Biomarkers in Neuroblastoma: An Insight into Their Potential Diagnostic and Prognostic Utilities

Prognostic Value of Metabolic Parameters and Textural Features in Pretreatment 18F-FDG PET/CT of Primary Lesions for Pediatric Patients with Neuroblastoma

RATIONALE AND OBJECTIVES

Our study evaluated the prognostic value of the metabolic parameters and textural features in pretreatment 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) of primary lesions for pediatric patients with neuroblastoma.

MATERIALS AND METHODS

In total, 107 pediatric patients with neuroblastoma who underwent pretreatment 18F-FDG PET/CT were retrospectively included and analyzed. All patients were diagnosed by pathology, and baseline characteristics and clinical data were collected. The four metabolic parameters and 43 textural features of 18F-FDG PET/CT of the primary lesions were measured. The prognostic significance of metabolic parameters and other clinical variables was assessed using Cox proportional hazards regression models. Differences in progression-free survival (PFS) and overall survival (OS) in relation to parameters were examined using the Kaplan-Meier method.

RESULTS

During a median follow-up period of 34.3 months, 45 patients (42.1%) experienced tumor recurrence or progression, and 21 patients (19.6%) died of cancer. In univariate Cox regression analysis, age, location of disease, International Neuroblastoma Risk Group Staging System (INRGSS) stage M, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), four positron emission tomography (PET) metabolic parameters, and 33 textural features were significant predictors of PFS. In multivariate analysis, INRGSS stage M (hazard ratio [HR] = 19.940, 95% confidence interval [CI] = 2.733-145.491, P = 0.003), skewness (＞0.173; PET first-order features; HR = 2.938, 95% CI = 1.389-6.215, P = 0.005), coarseness (＞0.003; neighborhood gray-tone difference matrix; HR = 0.253, 95% CI = 0.132-0.484, P ＜ 0.001), and variance (＞103.837; CT first-order gray histogram parameters; HR = 2.810, 95% CI = 1.160-6.807, P = 0.022) were independent predictors of PFS. In univariate Cox regression analysis, gender, INRGSS stage M, MYCN amplification, NSE, LDH, two PET metabolic parameters, and five textural features were significant predictors of OS. In multivariate analysis, INRGSS stage M (HR = 7.704, 95% CI = 1.031-57.576, P = 0.047), MYCN amplification (HR = 3.011, 95% CI = 1.164-7.786, P = 0.023), and metabolic tumor volume (＞138.788; HR = 3.930, 95% CI = 1.317-11.727, P = 0.014) were independent predictors of OS.

CONCLUSION

The metabolic parameters and textural features in pretreatment 18F-FDG PET/CT of primary lesions are predictive of survival in pediatric patients with neuroblastoma.

SESN1 functions as a new tumor suppressor gene via Toll-like receptor signaling pathway in neuroblastoma

AIMS

Neuroblastoma (NB) is the most common extracranial solid tumor in children, with a 5-year survival rate of <50% in high-risk patients. MYCN amplification is an important factor that influences the survival rate of high-risk patients. Our results indicated MYCN regulates the expression of SESN1. Therefore, this study aimed to investigate the role and mechanisms of SESN1 in NB.

METHODS

siRNAs or overexpression plasmids were used to change MYCN, SESN1, or MyD88's expression. The role of SESN1 in NB cell proliferation, migration, and invasion was elucidated. Xenograft mice models were built to evaluate SESN1's effect in vivo. The correlation between SESN1 expression and clinicopathological data of patients with NB was analyzed. RNA-Seq was done to explore SESN1's downstream targets.

RESULTS

SESN1 was regulated by MYCN in NB cells. Knockdown SESN1 promoted NB cell proliferation, cell migration, and cell invasion, and overexpressing SESN1 had opposite functions. Knockdown SESN1 promoted tumor growth and shortened tumor-bearing mice survival time. Low expression of SESN1 had a positive correlation with poor prognosis in patients with NB. RNA-Seq showed that Toll-like receptor (TLR) signaling pathway, and PD-L1 expression and PD-1 checkpoint pathway in cancer were potential downstream targets of SESN1. Knockdown MyD88 or TLRs inhibitor HCQ reversed the effect of knockdown SESN1 in NB cells. High expression of SESN1 was significantly associated with a higher immune score and indicated an active immune microenvironment for patients with NB.

CONCLUSIONS

SESN1 functions as a new tumor suppressor gene via TLR signaling pathway in NB.

Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction

OBJECTIVE

Seek new candidate prognostic markers for neuroblastoma outcome, relapse or progression.

