A 6-gene signature identifies four molecular subgroups of neuroblastoma

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

Targeting long noncoding RNAs in neuroblastoma: Progress and prospects

Neuroblastoma (NB) is the third most prevalent tumor that mostly influences infants and young children. Although different treatments have been developed for the treatment of NB, high-risk patients have been reported to have low survival rates. Currently, long noncoding RNAs (lncRNAs) have shown an attractive potential in cancer research and a party of investigations have been performed to understand mechanisms underlying tumor development through lncRNA dysregulation. Researchers have just newly initiated to exhibit the involvement of lncRNAs in NB pathogenesis. In this review article, we tried to clarify the point we stand with respect to the involvement of lncRNAs in NB. Moreover, implications for the pathologic roles of lncRNAs in the development of NB have been discussed. It seems that some of these lncRNAs have promising potential to be applied as biomarkers for NB prognosis and treatment.

Diagnostic classification of childhood cancer using multiscale transcriptomics

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.

MYCN in Neuroblastoma: "Old Wine into New Wineskins"

MYCN Proto-Oncogene, BHLH Transcription Factor (MYCN) has been one of the most studied genes in neuroblastoma. It is known for its oncogenetic mechanisms, as well as its role in the prognosis of the disease and it is considered one of the prominent targets for neuroblastoma therapy. In the present work, we attempted to review the literature, on the relation between MYCN and neuroblastoma from all possible mechanistic sites. We have searched the literature for the role of MYCN in neuroblastoma based on the following topics: the references of MYCN in the literature, the gene's anatomy, along with its transcripts, the protein's anatomy, the epigenetic mechanisms regulating MYCN expression and function, as well as MYCN amplification. MYCN plays a significant role in neuroblastoma biology. Its functions and properties range from the forming of G-quadraplexes, to the interaction with miRNAs, as well as the regulation of gene methylation and histone acetylation and deacetylation. Although MYCN is one of the most primary genes studied in neuroblastoma, there is still a lot to be learned. Our knowledge on the exact mechanisms of MYCN amplification, etiology and potential interventions is still limited. The knowledge on the molecular mechanisms of MYCN in neuroblastoma, could have potential prognostic and therapeutic advantages.

Neuronal lineages derived from the nerve-associated Schwann cell precursors

For a long time, neurogenic placodes and migratory neural crest cells were considered the immediate sources building neurons of peripheral nervous system. Recently, a number of discoveries revealed the existence of another progenitor type-a nerve-associated multipotent Schwann cell precursors (SCPs) building enteric and parasympathetic neurons as well as neuroendocrine chromaffin cells. SCPs are neural crest-derived and are similar to the crest cells by their markers and differentiation potential. Such similarities, but also considerable differences, raise many questions pertaining to the medical side, fundamental developmental biology and evolution. Here, we discuss the genesis of Schwann cell precursors, their role in building peripheral neural structures and ponder on their role in the origin in congenial diseases associated with peripheral nervous systems.

Identification of RNA-Binding Proteins as Targetable Putative Oncogenes in Neuroblastoma

Neuroblastoma is a common childhood cancer with almost a third of those affected still dying, thus new therapeutic strategies need to be explored. Current experimental therapies focus mostly on inhibiting oncogenic transcription factor signalling. Although LIN28B, DICER and other RNA-binding proteins (RBPs) have reported roles in neuroblastoma development and patient outcome, the role of RBPs in neuroblastoma is relatively unstudied. In order to elucidate novel RBPs involved in MYCN-amplified and other high-risk neuroblastoma subtypes, we performed differential mRNA expression analysis of RBPs in a large primary tumour cohort (n = 498). Additionally, we found via Kaplan–Meier scanning analysis that 685 of the 1483 tested RBPs have prognostic value in neuroblastoma. For the top putative oncogenic candidates, we analysed their expression in neuroblastoma cell lines, as well as summarised their characteristics and existence of chemical inhibitors. Moreover, to help explain their association with neuroblastoma subtypes, we reviewed candidate RBPs’ potential as biomarkers, and their mechanistic roles in neuronal and cancer contexts. We found several highly significant RBPs including RPL22L1, RNASEH2A, PTRH2, MRPL11 and AFF2, which remain uncharacterised in neuroblastoma. Although not all RBPs appear suitable for drug design, or carry prognostic significance, we show that several RBPs have strong rationale for inhibition and mechanistic studies, representing an alternative, but nonetheless promising therapeutic strategy in neuroblastoma treatment.

