk classification of neuroblastic tumors. Additionally, we review the clinical and biologic criteria used by various cooperative groups to define low, intermediate, and high-risk disease populations in clinical trials, highlighting the differences in risk classification internationally. Finally, we discuss the development of the International Neuroblastoma Risk Group classification system, designed to begin worldwide standardization of neuroblastoma pretreatment risk classification and allow comparison of clinical trials conducted through different cooperative groups.

Purpose: 5-Hydroxymethylcytosine (5-hmC) is an epigenetic marker of open chromatin and active gene expression. We profiled 5-hmC with Nano-hmC-Seal technology using 10 ng of plasma-derived cell-free DNA (cfDNA) in blood samples from patients with neuroblastoma to determine its utility as a biomarker. Experimental Design: For the Discovery cohort, 100 5-hmC profiles were generated from 34 well children and 32 patients (27 high-risk, 2 intermediate-risk, and 3 low-risk) at various time points during the course of their disease. An independent Validation cohort encompassed 5-hmC cfDNA profiles (n = 29) generated from 21 patients (20 high-risk and 1 intermediate-risk). Metastatic burden was classified as high, moderate, low, or none per Curie metaiodobenzylguanidine scores and percentage of tumor cells in bone marrow. Genes with differential 5-hmC levels between samples according to metastatic burden were identified using DESeq2. Results: Hierarchical clustering using 5-hmC levels of 347 genes identified from the Discovery cohort defined four clusters of samples that were confirmed in the Validation cohort and corresponded to high, high-moderate, moderate, and low/no metastatic burden. Samples from patients with increased metastatic burden had increased 5-hmC deposition on genes in neuronal stem cell maintenance and epigenetic regulatory pathways. Further, 5-hmC cfDNA profiles generated with 1,242 neuronal pathway genes were associated with subsequent relapse in the cluster of patients with predominantly low or no metastatic burden (sensitivity 65%, specificity 75.6%). Conclusions: cfDNA 5-hmC profiles in children with neuroblastoma correlate with metastatic burden and warrants development as a biomarker of treatment response and outcome.

A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide images from the largest reported cohort to date. The model showed promising performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification with validation on an external test dataset, suggesting potential for AI-assisted neuroblastoma classification.