Genetic predisposition to neuroblastoma mediated by a LMO1 super-enhancer polymorphism.

Neuroblastoma is a paediatric malignancy that typically arises in early childhood, and is derived from the developing sympathetic nervous system. Clinical phenotypes range from localized tumours with excellent outcomes to widely metastatic disease in which long-term survival is approximately 40% despite intensive therapy. A previous genome-wide association study identified common polymorphisms at the LMO1 gene locus that are highly associated with neuroblastoma susceptibility and oncogenic addiction to LMO1 in the tumour cells. Here we investigate the causal DNA variant at this locus and the mechanism by which it leads to neuroblastoma tumorigenesis. We first imputed all possible genotypes across the LMO1 locus and then mapped highly associated single nucleotide polymorphism (SNPs) to areas of chromatin accessibility, evolutionary conservation and transcription factor binding sites. We show that SNP rs2168101 G>T is the most highly associated variant (combined P = 7.47 × 10(-29), odds ratio 0.65, 95% confidence interval 0.60-0.70), and resides in a super-enhancer defined by extensive acetylation of histone H3 lysine 27 within the first intron of LMO1. The ancestral G allele that is associated with tumour formation resides in a conserved GATA transcription factor binding motif. We show that the newly evolved protective TATA allele is associated with decreased total LMO1 expression (P = 0.028) in neuroblastoma primary tumours, and ablates GATA3 binding (P < 0.0001). We demonstrate allelic imbalance favouring the G-containing strand in tumours heterozygous for this SNP, as demonstrated both by RNA sequencing (P < 0.0001) and reporter assays (P = 0.002). These findings indicate that a recently evolved polymorphism within a super-enhancer element in the first intron of LMO1 influences neuroblastoma susceptibility through differential GATA transcription factor binding and direct modulation of LMO1 expression in cis, and this leads to an oncogenic dependency in tumour cells.

Neuroblastoma and Its Zebrafish Model.

Neuroblastoma, an important developmental tumor arising in the peripheral sympathetic nervous system (PSNS), accounts for approximately 10 % of all cancer-related deaths in children. Recent genomic analyses have identified a spectrum of genetic alterations in this tumor. Amplification of the MYCN oncogene is found in 20 % of cases and is often accompanied by mutational activation of the ALK (anaplastic lymphoma kinase) gene, suggesting their cooperation in tumor initiation and spread. Understanding how complex genetic changes function together in oncogenesis has been a continuing and daunting task in cancer research. This challenge was addressed in neuroblastoma by generating a transgenic zebrafish model that overexpresses human MYCN and activated ALK in the PSNS, leading to tumors that closely resemble human neuroblastoma and new opportunities to probe the mechanisms that underlie the pathogenesis of this tumor. For example, coexpression of activated ALK with MYCN in this model triples the penetrance of neuroblastoma and markedly accelerates tumor onset, demonstrating the interaction of these modified genes in tumor development. Further, MYCN overexpression induces adrenal sympathetic neuroblast hyperplasia, blocks chromaffin cell differentiation, and ultimately triggers a developmentally-timed apoptotic response in the hyperplastic sympathoadrenal cells. In the context of MYCN overexpression, activated ALK provides prosurvival signals that block this apoptotic response, allowing continued expansion and oncogenic transformation of hyperplastic neuroblasts, thus promoting progression to neuroblastoma. This application of the zebrafish model illustrates its value in rational assessment of the multigenic changes that define neuroblastoma pathogenesis and points the way to future studies to identify novel targets for therapeutic intervention.

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.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

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.

Diverse Engraftment Capability of Neuroblastoma Cell Lines in Zebrafish Larvae.

