Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study

Proteomic analysis of serum small extracellular vesicles identifies diagnostic biomarkers for neuroblastoma

Background

Neuroblastoma (NB) primarily arises in children who are <10 years of age, and originates from developing sympathetic nervous system, which results in tumors in adrenal glands and/or sympathetic ganglia. The diagnosis of NB involves a combination of laboratory and imaging tests, and biopsies. Small extracellular vesicles (sEVs) have gained attention as potential biomarkers for various types of tumors. Here, we performed proteomic analysis of serum sEVs and identified potential biomarkers for NB.

Methods

Label-free proteomics of serum sEVs were performed in the discovery phase. A bulk RNA-seq dataset of NB tissues was used to analyze the association between genes encoding sEVs proteins and prognosis. Potential biomarkers were validated via multiple reaction monitoring (MRM) or western blot analysis in the validation phase. A public single-cell RNA-seq (scRNA-seq) dataset was integrated to analyze the tissue origin of sEVs harboring biomarkers.

Results

A total of 104 differentially expressed proteins were identified in NB patients with label-free proteomics, and 26 potential biomarkers were validated with MRM analysis. Seven proteins BSG, HSP90AB1, SLC44A1, CHGA, ATP6V0A1, ITGAL and SELL showed the strong ability to distinguish NB patients from healthy controls and non-NB patients as well. Integrated analysis of scRNA-seq and sEVs proteomics revealed that these sEVs-derived biomarkers originated from different cell populations in tumor tissues.

Conclusion

sEVs-based biomarkers may aid the molecular diagnosis of NB, representing an innovative strategy to improve NB detection and management.

Long Non-Coding RNAs in Neuroblastoma: Pathogenesis, Biomarkers and Therapeutic Targets

Neuroblastoma is the most common malignant extracranial solid tumor of childhood. Recent studies involving the application of advanced high-throughput "omics" techniques have revealed numerous genomic alterations, including aberrant coding-gene transcript levels and dysfunctional pathways, that drive the onset, growth, progression, and treatment resistance of neuroblastoma. Research conducted in the past decade has shown that long non-coding RNAs, once thought to be transcriptomic noise, play key roles in cancer development. With the recent and continuing increase in the amount of evidence for the underlying roles of long non-coding RNAs in neuroblastoma, the potential clinical implications of these RNAs cannot be ignored. In this review, we discuss their biological mechanisms of action in the context of the central driving mechanisms of neuroblastoma, focusing on potential contributions to the diagnosis, prognosis, and treatment of this disease. We also aim to provide a clear, integrated picture of future research opportunities.

Lineage specific transcription factor waves reprogram neuroblastoma from self-renewal to differentiation

Temporal regulation of super-enhancer (SE) driven transcription factors (TFs) underlies normal developmental programs. Neuroblastoma (NB) arises from an inability of sympathoadrenal progenitors to exit a self-renewal program and terminally differentiate. To identify SEs driving TF regulators, we use all-trans retinoic acid (ATRA) to induce NB growth arrest and differentiation. Time-course H3K27ac ChIP-seq and RNA-seq reveal ATRA coordinated SE waves. SEs that decrease with ATRA link to stem cell development (MYCN, GATA3, SOX11). CRISPR-Cas9 and siRNA verify SOX11 dependency, in vitro and in vivo. Silencing the SOX11 SE using dCAS9-KRAB decreases SOX11 mRNA and inhibits cell growth. Other TFs activate in sequential waves at 2, 4 and 8 days of ATRA treatment that regulate neural development (GATA2 and SOX4). Silencing the gained SOX4 SE using dCAS9-KRAB decreases SOX4 expression and attenuates ATRA-induced differentiation genes. Our study identifies oncogenic lineage drivers of NB self-renewal and TFs critical for implementing a differentiation program.

Introducing the f0% method: a reliable and accurate approach for qPCR analysis

BACKGROUND

qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%).

RESULTS

The performance of the new f0% method was compared against the CT method along with another two outstanding methods-LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds.

CONCLUSIONS

We recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases .

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.

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.

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.

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.

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.

Multi-omics integration reveals a six-malignant cell maker gene signature for predicting prognosis in high-risk neuroblastoma

Background

Neuroblastoma is the most common extracranial solid tumor of childhood, arising from the sympathetic nervous system. High-risk neuroblastoma (HRNB) remains a major therapeutic challenge with low survival rates despite the intensification of therapy. This study aimed to develop a malignant-cell marker gene signature (MMGS) that might serve as a prognostic indicator in HRNB patients.

