Noncoding cis-regulatory variants have gained interest as cancer drivers, yet progress in understanding their significance is hindered by the numerous challenges and limitations of variant prioritization. To overcome these limitations, we focused on active cis-regulatory elements (aCRE) to design a customized panel for the deep sequencing of 56 neuroblastoma tumor and normal DNA sample pairs. To search for driver mutations, aCREs were defined by reanalysis of H3K27ac chromatin immunoprecipitation sequencing peaks in 25 neuroblastoma cell lines. These regulatory genomic regions were tested for an excess of somatic mutations and assessed for statistical significance using a global approach that accounted for chromatin accessibility and replication timing. Additional validation was provided by whole genome sequence analysis of 151 neuroblastomas. Analysis of HiC data determined the presence of candidate target genes interacting with mutated regions. An excess of somatic mutations in aCREs of diverse genes were identified, including IPO7, HAND2, and ARID3A. CRISPR-Cas9 editing was utilized to assess the functional consequences of mutations in the IPO7-aCRE. Patients with noncoding mutations in aCREs showed inferior overall and event-free survival independent of age at diagnosis, stage, risk stratification, and MYCN status. Expression of aCRE-interacting genes correlated strongly with negative prognostic markers and low survival rates. Moreover, a convergence between the biological functions of aCRE target genes and transcription factors with mutated binding motifs was associated with embryonic development and immune system response. Overall, this strategy enabled the identification of somatic mutations in regulatory elements that collectively promote neuroblastoma tumorigenesis. Significance: Assessment of noncoding cis-regulatory variants and long-range interaction data highlight the combined effect of somatic mutations in regulatory elements in driving neuroblastoma.

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%–89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging.

Neuroblastoma (NB) is a pediatric tumor composed of adrenergic (ADRN) and mesenchymal-like (MES) cells which derive from the dysregulation of normal cell differentiation imposed by NB Core Regulatory Circuitries (CRCs)

Genome-wide association studies (GWASs) have contributed to the study of neuroblastoma (NB) genetics by identifying common risk variants that activate cancer-related processes associated with NB susceptibility