Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification

Identification of MYCN non-amplified neuroblastoma subgroups points towards molecular signatures for precision prognosis and therapy stratification

BACKGROUND

Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified cases. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown.

METHODS

A total of 1566 samples from 16 datasets were identified in Gene Expression Omnibus (GEO) and ArrayExpress. Characterisation of the subtypes was analysed by ConsensusClusterPlus. Independent predictors for subgrouping were constructed from the single sample predictor based on the multiclassPairs package. Findings were verified using immunohistochemistry and CIBERSORTx analysis.

RESULTS

We demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup_2 has the worst prognosis and this group shows a 'MYCN' signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup_3 is characterised by an 'inflamed' gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates a unique prognosis and vulnerability to investigational therapies. A total of 420 genes were identified as independent subgroup predictors with average balanced accuracy of 0.93 and 0.84 for train and test datasets, respectively.

CONCLUSION

We propose that transcriptional subtyping may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.

Target Genes of c-MYC and MYCN with Prognostic Power in Neuroblastoma Exhibit Different Expressions during Sympathoadrenal Development

Deregulation of the MYC family of transcription factors c-MYC (encoded by MYC), MYCN, and MYCL is prevalent in most human cancers, with an impact on tumor initiation and progression, as well as response to therapy. In neuroblastoma (NB), amplification of the MYCN oncogene and over-expression of MYC characterize approximately 40% and 10% of all high-risk NB cases, respectively. However, the mechanism and stage of neural crest development in which MYCN and c-MYC contribute to the onset and/or progression of NB are not yet fully understood. Here, we hypothesized that subtle differences in the expression of MYCN and/or c-MYC targets could more accurately stratify NB patients in different risk groups rather than using the expression of either MYC gene alone. We employed an integrative approach using the transcriptome of 498 NB patients from the SEQC cohort and previously defined c-MYC and MYCN target genes to model a multigene transcriptional risk score. Our findings demonstrate that defined sets of c-MYC and MYCN targets with significant prognostic value, effectively stratify NB patients into different groups with varying overall survival probabilities. In particular, patients exhibiting a high-risk signature score present unfavorable clinical parameters, including increased clinical risk, higher INSS stage, MYCN amplification, and disease progression. Notably, target genes with prognostic value differ between c-MYC and MYCN, exhibiting distinct expression patterns in the developing sympathoadrenal system. Genes associated with poor outcomes are mainly found in sympathoblasts rather than in chromaffin cells during the sympathoadrenal development.

Development and validation of a 21-gene prognostic signature in neuroblastoma

Survival outcomes for patients with neuroblastoma vary markedly and reliable prognostic markers and risk stratification tools are lacking. We sought to identify and validate a transcriptomic signature capable of predicting risk of mortality in patients with neuroblastoma. The TARGET NBL dataset (n = 243) was used to develop the model and two independent cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 240) were used as validation sets. EFS was the primary outcome and OS was the secondary outcome of interest for all analysis. We identified a 21-gene signature capable of stratifying neuroblastoma patients into high and low risk groups in the E-MTAB-179 (HR 5.87 [3.83-9.01], p < 0.0001, 5 year AUC 0.827) and GSE85047 (HR 3.74 [2.36-5.92], p < 0.0001, 5 year AUC 0.815) validation cohorts. Moreover, the signature remained independent of known clinicopathological variables, and remained prognostic within clinically important subgroups. Further, the signature was effectively incorporated into a risk model with clinicopathological variables to improve prognostic performance across validation cohorts (Pooled Validation HR 6.93 [4.89-9.83], p < 0.0001, 5 year AUC 0.839). Similar prognostic utility was also demonstrated with OS. The identified signature is a robust independent predictor of EFS and OS outcomes in neuroblastoma patients and can be combined with clinically utilized clinicopathological variables to improve prognostic performance.

Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network

Although induction of differentiation represents an effective strategy for neuroblastoma treatment, the mechanisms underlying neuroblastoma differentiation are poorly understood. We generated a computational model of neuroblastoma differentiation consisting of interconnected gene clusters identified based on symmetric and asymmetric gene expression relationships. We identified a differentiation signature consisting of series of gene clusters comprised of 1251 independent genes that predicted neuroblastoma differentiation in independent datasets and in neuroblastoma cell lines treated with agents known to induce differentiation. This differentiation signature was associated with patient outcomes in multiple independent patient cohorts and validated the role of MYCN expression as a marker of neuroblastoma differentiation. Our results further identified novel genes associated with MYCN via asymmetric Boolean implication relationships that would not have been identified using symmetric computational approaches and that were associated with both neuroblastoma differentiation and patient outcomes. Our differentiation signature included a cluster of genes involved in intracellular signaling and growth factor receptor trafficking pathways that is strongly associated with neuroblastoma differentiation, and we validated the associations of UBE4B, a gene within this cluster, with neuroblastoma cell and tumor differentiation. Our findings demonstrate that Boolean network analyses of symmetric and asymmetric gene expression relationships can identify novel genes and pathways relevant for neuroblastoma tumor differentiation that could represent potential therapeutic targets.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.

A Simple, Test-Based Method to Control the Overestimation Bias in the Analysis of Potential Prognostic Tumour Markers

The early evaluation of prognostic tumour markers is commonly performed by comparing the survival of two groups of patients identified on the basis of a cut-off value. The corresponding hazard ratio (HR) is usually estimated, representing a measure of the relative risk between patients with marker values above and below the cut-off. A posteriori methods identifying an optimal cut-off are appropriate when the functional form of the relation between the marker distribution and patient survival is unknown, but they are prone to an overestimation bias. In the presence of a small sample size, which is typical of rare diseases, the external validation sets are hardly available and internal cross-validation could be unfeasible. We describe a new method to obtain an unbiased estimate of the HR at an optimal cut-off, exploiting the simple relation between the HR and the associated p-value estimated by a random permutation analysis. We validate the method on both simulated data and set of gene expression profiles from two large, publicly available data sets. Furthermore, a reanalysis of a previously published study, which included 134 Stage 4S neuroblastoma patients, allowed for the identification of E2F1 as a new gene with potential oncogenic activity. This finding was confirmed by an immunofluorescence analysis on an independent cohort.

A perspective on oligonucleotide therapy: Approaches to patient customization

It is estimated that the human genome encodes 15% of proteins that are considered to be disease-modifying. Only 2% of these proteins possess a druggable site that the approved clinical candidates target. Due to this disparity, there is an immense need to develop therapeutics that may better mitigate the disease or disorders aroused by non-druggable and druggable proteins or enzymes. The recent surge in approved oligonucleotide therapeutics (OT) indicates the imminent potential of these therapies. Oligonucleotide-based therapeutics are of intermediate size with much-improved selectivity towards the target and fewer off-target effects than small molecules. The OTs include Antisense RNAs, MicroRNA (MIR), small interfering RNA (siRNA), and aptamers, which are currently being explored for their use in neurodegenerative disorders, cancer, and even orphan diseases. The present review is a congregated effort to present the past and present of OTs and the current efforts to make OTs for plausible future therapeutics. The review provides updated literature on the challenges and bottlenecks of OT and recent advancements in OT drug delivery. Further, this review deliberates on a newly emerging approach to personalized treatment for patients with rare and fatal diseases with OT.