MATERIALS AND METHODS

In this multicentre and retrospective study, Random Forests coupled with recursive feature elimination techniques were applied to electronic records (55 clinical features) of 3034 neuroblastoma patients. To assess model performance and feature importance, dataset was split into a training set (80%) and a test set (20%).

RESULTS

In the test set, the mean Matthews correlation coefficient for the Random Forests models was greater than 0.46. Feature importance analysis revealed that, together with maximum response to first-line treatment (D_MAX_RESP), time to maximum response to first-line treatment (TIME_MAX_RESP.days) is a relevant predictor of both patients' outcome and relapse\progression. We showed the prognostic value of the max response to first-line treatment in clinically relevant subsets of high-, intermediate-, and low-risk patients for both overall and relapse-free survival (Log-rank p-value<0.0001). In high-risk patients older than 18 months and stage 4 tumour achieving a complete response or very good partial response, patients who exhibited a D_MAX_RESP greater than 9 months showed a better prognosis with respect to patients achieving D_MAX_RESP earlier than 9 months (overall survival): hazard ratio 3.3 95% confidence interval 1.8-5.9, Log-rank p-value p < 0.0001; relapse-free survival: 3.2 95%CI 1.8-5.6, Log-rank p-value p < 0.0001).

CONCLUSION

Our findings evidence the emerging role of the TIME_MAX_RESP.days in addition to the D_MAX_RESP as relevant predictors of outcome and relapse\progression in neuroblastoma with potential clinical impact on the management and treatment of patients.

Preclinical Models of Neuroblastoma-Current Status and Perspectives

Preclinical in vitro and in vivo models remain indispensable tools in cancer research. These classic models, including two- and three-dimensional cell culture techniques and animal models, are crucial for basic and translational studies. However, each model has its own limitations and typically does not fully recapitulate the course of the human disease. Therefore, there is an urgent need for the development of novel, advanced systems that can allow for efficient evaluation of the mechanisms underlying cancer development and progression, more accurately reflect the disease pathophysiology and complexity, and effectively inform therapeutic decisions for patients. Preclinical models are especially important for rare cancers, such as neuroblastoma, where the availability of patient-derived specimens that could be used for potential therapy evaluation and screening is limited. Neuroblastoma modeling is further complicated by the disease heterogeneity. In this review, we present the current status of preclinical models for neuroblastoma research, discuss their development and characteristics emphasizing strengths and limitations, and describe the necessity of the development of novel, more advanced and clinically relevant approaches.

Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system

BACKGROUND

To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas.

PROCEDURE

This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated.

RESULTS

The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively.

CONCLUSIONS

CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.

Ewing Sarcoma Displaying Extensive Well Differentiated Neuroblastomatous Differentiation: A Case Report

INTRODUCTION

A tumor with EWSR1/FLI fusion displaying extensive well differentiated neuroblastomatous differentiation is presented.

CASE REPORT

A nine-year-old female patient had a thoracic vertebra 8 paraspinal mass. The lesion was resected incompletely. Histopathologically, a small round cell tumor with gangliomatous differentiation was seen. This was initially diagnosed as an intermixed ganglioneuroblastoma. In the completion surgery biopsy material, the small round cell component was more prominent. Immunohistochemistry for both samples showed membrane positivity for CD99 and nuclear positivity for NKX2.2 in the small round cell component of the tumor. Molecular analysis revealed EWSR1/FLI fusion. The diagnosis then considered a "Ewing Sarcoma Displaying Extensive Well Differentiated Neuroblastomatous Differentiation".

CONCLUSION

Tumors with the EWSR1/FLI fusion may show neuroblastomatous differentiation. We chose to treat this as an Ewing Sarcoma (ES). Recognition of this phenomenon in ES cases may prevent a possible misinterpretation and a failure in oncologic treatment.

Biomarkers and Corresponding Biosensors for Childhood Cancer Diagnostics

Although tremendous progress has been made in treating childhood cancer, it is still one of the leading causes of death in children worldwide. Because cancer symptoms overlap with those of other diseases, it is difficult to predict a tumor early enough, which causes cancers in children to be more aggressive and progress more rapidly than in adults. Therefore, early and accurate detection methods are urgently needed to effectively treat children with cancer therapy. Identification and detection of cancer biomarkers serve as non-invasive tools for early cancer screening, prevention, and treatment. Biosensors have emerged as a potential technology for rapid, sensitive, and cost-effective biomarker detection and monitoring. In this review, we provide an overview of important biomarkers for several common childhood cancers. Accordingly, we have enumerated the developed biosensors for early detection of pediatric cancer or related biomarkers. This review offers a restructured platform for ongoing research in pediatric cancer diagnostics that can contribute to the development of rapid biosensing techniques for early-stage diagnosis, monitoring, and treatment of children with cancer and reduce the mortality rate.

