METTL3/MYCN cooperation drives neural crest differentiation and provides therapeutic vulnerability in neuroblastoma.

Neuroblastoma (NB) is the most common extracranial childhood cancer, caused by the improper differentiation of developing trunk neural crest cells (tNCC) in the sympathetic nervous system. The N-methyladenosine (mA) epitranscriptomic modification controls post-transcriptional gene expression but the mechanism by which the mA methyltransferase complex METTL3/METTL14/WTAP is recruited to specific loci remains to be fully characterized. We explored whether the mA epitranscriptome could fine-tune gene regulation in migrating/differentiating tNCC.

The prognostic relevance of a gene expression signature in MRI-defined highly vascularized glioblastoma.

Background: The vascular heterogeneity of glioblastomas (GB) remains an important area of research, since tumor progression and patient prognosis are closely tied to this feature. With this study, we aim to identify gene expression profiles associated with MRI-defined tumor vascularity and to investigate its relationship with patient prognosis.

Methods: The study employed MRI parameters calculated with DSC Perfusion Quantification of ONCOhabitats glioma analysis software and RNA-seq data from the TCGA-GBM project dataset.

METTL3 drives telomere targeting of TERRA lncRNA through m6A-dependent R-loop formation: a therapeutic target for ALT-positive neuroblastoma.

Telomerase-negative tumors maintain telomere length by alternative lengthening of telomeres (ALT), but the underlying mechanism behind ALT remains poorly understood. A proportion of aggressive neuroblastoma (NB), particularly relapsed tumors, are positive for ALT (ALT+), suggesting that a better dissection of the ALT mechanism could lead to novel therapeutic opportunities. TERRA, a long non-coding RNA (lncRNA) derived from telomere ends, localizes to telomeres in a R-loop-dependent manner and plays a crucial role in telomere maintenance.

Digital image analysis workflows for evaluation of cell behavior and tumor microenvironment to aid therapeutic assessment in high-risk neuroblastoma.

Digital pathology and artificial intelligence are promising emerging tools in precision oncology as they provide more robust and reproducible analysis of histologic, morphologic and topologic characteristics of tumor cells and the surrounding microenvironment. This study aims to develop digital image analysis workflows for therapeutic assessment in preclinical in vivo models. For this purpose, we generated pipelines that enable automatic detection and quantification of vitronectin and αvβ3 in heterotopic high-risk neuroblastoma xenografts, demonstrating that digital analysis workflows can be used to provide robust detection of vitronectin secretion and αvβ3 expression by malignant neuroblasts and to evaluate the possibility of combining traditional chemotherapy (etoposide) with extracellular matrix-targeted therapies (cilengitide).