Targeting RAS in neuroblastoma: Is it possible?

Neuroblastoma is a common solid tumor in children and a leading cause of cancer death in children. Neuroblastoma exhibits genetic, morphological, and clinical heterogeneity that limits the efficacy of current monotherapies. With further research on neuroblastoma, the pathogenesis of neuroblastoma is found to be complex, and more and more treatment therapies are needed. The importance of personalized therapy is growing. Currently, various molecular features, including RAS mutations, are being used as targets for the development of new therapies for patients with neuroblastoma. A recent study found that RAS mutations are frequently present in recurrent neuroblastoma. RAS mutations have been shown to activate the MAPK pathway and play an important role in neuroblastoma. Treating RAS mutated neuroblastoma is a difficult challenge, but many preclinical studies have yielded effective results. At the same time, many of the therapies used to treat RAS mutated tumors also have good reference values for treating RAS mutated neuroblastoma. The success of KRAS-G12C inhibitors has greatly stimulated confidence in the direct suppression of RAS. This review describes the biological role of RAS and the frequency of RAS mutations in neuroblastoma. This paper focuses on the strategies, preclinical, and clinical progress of targeting carcinogenic RAS in neuroblastoma, and proposes possible prospects and challenges in the future.

Expression and activation of nuclear hormone receptors result in neuronal differentiation and favorable prognosis in neuroblastoma

BACKGROUND

Neuroblastoma (NB), a childhood tumor derived from the sympathetic nervous system, presents with heterogeneous clinical behavior. While some tumors regress spontaneously without medical intervention, others are resistant to therapy, associated with an aggressive phenotype. MYCN-amplification, frequently occurring in high-risk NB, is correlated with an undifferentiated phenotype and poor prognosis. Differentiation induction has been proposed as a therapeutic approach for high-risk NB. We have previously shown that MYCN maintains an undifferentiated state via regulation of the miR-17 ~ 92 microRNA cluster, repressing the nuclear hormone receptors (NHRs) estrogen receptor alpha (ERα) and the glucocorticoid receptor (GR).

METHODS

Cell viability was determined by WST-1. Expression of differentiation markers was analyzed by Western blot, RT-qPCR, and immunofluorescence analysis. Metabolic phenotypes were studied using Agilent Extracellular Flux Analyzer, and accumulation of lipid droplets by Nile Red staining. Expression of angiogenesis, proliferation, and neuronal differentiation markers, and tumor sections were assessed by immunohistochemistry. Gene expression from NB patient as well as adrenal gland cohorts were analyzed using GraphPad Prism software (v.8) and GSEA (v4.0.3), while pseudo-time progression on post-natal adrenal gland cells from single-nuclei transcriptome data was computed using scVelo.

RESULTS

Here, we show that simultaneous activation of GR and ERα potentiated induction of neuronal differentiation, reduced NB cell viability in vitro, and decreased tumor burden in vivo. This was accompanied by a metabolic reprogramming manifested by changes in the glycolytic and mitochondrial functions and in lipid droplet accumulation. Activation of the retinoic acid receptor alpha (RARα) with all-trans retinoic acid (ATRA) further enhanced the differentiated phenotype as well as the metabolic switch. Single-cell nuclei transcriptome analysis of human adrenal glands indicated a sequential expression of ERα, GR, and RARα during development from progenitor to differentiated chromaffin cells. Further, in silico analysis revealed that patients with higher combined expression of GR, ERα, and RARα mRNA levels had elevated expression of neuronal differentiation markers and a favorable outcome.

CONCLUSION

Together, our findings suggest that combination therapy involving activation of several NHRs could be a promising pharmacological approach for differentiation treatment of NB patients.

TP53 and TP53-associated genes are correlated with the prognosis of paediatric neuroblastoma

Background

TP53 is rarely mutated in paediatric neuroblastoma. The prognosis of TP53 and TP53-associated genes in paediatric neuroblastoma is unclear. The objectives of the study were to analyse datasets of 2477 paediatric neuroblastoma patients from eight independent cohorts to reveal the prognosis of TP53 and TP53-associated genes.

Results

High TP53 mRNA expression was associated with shortened event-free survival and overall survival in paediatric neuroblastoma. Moreover, a higher enrichment score of the TP53 signalling pathway was associated with worse clinical outcomes of paediatric neuroblastoma. Among the genes associated with TP53, CCNE1, CDK2 and CHEK2 were correlated with unfavourable clinical outcomes, while SESN1 was correlated with favourable clinical outcomes of paediatric neuroblastoma in the eight independent neuroblastoma cohorts. TP53, CCNE1, CDK2 and CHEK2 were overexpressed in neuroblastoma patients with MYCN amplification, while SESN1 was downregulated in neuroblastoma patients with MYCN amplification. CCNE1, SESN1, MYCN amplification and age at diagnosis were independent prognostic markers of neuroblastoma. CCNE1 was also highly expressed in paediatric neuroblastoma patients with an age at diagnosis ≥ 18 months, while SESN1 was downregulated in paediatric neuroblastoma patients with an age at diagnosis ≥ 18 months. Combinations of CCNE1 with age at diagnosis or combinations of SESN1 with age at diagnosis achieved superior prognostic effects in paediatric neuroblastoma. Finally, we constructed a nomogram risk model of paediatric neuroblastoma based on age and TP53, CCNE1, CDK2, CHEK2 and SESN1 expression. The nomogram model could predict the overall survival of paediatric neuroblastoma and MYCN nonamplified paediatric neuroblastoma with high specificity and sensitivity.

Conclusions

TP53 and TP53-associated genes CCNE1, CDK2, CHEK2 and SESN1 were significantly associated with the clinical outcomes of paediatric neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01059-5.

Growth factor signaling predicts therapy resistance mechanisms and defines neuroblastoma subtypes

Neuroblastoma (NB) has a low frequency of recurrent mutations compared to other cancers, which hinders the development of targeted therapies and novel risk stratification strategies. Multikinase inhibitors have shown potential in treating high-risk NB, but their efficacy is likely impaired by the cancer cells' ability to adapt to these drugs through the employment of alternative signaling pathways. Based on the expression of 48 growth factor-related genes in 1189 NB tumors, we have developed a model for NB patient survival prediction. This model discriminates between stage 4 NB tumors with favorable outcomes (>80% overall survival) and very poor outcomes (<10%) independently from MYCN-amplification status. Using signaling pathway analysis and gene set enrichment methods in 60 NB patients with known therapy response, we identified signaling pathways, including EPO, NGF, and HGF, upregulated in patients with no or partial response. In a therapeutic setting, we showed that among six selected growth factors, EPO, and NGF showed the most pronounced protective effects in vitro against several promising anti-NB multikinase inhibitors: imatinib, dasatinib, crizotinib, cabozantinib, and axitinib. Mechanistically kinase inhibitors potentiated NB cells to stronger ERK activation by EPO and NGF. The protective action of these growth factors strongly correlated with ERK activation and was ERK-dependent. ERK inhibitors combined with anticancer drugs, especially with dasatinib, showed a synergistic effect on NB cell death. Consideration of growth factor signaling activity benefits NB outcome prediction and tailoring therapy regimens to treat NB.

