High-risk neuroblastoma remains therapeutically challenging to treat, and the mechanisms promoting disease aggression are poorly understood. Here, we show that elevated expression of dihydrolipoamide S-succinyltransferase (DLST) predicts poor treatment outcome and aggressive disease in patients with neuroblastoma. DLST is an E2 component of the α-ketoglutarate (αKG) dehydrogenase complex, which governs the entry of glutamine into the tricarboxylic acid cycle (TCA) for oxidative decarboxylation. During this irreversible step, αKG is converted into succinyl-CoA, producing NADH for oxidative phosphorylation (OXPHOS). Utilizing a zebrafish model of MYCN-driven neuroblastoma, we demonstrate that even modest increases in DLST expression promote tumor aggression, while monoallelic dlst loss impedes disease initiation and progression. DLST depletion in human MYCN-amplified neuroblastoma cells minimally affected glutamine anaplerosis and did not alter TCA cycle metabolites other than αKG. However, DLST loss significantly suppressed NADH production and impaired OXPHOS, leading to growth arrest and apoptosis of neuroblastoma cells. In addition, multiple inhibitors targeting the electron transport chain, including the potent IACS-010759 that is currently in clinical testing for other cancers, efficiently reduced neuroblastoma proliferation in vitro. IACS-010759 also suppressed tumor growth in zebrafish and mouse xenograft models of high-risk neuroblastoma. Together, these results demonstrate that DLST promotes neuroblastoma aggression and unveils OXPHOS as an essential contributor to high-risk neuroblastoma. Significance: These findings demonstrate a novel role for DLST in neuroblastoma aggression and identify the OXPHOS inhibitor IACS-010759 as a potential therapeutic strategy for this deadly disease.

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.

Growing evidence suggests a major role for Src-homology-2-domain-containing phosphatase 2 (SHP2/PTPN11) in MYCN-driven high-risk neuroblastoma, although biologic confirmation and a plausible mechanism for this contribution are lacking. Using a zebrafish model of MYCN-overexpressing neuroblastoma, we demonstrate that mutant ptpn11 expression in the adrenal gland analog of MYCN transgenic fish promotes the proliferation of hyperplastic neuroblasts, accelerates neuroblastomagenesis, and increases tumor penetrance. We identify a similar mechanism in tumors with wild-type ptpn11 and dysregulated Gab2, which encodes a Shp2 activator that is overexpressed in human neuroblastomas. In MYCN transgenic fish, Gab2 overexpression activated the Shp2-Ras-Erk pathway, enhanced neuroblastoma induction, and increased tumor penetrance. We conclude that MYCN cooperates with either GAB2-activated or mutant SHP2 in human neuroblastomagenesis. Our findings further suggest that combined inhibition of MYCN and the SHP2-RAS-ERK pathway could provide effective targeted therapy for high-risk neuroblastoma patients with MYCN amplification and aberrant SHP2 activation.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

One of the greatest barriers to curative treatment of neuroblastoma is its frequent metastatic outgrowth prior to diagnosis, especially in cases driven by amplification of the MYCN oncogene. However, only a limited number of regulatory proteins that contribute to this complex MYCN-mediated process have been elucidated. Here we show that the growth arrest-specific 7 (GAS7) gene, located at chromosome band 17p13.1, is preferentially deleted in high-risk MYCN-driven neuroblastoma. GAS7 expression was also suppressed in MYCN-amplified neuroblastoma lacking 17p deletion. GAS7 deficiency led to accelerated metastasis in both zebrafish and mammalian models of neuroblastoma with overexpression or amplification of MYCN. Analysis of expression profiles and the ultrastructure of zebrafish neuroblastoma tumors with MYCN overexpression identified that GAS7 deficiency led to (i) downregulation of genes involved in cell–cell interaction, (ii) loss of contact among tumor cells as critical determinants of accelerated metastasis, and (iii) increased levels of MYCN protein. These results provide the first genetic evidence that GAS7 depletion is a critical early step in the cascade of events culminating in neuroblastoma metastasis in the context of MYCN overexpression.Significance: Heterozygous deletion or MYCN-mediated repression of GAS7 in neuroblastoma releases an important brake on tumor cell dispersion and migration to distant sites, providing a novel mechanism underlying tumor metastasis in MYCN-driven neuroblastoma.See related commentary by Menard, p. 2815