- Open Access
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
- Wenqian Zhang†1,
- Ying Yu†2,
- Falk Hertwig†3, 4,
- Jean Thierry-Mieg†5,
- Wenwei Zhang†1,
- Danielle Thierry-Mieg5,
- Jian Wang6,
- Cesare Furlanello7,
- Viswanath Devanarayan8,
- Jie Cheng9,
- Youping Deng10,
- Barbara Hero3,
- Huixiao Hong11,
- Meiwen Jia2,
- Li Li12,
- Simon M Lin13,
- Yuri Nikolsky14,
- André Oberthuer3,
- Tao Qing2,
- Zhenqiang Su11,
- Ruth Volland3,
- Charles Wang15,
- May D. Wang16,
- Junmei Ai10,
- Davide Albanese17,
- Shahab Asgharzadeh18,
- Smadar Avigad19,
- Wenjun Bao12,
- Marina Bessarabova14,
- Murray H. Brilliant20,
- Benedikt Brors21,
- Marco Chierici7,
- Tzu-Ming Chu12,
- Jibin Zhang1,
- Richard G. Grundy22,
- Min Max He13,
- Scott Hebbring20,
- Howard L. Kaufman10,
- Samir Lababidi23,
- Lee J. Lancashire14,
- Yan Li10,
- Xin X. Lu24,
- Heng Luo11, 25,
- Xiwen Ma26,
- Baitang Ning11,
- Rosa Noguera27,
- Martin Peifer4, 28,
- John H. Phan16,
- Frederik Roels3, 4,
- Carolina Rosswog3,
- Susan Shao12,
- Jie Shen11,
- Jessica Theissen3,
- Gian Paolo Tonini29,
- Jo Vandesompele30,
- Po-Yen Wu31,
- Wenzhong Xiao32,
- Joshua Xu11,
- Weihong Xu33,
- Jiekun Xuan11,
- Yong Yang6,
- Zhan Ye13,
- Zirui Dong1,
- Ke K. Zhang34,
- Ye Yin1,
- Chen Zhao2,
- Yuanting Zheng2,
- Russell D. Wolfinger12,
- Tieliu Shi35,
- Linda H. Malkas36,
- Frank Berthold3, 4,
- Jun Wang1, 37, 38, 39,
- Weida Tong11,
- Leming Shi2, 11Email author,
- Zhiyu Peng1, 40Email author and
- Matthias Fischer3, 4Email author
© Zhang et al. 2015
- Received: 10 February 2015
- Accepted: 12 June 2015
- Published: 25 June 2015
Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.
We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.
We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
- Overall Survival
- Prediction Performance
- Clinical Endpoint
- Gene Expression Model
Microarray-based gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction, such as diagnosis, prognosis, or prediction of treatment response [1–5]. The clinical value of some of these classifiers is currently being examined in prospective trials . Within the MicroArray Quality Control (MAQC)-II study , we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.
The advent of next-generation sequencing technologies has revolutionized eukaryotic transcriptome analysis. RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific expression [8–12]. RNA-seq data have also been used to develop gene expression-based predictive models in cancer research [13, 14]. Considering the vast amount of additional information provided by RNA-seq in comparison to microarrays, it is tempting to speculate that RNA-seq-based models may outperform microarray-based models for clinical endpoint prediction. A comprehensive comparison of RNA-seq and microarray-based predictive models, however, is lacking to date.
In this study of the Sequencing Quality Control (SEQC) consortium, we therefore aimed to systematically investigate the potential of RNA-seq-based classifiers in predicting clinical endpoints in comparison to microarrays. To this end, we selected neuroblastoma as a model, a pediatric malignancy arising from the developing sympathetic nervous system . The clinical courses of neuroblastoma are remarkably heterogeneous ranging from spontaneous regression to relentless progression. According to its diverse clinical presentations, patients are stratified into different prognostic subgroups, with therapeutic strategies ranging from ‘wait-and-see’ approaches to intensive multimodal treatments. Thus, accurate prediction of the natural course of the disease is an essential prerequisite for risk estimation and tailoring therapy intensities in individual patients. Treatment stratification in neuroblastoma is currently based on a combination of clinical and molecular parameters, including tumor stage, patient age at diagnosis, and the genomic amplification status of the MYCN proto-oncogene. In addition, a number of microarray-based gene expression models have been proposed to predict neuroblastoma patient outcome [16, 17]. However, while predictive models were highly accurate in risk assessment of current low- and intermediate-risk patients , the prediction of high-risk patient outcome has remained challenging [18–20].
