MYCN amplification plus 1p36 loss of heterozygosity predicts ultra high risk in bone marrow metastatic neuroblastoma

Segmental chromosome aberrations as a prognostic factor of neuroblastoma: a meta-analysis and systematic review

Background

Segmental chromosome aberrations, defined as presence of aberrations, deletion, or imbalance in the chromosomal arms, have long been considered as a predictor of poor prognosis of patients with neuroblastoma. The objective of this meta-analysis is to quantitively analyze the hazard ratios (HRs) of different whole or segmental chromosome aberrations for overall survival (OS) rate or event-free survival (EFS) rate of patients with neuroblastoma.

Methods

Relevant studies about chromosome, neuroblastoma, predictor, prognosis, and survival published from the inception to April 2023 in the databases of PubMed, Embase, and Web of Science were searched, screened, and reviewed. The risk of bias of included articles was assessed using the Quality In Prognosis Studies tool. Basic characteristics, HRs of long term (>3 years) EFS and OS with 95% confidence intervals (CIs) of included articles were extracted. A random effects model of DerSimonian-Laird was used to analyze the extracted HRs. For studies that did not report HRs, narrative synthesis was used for summarization.

Results

There were 34 (including 14,356 patients) in 844 searched studies finally included for narrative and quantitative analysis. There were 24 articles rated as low risk of bias and 10 articles rated as moderate. Although the results were inconsistent, the pooled effect of HR for 1p loss was 4.46 (1.88-10.59) for EFS and 2.29 (1.26-4.15) for OS; the pooled effect of HR for 17q gain was 4.81 (3.29-7.04) for EFS and 3.98 (2.11-7.54) for OS; the pooled effect of HR for 11q loss was 2.54 (2.32-3.73) for OS. Results of 1p36 loss, 1p22 loss, 11q23 loss, 11q13-q14 gain, 1q gain, 1q22 gain, 2p gain, 3p loss, 4p loss, 14q loss, 14q32 loss, and other segmental chromosome aberrations were also summarized.

Conclusions

1p loss, 11q loss, and 17q gain were identified as significant independent predictors for long-term OS and EFS of patients with neuroblastoma.

The Role of FDG- PET/CT in Detecting Bone Marrow Involvement in Childhood Solid Tumors

PURPOSE

To compare the results of 18F-Fluorodeoxy positron emission tomography/computed tomography (18 F-FDG-PET/CT) and bone marrow biopsy (BMB) procedures in the initial evaluation of bone marrow involvement (BMI) in pediatric solid tumors.

METHODS

We conducted a retrospective analysis of newly diagnosed pediatric cases with lymphoma, neuroblastoma, Ewing sarcoma, rhabdomyosarcoma. Each case underwent both PET-CT imaging and BMB. Presence of tumor infiltration in BMB specimens and/or positive FDG-PET/CT findings indicate as BMI were regarded as true positive results.

RESULTS

Sixty-four patients were included in the study. BMI was detected in 23/64 (36%) patients, FDG-PET/CT imaging and BMB results were concordant in 54/64 patients. In 9/64 patients the finding was FDG-PET/CT (+), BMB (-) indicating a false negative BMB result. In only 1/64 patients FDG- PET/CT (-), BMB (+), indicating a false negative FDG-PET/CT result. In the whole patient group, the sensitivity, specificity, positive predictive value and negative predictive value of PET/CT and BMB in detecting bone marrow involvement were 95.6%, 100%, 100% and 97.6% and 60.8 %, 100%, 100% and 82%, respectively.

CONCLUSION

PET/CT has a high sensitivity and specificity for the assessing marrow involvement in pediatric solid tumors. We believe that PET/CT imaging should be performed as the first step in diagnostic staging, and BMB may not be necessary in every patient, only in patients with suspicious PET/CT results for bone marrow involvement. Additionally, for a more precise determination of bone marrow involvement, it is reasonable to perform BMB from FDG-retaining areas, using PET/CT as a guide tool.

Identification of an Ultra-High-Risk Subgroup of Neuroblastoma Patients within the High-Risk Cohort Using a Computed Tomography-Based Radiomics Approach

RATIONALE AND OBJECTIVES

To identify ultra-high-risk (UHR) neuroblastoma patients who experienced disease-related mortality within 18 months of diagnosis within the high-risk cohort using computed tomography (CT)-based radiomics analysis.

MATERIALS AND METHODS

A retrospective analysis was conducted on 105 high-risk neuroblastoma patients, divided into a training set (n = 74) and a test set (n = 31). Radiomics features were extracted and selected from arterial phase CT images, and an optimal radiomics signature was established using the support vector machine algorithm. Evaluation metrics, including area under the curve (AUC) and 95% confidence interval (CI), were calculated. Furthermore, the fit and clinical benefit of the signature, along with its correlation with overall survival (OS), were analyzed.