Methods

Multi-omics datasets, including mRNA expression (single-cell and bulk), DNA methylation, and clinical information of HRNB patients, were used to identify prognostic malignant cell marker genes. MMGS was established by univariate Cox analysis, LASSO, and stepwise multivariable Cox regression analysis. Kaplan–Meier (KM) curve and time-dependent receiver operating characteristic curve (tROC) were used to evaluate the prognostic value and performance of MMGS, respectively. MMGS further verified its reliability and accuracy in the independent validation set. Finally, the characteristics of functional enrichment, tumor immune features, and inflammatory activity between different MMGS risk groups were also investigated.

Results

We constructed a prognostic model consisting of six malignant cell maker genes (MAPT, C1QTNF4, MEG3, NPW, RAMP1, and CDT1), which stratified patients into ultra-high-risk (UHR) and common-high-risk (CHR) group. Patients in the UHR group had significantly worse overall survival (OS) than those in the CHR group. MMGS was verified as an independent predictor for the OS of HRNB patients. The area under the curve (AUC) values of MMGS at 1-, 3-, and 5-year were 0.78, 0.693, and 0.618, respectively. Notably, functional enrichment, tumor immune features, and inflammatory activity analyses preliminarily indicated that the poor prognosis in the UHR group might result from the dysregulation of the metabolic process and immunosuppressive microenvironment.

Conclusion

This study established a novel six-malignant cell maker gene prognostic model that can be used to predict the prognosis of HRNB patients, which may provide new insight for the treatment and personalized monitoring of HRNB patients.

Chromosome instability in neuroblastoma: A pathway to aggressive disease

For over 100-years, genomic instability has been investigated as a central player in the pathogenesis of human cancer. Conceptually, genomic instability includes an array of alterations from small deletions/insertions to whole chromosome alterations, referred to as chromosome instability. Chromosome instability has a paradoxical impact in cancer. In most instances, the introduction of chromosome instability has a negative impact on cellular fitness whereas in cancer it is usually associated with a worse prognosis. One exception is the case of neuroblastoma, the most common solid tumor outside of the brain in children. Neuroblastoma tumors have two distinct patterns of genome instability: whole-chromosome aneuploidy, which is associated with a better prognosis, or segmental chromosomal alterations, which is a potent negative prognostic factor. Through a computational screen, we found that low levels of the de- ubiquitinating enzyme USP24 have a highly significant negative impact on survival in neuroblastoma. At the molecular level, USP24 loss leads to destabilization of the microtubule assembly factor CRMP2 - producing mitotic errors and leading to chromosome missegregation and whole-chromosome aneuploidy. This apparent paradox may be reconciled through a model in which whole chromosome aneuploidy leads to the subsequent development of segmental chromosome alterations. Here we review the mechanisms behind chromosome instability and the evidence for the progressive development of segmental alterations from existing numerical aneuploidy in support of a multi-step model of neuroblastoma progression.

Identification of a Five-Gene Signature Derived From MYCN Amplification and Establishment of a Nomogram for Predicting the Prognosis of Neuroblastoma

Background: Neuroblastoma (NB), the most common solid tumor in children, exhibits vastly different genomic abnormalities and clinical behaviors. While significant progress has been made on the research of relations between clinical manifestations and genetic abnormalities, it remains a major challenge to predict the prognosis of patients to facilitate personalized treatments. Materials and Methods: Six data sets of gene expression and related clinical data were downloaded from the Gene Expression Omnibus (GEO) database, ArrayExpress database, and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to the presence or absence of MYCN amplification, patients were divided into two groups. Differentially expressed genes (DEGs) were identified between the two groups. Enrichment analyses of these DEGs were performed to dig further into the molecular mechanism of NB. Stepwise Cox regression analyses were used to establish a five-gene prognostic signature whose predictive performance was further evaluated by external validation. Multivariate Cox regression analyses were used to explore independent prognostic factors for NB. The relevance of immunity was evaluated by using algorithms, and a nomogram was constructed. Results: A five-gene signature comprising CPLX3, GDPD5, SPAG6, NXPH1, and AHI1 was established. The five-gene signature had good performance in predicting survival and was demonstrated to be superior to International Neuroblastoma Staging System (INSS) staging and the MYCN amplification status. Finally, a nomogram based on the five-gene signature was established, and its clinical efficacy was demonstrated. Conclusion: Collectively, our study developed a novel five-gene signature and successfully built a prognostic nomogram that accurately predicted survival in NB. The findings presented here could help to stratify patients into subgroups and determine the optimal individualized therapy.