Xenotransplantation of neuroblastoma cells into larval zebrafish allows the characterization of their in vivo tumorigenic abilities and high-throughput treatment screening. This established preclinical model traditionally relies on microinjection into the yolk or perivitelline space, leaving the engraftment ability of cells at the hindbrain ventricle (HBV) and pericardial space (PCS), sites valuable for evaluating metastasis, angiogenesis, and the brain microenvironment, unknown. To address this gap in knowledge, Casper zebrafish at 48 h postfertilization were microinjected with approximately 200 Kelly, Be(2)-C, SK-N-AS, or SY5Y cells into either the HBV or PCS. Fish were imaged at 1, 3, and 6 days postinjection and tumor growth was monitored at each timepoint. We hypothesized that engraftment ability and location preference would be cell line dependent. Kelly and SK-N-AS cells were able to engraft at both the HBV and PCS, with a near doubling in size of tumor volume during the 6 days observation period, with cells appearing to grow better in the HBV. Be(2)-C tumors remained static while SY5Y tumors decreased in size, with almost complete loss of volume at both sites. Therefore, the capability of neuroblastoma cell engraftment in zebrafish larvae is cell line dependent with a location preference.

MRI Manifestations of Breast Cancer Stroma and their Role in Predicting Molecular Subtype: A Case-control Study.

OBJECTIVE: This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction. METHODS: 57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs). RESULTS: SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987). CONCLUSION: Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.

Golgi-localized Ring Finger Protein 121 is necessary for MYCN-driven neuroblastoma tumorigenesis.

MYCN amplification predicts poor prognosis in childhood neuroblastoma. To identify MYCN oncogenic signal dependencies we performed N-ethyl-N-nitrosourea (ENU) mutagenesis on the germline of neuroblastoma-prone TH-MYCN transgenic mice to generate founders which had lost tumorigenesis. Sequencing of the mutant mouse genomes identified the Ring Finger Protein 121 (RNF121WT) gene mutated to RNFM158R associated with heritable loss of tumorigenicity. While the RNF121WT protein localised predominantly to the cis-Golgi Complex, the RNF121M158R mutation in Helix 4 of its transmembrane domain caused reduced RNF121 protein stability and absent Golgi localisation. RNF121WT expression markedly increased during TH-MYCN tumorigenesis, whereas hemizygous RNF121WT gene deletion reduced TH-MYCN tumorigenicity. The RNF121WT-enhanced growth of MYCN-amplified neuroblastoma cells depended on RNF121WT transmembrane Helix 5. RNF121WT directly bound MYCN protein and enhanced its stability. High RNF121 mRNA expression associated with poor prognosis in human neuroblastoma tissues and another MYC-driven malignancy, laryngeal cancer. RNF121 is thus an essential oncogenic cofactor for MYCN and a target for drug development.

Manifold epigenetics: A conceptual model that guides engineering strategies to improve whole-body regenerative health.

Despite the promising advances in regenerative medicine, there is a critical need for improved therapies. For example, delaying aging and improving healthspan is an imminent societal challenge. Our ability to identify biological cues as well as communications between cells and organs are keys to enhance regenerative health and improve patient care. Epigenetics represents one of the major biological mechanisms involving in tissue regeneration, and therefore can be viewed as a systemic (body-wide) control. However, how epigenetic regulations concertedly lead to the development of biological memories at the whole-body level remains unclear. Here, we review the evolving definitions of epigenetics and identify missing links. We then propose our Manifold Epigenetic Model (MEMo) as a conceptual framework to explain how epigenetic memory arises and discuss what strategies can be applied to manipulate the body-wide memory. In summary we provide a conceptual roadmap for the development of new engineering approaches to improve regenerative health.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms.

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Genetic Predisposition to Neuroblastoma Results from a Regulatory Polymorphism that Promotes the Adrenergic Cell State.

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like "mesenchymal" cell state and a more differentiated sympathetic "adrenergic" cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional co-factor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism G/T that affects a G ATA motif in the first intron of LMO1. Here we show that wild-type zebrafish with the G ATA genotype develop adrenergic neuroblastoma, while knock-in of the protective T ATA allele at this locus reduces the penetrance of MYCN-driven tumors, which are restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrates that T ATA/ T ATA tumors also exhibit a mesenchymal cell state and are low risk at diagnosis. Thus, conversion of the regulatory G ATA to a T ATA allele in the first intron of LMO1 reduces the neuroblastoma initiation rate by preventing formation of the adrenergic cell state, a mechanism that is conserved over 400 million years of evolution separating zebrafish and humans.

