Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.

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.

Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.

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 -amplification status using H&E-stained whole slide digital images. The model demonstrated strong performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and -amplification on an external test dataset. This AI-based approach establishes a valuable tool for automating diagnosis and precise classification of neuroblastoma tumors.

5-Hydroxymethylcytosine Profiling of Cell-Free DNA Identifies Bivalent Genes That Are Prognostic of Survival in High-Risk Neuroblastoma.

Purpose: Neuroblastoma is the most common extracranial solid tumor in childhood. We previously showed that circulating cell-free DNA (cfDNA) and tumor biopsy derived 5-hydroxymethylcytosime (5-hmC) profiles identified patients with neuroblastoma who experienced subsequent relapse. Here, we hypothesized that 5-hmC modifications selectively enriched in cfDNA compared with tumor biopsy samples would identify epigenetic changes associated with aggressive tumor behavior and identify novel biomarkers of outcome in patients with high-risk neuroblastoma.

Adrenergic and mesenchymal signatures are identifiable in cell-free DNA and correlate with metastatic disease burden in children with neuroblastoma.

Background: Cell-free DNA (cfDNA) profiles of 5-hydroxymethylcytosine (5-hmC), an epigenetic marker of open chromatin and active gene expression, are correlated with metastatic disease burden in patients with neuroblastoma. Neuroblastoma tumors are comprised of adrenergic (ADRN) and mesenchymal (MES) cells, and the relative abundance of each in tumor biopsies has prognostic implications. We hypothesized that ADRN and MES-specific signatures could be quantified in cfDNA 5-hmC profiles and would augment the detection of metastatic burden in patients with neuroblastoma.

5-hydroxymethylcytosine profiling of cell-free DNA identifies bivalent genes that are prognostic of survival in high-risk neuroblastoma.

Neuroblastoma is the most common extra-cranial solid tumor in childhood and epigenetic dysregulation is a key driver of this embryonal disease. In cell-free DNA from neuroblastoma patients with high-risk disease, we found increased 5-hydroxymethylcytosine (5-hmC) deposition on Polycomb Repressive Complex 2 (PRC2) target genes, a finding previously described in the context of bivalent genes. As bivalent genes, defined as genes bearing both activating (H3K4me3) and repressive (H3K27me3) chromatin modifications, have been shown to play an important role in development and cancer, we investigated the potential role of bivalent genes in maintaining a de-differentiated state in neuroblastoma and their potential use as a biomarker.