Evaluation validation of a qPCR curve analysis method and conventional approaches

BACKGROUND

Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle).

RESULTS

We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method--CqMAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system.

CONCLUSIONS

The results show that CqMAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma

BACKGROUND

Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved.

METHODS

Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism.

RESULTS

This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients.

CONCLUSIONS

In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

Efficiency Correction Is Required for Accurate Quantitative PCR Analysis and Reporting

BACKGROUND

Quantitative PCR (qPCR) aims to measure the DNA or RNA concentration in diagnostic and biological samples based on the quantification cycle (Cq) value observed in the amplification curves. Results of qPCR experiments are regularly calculated as if all assays are 100% efficient or reported as just Cq, ΔCq, or ΔΔCq values.

CONTENTS

When the reaction shows specific amplification, it should be deemed to be positive, regardless of the observed Cq. Because the Cq is highly dependent on amplification efficiency that can vary among targets and samples, accurate calculation of the target quantity and relative gene expression requires that the actual amplification efficiency be taken into account in the analysis and reports. PCR efficiency is frequently derived from standard curves, but this approach is affected by dilution errors and hampered by properties of the standard and the diluent. These factors affect accurate quantification of clinical and biological samples used in diagnostic applications and collected in challenging conditions. PCR efficiencies determined from individual amplification curves avoid these confounders. To obtain unbiased efficiency-corrected results, we recommend absolute quantification with a single undiluted calibrator with a known target concentration and efficiency values derived from the amplification curves of the calibrator and the unknown samples.

SUMMARY

For meaningful diagnostics or biological interpretation, the reported results of qPCR experiments should be efficiency corrected. To avoid ambiguity, the Minimal Information for Publications on Quantitative Real-Time PCR Experiments (MIQE) guidelines checklist should be extended to require the methods that were used (1) to determine the PCR efficiency and (2) to calculate the reported target quantity and relative gene expression value.

The Role of Extracellular Vesicles in the Progression of Human Neuroblastoma

The long-underestimated role of extracellular vesicles in cancer is now reconsidered worldwide by basic and clinical scientists, who recently highlighted novel and crucial activities of these moieties. Extracellular vesicles are now considered as king transporters of specific cargoes, including molecular components of parent cells, thus mediating a wide variety of cellular activities both in normal and neoplastic tissues. Here, we discuss the multifunctional activities and underlying mechanisms of extracellular vesicles in neuroblastoma, the most frequent common extra-cranial tumor in childhood. The ability of extracellular vesicles to cross-talk with different cells in the tumor microenvironment and to modulate an anti-tumor immune response, tumorigenesis, tumor growth, metastasis and drug resistance will be pinpointed in detail. The results obtained on the role of extracellular vesicles may represent a panel of suggestions potentially useful in practice, due to their involvement in the response to chemotherapy, and, moreover, their ability to predict resistance to standard therapies—all issues of clinical relevance.

A G316A Polymorphism in the Ornithine Decarboxylase Gene Promoter Modulates MYCN-Driven Childhood Neuroblastoma

Simple Summary

Neuroblastoma is a devasting childhood cancer in which multiple copies (amplification) of the cancer-causing gene MYCN strongly predict poor outcome. Neuroblastomas are reliant on high levels of cellular components called polyamines for their growth and malignant behavior, and the gene regulating polyamine synthesis is called ODC1. ODC1 is often coamplified with MYCN, and in fact is regulated by MYCN, and like MYCN is prognostic of poor outcome. Here we studied a naturally occurring genetic variant or polymorphism that occurs in the ODC1 gene, and used gene editing to demonstrate the functional importance of this variant in terms of ODC1 levels and growth of neuroblastoma cells. We showed that this variant impacts the ability of MYCN to regulate ODC1, and that it also influences outcome in neuroblastoma, with the rarer variant associated with a better survival. This study addresses the important topic of genetic polymorphisms in cancer.