MCM6 indicates adverse tumor features and poor outcomes and promotes G1/S cell cycle progression in neuroblastoma

Background

Minichromosome maintenance complex component 6 (MCM6), as an important replication permission factor, is involved in the pathogenesis of various tumors. Here we studied the expression of MCM6 in neuroblastoma and its influence on tumor characteristics and prognosis.

Methods

Publicly available datasets were used to explore the influence of the differential expression of MCM6 on neuroblastoma tumor stage, risk and prognosis. In cell experiments, human neuroblastoma cell lines SK-N-SH and SK-N-BE [ (2)] were utilized to verify the ability of MCM6 to promote cell proliferation, migration and invasion. We further explored the possible molecular mechanism of MCM6 affecting the phenotype of neuroblastoma cells by mutual verification of RNA-seq and western blotting, and flow cytometry to inquire about its potential specific roles in the cell cycle.

Results

Through multiple datasets mining, we found that high expression of MCM6 was positively correlated with elevated tumor stage, high risk and poor prognosis in neuroblastoma. At the cellular level, neuroblastoma cell proliferation, migration and invasion were significantly inhibited after MCM6 was interfered by siRNA. Mutual verification of RNA-seq and western blotting suggested that the downstream cell cycle-related genes were differentially expressed after MCM6 interference. Flow cytometric analysis revealed that neuroblastoma cells were blocked in G1/S phase after MCM6 interference.

Conclusion

MCM6 is considered to be the driving force of G1/S cell cycle progression, and it is also a prognostic marker and a potential novel therapeutic target in neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08344-z.

Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

The migrational propensity of neuroblastoma is affected by cell identity, but the mechanisms behind the divergence remain unknown. Using RNAi and time-lapse imaging, we show that ADRN-type NB cells exhibit RAC1- and kalirin-dependent nucleokinetic (NUC) migration that relies on several integral components of neuronal migration. Inhibition of NUC migration by RAC1 and kalirin-GEF1 inhibitors occurs without hampering cell proliferation and ADRN identity. Using three clinically relevant expression dichotomies, we reveal that most of up-regulated mRNAs in RAC1- and kalirin-GEF1-suppressed ADRN-type NB cells are associated with low-risk characteristics. The computational analysis shows that, in a context of overall gene set poverty, the upregulomes in RAC1- and kalirin-GEF1-suppressed ADRN-type cells are a batch of AU-rich element-containing mRNAs, which suggests a link between NUC migration and mRNA stability. Gene set enrichment analysis-based search for vulnerabilities reveals prospective weak points in RAC1- and kalirin-GEF1-suppressed ADRN-type NB cells, including activities of H3K27- and DNA methyltransferases. Altogether, these data support the introduction of NUC inhibitors into cancer treatment research.

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.

Biomarker discovery by feature ranking: Evaluation on a case study of embryonal tumors

The task of biomarker discovery is best translated to the machine learning task of feature ranking. Namely, the goal of biomarker discovery is to identify a set of potentially viable targets for addressing a given biological status. This is aligned with the definition of feature ranking and its goal - to produce a list of features ordered by their importance for the target concept. This differs from the task of feature selection (typically used for biomarker discovery) in that it catches viable biomarkers that have redundant or overlapping information with often highly important biomarkers, while with feature selection this is not the case. We propose to use a methodology for evaluating feature rankings to assess the quality of a given feature ranking and to discover the best cut-off point. We demonstrate the effectiveness of the proposed methodology on 10 datasets containing data about embryonal tumors. We evaluate two most commonly used feature ranking algorithms (Random forests and RReliefF) and using the evaluation methodology identifies a set of viable biomarkers that have been confirmed to be related to cancer.

The Over-Expression of E2F3 Might Serve as Prognostic Marker for Neuroblastoma Patients with Stage 4S Disease

Stage 4S neuroblastoma is a childhood cancer occurring in infants (<12 months at diagnosis) with metastases limited to liver, skin, and bone marrow (<10%). It is associated with an excellent outcome, due to its notable ability to undergo spontaneous regression without any therapeutic intervention. However, a subgroup of patients is doomed to relapse and eventually to die in spite of aggressive therapies. Stage 4S neuroblastoma shows characteristic hypermethylation of genes involved in the telomere maintenance, indicating that the dysregulation of these genes might serve as prognostic marker. The retinoblastoma tumor suppressor protein (RB)-E2F transcription factors pathway is one of the critical tumor-suppressor/oncogene pathways involved in regulating telomerase expression. We have interrogated in silicopublic neuroblastoma databases for regulators involved in the RB-E2F pathway especially for E2F factors themselves, and we identified the E2F transcription factor 3 (E2F3) expression as a potential prognostic marker in stage 4S neuroblastoma. In order to confirm this finding, we screened 38 paraffin-embedded tissue samples stage 4S neuroblastoma for E2F3 protein expression using immunofluorescence, and we observed that augmented expression was strongly associated with impaired event-free survival. These results indicate that E2F3 expression might serve as prognostic marker in patients with stage 4S disease.

Link between spermine oxidase and apoptosis antagonizing transcription factor: A new pathway in neuroblastoma

Neuroblastoma (NB) is a heterogeneous extra‑cranial childhood type of cancer, responsible for approximately 15% of all paediatric cancer‑related deaths. Although several critical genetic aberrations have been related to NB, only a few established molecular markers have been associated with prognosis [V‑myc avian myelocytomatosis viral oncogene (MYCN) locus amplification, deletions of part of chromosome 1p, 11q23 and gain of 17q]. Regrettably, direct evidence of NB‑related tumour suppressors or oncogenes has not been currently identified at these chromosomal regions. MYCN locus amplification is present in approximately 20‑30% of cases and is associated with a poor clinical outcome, representing the most important genetic prognostic marker. The functional guidelines for the prognosis of NB identify high‑risk patients (<40% survival probabilities), but fail to identify patients at low and intermediate stages of the disease, which remains an issue to be resolved in NB. It has been shown that in NB cell lines and in a total‑spermine oxidase (SMOX) transgenic mouse model, SMOX overexpression induces cellular stress via reactive oxygen species (ROS) imbalance. In this study, we demonstrated that the high expression level of the cytoprotective gene, apoptosis-antagonizing transcription factor (AATF), was driven by SMOX gene overexpression in both NB cells and Total‑SMOX mice. The anti‑apoptotic effect of AATF was supported by analysing the inhibition of the expression of the pro‑apoptotic genes, BAX, BAK and PUMA, which were decreased, in both the in vitro and in vivo SMOX overexpressing model systems investigated. On the whole, this study supports the hypothesis that the SMOX gene can be considered as a novel anti‑apoptotic marker in NB.

