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Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification.

ABSTRACT: Neuroblastoma is a pediatric tumor of the sympathetic nervous system. MYCN (V-myc myelocytomatosis viral-related oncogene, neuroblastoma derived [avian]) is amplified in 20% of neuroblastomas, and these tumors carry a poor prognosis. However, tumors without MYCN amplification also may have a poor outcome. Here, we identified downstream targets of MYCN by shRNA-mediated silencing MYCN in neuroblastoma cells. From these targets, 157 genes showed an expression profile correlating with MYCN mRNA levels in NB88, a series of 88 neuroblastoma tumors, and therefore represent in vivo relevant MYCN pathway genes. This 157-gene signature identified very poor prognosis tumors in NB88 and independent neuroblastoma cohorts and was more powerful than MYCN amplification or MYCN expression alone. Remarkably, this signature also identified poor outcome of a group of tumors without MYCN amplification. Most of these tumors have low MYCN mRNA levels but high nuclear MYCN protein levels, suggesting stabilization of MYCN at the protein level. One tumor has an MYC amplification and high MYC expression. Chip-on-chip analyses showed that most genes in this signature are directly regulated by MYCN. MYCN induces genes functioning in cell cycle and DNA repair while repressing neuronal differentiation genes. The functional MYCN-157 signature recognizes classical neuroblastoma with MYCN amplification, as well as a newly identified group marked by MYCN protein stabilization.

SUBMITTER: Valentijn LJ

PROVIDER: S-EPMC3511149 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification.

Valentijn Linda J LJ Koster Jan J Haneveld Franciska F Aissa Rachida Ait RA van Sluis Peter P Broekmans Marloes E C ME Molenaar Jan J JJ van Nes Johan J Versteeg Rogier R

Proceedings of the National Academy of Sciences of the United States of America 20121022 47

Neuroblastoma is a pediatric tumor of the sympathetic nervous system. MYCN (V-myc myelocytomatosis viral-related oncogene, neuroblastoma derived [avian]) is amplified in 20% of neuroblastomas, and these tumors carry a poor prognosis. However, tumors without MYCN amplification also may have a poor outcome. Here, we identified downstream targets of MYCN by shRNA-mediated silencing MYCN in neuroblastoma cells. From these targets, 157 genes showed an expression profile correlating with MYCN mRNA lev ...[more]

PMID: 23091029

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Project description:PurposeMYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma.MethodsA total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups.ResultsIn total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase.ConclusionThe CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.

Dataset Information

Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification.

Publications

Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification.

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