Abstract

Ornithine decarboxylase (ODC1), a critical regulatory enzyme in polyamine biosynthesis, is a direct transcriptional target of MYCN, amplification of which is a powerful marker of aggressive neuroblastoma. A single nucleotide polymorphism (SNP), G316A, within the first intron of ODC1, results in genotypes wildtype GG, and variants AG/AA. CRISPR-cas9 technology was used to investigate the effects of AG clones from wildtype MYCN-amplified SK-N-BE(2)-C cells and the effect of the SNP on MYCN binding, and promoter activity was investigated using EMSA and luciferase assays. AG clones exhibited decreased ODC1 expression, growth rates, and histone acetylation and increased sensitivity to ODC1 inhibition. MYCN was a stronger transcriptional regulator of the ODC1 promoter containing the G allele, and preferentially bound the G allele over the A. Two neuroblastoma cohorts were used to investigate the clinical impact of the SNP. In the study cohort, the minor AA genotype was associated with improved survival, while poor prognosis was associated with the GG genotype and AG/GG genotypes in MYCN-amplified and non-amplified patients, respectively. These effects were lost in the GWAS cohort. We have demonstrated that the ODC1 G316A polymorphism has functional significance in neuroblastoma and is subject to allele-specific regulation by the MYCN oncoprotein.

Non-Coding RNAs Participate in the Pathogenesis of Neuroblastoma

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

Prp19 Is an Independent Prognostic Marker and Promotes Neuroblastoma Metastasis by Regulating the Hippo-YAP Signaling Pathway

Pre-mRNA processing factor 19 (Prp19) was previously reported to be involved in tumor progression. However, Prp19 expression and its functions remain elusive in neuroblastoma. Here, we aim to identify the functions and mechanisms of Prp19 in neuroblastoma. Neuroblastic tumor tissue microarrays and two independent validation data sets indicate that Prp19 is associated with high-risk markers and bone marrow metastasis and serves as a prognostic marker for worse clinical outcomes with neuroblastoma. Gain- and loss-of-expression assays reveal that Prp19 promotes invasion, migration, and epithelial-mesenchymal transition (EMT) of neuroblastoma cells in vitro. Bioinformatics analysis of RNA-seq data shows that the expressions of YAP and its downstream genes are significantly inhibited after downregulation of Prp19. Prp19 and YAP expression in metastatic lymph nodes is higher than in situ neuroblastoma tissue. Further experiments show that Prp19 regulates YAP expression and consequently affects cell invasion, migration, and EMT in neuroblastoma by pre-mRNA splicing of YAP. In conclusion, our findings provide the first evidence that Prp19 is a potential therapeutic target and prognostic biomarker for patients with neuroblastoma.

New Prognostic Indicators in Pediatric Adrenal Tumors: Neuroblastoma and Adrenal Cortical Tumors, Can We Predict When These Will Behave Badly?

Pediatric adrenal tumors are unique entities with specific diagnostic, prognostic, and therapeutic challenges. The adrenal medulla gives rise to peripheral neuroblastic tumors (pNTs), pathologically defined by their architecture, stromal content, degree of differentiation, and mitotic-karyorrhectic index. Successful risk stratification of pNTs uses patient age, stage, tumor histology, and molecular/genetic aberrations. The adrenal cortex gives rise to adrenocortical tumors (ACTs), which present diagnostic and prognostic challenges. Histologic features that signify poor prognosis in adults can be meaningless in children, who have superior outcomes. The key clinical, pathologic, and molecular findings of pediatric ACTs have yet to be completely identified.

Prognostic significance of MYCN related genes in pediatric neuroblastoma: a study based on TARGET and GEO datasets

Background

Neuroblastoma patients with MYCN amplification are associated with poor prognosis. However, the prognostic relevance of MYCN associated genes in neuroblastoma is unclear.

Methods

The expression profiles of MYCN associated genes were identified from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) datasets. Enriched transcription factors and signaling pathways were determined using gene set enrichment analysis (GSEA). Kaplan-Meier plotter was used to identify the prognostic relevance of MYCN associated genes. Multivariate cox regression and Spearman’s correlation were used to determine the correlation coefficients of MYCN associated genes.

Results

In TARGET and GSE85047 datasets, neuroblastoma patients with MYCN amplification were associated with worse prognosis. Transcription factor MYC was positively associated with MYCN amplification in GSEA assay. We identified 13 MYC target genes which were increased in neuroblastoma patients with MYCN amplification in TARGET, GSE19274 and GSE85047 datasets. Moreover, six out of the 13 MYC target genes ARMC6, DCTPP1, EIF4G1, ELOVL6, FBL and PRMT1 were associated with adverse prognosis in TARGET and GSE85047 datasets. Transcription factor E2F1 was up-regulated by MYCN amplification and associated with the poor prognosis of neuroblastoma. Furthermore, RPS19 in ribosome signaling pathway was also associated with MYCN amplification and correlated with the poor prognosis of neuroblastoma. At last, we showed that most of MYCN target genes were correlated with each other. However, EIF4G1 was an independent prognostic marker. And the prognostic effects of the combination of MYCN amplification and EIF4G1 expression were more significant than MYCN or EIF4G1 alone.