MYCN-enhanced Oxidative and Glycolytic Metabolism Reveals Vulnerabilities for Targeting Neuroblastoma

In pediatric neuroblastoma, MYCN-amplification correlates to poor clinical outcome and new treatment options are needed for these patients. Identifying the metabolic adaptations crucial for tumor progression may be a promising strategy to discover novel therapeutic targets. Here, we have combined proteomics, gene expression profiling, functional analysis, and metabolic tracing to decipher the impact of MYCN on neuroblastoma cell metabolism. We found that high MYCN levels are correlated with altered expression of proteins involved in multiple metabolic processes, including enhanced glycolysis and increased oxidative phosphorylation. Unexpectedly, we discovered that MYCN-amplified cells showed de novo glutamine synthesis. Furthermore, inhibition of β-oxidation reduced the viability of MYCN-amplified cells in vitro and decreased tumor burden in vivo, while not affecting non-MYCN–amplified tumors. Our data provide information on metabolic processes in MYCN expressing tumors, which could be exploited for the development of novel targeted therapies.

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High MYCN expression enhances glycolysis and oxidative phosphorylation in neuroblastoma

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Neuroblastoma cells with MYCN-amplification display de novo glutamine synthesis

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MYCN-amplified cells show fatty acid–dependent mitochondrial respiration

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Fatty acid oxidation is a vulnerability in MYCN-amplified neuroblastoma

Genomic Amplifications and Distal 6q Loss: Novel Markers for Poor Survival in High-risk Neuroblastoma Patients

Background

Neuroblastoma is characterized by substantial clinical heterogeneity. Despite intensive treatment, the survival rates of high-risk neuroblastoma patients are still disappointingly low. Somatic chromosomal copy number aberrations have been shown to be associated with patient outcome, particularly in low- and intermediate-risk neuroblastoma patients. To improve outcome prediction in high-risk neuroblastoma, we aimed to design a prognostic classification method based on copy number aberrations.

Methods

In an international collaboration, normalized high-resolution DNA copy number data (arrayCGH and SNP arrays) from 556 high-risk neuroblastomas obtained at diagnosis were collected from nine collaborative groups and segmented using the same method. We applied logistic and Cox proportional hazard regression to identify genomic aberrations associated with poor outcome.

Results

In this study, we identified two types of copy number aberrations that are associated with extremely poor outcome. Distal 6q losses were detected in 5.9% of patients and were associated with a 10-year survival probability of only 3.4% (95% confidence interval [CI] = 0.5% to 23.3%, two-sided P = .002). Amplifications of regions not encompassing the MYCN locus were detected in 18.1% of patients and were associated with a 10-year survival probability of only 5.8% (95% CI = 1.5% to 22.2%, two-sided P < .001).

Conclusions

Using a unique large copy number data set of high-risk neuroblastoma cases, we identified a small subset of high-risk neuroblastoma patients with extremely low survival probability that might be eligible for inclusion in clinical trials of new therapeutics. The amplicons may also nominate alternative treatments that target the amplified genes.

ARID1A-SIN3A drives retinoic acid-induced neuroblastoma differentiation by transcriptional repression of TERT

Aggressive, high-risk neuroblastoma (NB) exhibits an immature differentiation state, profound epigenetic dysregulation and high telomerase activity. It has been suggested that aggressive NB may be treatable by inducing differentiation whereas therapeutic targeting of telomerase is under investigation for multiple cancer types. While epigenetic regulation of the telomerase reverse transcriptase (TERT) promoter has been described in high-risk NB, the exact molecular mechanisms are still not completely understood. Here we used quantitative real-time polymerase chain reaction (PCR), chromatin immunoprecipitation qPCR, quantitative telomeric repeat amplification protocol, and immunoblot techniques to investigate epigenetic regulation of TERT in wild-type and genetically modified NB cell lines. We demonstrated that TERT expression is reduced during 13-cis retinoic acid-induced NB differentiation and that this inversely correlated with increased expression of AT-rich interaction domain 1A (ARID1A), a subunit of the SWItch/sucrose nonfermentable chromatin remodeling complex. We showed that ARID1A directly caused suppression of TERT and was reliant on DNA binding and co-occupancy of the TERT promoter by the SIN3 transcription regulator family member A (SIN3A) repressor complex allowing NB differentiation to proceed. Finally, using data from NB patient cohorts, we reported a significant correlation between low ARID1A expression, elevated expression of TERT, and poorly differentiated, high-risk NB. These results provide insights into a key epigenetic pathway responsible for modulating TERT-driven NB progression, which could represent a target for therapeutic intervention.

Inhibition of Ubiquitin-Specific Protease 14 Suppresses Cell Proliferation and Synergizes with Chemotherapeutic Agents in Neuroblastoma

Neuroblastoma is the most common extracranial malignant solid tumor in children, and drug resistance is a major reason for poor outcomes. Elevated proteasome activity plays an important role in neuroblastoma tumor development and resistance to conventional chemotherapy. Ubiquitin-specific protease 14 (USP14), one of three deubiquitinases associated with the regulatory subunit of the proteasome, is emerging as a potential therapeutic target in multiple tumor types. However, the role of USP14 in neuroblastoma is yet to be elucidated. We found that USP14 inhibition in neuroblastoma via knockdown or a specific inhibitor such as b-AP15 suppressed cell proliferation by inducing cell apoptosis. Furthermore, b-AP15 significantly inhibited neuroblastoma tumor growth in NGP and SH-SY5Y xenograft mouse models. For combination treatment, b-AP15 plus conventional chemotherapeutic agents such as doxorubicin or VP-16 resulted in synergistic antitumor effects on neuroblastoma. Our study demonstrates that USP14 is required for cell viability and is a novel therapeutic target in neuroblastoma. Moreover, USP14 inhibition may add value in combination therapy due to its powerful synergistic effects in treating neuroblastoma.

The challenge of defining "ultra-high-risk" neuroblastoma

Given the biological and clinical heterogeneity of neuroblastoma, risk stratification is vital to determining appropriate treatment. Historically, most patients with high-risk neuroblastoma (HR-NBL) have been treated uniformly without further stratification. Attempts have been made to identify factors that can be used to risk stratify these patients and to characterize an "ultra-high-risk" (UHR) subpopulation with particularly poor outcome. However, among published data, there is a lack of consensus in the definition of the UHR population and heterogeneity in the endpoints and statistical methods used. This review summarizes our current understanding of stratification of HR-NBL and discusses the complex issues in defining UHR neuroblastoma.

FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To address these challenges, in this paper we propose a Factor-Adjusted Robust Multiple Testing (FarmTest) procedure for large-scale simultaneous inference with control of the false discovery proportion. We demonstrate that robust factor adjustments are extremely important in both controlling the FDP and improving the power. We identify general conditions under which the proposed method produces consistent estimate of the FDP. As a byproduct that is of independent interest, we establish an exponential-type deviation inequality for a robust U-type covariance estimator under the spectral norm. Extensive numerical experiments demonstrate the advantage of the proposed method over several state-of-the-art methods especially when the data are generated from heavy-tailed distributions. The proposed procedures are implemented in the R-package FarmTest.

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children’s Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients’ survival.

The deubiquitinating enzyme UCHL1 is a favorable prognostic marker in neuroblastoma as it promotes neuronal differentiation

Background

Neuroblastoma (NB) is the most common pediatric solid tumor that originates from neural crest-derived sympathoadrenal precursor cells that are committed to development of sympathetic nervous system. The well differentiated histological phenotype of NB tumor cells has been reportedly associated with favorable patient outcome. Retinoic acid (RA) can effectively induce NB cell differentiation, thereby being used in the clinic as a treatment agent for inducing the differentiation of high-risk NB. However, the underlying molecular mechanisms of regulating differentiation remain elusive.

Methods

The correlation between clinical characteristics, survival and the deubiquitinating enzyme ubiquitin C-terminal hydrolase 1 (UCHL1) expression were assessed using a neuroblastic tumor tissue microarray, and then validated in three independent patient datasets. The different expression of UCHL1 in ganglioneuroblastoma, ganglioneuroma and NB was detected by immunohistochemistry, mass spectra and immunoblotting analysis, and the correlation between UCHL1 expression and the differentiated histology was analyzed, which was also validated in three independent patient datasets. Furthermore, the roles of UCHL1 in NB cell differentiation and proliferation and the underlying mechanisms were studied by using short hairpin RNA and its inhibitor LDN57444 in vitro.

Results

Based on our neuroblastic tumor tissue microarrays and three independent validation datasets (Oberthuer, Versteeg and Seeger), we identified that UCHL1 served as a prognostic marker for better clinical outcome in NB. We further demonstrated that high UCHL1 expression was associated with NB differentiation, indicated by higher UCHL1 expression in ganglioneuroblastomas/ganglioneuromas and well-differentiated NB than poorly differentiated NB, and the positive correlation between UCHL1 and differentiation markers. As expected, inhibiting UCHL1 by knockdown or LDN57444 could significantly inhibit RA-induced neural differentiation of NB tumor cells, characterized by decreased neurite outgrowth and neural differentiation markers. This effect of UCHL1 was associated with positively regulating RA-induced AKT and ERK1/2 signaling activation. What’s more, knockdown of UCHL1 conferred resistance to RA-induced growth arrest.

Conclusion

Our findings identify a pivotal role of UCHL1 in NB cell differentiation and as a prognostic marker for survival in patients with NB, potentially providing a novel therapeutic target for NB.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0931-z) contains supplementary material, which is available to authorized users.

A NEW PERSPECTIVE ON ROBUST M-ESTIMATION: FINITE SAMPLE THEORY AND APPLICATIONS TO DEPENDENCE-ADJUSTED MULTIPLE TESTING

Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber in 1973 [Ann. Statist.1 (1973) 799-821], robust alternatives to the method of least squares are sorely needed. To achieve robustness against heavy-tailed sampling distributions, we revisit the Huber estimator from a new perspective by letting the tuning parameter involved diverge with the sample size. In this paper, we develop nonasymptotic concentration results for such an adaptive Huber estimator, namely, the Huber estimator with the tuning parameter adapted to sample size, dimension, and the variance of the noise. Specifically, we obtain a sub-Gaussian-type deviation inequality and a nonasymptotic Bahadur representation when noise variables only have finite second moments. The nonasymptotic results further yield two conventional normal approximation results that are of independent interest, the Berry-Esseen inequality and Cramér-type moderate deviation. As an important application to large-scale simultaneous inference, we apply these robust normal approximation results to analyze a dependence-adjusted multiple testing procedure for moderately heavy-tailed data. It is shown that the robust dependence-adjusted procedure asymptotically controls the overall false discovery proportion at the nominal level under mild moment conditions. Thorough numerical results on both simulated and real datasets are also provided to back up our theory.

Predicting clinical outcomes in neuroblastoma with genomic data integration

BACKGROUND

Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.

RESULTS

Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions.

CONCLUSION

Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients.

REVIEWERS

This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

Predicting neuroblastoma using developmental signals and a logic-based model

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.

Assessment of citalopram and escitalopram on neuroblastoma cell lines. Cell toxicity and gene modulation

Selective serotonin reuptake inhibitors (SSRI) are common antidepressants which cytotoxicity has been assessed in cancers notably colorectal carcinomas and glioma cell lines. We assessed and compared the cytotoxicity of 2 SSRI, citalopram and escitalopram, on neuroblastoma cell lines. The study was performed on 2 non-MYCN amplified cell lines (rat B104 and human SH-SY5Y) and 2 human MYCN amplified cell lines (IMR32 and Kelly). Citalopram and escitalopram showed concentration-dependent cytotoxicity on all cell lines. Citalopram was more cytotoxic than escitalopram. IMR32 was the most sensitive cell line. The absence of toxicity on human primary Schwann cells demonstrated the safety of both molecules for myelin. The mechanisms of cytotoxicity were explored using gene-expression profiles and quantitative real-time PCR (qPCR). Citalopram modulated 1 502 genes and escitalopram 1 164 genes with a fold change ≥ 2. 1 021 genes were modulated by both citalopram and escitalopram; 481 genes were regulated only by citalopram while 143 genes were regulated only by escitalopram. Citalopram modulated 69 pathways (KEGG) and escitalopram 42. Ten pathways were differently modulated by citalopram and escitalopram. Citalopram drastically decreased the expression of MYBL2, BIRC5 and BARD1 poor prognosis factors of neuroblastoma with fold-changes of -107 (p<2.26 10⁻⁷), -24.1 (p<5.6 10⁻⁹) and -17.7 (p<1.2 10⁻⁷). CCNE1, AURKA, IGF2, MYCN and ERBB2 were more moderately down-regulated by both molecules. Glioma markers E2F1, DAPK1 and CCND1 were down-regulated. Citalopram displayed more powerful action with broader and distinct spectrum of action than escitalopram.

