Association of heterogeneous MYCN amplification with clinical features, biological characteristics and outcomes in neuroblastoma: A report from the Children's Oncology Group

Construction of methylation driver gene-related prognostic signature and development of a new prognostic stratification strategy in neuroblastoma

BACKGROUND

Aberrant DNA methylation is one of the major epigenetic alterations in neuroblastoma.

OBJECTIVE

Exploring the prognostic significance of methylation driver genes in neuroblastoma could help to comprehensively assess patient prognosis.

METHODS

After identifying methylation driver genes (MDGs), we used the LASSO algorithm and stepwise Cox regression to construct methylation driver gene-related risk score (MDGRS), and evaluated its predictive performance by multiple methods. By combining risk grouping and MDGRS grouping, we developed a new prognostic stratification strategy and explored the intrinsic differences between the different groupings.

RESULTS

We identified 44 stably expressed MDGs in neuroblastoma. MDGRS showed superior predictive performance in both internal and external cohorts and was strongly correlated with immune-related scores. MDGRS can be an independent prognostic factor for neuroblastoma, and we constructed the nomogram to facilitate clinical application. Based on the new prognostic stratification strategy, we divided the patients into three groups and found significant differences in overall prognosis, clinical characteristics, and immune infiltration between the different subgroups.

CONCLUSION

MDGRS was an accurate and promising tool to facilitate comprehensive pre-treatment assessment. And the new prognostic stratification strategy could be helpful for clinical decision making.

Narrative review: precision medicine applications in neuroblastoma-current status and future prospects

Background and Objective

Neuroblastoma (NB) is a common malignant tumor in children, and its treatment remains challenging. Precision medicine, as an individualized treatment strategy, aims to improve efficacy and reduce toxicity by combining unique patient- and tumor-related factors, bringing new hope for NB treatment. In this article, we review the evidence related to precision medicine in NB, with a focus on potential clinically actionable targets and a series of targeted drugs associated with NB.

Methods

We conducted an extensive search in PubMed, EMBASE, and Web of Science using key terms and database-specific strategies, filtered for time and language, to ensure a comprehensive collection of literature related to precision medicine in NB. The main search terms consisted of "neuroblastoma", "precision medicine", "pediatrics", and "targeting". The articles included in this study encompass those published from 1985 to the present, without restrictions on the type of articles.

Key Content and Findings

ALK inhibitors and MYCN inhibitors have been developed to interfere with tumor cell growth and dissemination, thereby improving treatment outcomes. Additionally, systematic testing to identify relevant driver mutations is crucial and can be used for diagnosis and prognostic assessment through the detection of many associated molecular markers. Furthermore, liquid biopsy, a non-invasive tumor detection method, can complement tissue biopsy and play a role in NB by analyzing circulating tumor DNA and circulating tumor cells to provide genetic information and molecular characteristics of the tumor. Recently, trials conducted by many pediatric oncology groups have shown the urgent need for new approaches to cure relapsed and refractory patients.

Conclusions

The purpose of this review is to summarize the latest advances in clinical treatment of NB, to better understand and focus on the development of promising treatment approaches, and to expedite the transition to the precision medicine clinical relevance in NB patients.

Risk factors and novel predictive models for metastatic neuroblastoma in children

INTRODUCTION

Neuroblastoma (NB) with distant metastasis (DM) is a high-risk condition with a poor prognosis. Early identify the risk and prognostic differences of DM in children, which is helpful for the development of clinical diagnosis and treatment.

METHODS

The study cohort included patients with NB in surveillance, epidemiological, and final outcome databases between 2010 and 2018. To identify the risk and prognostic factors for DM, both univariate and multivariate logistic and Cox regression analyses were conducted. In addition, we created and verified three online clinical prediction models. Finally, we assess the performance of the proposed predictive model.