Here, we determined global gene expression profiles from 498 primary neuroblastoma samples using both RNA-seq and Agilent’s 44 k oligonucleotide-microarrays to compare the performance of RNA-seq and microarray-based models in predicting clinical endpoints. We generated 360 gene expression-based models using a broad range of algorithms to predict six different endpoints with a varying degree of predictability, and analyzed the effects of a range of variables on the prediction performances. We found that prediction accuracies were most strongly influenced by the nature of the clinical endpoint, whereas neither the expression profiling technology nor the RNA-seq data analysis pipeline affected prediction accuracy systematically. To our knowledge, we present the first study on the evaluation of predictive models using RNA-seq in comparison to microarrays, which may provide valuable information for designing future experiments on gene expression-based classifiers using high-throughput technologies.
Characterization of the neuroblastoma transcriptome using RNA-seq
Clinical characteristics of neuroblastoma patients
Percent of total
Age at diagnosis
Analysis of differentially expressed genes (DEGs) in four neuroblastoma subgroups
Since gene expression-based prediction of clinical endpoints depends on differing mRNA levels in clinically relevant disease subgroups, we evaluated how analysis of RNA-seq data compares to microarrays in determining differential gene expression in four major clinico-genetic subtypes of neuroblastoma (Additional file 1: Table S1). We first restricted our analysis to the gene level considering only genes that were common to both platforms. DEGs were identified from both platforms using two different analytical methods: (1) a method based on the recommendations of the MAQC-I project utilizing a fold-change ranking and a non-stringent P value cutoff (MAQC-I) [7, 24, 25]; and (2) a novel method developed within the MAQC-III project utilizing the expression distributions, corrected for noise and batch effects, and assisted by random resampling, to compute DEG scores related to the Wilcoxon U test (Magic, see Additional file 1: Supplementary Note 2). On the platform level, we found that RNA-seq detected 5,488 (69.2 %) and 7,827 (67.0 %) of the DEGs identified by microarrays using the MAQC-I and Magic methods, respectively (Additional file 1: Figure S5). On the analytical method level, Magic detected 7,423 (93.5 %) and 6,728 (80.4 %) of the DEGs identified by the MAQC-I method using microarray and RNA-seq data, respectively (Additional file 1: Figure S5). Together, these results demonstrate that both the different platforms and the different analytical methods provide largely comparable results, indicating the validity of the methods used in our study.
To appreciate the comprehensive information provided by RNA-seq for differential gene expression analysis, we performed a second approach in which we applied the Magic pipeline on the full AceView-annotated transcript information in the four neuroblastoma subgroups. In total, we detected 54,164 differentially expressed transcripts corresponding to 21,315 DEGs using RNA-seq (RefSeq, n = 14,251; AceView only, n = 7,064; Additional file 3). By contrast, the 16,245 microarray probes found to be differentially represented in the four subgroups correspond to 11,688 DEGs (RefSeq, n = 10,308; AceView only, n = 1,380). Notably, RNA-seq analysis identified 80.1 % of the DEGs detected by microarrays (Fig. 1c), but in addition many more genes (annotated both in the RefSeq database and in the AceView database only) than microarrays (Additional file 1: Figure S6a). Furthermore, the power of RNA-seq in discovering tumor subtype-specific expression patterns became evident by detecting genes with discordant expression patterns, that is, genes with multiple transcript variants, of which at least one variant was differentially expressed while at least one other was not. We noted that 65.9 % of the 21,315 DEGs identified by RNA-seq showed such discordant expression patterns. As an example, we detected differentially expressed transcript variants of the cancer genes NF1 and MDM4 (Additional file 1: Figure S7 and S8). Both variants have been previously described to be of functional relevance in other cancer entities [26, 27], and were identified by the transcript-based approach only, while the gene-level analysis failed to report the two genes as DEGs. As a particular subgroup of genes with discordant expression patterns, we also determined DEGs of which at least one transcript variant was upregulated while at least one other variant was downregulated in the same subgroup. In this category, we identified 1,073 DEGs by RNA-seq, as opposed to 129 DEGs by microarrays (Additional file 1: Figure S6b). Focusing on cancer census genes , we observed such complex expression patterns for 26 of the current 513 cancer census genes (Additional file 1: Table S2). Together, our findings substantiate that RNA-seq is a powerful tool to determine the complex transcriptomic characteristics of cancer.