Conclusions

MYCN target genes ARMC6, DCTPP1, EIF4G1, ELOVL6, FBL, PRMT1, E2F1 and RPS19 had significant prognostic effects in pediatric neuroblastoma. And neuroblastoma patients without MYCN amplification and low EIF4G1 expression had best prognosis.

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.

Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma

BACKGROUND

Neuroblastoma (NB) is a heterogeneous disease with respect to genomic abnormalities and clinical behaviors. Despite recent advances in our understanding of the association between the genetic aberrations and clinical features, it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this disease. The aim of this study was to develop an effective prognosis prediction model for NB patients.

METHODS

We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p (Chr1p_del) and chromosome 11q (Chr11q_del) as well as chromosome 11q whole loss (Chr11q_wls). We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets (the number of samples ranges from 94 to 498, with a total of 2120) generated from both RNA sequencing and microarray platforms.

RESULTS

MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN expression. Similarly, the Chr1p_del score was more prognostic than Chr1p status. The activity scores of MYCN, Chr1p_del and Chr11q_del were associated with poor prognosis, while the Chr11q_wls score was linked to good outcome. We integrated the activity scores of MYCN, Chr1p_del, Chr11q_del, and Chr11q_wls and clinical variables into an integrative prognostic model, which displayed significant performance over the clinical variables or each genomic aberration alone.

CONCLUSIONS

Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information, and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.

Identifying a ten-microRNA signature as a superior prognosis biomarker in colon adenocarcinoma

Background

Increasing studies have suggested that aberrant expression of microRNAs might play essential roles in the progression of cancers. In this study, we sought to construct a high-specific and superior microRNAs signature to improve the survival prediction of colon adenocarcinoma (COAD) patients.

Methods

The genome-wide miRNAs, mRNA and lncRNA expression profiles and corresponding clinical information of COAD were collected from the TCGA database. Differential expression analysis, Kaplan–Meier curve and time-dependent ROC curve were calculated and performed using R software and GraphPad Prism7. Univariate and multivariate Cox analysis was performed to evaluate the prognostic ability of signature. Functional enrichment analysis was analyzed using STRING database.

Results

We identified ten prognosis-related microRNAs, including seven risky factors (hsa-miR-197, hsa-miR-32, hsa-miR-887, hsa-miR-3199-2, hsa-miR-4999, hsa-miR-561, hsa-miR-210) and three protective factors (hsa-miR-3917, hsa-miR-3189, hsa-miR-6854). The Kaplan–Meier survival analysis showed that the patients with high risk score had shorter overall survival (OS) in test series. And the similar results were observed in both validation and entire series. The time-dependent ROC curve suggested this signature have high accuracy of OS for COAD. The Multivariate Cox regression analysis and stratification analysis suggested that the ten-microRNA signature was an independent factor after being adjusted with other clinical characteristics. In addition, we also found microRNA signature have higher AUC than other signature. Furthermore, we identified some miRNA-target genes that affect lymphatic metastasis and invasion of COAD patients.

Conclusion

In this study, we established a ten-microRNA signature as a potentially reliable and independent biomarker for survival prediction of COAD patients.

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.

A novel data processing method CyC* for quantitative real time polymerase chain reaction minimizes cumulative error

Quantitative real-time polymerase chain reaction (qPCR) is routinely conducted for DNA quantitative analysis using the cycle-threshold (Ct) method, which assumes uniform/optimum template amplification. In practice, amplification efficiencies vary from cycle to cycle in a PCR reaction, and often decline as the amplification proceeds, which results in substantial errors in measurement. This study reveals the cumulative error for quantification of initial template amounts, due to the difference between the assumed perfect amplification efficiency and actual one in each amplification cycle. The novel CyC* method involves determination of both the earliest amplification cycle detectable above background (“outlier” C*) and the amplification efficiency over the cycle range from C* to the next two amplification cycles; subsequent analysis allows the calculation of initial template amount with minimal cumulative error. Simulation tests indicated that the CyC* method resulted in significantly less variation in the predicted initial DNA level represented as fluorescence intensity F₀ when the outlier cycle C^* was advanced to an earlier cycle. Performance comparison revealed that CyC* was better than the majority of 13 established qPCR data analysis methods in terms of bias, linearity, reproducibility, and resolution. Actual PCR test also suggested that relative expression levels of nine genes in tea leaves obtained using CyC* were much closer to the real value than those obtained with the conventional 2^-ΔΔCt method. Our data indicated that increasing the input of initial template was effective in advancing emergence of the earliest amplification cycle among the tested variants. A computer program (CyC* method) was compiled to perform the data processing. This novel method can minimize cumulative error over the amplification process, and thus, can improve qPCR analysis.