RESULTS

Among the 1224 children with NB included in the study, 599 developed DM. Primary site is the most important factor affecting metastasis and prognosis. The training and validation groups of the diagnostic nomograms had area under curves (AUC) values of 0.872 and 0.824, respectively. In addition, in the training group, the AUC values at 12, 36, and 60 months were 0.68, 0.71, and 0.75 for the OS nomogram and 0.70, 0.72, and 0.75 for the CSS nomogram. In the validation group, the AUC values at 12, 36, and 60 months were 0.68, 0.72, and 0.70 for the OS nomogram and 0.67, 0.71, and 0.69 for the CSS nomogram, respectively. Calibration curve and decision curve analyses revealed good performance of the nomogram.

CONCLUSIONS

The nomogram developed in this study could appropriately predict DM and assess its prognosis in patients with NB.

Development and validation of a nomogram for predicting survival in intermediate- and high-risk neuroblastoma of the Children's Oncology Group risk stratification

PURPOSE

To develop and validate a nomogram for predicting survival in intermediate- and high-risk neuroblastoma patients and to compare the accuracy of the nomogram in predicting survival with Children's Oncology Group (COG) risk stratification.

METHODS

A total of 885 intermediate- and high-risk neuroblastoma patients were enrolled in this study, including 243 patients from our hospital (the training set) and 642 patients from the TARGET database (the validation set). The factors related to event-free survival (EFS) and overall survival (OS) in neuroblastoma were determined to construct the nomogram by Cox regression analysis. The C-index, calibration curves, and area under the time-dependent receiver operating characteristic curves (AUCs) were used to assess the predictive performance of the nomogram.

RESULTS

International Neuroblastoma Staging System stage and Mitosis-karyorrhexis index (MKI) were significant unfavorable factors for EFS, while MKI and MYCN status were significant unfavorable factors for OS. The C-index of the nomogram was 0.621 and 0.586 for predicting EFS, 0.650 and 0.570 for predicting OS in the training and validation sets, respectively. The calibration curves revealed good agreement in the EFS and OS predicted by the nomogram. The AUCs of the nomogram for 1-, 2-, 3-year EFS and OS were 0.633, 0.669, 0.604 and 0.672, 0.670, 0.702 in the training set, respectively. Moreover, the nomogram was able to classify patients into two groups according to risk scores, with the "high-risk" group having a lower survival rate than the "intermediate-risk" group. And the nomogram performed better than the COG risk stratification, which had a C-index of 0.537, 0.502 and 0.565, 0.572 for predicting EFS, OS in the training and validation sets, respectively.

CONCLUSION

We developed and validated a prognostic nomogram for intermediate- and high-risk neuroblastoma patients that clinicians can use to make more informed decisions for individual patients.

Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature

OBJECTIVES

To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients.

METHODS

A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757-0.963) and 0.824 (95% CI, 0.657-0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility.

CONCLUSION

Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy.

CRITICAL RELEVANCE STATEMENT

Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma.

KEY POINTS

• Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification.

Mouse models of pediatric high-grade gliomas with MYCN amplification reveal intratumoral heterogeneity and lineage signatures

Pediatric high-grade gliomas of the subclass MYCN (HGG-MYCN) are highly aggressive tumors frequently carrying MYCN amplifications, TP53 mutations, or both alterations. Due to their rarity, such tumors have only recently been identified as a distinct entity, and biological as well as clinical characteristics have not been addressed specifically. To gain insights into tumorigenesis and molecular profiles of these tumors, and to ultimately suggest alternative treatment options, we generated a genetically engineered mouse model by breeding hGFAP-cre::Trp53Fl/Fl::lsl-MYCN mice. All mice developed aggressive forebrain tumors early in their lifetime that mimic human HGG-MYCN regarding histology, DNA methylation, and gene expression. Single-cell RNA sequencing revealed a high intratumoral heterogeneity with neuronal and oligodendroglial lineage signatures. High-throughput drug screening using both mouse and human tumor cells finally indicated high efficacy of Doxorubicin, Irinotecan, and Etoposide as possible therapy options that children with HGG-MYCN might benefit from.

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.