RESULTS

The optimal radiomics signature comprised 11 features. In the training set, AUC and accuracy were 0.911 (95% CI: 0.840-0.982) and 0.892, respectively. In the test set, AUC and accuracy were 0.828 (95% CI: 0.669-0.987) and 0.839, respectively. There was no significant difference between predicted probability and actual probability, and the signature demonstrated net benefit. The concordance index of this signature for predicting OS was 0.743 (95% CI: 0.672-0.814) in the training set and 0.688 (95% CI: 0.566-0.810) in the test set. Moreover, the signature achieved AUC values of 0.832, 0.863, and 0.721 for 1-year, 2-year, and 3-year OS in the training set, and 0.870, 0.836, and 0.638 in the test set for the respective time periods.

CONCLUSION

The utilization of CT-based radiomics signature to identify an UHR subgroup of neuroblastoma patients within the high-risk cohort can help aid in predicting early disease progression.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses

Background

Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy.

Methods

The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248.

Results

Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage.

Conclusions

Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

BPTF in bone marrow provides a potential progression biomarker regulated by TFAP4 through the PI3K/AKT pathway in neuroblastoma

BACKGROUND

Neuroblastoma (NB) is the most common extracranial malignant solid tumor in children, which is highly prone to bone marrow (BM) metastasis. BM can monitor early signs of mild disease and metastasis. Existing biomarkers are insufficient for the diagnosis and treatment of NB. Bromodomain PHD finger transcription factor (BPTF) is an important subunit of the chromatin-remodeling complex that is closely associated with tumors. Here, we evaluated whether BPTF in BM plays an important role in predicting NB progression, and explore the molecular mechanism of BPTF in NB.

METHODS

The clinical relevance of the BPTF was predicted in the GEO (GSE62564) and TARGET database. The biological function of BPTF in NB was investigated by constructing cell lines and employing BPTF inhibitor AU1. Western blot was used to determine the changes of BPTF, TFAP4, PI3K/AKT signaling and Epithelial-mesenchymal transition (EMT) related markers. A total of 109 children with newly diagnosed NB in Beijing Children's Hospital from January 2018 to March 2021 were included in this study. RT-PCR was used to measure the BPTF and TFAP4 expression in BM. The cut-off level was set at the median value of BPTF expression levels.

RESULTS

Databases suggested that BPTF expression was higher in NB and was significantly associated with stage and grade. Proliferation and migration of NB cells were slowed down when BPTF was silenced. Mechanistically, TFAP4 could positively regulate BPTF and promotes EMT process through activating the PI3K/AKT signaling pathway. Moreover, detection of the newly diagnosed BM specimens showed that BPTF expression was significantly higher in high-risk group, stage IV group and BM metastasis group. Children with high BPTF at initial diagnosis were considered to have high risk for disease progression and recurrence. BPTF is an independent risk factor for predicting NB progression.

CONCLUSIONS

A novel and convenient BPTF-targeted humoral detection that can prompt minimal residual and predict NB progression in the early stages of the disease were identified. BPTF inhibitor AU1 is expected to become a new targeted drug for NB therapy. It's also reveal previously unknown mechanisms of BPTF in NB cell proliferation and metastasis through TFAP4 and PI3K/AKT pathways.

Combined analysis of PHOX2B at two time points and its value for further risk stratification in high-risk neuroblastoma

BACKGROUND

Risk stratification of high-risk neuroblastoma (NB) is crucial for exploring treatments. This study aimed to explore the value of minimal residual disease (MRD) based on PHOX2B levels for further risk stratification in high-risk NB.

METHODS

The expression of PHOX2B was monitored at two time points (after two and six cycles of induction chemotherapy, TP1 and TP2, respectively) by real-time polymerase chain reaction (RT-PCR). The clinical characteristics between groups and survival rates were analyzed.

RESULTS

The study included 151 high-risk patients. Positive expression of PHOX2B at diagnosis was seen in 129 cases. PHOX2B was mainly expressed in patients with high lactate dehydrogenase (LDH) and neuron-specific enolase (NSE) levels (p < .001), bone marrow metastasis (p < .001), more than three metastatic organs (p < .001), 11q23 loss of heterozygosity (LOH) (p = .007), and when more events occurred (p = .012). The 4-year EFS rate was significantly lower in patients with positive PHOX2B expression compared to the negative group at diagnosis (32.9% ± 6.2% vs. 74.5% ± 10.1%, p = .005). We stratified the 151 patients into three MRD risk groups: low high-risk (low-HR), with TP1 less than 10^-4 and TP2 less than 10^-4 ; ultra-HR, with TP1 greater than or equal to 10^-2 or TP2 greater than or equal to 10^-4 , and others classified as intermediate-HR. Patients in ultra-HR had the worst survival rate compared with other two groups (p = .02). In a multivariate model, MRD risk stratification based on PHOX2B levels at TP1 and TP2 was an independent prognostic factor for high-risk patients (p = .001). Patients in ultra-HR were associated with 11q23 LOH (p < .001), more than three organs of metastasis (p = .005), bone marrow metastasis (p < .001), and occurrence of more events (p = .009).

CONCLUSIONS

MRD risk stratification based on PHOX2B levels at two time points (after two and six cycles of induction chemotherapy) provided a stratification system for high-risk NB, which successfully predicted treatment outcomes. Our results present an effective method for further stratification of high-risk NB.

18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma

Objective

To develop and validate an ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma.

Methods

A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).

Results

Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility.

Conclusion

The ¹⁸F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01283-8.