The Transcribed-Ultra Conserved Regions: Novel Non-Coding RNA Players in Neuroblastoma Progression

The Transcribed-Ultra Conserved Regions (T-UCRs) are a class of novel non-coding RNAs that arise from the dark matter of the genome. T-UCRs are highly conserved between mouse, rat, and human genomes, which might indicate a definitive role for these elements in health and disease. The growing body of evidence suggests that T-UCRs contribute to oncogenic pathways. Neuroblastoma is a type of childhood cancer that is challenging to treat. The role of non-coding RNAs in the pathogenesis of neuroblastoma, in particular for cancer development, progression, and therapy resistance, has been documented. Exosmic non-coding RNAs are also involved in shaping the biology of the tumor microenvironment in neuroblastoma. In recent years, the involvement of T-UCRs in a wide variety of pathways in neuroblastoma has been discovered. Here, we present an overview of the involvement of T-UCRs in various cellular pathways, such as DNA damage response, proliferation, chemotherapy response, MYCN (v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)) amplification, gene copy number, and immune response, as well as correlate it to patient survival in 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.

Non-High-Risk Neuroblastoma: Classification and Achievements in Therapy

Neuroblastoma, a tumor of the sympathetic nervous system, is the most common extra-cranial neoplasm of childhood. Variables with prognostic significance in patients with neuroblastoma, including age at diagnosis, disease stage, tumor histology, MYCN gene amplification, tumor cell ploidy, and the presence of segmental chromosomal aberrations are utilized to classify patients based on risk of disease recurrence. Patients with non-high-risk neuroblastoma, low- and intermediate-risk categories, represent nearly half of all newly diagnosed cases. This group has an excellent event-free and overall survival with current therapy. Over time, the objective in treatment of non-high-risk neuroblastoma has been reduction of therapy intensity to minimize short- and long-term adverse events all the while maintaining excellent outcomes.

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.

Analysis of the risk factor for the poor prognosis of localized neuroblastoma after the surgical

Neuroblastoma is a unique malignancy in infants often presenting with either localized or metastatic disease. The study was carried out to explore the risk stratification of the poor prognosis for patients underwent surgical treatment.60 patients diagnosed with neuroblastoma were primarily enrolled in the study from April 2008 to April 2016. All the patients underwent surgical treatment and received 5-year follow-up. Clinical variables, including age, International Neuroblastoma Staging System (INSS) stage, tumor size and site, histology, and MYCN status were retrospectively analyzed, and EFS was chosen as the endpoint.The median age of patients was 8.2 months and average follow-up period was 40.2 ± 8.6 months. Among 60 patients, complete remission was achieved in 35 patients and partial remission in 14 subjects. Poor prognosis including patient death and tumor progression were overserved in 11 patients. Cox multifactor regression analysis revealed that age, histology and MYCN status had significant prognostic effect on event-free survival (EFS) rate for neuroblastoma patients underwent surgical treatment.In our study, we identified a series of prognostic factors including age, histology, and MYCN status predicting the prognosis of neuroblastoma patients after surgical treatment.

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.

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.

Too many targets, not enough patients: rethinking neuroblastoma clinical trials

Neuroblastoma is a rare solid tumour of infancy and early childhood with a disproportionate contribution to paediatric cancer mortality and morbidity. Combination chemotherapy, radiation therapy and immunotherapy remains the standard approach to treat high-risk disease, with few recurrent, actionable genetic aberrations identified at diagnosis. However, recent studies indicate that actionable aberrations are far more common in relapsed neuroblastoma, possibly as a result of clonal expansion. In addition, although the major validated disease driver, MYCN, is not currently directly targetable, multiple promising approaches to target MYCN indirectly are in development. We propose that clinical trial design needs to be rethought in order to meet the challenge of providing rigorous, evidence-based assessment of these new approaches within a fairly small patient population and that experimental therapies need to be assessed at diagnosis in very-high-risk patients rather than in relapsed and refractory patients.