Gene expression-based dissection of inter-histotypes, intra-histotype and intra-tumor heterogeneity in pediatric tumors

Intra-tumor heterogeneity (ITH) fosters tumor evolution, resistance to therapy, and relapse. Recently, many evidence have been accumulated on the occurrence of genetic ITH in pediatric cancers. With this study we aimed to address the downstream effects that genetic and epigenetic ITH, and tumor-microenvironment interactions may produce within a tumor mass. To this aim, we investigated by high-throughput gene expression multiple samples of 5 hepatoblastomas, 5 neuroblastomas, 5 rhabdomyosarcomas, and 5 Wilms tumors. Principal component analysis, single sample hallmark gene sets analysis, and weighted gene co-expression network analysis were performed on gene expression data. We observed that the different tumors clustered by histotype, and then by case, and in addition, a variable degree of ITH was visible in all the investigated cases. The ITH highlighted in this study can represent a challenge in tumor treatment since we demonstrated that different druggable hallmarks and targets may be heterogeneously present within the same tumor mass, and this can potentially lead to therapeutic failure. Despite this heterogeneity, we could highlight some commonalities among the different histotypes investigated, supporting the feasibility to move in the clinic from a histotype-driven to a target-driven, sometimes agnostic, approach at least in some cases.

CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma

PURPOSE

MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)-based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma.

METHODS

From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children's hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features.

RESULTS

One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85-1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64-1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher "GreyLevelNonUniformity" values, lower "Strength" values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification.

CONCLUSION

The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.

Radiogenomics prediction for MYCN amplification in neuroblastoma: A hypothesis generating study

BACKGROUND

MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information.

PROCEDURE

In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status. NB lesions were segmented on pretherapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome.

RESULTS

Seventy-eight patients were included in this study, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p = .0082) and zone size non-uniformity (p = .038). Five-times repeated three-fold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data.

CONCLUSIONS

CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.

Targeting MYCN in Pediatric and Adult Cancers

The deregulation of the MYC family of oncogenes, including c-MYC, MYCN and MYCL occurs in many types of cancers, and is frequently associated with a poor prognosis. The majority of functional studies have focused on c-MYC due to its broad expression profile in human cancers. The existence of highly conserved functional domains between MYCN and c-MYC suggests that MYCN participates in similar activities. MYC encodes a basic helix-loop-helix-leucine zipper (bHLH-LZ) transcription factor (TF) whose central oncogenic role in many human cancers makes it a highly desirable therapeutic target. Historically, as a TF, MYC has been regarded as “undruggable”. Thus, recent efforts focus on investigating methods to indirectly target MYC to achieve anti-tumor effects. This review will primarily summarize the recent progress in understanding the function of MYCN. It will explore efforts at targeting MYCN, including strategies aimed at suppression of MYCN transcription, destabilization of MYCN protein, inhibition of MYCN transcriptional activity, repression of MYCN targets and utilization of MYCN overexpression dependent synthetic lethality.

Molecular targeting therapies for neuroblastoma: Progress and challenges

There is an urgent need to identify novel therapies for childhood cancers. Neuroblastoma is the most common pediatric solid tumor, and accounts for ~15% of childhood cancer‐related mortality. Neuroblastomas exhibit genetic, morphological and clinical heterogeneity, which limits the efficacy of existing treatment modalities. Gaining detailed knowledge of the molecular signatures and genetic variations involved in the pathogenesis of neuroblastoma is necessary to develop safer and more effective treatments for this devastating disease. Recent studies with advanced high‐throughput “omics” techniques have revealed numerous genetic/genomic alterations and dysfunctional pathways that drive the onset, growth, progression, and resistance of neuroblastoma to therapy. A variety of molecular signatures are being evaluated to better understand the disease, with many of them being used as targets to develop new treatments for neuroblastoma patients. In this review, we have summarized the contemporary understanding of the molecular pathways and genetic aberrations, such as those in MYCN, BIRC5, PHOX2B, and LIN28B, involved in the pathogenesis of neuroblastoma, and provide a comprehensive overview of the molecular targeted therapies under preclinical and clinical investigations, particularly those targeting ALK signaling, MDM2, PI3K/Akt/mTOR and RAS‐MAPK pathways, as well as epigenetic regulators. We also give insights on the use of combination therapies involving novel agents that target various pathways. Further, we discuss the future directions that would help identify novel targets and therapeutics and improve the currently available therapies, enhancing the treatment outcomes and survival of patients with neuroblastoma.