Genetic predisposition to neuroblastoma results from a regulatory polymorphism that promotes the adrenergic cell state.

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like mesenchymal cell state and a more differentiated sympathetic adrenergic cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional cofactor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism, G/T, that affects a GATA motif in the first intron of LMO1. Here, we showed that WT zebrafish with the GATA genotype developed adrenergic neuroblastoma, while knock-in of the protective TATA allele at this locus reduced the penetrance of MYCN-driven tumors, which were restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrated that TATA/TATA tumors also exhibited a mesenchymal cell state and were low risk at diagnosis. Thus, conversion of the regulatory GATA to a TATA allele in the first intron of LMO1 reduced the neuroblastoma-initiation rate by preventing formation of the adrenergic cell state. This mechanism was conserved over 400 million years of evolution, separating zebrafish and humans.

Oligodendrocyte progenitor cell numbers and migration are regulated by the zebrafish orthologs of the NF1 tumor suppressor gene.

Neurofibromatosis type 1 is the most commonly inherited human cancer predisposition syndrome. Neurofibromin (NF1) gene mutations lead to increased risk of neurofibromas, schwannomas, low grade, pilocytic optic pathway gliomas, as well as malignant peripheral nerve sheath tumors and glioblastomas. Despite the evidence for NF1 tumor suppressor function in glial cell tumors, the mechanisms underlying transformation remain poorly understood. In this report, we used morpholinos to knockdown the two nf1 orthologs in zebrafish and show that oligodendrocyte progenitor cell (OPC) numbers are increased in the developing spinal cord, whereas neurons are unaffected. The increased OPC numbers in nf1 morphants resulted from increased proliferation, as detected by increased BrdU labeling, whereas TUNEL staining for apoptotic cells was unaffected. This phenotype could be rescued by the forced expression of the GTPase-activating protein (GAP)-related domain of human NF1. In addition, the in vivo analysis of OPC migration following nf1 loss using time-lapse microscopy demonstrated that olig2-EGFP(+) OPCs exhibit enhanced cell migration within the developing spinal cord. OPCs pause intermittently as they migrate, and in nf1 knockdown animals, they covered greater distances due to a decrease in average pause duration, rather than an increase in velocity while in motion. Interestingly, nf1 knockdown also leads to an increase in ERK signaling, principally in the neurons of the spinal cord. Together, these results show that negative regulation of the Ras pathway through the GAP activity of NF1 limits OPC proliferation and motility during development, providing insight into the oncogenic mechanisms through which NF1 loss contributes to human glial tumors.

Gene utility recapitulates chromosomal aberrancies in advanced stage neuroblastoma.

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Although only a few recurrent somatic mutations have been identified, chromosomal abnormalities, including the loss of heterozygosity (LOH) at the chromosome 1p and gains of chromosome 17q, are often seen in the high-risk cases. The biological basis and evolutionary forces that drive such genetic abnormalities remain enigmatic. Here, we conceptualize the Gene Utility Model (GUM) that seeks to identify genes driving biological signaling via their collective gene utilities and apply it to understand the impact of those differentially utilized genes on constraining the evolution of NB karyotypes. By employing a computational process-guided flow algorithm to model gene utility in protein-protein networks that built based on transcriptomic data, we conducted several pairwise comparative analyses to uncover genes with differential utilities in stage 4 NBs with distinct classification. We then constructed a utility karyotype by mapping these differentially utilized genes to their respective chromosomal loci. Intriguingly, hotspots of the utility karyotype, to certain extent, can consistently recapitulate the major chromosomal abnormalities of NBs and also provides clues to yet identified predisposition sites. Hence, our study not only provides a new look, from a gene utility perspective, into the known chromosomal abnormalities detected by integrative genomic sequencing efforts, but also offers new insights into the etiology of NB and provides a framework to facilitate the identification of novel therapeutic targets for this devastating childhood cancer.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Zebrafish Model of Neuroblastoma Metastasis.