EZH2 regulates neuroblastoma cell differentiation via NTRK1 promoter epigenetic modifications

The polycomb repressor complex 2 molecule EZH2 is now known to play a role in essential cellular processes, namely, cell fate decisions, cell cycle regulation, senescence, cell differentiation, and cancer development/progression. EZH2 inhibitors have recently been developed; however, their effectiveness and underlying molecular mechanisms in many malignancies have not yet been elucidated in detail. Although the functional role of EZH2 in tumorigenesis in neuroblastoma (NB) has been investigated, mutations of EZH2 have not been reported. A Kaplan–Meier analysis on the event free survival and overall survival of NB patients indicated that the high expression of EZH2 correlated with an unfavorable prognosis. In order to elucidate the functional roles of EZH2 in NB tumorigenesis and its aggressiveness, we knocked down EZH2 in NB cell lines using lentivirus systems. The knockdown of EZH2 significantly induced NB cell differentiation, e.g., neurite extension, and the neuronal differentiation markers, NF68 and GAP43. EZH2 inhibitors also induced NB cell differentiation. We performed a comprehensive transcriptome analysis using Human Gene Expression Microarrays and found that NTRK1 (TrkA) is one of the EZH2-related suppression targets. The depletion of NTRK1 canceled EZH2 knockdown-induced NB cell differentiation. Our integrative methylome, transcriptome, and chromatin immunoprecipitation assays using NB cell lines and clinical samples clarified that the NTRK1 P1 and P2 promoter regions were regulated differently by DNA methylation and EZH2-related histone modifications. The NTRK1 transcript variants 1/2, which were regulated by EZH2-related H3K27me3 modifications at the P1 promoter region, were strongly expressed in favorable, but not unfavorable NB. The depletion and inhibition of EZH2 successfully induced NTRK1 transcripts and functional proteins. Collectively, these results indicate that EZH2 plays important roles in preventing the differentiation of NB cells and also that EZH2-related NTRK1 transcriptional regulation may be the key pathway for NB cell differentiation.

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.

Immunohistochemical analysis of PDK1, PHD3 and HIF-1α expression defines the hypoxic status of neuroblastoma tumors

Neuroblastoma (NB) is the most common solid tumor during infancy and the first cause of death among the preschool age diseases. The availability of several NB genomic profiles improves the prognostic ability, but the outcome prediction for this pathology remains imperfect. We previously produced a novel prognostic gene signature based on the response of NB cells to hypoxia, a condition of tumor microenvironment strictly connected with cancer aggressiveness. Here we attempted to further define the expression of hypoxia-modulated specific genes, looking at their protein level in NB specimens, considering in particular the hypoxia inducible factor-1α (HIF-1α), the mitochondrial pyruvate dehydrogenase kinase 1 (PDK1), and the HIF-prolyl hydroxylase domain 3 (PHD3). The evaluation of expression was performed by Western blot and immunocytochemistry on NB cell lines and by immunohistochemistry on tumor specimens. Stimulation of both HIF-1α and PDK1 and inhibition of PHD3 expression were observed in NB cell lines cultured under prolonged hypoxic conditions as well as in most of the tumors with poor outcome. Our results indicate that the immunohistochemistry analysis of the protein expression of PDK1, PHD3, and HIF-1α defines the hypoxic status of NB tumors and can be used as a simple and relevant tool to stratify high-risk patients.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment

BACKGROUND: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. METHODS: A cohort of 695 neuroblastoma patients was divided into a discovery set (n = 75) for multigene predictor generation, a training set (n = 411) for risk score development, and a validation set (n = 209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. RESULTS: The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9 ± 3.4 vs 63.6 ± 14.5 vs 31.0 ± 5.4; P < .001), and its prognostic value was validated by multivariable analysis. CONCLUSION: We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients.

Identification of Plagl1/Zac1 binding sites and target genes establishes its role in the regulation of extracellular matrix genes and the imprinted gene network

PLAGL1/ZAC1 undergoes parental genomic imprinting, is paternally expressed, and is a member of the imprinted gene network (IGN). It encodes a zinc finger transcription factor with anti-proliferative activity and is a candidate tumor suppressor gene on 6q24 whose expression is frequently lost in various neoplasms. Conversely, gain of PLAGL1 function is responsible for transient neonatal diabetes mellitus, a rare genetic disease that results from defective pancreas development. In the present work, we showed that Plagl1 up-regulation was not associated with DNA damage-induced cell cycle arrest. It was rather associated with physiological cell cycle exit that occurred with contact inhibition, growth factor withdrawal, or cell differentiation. To gain insights into Plagl1 mechanism of action, we identified Plagl1 target genes by combining chromatin immunoprecipitation and genome-wide transcriptomics in transfected cell lines. Plagl1-elicited gene regulation correlated with multiple binding to the proximal promoter region through a GC-rich motif. Plagl1 target genes included numerous genes involved in signaling, cell adhesion, and extracellular matrix composition, including collagens. Plagl1 targets also included 22% of the 409 genes that make up the IGN. Altogether, this work identified Plagl1 as a transcription factor that coordinated the regulation of a subset of IGN genes and controlled extracellular matrix composition.