Coexpression network analysis identifies transcriptional modules associated with genomic alterations in neuroblastoma

Neuroblastoma is a highly complex and heterogeneous cancer in children. Acquired genomic alterations including MYCN amplification, 1p deletion and 11q deletion are important risk factors and biomarkers in neuroblastoma. Here, we performed a co-expression-based gene network analysis to study the intrinsic association between specific genomic changes and transcriptome organization. We identified multiple gene coexpression modules which are recurrent in two independent datasets and associated with functional pathways including nervous system development, cell cycle, immune system process and extracellular matrix/space. Our results also indicated that modules involved in nervous system development and cell cycle are highly associated with MYCN amplification and 1p deletion, while modules responding to immune system process are associated with MYCN amplification only. In summary, this integrated analysis provides novel insights into molecular heterogeneity and pathogenesis of neuroblastoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Neuroblastoma cells undergo transcriptomic alterations upon dissemination into the bone marrow and subsequent tumor progression

Neuroblastoma is the most common extracranial solid tumor in childhood. The vast majority of metastatic (M) stage patients present with disseminated tumor cells (DTCs) in the bone marrow (BM) at diagnosis and relapse. Although these cells represent a major obstacle in the treatment of neuroblastoma patients, insights into their expression profile remained elusive. The present RNA‐Seq study of stage 4/M primary tumors, enriched BM‐derived diagnostic and relapse DTCs, as well as the corresponding BM‐derived mononuclear cells (MNCs) from 53 patients revealed 322 differentially expressed genes in DTCs as compared to the tumors (q < 0.001, |log₂FC|>2). Particularly, the levels of transcripts encoded by mitochondrial DNA were elevated in DTCs, whereas, for example, genes involved in angiogenesis were downregulated. Furthermore, 224 genes were highly expressed in DTCs and only slightly, if at all, in MNCs (q < 8 × 10⁻⁷⁵ log₂FC > 6). Interestingly, we found the transcriptome of relapse DTCs largely resembling those of diagnostic DTCs with only 113 differentially expressed genes under relaxed cut‐offs (q < 0.01, |log₂FC|>0.5). Notably, relapse DTCs showed a positional enrichment of 31 downregulated genes on chromosome 19, including five tumor suppressor genes: SIRT6, BBC3/PUMA, STK11, CADM4 and GLTSCR2. This first RNA‐Seq analysis of neuroblastoma DTCs revealed their unique expression profile in comparison to the tumors and MNCs, and less pronounced differences between diagnostic and relapse DTCs. The latter preferentially affected downregulation of genes encoded by chromosome 19. As these alterations might be associated with treatment failure and disease relapse, further functional studies on DTCs should be considered.

What's new?

More than 90% of patients diagnosed with stage 4 metastatic (4/M) neuroblastoma present with disseminated tumor cells (DTCs) in the bone marrow (BM). Despite treatment, a substantial fraction of these patients experience disease relapse. Here, sequencing analysis of tumor tissue, BM‐derived mononuclear cells (MNCs), and DTCs from stage 4/M neuroblastoma patients indicates that numerous genes are differentially expressed in DTCs but are not or are only slightly altered in tumors and MNCs. Moreover, DTCs exhibited significant downregulation of tumor suppressor genes specifically on chromosome 19. Further studies are needed to determine whether DTC transcriptomic alterations are associated with neuroblastoma relapse.

Three-dimensional tumor cell growth stimulates autophagic flux and recapitulates chemotherapy resistance

Current preclinical models in tumor biology are limited in their ability to recapitulate relevant (patho-) physiological processes, including autophagy. Three-dimensional (3D) growth cultures have frequently been proposed to overcome the lack of correlation between two-dimensional (2D) monolayer cell cultures and human tumors in preclinical drug testing. Besides 3D growth, it is also advantageous to simulate shear stress, compound flux and removal of metabolites, e.g., via bioreactor systems, through which culture medium is constantly pumped at a flow rate reflecting physiological conditions. Here we show that both static 3D growth and 3D growth within a bioreactor system modulate key hallmarks of cancer cells, including proliferation and cell death as well as macroautophagy, a recycling pathway often activated by highly proliferative tumors to cope with metabolic stress. The autophagy-related gene expression profiles of 2D-grown cells are substantially different from those of 3D-grown cells and tumor tissue. Autophagy-controlling transcription factors, such as TFEB and FOXO3, are upregulated in tumors, and 3D-grown cells have increased expression compared with cells grown in 2D conditions. Three-dimensional cultures depleted of the autophagy mediators BECN1, ATG5 or ATG7 or the transcription factor FOXO3, are more sensitive to cytotoxic treatment. Accordingly, combining cytotoxic treatment with compounds affecting late autophagic flux, such as chloroquine, renders the 3D-grown cells more susceptible to therapy. Altogether, 3D cultures are a valuable tool to study drug response of tumor cells, as these models more closely mimic tumor (patho-)physiology, including the upregulation of tumor relevant pathways, such as autophagy.

Lysine-specific demethylase LSD1 regulates autophagy in neuroblastoma through SESN2-dependent pathway

Autophagy is a physiological process, important for recycling of macromolecules and maintenance of cellular homeostasis. Defective autophagy is associated with tumorigenesis and has a causative role in chemotherapy resistance in leukemia and in solid cancers. Here, we report that autophagy is regulated by the lysine-specific demethylase LSD1/KDM1A, an epigenetic marker whose overexpression is a feature of malignant neoplasia with an instrumental role in cancer development. In the present study, we determine that two different LSD1 inhibitors (TCP and SP2509) as well as selective ablation of LSD1 expression promote autophagy in neuroblastoma cells. At a mechanistic level, we show that LSD1 binds to the promoter region of Sestrin2 (SESN2), a critical regulator of mTORC1 activity. Pharmacological inhibition of LSD1 triggers SESN2 expression that hampers mTORC1 activity, leading to enhanced autophagy. SESN2 overexpression suffices to promote autophagy in neuroblastoma cells, while loss of SESN2 expression reduces autophagy induced by LSD1 inhibition. Our findings elucidate a mechanism whereby LSD1 controls autophagy in neuroblastoma cells through SESN2 transcription regulation, and we suggest that pharmacological targeting of LSD1 may have effective therapeutic relevance in the control of autophagy in neuroblastoma.

A c-Myc-regulated stem cell-like signature in high-risk neuroblastoma: A systematic discovery (Target neuroblastoma ESC-like signature)

c-Myc dysregulation is hypothesized to account for the ‘stemness’ – self-renewal and pluripotency – shared between embryonic stem cells (ESCs) and adult aggressive tumours. High-risk neuroblastoma (HR-NB) is the most frequent, aggressive, extracranial solid tumour in childhood. Using HR-NB as a platform, we performed a network analysis of transcriptome data and presented a c-Myc subnetwork enriched for genes previously reported as ESC-like cancer signatures. A subsequent drug-gene interaction analysis identified a pharmacogenomic agent that preferentially interacted with this HR-NB-specific, ESC-like signature. This agent, Roniciclib (BAY 1000394), inhibited neuroblastoma cell growth and induced apoptosis in vitro. It also repressed the expression of the oncogene c-Myc and the neural ESC marker CDK2 in vitro, which was accompanied by altered expression of the c-Myc-targeted cell cycle regulators CCND1, CDKN1A and CDKN2D in a time-dependent manner. Further investigation into this HR-NB-specific ESC-like signature in 295 and 243 independent patients revealed and validated the general prognostic index of CDK2 and CDKN3 compared with CDKN2D and CDKN1B. These findings highlight the very potent therapeutic benefits of Roniciclib in HR-NB through the targeting of c-Myc-regulated, ESC-like tumorigenesis. This work provides a hypothesis-driven systems computational model that facilitates the translation of genomic and transcriptomic signatures to molecular mechanisms underlying high-risk tumours.