Generation of predictive models from RNA-seq and microarray expression data
Definition of clinical endpoints analyzed in this study
Endpoint (bin 1/0)
All patients (498)
Class labeled patients (272)
High-risk patients (176)
Analogous to the strategy used in the MAQC-II study , the following best practice strategies were applied in model development and validation to obtain reliable and robust results: (1) Considering the fact that the nature of the clinical endpoint strongly affects a classifier’s performance [7, 18], we included endpoints that are known to cover a broad range of predictive difficulties (low difficulty, SEX, and CLASS LABEL; intermediate difficulty, EFS ALL, and OS ALL; high difficulty, EFS HR, and OS HR). (2) We involved six data analysis teams and applied various classification methods to take into account that the proficiency of data analysis teams and the choice of the classifier may impact the prediction results. (3) We implemented a two-step modeling strategy to ensure an unbiased validation: first, the models were developed based on a training set and frozen; then, the validation set was released for evaluation of the frozen models. (4) We decided to focus not only on RefSeq-annotated genes being the primary source of common microarrays designs, but also to consider more comprehensive transcriptomic information provided by the AceView database.
Clinical endpoint prediction by RNA-seq- and microarray-based models
We determined prediction performances of all models in terms of MCC for each endpoint in the validation set, and compared performances of RNA-Seq and microarray-based models. RNA-seq-based models performed significantly better than microarray-based models in predicting endpoint EFS ALL (P = 0.043), while models based on the two platforms performed similarly well in predicting the remaining five endpoints (Fig. 2b).
In line with our findings from the MAQC II study , we noticed that prediction performances were strongly influenced by the respective clinical endpoint. While patient’s SEX and CLASS LABEL were predicted accurately, performances were substantially inferior for predicting EFS and OS in the entire cohort, and even worse in the high-risk subgroup by both RNA-seq and microarray-based models (Fig. 2b). To assess the potential clinical value of gene expression-based outcome prediction, we performed univariate Cox regression analysis and Kaplan-Meier survival estimates for patients of the validation set using the best performing RNA-seq and microarray models. The models significantly discriminated patients with favorable and unfavorable outcome in the entire cohort and in the class labeled cohort (Additional file 1: Figure S9a-d, Table S3 and S4), which is consistent with previous observations made by us and others [16–18, 20]. In the high-risk cohort, the best performing models were also able to predict patient outcome, which was in contrast to the prognostic markers age, stage and MYCN status (Additional file 1: Figure S9e and f, Fig. S10, and Table S5). The potential relevance of gene expression-based outcome prediction for neuroblastoma patients was substantiated by multivariate Cox regression analysis (Additional file 1: Table S6).
We also assessed whether performances observed in the validation set could have been estimated from performances determined in the internal cross-validation processes (Fig. 2c and d). Strikingly, correlations of internal and external validation performances for microarray- and RNA-seq-based models were almost identical (r = 0.716 and r = 0.723, respectively), indicating that the technical platform does not affect the reliability of performance estimates obtained during model training. It has to be noted, though, that performances of both RNA-seq- and microarray-based models for endpoints EFS HR and OS HR tended to be biased strongly towards internal validation, which is indicative of model overfitting on the training set. These results suggest that the endpoint itself influences the reliability of performance estimates derived from training cohorts.