Transcription instability in high-risk neuroblastoma is associated with a global perturbation of chromatin domains

Chromosome instability has a pivotal role among the hallmarks of cancer, but its transcriptional counterpart is rarely considered a relevant factor in cell destabilization. To examine transcription instability (TIN), we first devised a metric we named TIN index and used it to evaluate TIN on a dataset containing more than 500 neuroblastoma samples. We found that metastatic tumors from high-risk (HR) patients are characterized by significantly different TIN index values compared to low/intermediate-risk patients. Our results indicate that the TIN index is a good predictor of neuroblastoma patient's outcome, and a related TIN index gene signature (TIN-signature) is also able to predict the neuroblastoma patient's outcome with high confidence. Interestingly, we find that TIN-signature genes have a strong positional association with superenhancers in neuroblastoma tumors. Finally, we show that TIN is linked to chromatin structural domains and interferes with their integrity in HR neuroblastoma patients. This novel approach to gene expression analysis broadens the perspective of genome instability investigations to include functional aspects.

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.

A unified censored normal regression model for qPCR differential gene expression analysis

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as "undetermined". Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.

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.

Metronomic topotecan impedes tumor growth of MYCN-amplified neuroblastoma cells in vitro and in vivo by therapy induced senescence

Poor prognosis and frequent relapses are major challenges for patients with high-risk neuroblastoma (NB), especially when tumors show MYCN amplification. High-dose chemotherapy triggers apoptosis, necrosis and senescence, a cellular stress response leading to permanent proliferative arrest and a typical senescence-associated secretome (SASP). SASP components reinforce growth-arrest and act immune-stimulatory, while others are tumor-promoting. We evaluated whether metronomic, i.e. long-term, repetitive low-dose, drug treatment induces senescence in vitro and in vivo. And importantly, by using the secretome as a discriminator for beneficial versus adverse effects of senescence, drugs with a tumor-inhibiting SASP were identified.

We demonstrate that metronomic application of chemotherapeutic drugs induces therapy-induced senescence, characterized by cell cycle arrest, p21^WAF/CIP1 up-regulation and DNA double-strand breaks selectively in MYCN-amplified NB. Low-dose topotecan (TPT) was identified as an inducer of a favorable SASP while lacking NFKB1/p50 activation. In contrast, Bromo-deoxy-uridine induced senescent NB-cells secret a tumor-promoting SASP in a NFKB1/p50-dependent manner. Importantly, TPT-treated senescent tumor cells act growth-inhibitory in a dose-dependent manner on non-senescent tumor cells and MYCN expression is significantly reduced in vitro and in vivo. Furthermore, in a mouse xenotransplant-model for MYCN-amplified NB metronomic TPT leads to senescence selectively in tumor cells, complete or partial remission, prolonged survival and a favorable SASP.

This new mode-of-action of metronomic TPT treatment, i.e. promoting a tumor-inhibiting type of senescence in MYCN-amplified tumors, is clinically relevant as metronomic regimens are increasingly implemented in therapy protocols of various cancer entities and are considered as a feasible maintenance treatment option with moderate adverse event profiles.

Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma

Accurate assessment of neuroblastoma outcome prediction remains challenging. Therefore, this study aims at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was used to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature.

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.

Neuroblastoma (Peripheral neuroblastic tumours)

Peripheral neuroblastic tumours (PNTs), a family of tumours arising in the embryonal remnants of the sympathetic nervous system, account for 7-10% of all tumours in children. In two-thirds of cases, PNTs originate in the adrenal glands or the retroperitoneal ganglia. At least one third present metastases at onset, with bone and bone marrow being the most frequent metastatic sites. Disease extension, MYCN oncogene status and age are the most relevant prognostic factors, and their influence on outcome have been considered in the design of the recent treatment protocols. Consequently, the probability of cure has increased significantly in the last two decades. In children with localised operable disease, surgical resection alone is usually a sufficient treatment, with 3-year event-free survival (EFS) being greater than 85%. For locally advanced disease, primary chemotherapy followed by surgery and/or radiotherapy yields an EFS of around 75%. The greatest problem is posed by children with metastatic disease or amplified MYCN gene, who continue to do badly despite intensive treatments. Ongoing trials are exploring the efficacy of new drugs and novel immunological approaches in order to save a greater number of these patients.