Impact of Disseminated Neuroblastoma Cells on the Identification of the Relapse-Seeding Clone

Purpose: Tumor relapse is the most frequent cause of death in stage 4 neuroblastomas. Since genomic information on the relapse precursor cells could guide targeted therapy, our aim was to find the most appropriate tissue for identifying relapse-seeding clones.Experimental design: We analyzed 10 geographically and temporally separated samples of a single patient by SNP array and validated the data in 154 stage 4 patients.Results: In the case study, aberrations unique to certain tissues and time points were evident besides concordant aberrations shared by all samples. Diagnostic bone marrow-derived disseminated tumor cells (DTCs) as well as the metastatic tumor and DTCs at relapse displayed a 1q deletion, not detected in any of the seven primary tumor samples. In the validation cohort, the frequency of 1q deletion was 17.8%, 10%, and 27.5% in the diagnostic DTCs, diagnostic tumors, and DTCs at relapse, respectively. This aberration was significantly associated with 19q and ATRX deletions. We observed a significant increased likelihood of an adverse event in the presence of 19q deletion in the diagnostic DTCs.Conclusions: Different frequencies of 1q and 19q deletions in the primary tumors as compared with DTCs, their relatively high frequency at relapse, and their effect on event-free survival (19q deletion) indicate the relevance of analyzing diagnostic DTCs. Our data support the hypothesis of a branched clonal evolution and a parallel progression of primary and metastatic tumor cells. Therefore, searching for biomarkers to identify the relapse-seeding clone should involve diagnostic DTCs alongside the tumor tissue. Clin Cancer Res; 23(15); 4224-32. ©2017 AACR.

A Metastatic Mouse Model Identifies Genes That Regulate Neuroblastoma Metastasis

Metastatic relapse is the major cause of death in pediatric neuroblastoma, where there remains a lack of therapies to target this stage of disease. To understand the molecular mechanisms mediating neuroblastoma metastasis, we developed a mouse model using intracardiac injection and in vivo selection to isolate malignant cell subpopulations with a higher propensity for metastasis to bone and the central nervous system. Gene expression profiling revealed primary and metastatic cells as two distinct cell populations defined by differential expression of 412 genes and of multiple pathways, including CADM1, SPHK1, and YAP/TAZ, whose expression independently predicted survival. In the metastatic subpopulations, a gene signature was defined (MET-75) that predicted survival of neuroblastoma patients with metastatic disease. Mechanistic investigations demonstrated causal roles for CADM1, SPHK1, and YAP/TAZ in mediating metastatic phenotypes in vitro and in vivo Notably, pharmacologic targeting of SPHK1 or YAP/TAZ was sufficient to inhibit neuroblastoma metastasis in vivo Overall, we identify gene expression signatures and candidate therapeutics that could improve the treatment of metastatic neuroblastoma. Cancer Res; 77(3); 696-706. ©2017 AACR.

Artificial neural network classifier predicts neuroblastoma patients' outcome

Background

More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients’ outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease.

Methods

Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients’ outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with “Poor” or “Good” outcome.

Results

We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients’ outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors’ set. The NB-hypo classifier predicted the patients’ outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis.

Conclusions

We developed a robust classifier predicting neuroblastoma patients’ outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1194-3) contains supplementary material, which is available to authorized users.

Stage 4S neuroblastoma tumors show a characteristic DNA methylation portrait

Stage 4S neuroblastoma (NB) is a special type of NB found in infants with metastases at diagnosis and is associated with an excellent outcome due to its remarkable capacity to undergo spontaneous regression. As genomics have not been able to explain this intriguing clinical presentation, we here aimed at profiling the DNA methylome of stage 4S NB to better understand this phenomenon. To this purpose, differential methylation analyses between International Neuroblastoma Staging System (INSS) stage 4S, stage 4 and stage 1/2 were performed, using methyl-CpG-binding domain (MBD) sequencing data of 14 stage 4S, 14 stage 4, and 13 stage 1/2 primary NB tumors (all MYCN non-amplified in order not to confound results). Stage 4S-specific hyper- and hypomethylated promoters were determined and further characterized for genomic localization and function by cytogenetic band enrichment, gene set enrichment, transcription factor target enrichment and differential RNA expression analyses. We show that specific chromosomal locations are enriched for stage 4S differentially methylated promoters and that stage 4S tumors show characteristic hypermethylation of specific subtelomeric promoters. Furthermore, genes involved in important oncogenic pathways, in neural crest development and differentiation, and in epigenetic processes are differentially methylated and expressed in stage 4S tumors. Based on these findings, we describe new biological mechanisms possibly contributing to the stage 4S-specific tumor biology and spontaneous regression. In conclusion, this study is the first to describe the highly characteristic stage 4S DNA methylome. These findings will open new avenues to further unravel the NB pathology in general and stage 4S disease specifically.

Transcript signatures that predict outcome and identify targetable pathways in MYCN-amplified neuroblastoma

BACKGROUND

In the pediatric cancer neuroblastoma (NB), patients are stratified into low, intermediate or high-risk subsets based in part on MYCN amplification status. While MYCN amplification in general predicts unfavorable outcome, no clinical or genomic factors have been identified that predict outcome within these cohorts of high-risk patients. In particular, it is currently not possible at diagnosis to determine which high-risk neuroblastoma patients will ultimately fail upfront therapy.

EXPERIMENTAL DESIGN

We analyzed the prognostic potential of most published gene expression signatures for NB and developed a new prognostic signature to predict outcome for patients with MYCN amplification. Network and pathway analyses identified candidate therapeutic targets for this MYCN-amplified patient subset with poor outcome.

RESULTS

Most signatures have a high capacity to predict outcome of unselected NB patients. However, the majority of published signatures, as well as most randomly generated signatures, are highly confounded by MYCN amplification, and fail to predict outcome in subpopulations of high-risk patients with MYCN-amplified NB. We identify a MYCN module signature that predicts patient outcome for those with MYCN-amplified tumors, that also predicts potential tractable therapeutic signaling pathways and targets including the DNA repair enzyme Poly [ADP-ribose] polymerase 1 (PARP1).