Metabolomic profiling in neuroblastoma

BACKGROUND AND OBJECTIVES

Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB.

DESIGN AND METHODS

Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS).

RESULTS

The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment.

CONCLUSIONS

To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.

High-risk, Expression-Based Prognostic Long Noncoding RNA Signature in Neuroblastoma

Background

Current clinical risk factors stratify patients with neuroblastoma (NB) for appropriate treatments, yet patients with similar clinical behaviors evoke variable responses. MYCN amplification is one of the established drivers of NB and, when combined with high-risk displays, worsens outcomes. Growing high-throughput transcriptomics studies suggest long noncoding RNA (lncRNA) dysregulation in cancers, including NB. However, expression-based lncRNA signatures are altered by MYCN amplification, which is associated with high-risk, and patient prognosis remains limited.

Methods

We investigated RNA-seq-based expression profiles of lncRNAs in MYCN status and risk status in a discovery cohort (n = 493) and validated them in three independent cohorts. In the discovery cohort, a prognostic association of lncRNAs was determined by univariate Cox regression and integrated into a signature using the risk score method. A novel risk score threshold selection criterion was developed to stratify patients into risk groups. Outcomes by risk group and clinical subgroup were assessed using Kaplan-Meier survival curves and multivariable Cox regression. The performance of lncRNA signatures was evaluated by receiver operating characteristic curve. All statistical tests were two-sided.

Results

In the discovery cohort, 16 lncRNAs that were differentially expressed (fold change ≥ 2 and adjusted P ≤ 0.01) integrated into a prognostic signature. A high risk score group of lncRNA signature had poor event-free survival (EFS; P < 1E-16). Notably, lncRNA signature was independent of other clinical risk factors when predicting EFS (hazard ratio = 3.21, P = 5.95E-07). The findings were confirmed in independent cohorts (P = 2.86E-02, P = 6.18E-03, P = 9.39E-03, respectively). Finally, the lncRNA signature had higher accuracy for EFS prediction (area under the curve = 0.788, 95% confidence interval = 0.746 to 0.831).

Conclusions

Here, we report the first (to our knowledge) RNA-seq 16-lncRNA prognostic signature for NB that may contribute to precise clinical stratification and EFS prediction.

Investigation of major genetic alterations in neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor in childhood. This malignancy shows a wide spectrum of clinical outcome and its prognosis is conditioned by manifold biological and genetic factors. We investigated the tumor genetic profile and clinical data of 29 patients with NB by multiplex ligation-dependent probe amplification (MLPA) to assess therapeutic risk. In 18 of these tumors, MYCN status was assessed by fluorescence in situ hybridization (FISH). Copy number variation was also determined for confirming MLPA findings in two 6p loci. We found 2p, 7q and 17q gains, and 1p and 11q losses as the most frequent chromosome alterations in this cohort. FISH confirmed all cases of MYCN amplification detected by MLPA. In view of unexpected 6p imbalance, copy number variation of two 6p loci was assessed for validating MLPA findings. Based on clinical data and genetic profiles, patients were stratified in pretreatment risk groups according to international consensus. MLPA proved to be effective for detecting multiple genetic alterations in all chromosome regions as requested by the International Neuroblastoma Risk Group (INRG) for therapeutic stratification. Moreover, this technique proved to be cost effective, reliable, only requiring standard PCR equipment, and attractive for routine analysis. However, the observed 6p imbalances made PKHD1 and DCDC2 inadequate for control loci. This must be considered when designing commercial MLPA kits for NB. Finally, four patients showed a normal MLPA profile, suggesting that NB might have a more complex genetic pattern than the one assessed by presently available MLPA kits.

PHOX2B is a suppressor of neuroblastoma metastasis

Paired like homeobox 2B (PHOX2B) is a minimal residual disease (MRD) marker of neuroblastoma. The presence of MRD, also referred to as micro-metastases, is a powerful marker of poor prognosis in neuroblastoma.

Lung metastasis is considered a terminal event in neuroblastoma. Lung micro-metastatic neuroblastoma (MicroNB) cells show high expression levels of PHOX2B and possess a less malignant and metastatic phenotype than lung macro metastatic neuroblastoma (MacroNB) cells, which hardly express PHOX2B.