Establishment and validation of a nomogram to predict cancer-specific survival in pediatric neuroblastoma patients

Background

The term "neuroblastoma (NB)" refers to a type of solid pediatric tumor that develops from undivided neuronal cells. According to the American Cancer Society report, between 700 and 800 children under the age of 14 are diagnosed with NB every year in the United States (U.S.). About 6% of all cases of pediatric cancer in the U.S. are caused by NB. NB is the most frequent malignancy in children younger than 1 year; however, it is rarely found in those over the age of 10 and above.

Objective

To accurately predict cancer-specific survival (CSS) in children with NB, this research developed and validated an all-encompassing prediction model.

Methods

The present retrospective study used the Surveillance, Epidemiology, and End Results (SEER) database to collect information on 1,448 individuals diagnosed with NB between 1998 and 2019. The pool of potentially eligible patients was randomly split into two groups, a training cohort (N = 1,013) and a validation cohort (N = 435). Using multivariate Cox stepwise regression, we were able to identify the components that independently predicted outcomes. The accuracy of this nomogram was measured employing the consistency index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis (DCA).

Results

In this study, we found that age, primary location, tumor size, summary stage, chemotherapy, and surgery were all significant predictors of CSS outcomes and integrated them into our model accordingly. The C-index for the validation cohort was 0.812 (95% CI: 0.773-0.851), while for the training cohort it was 0.795 (95% CI: 0.767-0.823). The C-indexes and AUC values show that the nomogram is able to discriminate well enough. The calibration curves suggest that the nomogram is quite accurate. Also, the DCA curves demonstrated the prediction model's value.

Conclusion

A novel nomogram was developed and validated in this work to assess personalized CSS in NB patients, and it has been indicated that this model could be a useful tool for calculating NB patients' survival on an individual basis and enhancing therapeutic decision-making.

Identification of Key Genes Associated with Risk and Prognosis of Neuroblastoma

Neuroblastoma is a childhood malignancy with high morbidity and mortality. We identified key biomarkers associated with neuroblastoma risk and prognosis. The gene modules most associated with neuroblastoma risk were derived by WGCNA. Modular genes were intersected with differentially expressed genes between patients with high-risk (HR) and non-high-risk (NHR) to obtain risk genes, and enrichment analysis was performed. After incorporating risk genes into Cox regression analysis, LASSO algorithm, and K-M survival analysis, key genes were identified and introduced into four external datasets for validation. We performed short time-series expression miner analysis and single-sample genome enrichment analysis. Finally, we evaluated the difference in DNA methylation levels to identify meaningful methylation marks. We identified 5 key genes (ANO6, CPNE2, DST, PLXNC1, SCN3A) for neuroblastoma risk and prognosis, which correlated closely with known neuroblastoma biomarkers. All key genes showed a progressive downregulation trend with increasing risk levels of neuroblastoma. The immune infiltration of 14 immune cells was significantly different between HR-NB and NHR-NB, and most immune cells were negatively correlated with key genes. Furthermore, the expression of ANO6, CPNE2, DST, and PLXNC1 was modified by DNA methylation. This study identified 5 key genes for neuroblastoma risk and prognosis that were potential biomarkers.

From the ashes of "Ewing-like" sarcoma: A contemporary update of the classification, immunohistochemistry, and molecular genetics of round cell sarcomas

Round cell sarcomas include a diverse group of bone and soft tissue tumors, which comprise well-defined entities as well as several nascent categories presented in the 2020 World Health Organization classification. The morphologic overlap yet disparate nosology, prognostic implications, and management strategies places a high value on ancillary testing, including a strategic immunohistochemical approach and directed confirmation by cytogenetic and molecular genetic methods. We review the diagnostic categories that have emerged from the former wastebasket "undifferentiated round cell sarcoma" ("Ewing-like" sarcomas), with an emphasis on algorithmic exclusion of nonsarcomatous entities, diagnostic stratification of well-defined entities (Ewing sarcoma, rhabdomyosarcomas, poorly differentiated synovial sarcoma), and a discussion of the new categories with novel genetic alterations (CIC-rearranged sarcomas, sarcomas with BCOR genetic alterations, and round cell sarcomas with EWSR1-non-ETS fusions).