CONCLUSION

Many prognostic signatures for NB are confounded by MYCN amplification and fail to predict outcome for the subset of high-risk patients with MYCN amplification. We report a MYCN module signature that is associated with distinct patient outcomes, and predicts candidate therapeutic targets in DNA repair pathways, including PARP1 in MYCN-amplified NB.

Rac GTPase regulation of 3D invasion in neuroblastomas lacking MYCN amplification

Neuroblastomas are highly invasive tumors that occur in pediatric patients and treatment of invasive disease remains a challenge. The study of cells invading in 3-dimensional (3D) hydrogels has revealed morphologically distinct modes of invasion by which cancer cells adapt to the local tissue environment in order to invade local tissue. Specifically, the small G protein Rac GTPase has been implicated as regulating the elongated/mesenchymal mode of cell invasion. In the present study we demonstrate an inverse association between Rac expression and amplification of MYCN, a well-established prognostic indicator in neuroblastoma. Moreover, the association further tracks with previously described morphological variants of neuroblastoma. Importantly, while MYCN amplification is associated with universally poor prognosis, the clinical course of patients whose tumors lack MYCN amplification are more difficult to predict. Therefore, we analyzed the role that Rac plays in regulating the invasive behavior of neuroblastoma cells lacking MYCN amplification. Using siRNA targeting Rac in single cell suspensions in 3D collagen gels and Rac inhibition of multicellular spheroids (MCS) embedded in collagen gels, we find that the high Rac-expressing lines differ in their morphological response to Rac depletion and inhibition. Live cell imaging of embedded MCS reveals distinct individual and collective modes of invasion between the cell lines. Critically, Rac inhibition blocked both individual and collective invasion in 2 of the 3 high Rac expressing cell lines. Our study suggests that Rac activity may be an important determinant of metastatic capability in subsets of neuroblastoma cells lacking MYCN amplification.

An 18 gene expression-based score classifier predicts the clinical outcome in stage 4 neuroblastoma

Background

The prognosis of children with metastatic stage 4 neuroblastoma (NB) has remained poor in the past decade.

Patients and methods

Using microarray analyses of 342 primary tumors, we here developed and validated an easy to use gene expression-based risk score including 18 genes, which can robustly predict the outcome of stage 4 patients.

Results

This classifier was a significant predictor of overall survival in two independent validation cohorts [cohort 1 (n = 214): P = 6.3 × 10⁻⁵; cohort 2 (n = 27): P = 3.1 × 10⁻²]. The prognostic value of the risk score was validated by multivariate analysis including the established markers age and MYCN status (P = 0.027). In the pooled validation cohorts (n = 241), integration of the risk score with the age and/or MYCN status identified subgroups with significantly differing overall survival (ranging from 35 to 100 %).

Conclusion

Together, the 18-gene risk score classifier can identify patients with stage 4 NB with favorable outcome and may therefore improve risk assessment and treatment stratification of NB patients with disseminated disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0896-7) contains supplementary material, which is available to authorized users.

PI3K inhibition synergizes with glucocorticoids but antagonizes with methotrexate in T-cell acute lymphoblastic leukemia

The PI3K pathway is frequently hyperactivated in primary T-cell acute lymphoblastic leukemia (T-ALL) cells. Activation of the PI3K pathway has been suggested as one mechanism of glucocorticoid resistance in T-ALL, and patients harboring mutations in the PI3K negative regulator PTEN may be at increased risk of induction failure and relapse. By gene expression microarray analysis of T-ALL cells treated with the PI3K inhibitor AS605240, we identified Myc as a prominent downstream target of the PI3K pathway. A significant association was found between the AS605240 gene expression signature and that of glucocorticoid resistance and relapse in T-ALL. AS605240 showed anti-leukemic activity and strong synergism with glucocorticoids both in vitro and in a NOD/SCID xenograft model of T-ALL. In contrast, PI3K inhibition showed antagonism with methotrexate and daunorubicin, drugs that preferentially target dividing cells. This antagonistic interaction, however, could be circumvented by the use of correct drug scheduling schemes. Our data indicate the potential benefits and difficulties for the incorporation of PI3K inhibitors in T-ALL therapy.

Hexokinase 2 is a determinant of neuroblastoma metastasis

Background:

Intersecting a genome-wide expression profile of metastatic and nonmetastatic human neuroblastoma xenograft variants with expression profiles of tumours from stage 1 and 4 neuroblastoma patients, we previously characterised hexokinase 2 (HK2) as a gene whose expression was upregulated in both metastatic neuroblastoma variants and tumours from stage 4 neuroblastoma patients.

Methods:

Local and metastatic neuroblastoma cell variants as well as metastatic neuroblastoma cells genetically manipulated to downregulate the expression of HK2 were utilised for in vitro and in vivo examinations of the involvement of HK2 in neuroblastoma.

Results:

Hexokinase 2 expression and its activity levels were increased in neuroblastoma metastatic variants as compared with the local variants. The upregulation of HK2 confers upon the metastatic cells high resistance to the antiproliferative effect of the HK2 inhibitor 3-BrPa and to the chemotherapy agent Deferoxamine. The inhibition of HK2 transcript lowered the proliferation and motility of sh-HK2 cells as compared with sh-control cells. Mice that were inoculated with sh-HK2 cells had a lower incidence of local tumours, smaller tumour volumes and a diminished load of lung metastasis compared with mice inoculated with sh-control cells.

Conclusions:

Hexokinase 2 plays a significant role in shaping the malignant phenotype of neuroblastoma and influences the progression of this disease.

Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm

Background

Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis.

Method

We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary.

Results

The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases.

Conclusion

These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0132-y) contains supplementary material, which is available to authorized users.

Screening for genes involved in antibody response to sheep red blood cells in the chicken, Gallus gallus

Antibody response, an important trait in both agriculture and biomedicine, plays a part in protecting animals from infection. Dissecting molecular basis of antibody response may improve artificial selection for natural disease resistance in livestock and poultry. A number of genetic markers associated with antibody response have been identified in the chicken and mouse by linkage-based association studies, which only define genomic regions by genetic markers but do not pinpoint genes for antibody response. In contrast, global expression profiling has been applied to define the molecular bases of a variety of biological traits through identification of differentially expressed genes (DEGs). Here, we employed Affimetrix GeneChip Chicken Genome Arrays to identify differentially expressed genes for antibody response to sheep red blood cells (SRBC) using chickens challenged with and without SRBC or chickens with high and low anti-SRBC titers. The DEGs include those with known (i.e., MHC class I and IgH genes) or unknown function in antibody response. Classification test of these genes suggested that the response of the chicken to intravenous injection of SRBC involved multiple biological processes, including response to stress or other different stimuli, sugar, carbohydrate or protein binding, and cell or soluble fraction, in addition to antibody response. This preliminary study thus provides an insight into molecular basis of antibody response to SRBC in the chicken.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Background

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Results

We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.

Conclusions

We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.