In vitro assays showed that PHOX2B knockdown in MicroNB cells did not affect cell viability; however it decreased the migratory capacity of the MicroNB-shPHOX2B cells. An orthotopic inoculation of MicroNB-shPHOX2B cells into the adrenal gland of nude mice resulted in significantly larger primary tumors and a heavier micro-metastatic load in the lungs and bone-marrow, than when control cells were inoculated.

PHOX2B expression was found to be regulated by methylation. The PHOX2B promoter in MacroNB cells is significantly more methylated than in MicroNB cells. Demethylation assays using 5-azacytidine demonstrated that methylation can indeed inhibit PHOX2B transcription in MacroNB cells.

These pre-clinical data strongly suggest that PHOX2B functions as a suppressor of neuroblastoma progression.

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.

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.

Genetic signatures of heroin addiction

Heroin addiction is a complex psychiatric disorder with a chronic course and a high relapse rate, which results from the interaction between genetic and environmental factors. Heroin addiction has a substantial heritability in its etiology; hence, identification of individuals with a high genetic propensity to heroin addiction may help prevent the occurrence and relapse of heroin addiction and its complications. The study aimed to identify a small set of genetic signatures that may reliably predict the individuals with a high genetic propensity to heroin addiction. We first measured the transcript level of 13 genes (RASA1, PRKCB, PDK1, JUN, CEBPG, CD74, CEBPB, AUTS2, ENO2, IMPDH2, HAT1, MBD1, and RGS3) in lymphoblastoid cell lines in a sample of 124 male heroin addicts and 124 male control subjects using real-time quantitative PCR. Seven genes (PRKCB, PDK1, JUN, CEBPG, CEBPB, ENO2, and HAT1) showed significant differential expression between the 2 groups. Further analysis using 3 statistical methods including logistic regression analysis, support vector machine learning analysis, and a computer software BIASLESS revealed that a set of 4 genes (JUN, CEBPB, PRKCB, ENO2, or CEBPG) could predict the diagnosis of heroin addiction with the accuracy rate around 85% in our dataset. Our findings support the idea that it is possible to identify genetic signatures of heroin addiction using a small set of expressed genes. However, the study can only be considered as a proof-of-concept study. As the establishment of lymphoblastoid cell line is a laborious and lengthy process, it would be more practical in clinical settings to identify genetic signatures for heroin addiction directly from peripheral blood cells in the future study.

Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system

Microarray-based molecular signatures have not been widely integrated into neuroblastoma diagnostic classification systems due to the complexities of the assay and requirement for high-quality RNA. New digital technologies that accurately quantify gene expression using RNA isolated from formalin-fixed paraffin embedded (FFPE) tissues are now available. In this study, we describe the first use of a high-throughput digital system to assay the expression of genes in an "ultra-high risk" microarray classifier in FFPE high-risk neuroblastoma tumors. Customized probes corresponding to the 42 genes in a published multi-gene neuroblastoma signature were hybridized to RNA isolated from 107 FFPE high-risk neuroblastoma samples using the NanoString nCounter™ Analysis System. For classification of each patient, the Pearson's correlation coefficient was calculated between the standardized nCounter™ data and the molecular signature from the microarray data. We demonstrate that the nCounter™ 42-gene panel sub-stratified the high-risk cohort into two subsets with statistically significantly different overall survival (p = 0.0027) and event-free survival (p = 0.028). In contrast, none of the established prognostic risk markers (age, stage, tumor histology, MYCN status, and ploidy) were significantly associated with survival. We conclude that the nCounter™ System can reproducibly quantify expression levels of signature genes in FFPE tumor samples. Validation of this microarray signature in our high-risk patient cohort using a completely different technology emphasizes the prognostic relevance of this classifier. Prospective studies testing the prognostic value of molecular signatures in high-risk neuroblastoma patients using FFPE tumor samples and the nCounter™ System are warranted.

New insights into the genetics of neuroblastoma

Neuroblastoma is a genetically and clinically heterogeneous tumor of childhood, arising from precursor cells of the sympathetic nervous system. It is still a challenging cancer for pediatric oncology, as some tumors will spontaneously regress, while others will become refractory to all forms of therapy. The clinical course of this disease is greatly influenced by both patient age and the genetic abnormalities that occur within the tumors. MYCN (v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)) amplification and loss of chromosome 11q heterozygosity have been known to be indicative of poor prognosis. In this article, we review how mutations and structural alterations in specific genes contribute to inheritable predisposition to neuroblastoma and/or to aggressive disease pathogenesis, as well as implications for diagnosis and therapy. These genes include PHOX2B (paired-like homeobox 2b), ALK (anaplastic lymphoma receptor tyrosine kinase), and ATRX (alpha thalassemia/mental retardation syndrome X-linked).