Zebrafish has emerged as an important animal model to study human diseases, especially cancer. Along with the robust transgenic and genome editing technologies applied in zebrafish modeling, the ease of maintenance, high-yield productivity, and powerful live imaging altogether make the zebrafish a valuable model system to study metastasis and cellular and molecular bases underlying this process in vivo. The first zebrafish neuroblastoma (NB) model of metastasis was developed by overexpressing two oncogenes, MYCN and LMO1, under control of the dopamine-beta-hydroxylase (dßh) promoter. Co-overexpressed MYCN and LMO1 led to the reduced latency and increased penetrance of neuroblastomagenesis, as well as accelerated distant metastasis of tumor cells. This new model reliably reiterates many key features of human metastatic NB, including involvement of clinically relevant and metastasis-associated genetic alterations; natural and spontaneous development of metastasis in vivo; and conserved sites of metastases. Therefore, the zebrafish model possesses unique advantages to dissect the complex process of tumor metastasis in vivo.

Zebrafish as a Neuroblastoma Model: Progress Made, Promise for the Future.

For nearly a decade, researchers in the field of pediatric oncology have been using zebrafish as a model for understanding the contributions of genetic alternations to the pathogenesis of neuroblastoma (NB), and exploring the molecular and cellular mechanisms that underlie neuroblastoma initiation and metastasis. In this review, we will enumerate and illustrate the key advantages of using the zebrafish model in NB research, which allows researchers to: monitor tumor development in real-time; robustly manipulate gene expression (either transiently or stably); rapidly evaluate the cooperative interactions of multiple genetic alterations to disease pathogenesis; and provide a highly efficient and low-cost methodology to screen for effective pharmaceutical interventions (both alone and in combination with one another). This review will then list some of the common challenges of using the zebrafish model and provide strategies for overcoming these difficulties. We have also included visual diagram and figures to illustrate the workflow of cancer model development in zebrafish and provide a summary comparison of commonly used animal models in cancer research, as well as key findings of cooperative contributions between MYCN and diverse singling pathways in NB pathogenesis.

Metabolic Enzyme DLST Promotes Tumor Aggression and Reveals a Vulnerability to OXPHOS Inhibition in High-Risk Neuroblastoma.

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.

A novel combination therapy targeting ubiquitin-specific protease 5 in MYCN-driven neuroblastoma.

Histone deacetylase (HDAC) inhibitors are effective in MYCN-driven cancers, because of a unique need for HDAC recruitment by the MYCN oncogenic signal. However, HDAC inhibitors are much more effective in combination with other anti-cancer agents. To identify novel compounds which act synergistically with HDAC inhibitor, such as suberanoyl hydroxamic acid (SAHA), we performed a cell-based, high-throughput drug screen of 10,560 small molecule compounds from a drug-like diversity library and identified a small molecule compound (SE486-11) which synergistically enhanced the cytotoxic effects of SAHA. Effects of drug combinations on cell viability, proliferation, apoptosis and colony forming were assessed in a panel of neuroblastoma cell lines. Treatment with SAHA and SE486-11 increased MYCN ubiquitination and degradation, and markedly inhibited tumorigenesis in neuroblastoma xenografts, and, MYCN transgenic zebrafish and mice. The combination reduced ubiquitin-specific protease 5 (USP5) levels and increased unanchored polyubiquitin chains. Overexpression of USP5 rescued neuroblastoma cells from the cytopathic effects of the combination and reduced unanchored polyubiquitin, suggesting USP5 is a therapeutic target of the combination. SAHA and SE486-11 directly bound to USP5 and the drug combination exhibited a 100-fold higher binding to USP5 than individual drugs alone in microscale thermophoresis assays. MYCN bound to the USP5 promoter and induced USP5 gene expression suggesting that USP5 and MYCN expression created a forward positive feedback loop in neuroblastoma cells. Thus, USP5 acts as an oncogenic cofactor with MYCN in neuroblastoma and the novel combination of HDAC inhibitor with SE486-11 represents a novel therapeutic approach for the treatment of MYCN-driven neuroblastoma.