Variables affecting prediction performances of RNA-seq-based models
Here, we evaluated the potential of RNA-seq to predict clinical endpoints in comparison to microarrays. We generated gene expression profiles from 498 primary neuroblastoma samples using RNA-seq and microarrays, which represents, to the best of our knowledge, the most comprehensive description of a single cancer entity’s transcriptome. We demonstrate that gene expression profiles of neuroblastoma are tremendously complex, corresponding to findings on the transcriptomic landscape of other human cells published recently [9, 12, 30]. In the entire neuroblastoma cohort, we found 48,415 genes and 204,352 transcripts to be expressed, comprising 86.7 % and 77.3 % of all features annotated in the AceView database, respectively. We also identified >39,000 novel exons to be expressed in neuroblastoma, providing further evidence that the human transcriptome still exceeds the complexity reflected by current reference databases such as RefSeq, Gencode, and AceView. The comparison of gene expression profiles of four major clinico-genetic subgroups revealed that RNA-seq identified almost twice as many DEGs as microarrays. Of note, DEGs determined by RNA-seq comprised 80.1 % of the DEGs detected by microarrays, pointing towards the reliability of identifying DEGs by either method. One reason for the discrepant numbers received by RNA-seq and microarrays derives from the fact that 6,939 DEGs identified by RNA-seq were not represented by a probe on the microarray. In addition, 4,776 DEGs were not detected by microarrays although the genes were represented by a probe, which may be at least partly attributed to our analytical approach which was taking expression profiles at the transcript level into account. Taken together, our study substantiates that RNA-seq is capable of providing much more detailed insights into the transcriptomic characteristics of neuroblastoma than microarrays.
To systematically compare the potential of RNA-seq- and microarray-based models for clinical endpoint prediction, we utilized various data annotation pipelines and considered different feature levels to establish nine expression profiles per sample derived from RNA-seq data, complemented by one expression profile derived from microarray analyses. We generated 360 predictive models for six endpoints covering a broad range of prediction difficulties. Evaluation of the prediction performances in the validation set revealed that the endpoint represents the most relevant factor affecting model performances, which is well in line with the findings of the MAQC-II study . By contrast, neither the technical platform (that is, RNA-seq vs. microarrays) nor the RNA-seq data annotation pipeline significantly affected the variability of prediction performances. Collectively, our data demonstrate that RNA-seq and microarray-based models perform similarly in clinical endpoint prediction.
We also noticed that models based on different feature levels predicted clinical endpoints with comparable accuracies. In turn, this result implies that models based on exon-junction levels perform equally well as models based on gene levels. These findings may impact the development of expression-based classifiers to be used in clinical settings, which are frequently transferred from high-throughput analyses to RT-qPCR-based assays [6, 20]: While assays based on gene expression levels may lack specificity due to uncertainties on the underlying relevant transcript variants, exon-junctions identified by RNA-seq provide an unambiguous source of expression information for developing specific diagnostic tests.
Our results do not support the hypothesis that the more extensive transcriptomic information provided by RNA-seq in comparison to microarrays may improve gene expression-based prediction performances in general. A possible explanation for this finding might be that the inherent complexity of RNA-seq data may promote over-fitting effects in the model development process, leading to over-optimistic internal prediction performances that cannot be reproduced in external validation cohorts . We noted, however, that the correlation of internal and external validation performances was almost identical for RNA-seq and microarray-based models, indicating that over-fitting effects are independent of the technological platform. An alternative explanation for our results may be inferred from the observation that the proportion of RefSeq-annotated features in the prediction models was in the range of, or even above their proportion in the AceView database for most endpoints. This finding may suggest that the predictive information of RefSeq-annotated genes represented by standard microarrays is saturated, and that predictive information of more complex transcriptomic data provided by RNA-seq is largely redundant. It has to be noted, though, that models for endpoints that were difficult to predict (that is, EFS HR, OS HR) tended to disproportionately recruit features that are not annotated in RefSeq, suggesting that these features may considerably contribute to the prediction accuracy in these endpoints.