Neuroblastoma tumorigenesis is regulated through the Nm23-H1/h-Prune C-terminal interaction

Nm23-H1 is one of the most interesting candidate genes for a relevant role in Neuroblastoma pathogenesis. H-Prune is the most characterized Nm23-H1 binding partner, and its overexpression has been shown in different human cancers. Our study focuses on the role of the Nm23-H1/h-Prune protein complex in Neuroblastoma. Using NMR spectroscopy, we performed a conformational analysis of the h-Prune C-terminal to identify the amino acids involved in the interaction with Nm23-H1. We developed a competitive permeable peptide (CPP) to impair the formation of the Nm23-H1/h-Prune complex and demonstrated that CPP causes impairment of cell motility, substantial impairment of tumor growth and metastases formation. Meta-analysis performed on three Neuroblastoma cohorts showed Nm23-H1 as the gene highly associated to Neuroblastoma aggressiveness. We also identified two other proteins (PTPRA and TRIM22) with expression levels significantly affected by CPP. These data suggest a new avenue for potential clinical application of CPP in Neuroblastoma treatment.

ERBB3 is a marker of a ganglioneuroblastoma/ganglioneuroma-like expression profile in neuroblastic tumours

Background

Neuroblastoma (NB) tumours are commonly divided into three cytogenetic subgroups. However, by unsupervised principal components analysis of gene expression profiles we recently identified four distinct subgroups, r1-r4. In the current study we characterized these different subgroups in more detail, with a specific focus on the fourth divergent tumour subgroup (r4).

Methods

Expression microarray data from four international studies corresponding to 148 neuroblastic tumour cases were subject to division into four expression subgroups using a previously described 6-gene signature. Differentially expressed genes between groups were identified using Significance Analysis of Microarray (SAM). Next, gene expression network modelling was performed to map signalling pathways and cellular processes representing each subgroup. Findings were validated at the protein level by immunohistochemistry and immunoblot analyses.

Results

We identified several significantly up-regulated genes in the r4 subgroup of which the tyrosine kinase receptor ERBB3 was most prominent (fold change: 132–240). By gene set enrichment analysis (GSEA) the constructed gene network of ERBB3 (n = 38 network partners) was significantly enriched in the r4 subgroup in all four independent data sets. ERBB3 was also positively correlated to the ErbB family members EGFR and ERBB2 in all data sets, and a concurrent overexpression was seen in the r4 subgroup. Further studies of histopathology categories using a fifth data set of 110 neuroblastic tumours, showed a striking similarity between the expression profile of r4 to ganglioneuroblastoma (GNB) and ganglioneuroma (GN) tumours. In contrast, the NB histopathological subtype was dominated by mitotic regulating genes, characterizing unfavourable NB subgroups in particular. The high ErbB3 expression in GN tumour types was verified at the protein level, and showed mainly expression in the mature ganglion cells.

Conclusions

Conclusively, this study demonstrates the importance of performing unsupervised clustering and subtype discovery of data sets prior to analyses to avoid a mixture of tumour subtypes, which may otherwise give distorted results and lead to incorrect conclusions. The current study identifies ERBB3 as a clear-cut marker of a GNB/GN-like expression profile, and we suggest a 7-gene expression signature (including ERBB3) as a complement to histopathology analysis of neuroblastic tumours. Further studies of ErbB3 and other ErbB family members and their role in neuroblastic differentiation and pathogenesis are warranted.

The role of genetic and epigenetic alterations in neuroblastoma disease pathogenesis

Neuroblastoma is a highly heterogeneous tumor accounting for 15 % of all pediatric cancer deaths. Clinical behavior ranges from the spontaneous regression of localized, asymptomatic tumors, as well as metastasized tumors in infants, to rapid progression and resistance to therapy. Genomic amplification of the MYCN oncogene has been used to predict outcome in neuroblastoma for over 30 years, however, recent methodological advances including miRNA and mRNA profiling, comparative genomic hybridization (array-CGH), and whole-genome sequencing have enabled the detailed analysis of the neuroblastoma genome, leading to the identification of new prognostic markers and better patient stratification. In this review, we will describe the main genetic factors responsible for these diverse clinical phenotypes in neuroblastoma, the chronology of their discovery, and the impact on patient prognosis.

