[Novel micro RNA-based therapies for the treatment of neuroblastoma]

Engineering pH-Sensitive Stable Nanovesicles for Delivery of MicroRNA Therapeutics

MicroRNAs (miRNAs) are small non-coding endogenous RNAs, which are attracting a growing interest as therapeutic molecules due to their central role in major diseases. However, the transformation of these biomolecules into drugs is limited due to their unstability in the bloodstream, caused by nucleases abundantly present in the blood, and poor capacity to enter cells. The conjugation of miRNAs to nanoparticles (NPs) could be an effective strategy for their clinical delivery. Herein, the engineering of non-liposomal lipid nanovesicles, named quatsomes (QS), for the delivery of miRNAs and other small RNAs into the cytosol of tumor cells, triggering a tumor-suppressive response is reported. The engineered pH-sensitive nanovesicles have controlled structure (unilamellar), size (<150 nm) and composition. These nanovesicles are colloidal stable (>24 weeks), and are prepared by a green, GMP compliant, and scalable one-step procedure, which are all unavoidable requirements for the arrival to the clinical practice of NP based miRNA therapeutics. Furthermore, QS protect miRNAs from RNAses and when injected intravenously, deliver them into liver, lung, and neuroblastoma xenografts tumors. These stable nanovesicles with tunable pH sensitiveness constitute an attractive platform for the efficient delivery of miRNAs and other small RNAs with therapeutic activity and their exploitation in the clinics.

Non-Coding RNAs Participate in the Pathogenesis of Neuroblastoma

Neuroblastoma is one of the utmost frequent neoplasms during the first year of life. This pediatric cancer is believed to be originated during the embryonic life from the neural crest cells. Previous studies have detected several types of chromosomal aberrations in this tumor. More recent studies have emphasized on expression profiling of neuroblastoma samples to identify the dysregulated genes in this type of cancer. Non-coding RNAs are among the mostly dysregulated genes in this type of cancer. Such dysregulation has been associated with a number of chromosomal aberrations that are frequently detected in neuroblastoma. In this study, we explain the role of non-coding transcripts in the malignant transformation in neuroblastoma and their role as biomarkers for this pediatric cancer.

Functional high-throughput screening reveals miR-323a-5p and miR-342-5p as new tumor-suppressive microRNA for neuroblastoma

Current therapies for most non-infectious diseases are directed at or affect functionality of the human translated genome, barely 2% of all genetic information. By contrast, the therapeutic potential of targeting the transcriptome, ~ 70% of the genome, remains largely unexplored. RNA therapeutics is an emerging field that widens the range of druggable targets and includes elements such as microRNA. Here, we sought to screen for microRNA with tumor-suppressive functions in neuroblastoma, an aggressive pediatric tumor of the sympathetic nervous system that requires the development of new therapies. We found miR-323a-5p and miR-342-5p to be capable of reducing cell proliferation in multiple neuroblastoma cell lines in vitro and in vivo, thereby providing a proof of concept for miRNA-based therapies for neuroblastoma. Furthermore, the combined inhibition of the direct identified targets such as CCND1, CHAF1A, INCENP and BCL-XL could reveal new vulnerabilities of high-risk neuroblastoma.

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.