Both gene expression-based models derived from RNA-seq and microarray analyses were capable of predicting patient outcome in the entire neuroblastoma cohort accurately, thereby validating results from previous studies and underscoring their potential clinical utility for risk estimation in neuroblastoma [16–18, 20]. Notably, we observed that models containing 100 to 1,000 features on average performed better than models containing fewer features. This finding may argue against ambitious efforts to minimize feature numbers in predictive models, as has been done in the past [20, 32]. In addition, we found that the best performing models were able to predict outcome of high-risk patients with a similar precision as previously published multigene signatures [18, 20, 33], and independently from current prognostic markers. While the prognostic value of such multigene signatures needs to be validated in independent high-risk neuroblastoma cohorts, these findings may represent a starting point to establish biomarker-based risk assessment in this challenging patient subgroup.
Our study demonstrates that RNA-seq based models are suitable for clinical endpoint prediction, and that prediction performances are similar to those of microarray-based models. Our findings may be used to guide the design of future studies for developing gene expression-based predictive models as well as their implementation in clinical practice. The key advantage of RNA deep-sequencing, however, resides in its ability to characterize transcriptomes at an unprecedented level of detail, which may lead to new insights into the molecular mechanisms of disease, thereby providing starting points for the development of rational targeted therapeutic strategies.
This project comprised tumor samples of 498 neuroblastoma patients from seven countries: Belgium (n = 1), Germany (n = 420), Israel (n = 11), Italy (n = 5), Spain (n = 14), United Kingdom (n = 5), and United States (n = 42). All patients were registered in respective clinical trials with informed consent. The patients’ age at diagnosis varied from 0 to 295.5 months (median age, 14.6 months). Tumor stage was classified according to the International Neuroblastoma Staging System (INSS): stage 1 (n = 121; MYCN-amplified (MNA), n = 3), stage 2 (n = 78; MNA, n = 5), stage 3 (n = 63; MNA, n = 15), stage 4 (n = 183; MNA, n = 65), stage 4S (n = 53; MNA, n = 4). Events were defined according to a revised version of the International Neuroblastoma Response Criteria .
Gene expression analysis using oligonucleotide microarrays and RNA-sequencing
Tumor material preparation was performed as described previously . For microarray analysis, gene expression profiles were generated using customized 4x44k oligonucleotide microarrays (Agilent Technologies). Sample preparation, labeling, and hybridization were performed according to the manufacturer’s protocol. Microarray expression profiles were generated using Agilent’s Feature Extraction software (Version 9.5.1) . For RNA sequencing, Dynabeads® mRNA Purification Kit (Invitrogen) was used to purify mRNA from total RNA, and ERCC RNA spike-in was added according to the user guide. Library construction was performed according to the non-stranded TruSeqs™ protocol. Clusters were generated according to the TruSeq PE cluster Kit v3 reagent preparation guide (for cBot-HiSeq/HiScanSQ). High-throughput shotgun sequencing was performed on the Illumina HiSeq 2000 platform. Paired-end reads with lengths of 90 and 100 nucleotides were generated for 12 samples and 486 samples, respectively.
Raw data preprocessing, read mapping, and gene expression quantification
The Magic-AceView pipeline (MAV) is based on the Magic analysis tool  (ftp://ftp.ncbi.nlm.nih.gov/repository/acedb/Software/Magic). Magic includes quality control, alignment, annotation, quantification, and normalization of RNA-seq data. Magic maps the read pairs in parallel on all targets, including the genome (NCBI 37), the RefSeq 37.104, the Gencode v15, and the AceView 2011 gene models. Gene expression is measured in sFPKM, adding to the standard FPKM (fragments per kilobase of transcript per million mapped reads) a gene-specific and experiment-specific threshold of significance (sFPKM) for low counts, together with a number of batch effect corrections. The Magic-AceView pipeline is described in detail as a Supplementary Note 1 (Additional file 1).