New prognostic markers in neuroblastoma

INTRODUCTION

The hallmark of neuroblastoma is its clinical and biological heterogeneity, with the likelihood of cure varying widely according to age at diagnosis, extent of disease and tumor biology. We hope this review will be useful for understanding part of the unfamiliar neuroblastoma codex.

AREAS COVERED

In the first part of this review, the authors summarize the currently used prognostic factors for risk-adapted therapy, with the focus on clinical management of neuroblastoma patients. In the second part, the authors discuss the evolving prognostic factors for future treatment schemes. A search of online medical research databases was undertaken focusing especially on literature published in the last six years.

EXPERT OPINION

Harnessing the synergy of the various forms of data, including clinical variables and biomarker profiles, would allow mathematical predictive models to be built for the individual patient, which could eventually become molecular targets of specific therapies.

High-throughput molecular and histopathologic profiling of tumor tissue in a novel transplantable model of murine neuroblastoma: new tools for pediatric drug discovery

Using two MYCN transgenic mouse strains, we established 10 transplantable neuroblastoma cell lines via serial orthotopic passage in the adrenal gland. Tissue arrays demonstrate that by histochemistry, vascularity, immunohistochemical staining for neuroblastoma markers, catecholamine analysis, and concurrent cDNA microarray analysis, there is a close correspondence between the transplantable lines and the spontaneous tumors. Several genes closely associated with the pathobiology and immune evasion of neuroblastoma, novel targets that warrant evaluation, and decreased expression of tumor suppressor genes are demonstrated. These studies describe a unique and generalizable approach to expand the utility of transgenic models of spontaneous tumor, providing new tools for preclinical investigation.

Enrichment of neural-related genes in human mesenchymal stem cells from neuroblastoma patients

Neuroblastoma (NB) is one of the most common pediatric solid tumors and, like most human cancers, is characterized by a broad variety of genomic alterations. Although mesenchymal stem cells (MSCs) are known to interact with cancer cells, the relationship between MSCs and metastatic NB cancer cells in bone marrow (BM) is unknown. To obtain genetic evidence about this interaction, we isolated ΒΜ-derived MSCs from children with NB and compared their global expression patterns with MSCs obtained from normal pediatric donors, using the Agilent 44K microarrays. Significance analysis of microarray results with a false discovery rate (FDR) <5% identified 496 differentially expressed genes showing either a 2-fold upregulation or downregulation between both groups of samples. Comparison of gene ontology categories of differentially expressed genes revealed the upregulation of genes categorized as 'neurological system process', 'cell adhesion', 'apoptosis', 'cell surface receptor linked signal transduction', 'intrinsic to membrane' and 'extracellular region'. Among the downregulated genes, several immunology-related terms were the most abundant. These findings provide preliminary genetic evidence of the interaction between MSCs and NB cancer cells in ΒΜ as well as identify relevant biological processes potentially altered in MSCs in response to NB.

New strategies in refractory and recurrent neuroblastoma: translational opportunities to impact patient outcome

Neuroblastoma remains responsible for a disproportionate amount of childhood cancer morbidity and mortality despite recent significant advances in understanding the genetic basis of tumor initiation and progression. About half of newly diagnosed patients can be reliably identified as having tumors of low malignant potential, and these children have cure rates of greater than 95% with little or no cytotoxic therapy. On the other hand, the other half of neuroblastomas typically present in an explosive fashion with widely metastatic disease, and reliable tumor-specific biomarkers have been defined for this phenotype as well. Empiric approaches to high-risk neuroblastoma therapy have relied on dramatic escalation of chemotherapy dose intensity and, recently, the incorporation of targeted immunotherapy, but nearly 50% of children with high-risk disease will be refractory to therapy or suffer a relapse, both of which are invariably fatal. Future improvements in high-risk neuroblastoma outcomes will require the identification of disease and patient-specific oncogenic vulnerabilities that can be leveraged therapeutically. Rational development of novel approaches to neuroblastoma therapy requires forward-thinking strategies to unequivocally prove activity in the relapse setting and, ultimately, efficacy in curing patients when integrated into frontline treatment plans.