For the TopHat-AceView (TAV) pipeline, raw reads of RNA-seq were filtered using an in-house pipeline (BGI, Shenzhen, PR China). Clean RNA reads were aligned to the human genome (hg19) using TopHat . Cufflinks was used to quantify the gene as well as transcript expression . FPKM values for each annotated gene in the AceView database were calculated.
For the TopHat-UCSC pipeline (TUC), RNA-seq reads were mapped by TopHat  to a reference sequence consisting of the UCSC hg19 human genome and the RefSeq annotated genes. The mapped reads were processed by Cufflinks 1.3.0 with default parameters to assemble transcripts relying on RefSeq annotation . Gene- and transcript-level expression values were computed by Cufflinks in terms of FPKM and transformed as log2(1 + FPKM) for downstream processing. Reads that align to known junctions were quantified by the Open Source software bam2ssj .
Construction of classification models
Six data analysis teams received 10 expression profiles (nine profiles based on RNA-seq data and one profile based on microarray data) to predict six different endpoints as extensively described in the Results. Data analysts were asked to include a five-fold cross validation for 10 iterations to assess model performances in the training datasets, but were otherwise free in their choice of modeling algorithms and statistical tests to generate and select suitable prediction models. Models with highest average performance metrics were selected and submitted for testing on adequate blinded validation sets for each endpoint. More details and an overview of the applied classification algorithms are given in Additional file 1 (Supplementary Note 3 and Table S9).
Prediction performance evaluation and statistical analyses
Matthew’s correlation coefficient (MCC) was used to evaluate the prediction performance of classification models as described . Differences in model performances were subjected to variance component analysis using Mixed Modeling . All the six major effects were assigned to random effects for partitioning their corresponding variances from total variance. Statistical F- and T- tests were applied for evaluating the significance of corresponding variance components and comparisons among the levels within each variance component, respectively. For statistical analysis of clinical data, IBM SPSS package release 20.0.0 and version 2.15.0 of the survival package in R was applied . Overall survival (OS) was calculated as the time from diagnosis to death from disease or the last follow-up if the patient survived. Event-free survival (EFS) was calculated from diagnosis to the time of tumor progression, relapse, or death from disease or to the last follow-up if no event occurred. Survival curves were computed according to Kaplan-Meier estimates and compared with the log-rank test. Univariate Cox proportional hazards or logistic regression was applied with respect to EFS and OS to analyze the separate prognostic value of gene expression-based classification models or clinical markers, considering P <0.05 as significant. All analyses are regarded as explorative.
Microarray and RNA-seq data can be accessed from the GEO database (www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE49710 and GSE49711, respectively, which are included in SEQC Project SuperSeries GSE47792.
We are grateful to Yvonne Kahlert, Anne Engesser, and Simona Coco for technical assistance. This work was supported by grants from the National High Technology Research and Development Program of China - 863 Program (2015AA020104 to LS and 2012AA02A201 to TS), the National Science Foundation of China (31471239 to LS), the Intramural Research Program of the NIH, National Library of Medicine (USA), the Cologne Center for Molecular Medicine (to MF), the Bundesministerium für Bildung und Forschung (BMBF) through the National Genome Research Network plus (NGFNplus, grant 01GS0895 to MF), the German Cancer Aid (Deutsche Krebshilfe, grant 110122 to MF), the Fördergesellschaft Kinderkrebs-Neuroblastom-Forschung e.V. (to FB and MF), and the National Institutes of Health (U54CA119338, 1RC2CA148265, and R01CA163256) and Georgia Cancer Coalition (to MDW). Tumor sample collection was supported by the BMBF through the Competence Network Pediatric Oncology and Hematology (KPOH) and by the Italian Neuroblastoma Foundation. We gratefully acknowledge support by China’s National Supercomputing Centers in Guangzhou and Tianjin.
The views presented in this article do not necessarily reflect current or future opinion or policy of the US Food and Drug Administration. Any mention of commercial products is for clarification and not intended as endorsement.
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