Volume 13 Supplement 4
Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome
- Andrea Cornero†1,
- Massimo Acquaviva†1,
- Paolo Fardin1,
- Rogier Versteeg2,
- Alexander Schramm3,
- Alessandra Eva1,
- Maria Carla Bosco1,
- Fabiola Blengio1,
- Sara Barzaghi1 and
- Luigi Varesio1Email author
© Cornero et al.; licensee BioMed Central Ltd. 2012
Published: 28 March 2012
Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.
Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.
We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.
The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.
Neuroblastoma is the most common pediatric solid tumor, deriving from ganglionic lineage precursors of the sympathetic nervous system . It is diagnosed during infancy and shows notable heterogeneity with regard to histology and clinical behavior, ranging from rapid progression associated with metastatic spread and poor clinical outcome to spontaneous, or therapy-induced regression into benign ganglioneuroma. Age at diagnosis, stage, histology, DNA index, chromosomal aberrations, and amplification of the N-myc proto-oncogene (MYCN) are clinical and molecular risk factors commonly combined to classify patients into high, intermediate and low risk subgroups on which current therapeutic strategy is based. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory .
The availability of genomic profiles improved our prognostic ability in many types of cancers including neuroblastoma . Several groups have developed gene expression-based approaches to stratify neuroblastoma patients [4–10]. One approach for patients stratification is to apply feature selection techniques to the patients' datasets to derive gene expression signatures representative of either biological processes related to tumor progression (biology-driven), such as tumor hypoxia [11, 12], risk estimation (risk-driven)  or unsupervised clustering. Several groups used gene expression-based approaches to stratify neuroblastoma patients. Prognostic gene signatures were described and neuroblastoma classifiers were trained to predict the risk class and/or patients 'outcome [4–10].
Prognostic gene expression signatures have often similar performances despite the lack of gene overlapping suggesting that they relate to a common biological feature but derive from a highly variable environment . Combination of the information contained in these signatures should improve the accuracy and/or the predictive power suggesting the potential application of ensemble learning approaches to increase not only the accuracy of the classification, but also the confidence of the results. Ensemble methods were originally developed to enhance classification performance  and have been recently applied to biomarkers identification and feature selection . The general idea of this family of techniques consists in combining lots of different models in a global, more robust, model. The task of combining existing neuroblastoma gene expression signatures is rather complex because they were designed by biology or risk-driven approaches, hence with different finalities and applicability. Furthermore, these signatures were derived using different platforms and datasets thus preventing a straightforward integration. The problem of merging signatures or datasets was recently addressed in breast cancer where it was shown that multiple signatures can lead to robust prognostic when combined to clinical variables and large databases of gene expression . Furthermore, Nuyten et al. demonstrated the relevance of combining biological gene expression signatures into an independent predictor for outcome in breast cancer patients . Recently, Fan et al.  reported the generation of a prognostic model combining hundreds of gene expression signatures to clinical-pathological factors utilizing the Least Absolute Shrinkage and Selection Operator method and a Cox proportional hazards approach.
These results raised the question as to whether we could design an ensemble-based learning approach suitable for integrating gene expression signatures of neuroblastomas tumors where patients stratification is critical for the choice of treatment. Each tumor type has unique biological an clinical attributes and the best performing approaches must be designed accordingly. The potential problem of merging previously established signatures is that their implementation takes them out of the context in which they were generated in terms of experimental platform, dataset, paradigm and finality. We addressed this issue by introducing a meticulous selection of the algorithms for optimal performance of each signature and by building the final single classifier on the predictions rather than on gene expression values.
Clinical characteristics of the 182 neuroblastoma patients analyzed
Age at diagnosis
< = 1 year
> 1 year
5 years survival
Phase 1. Single signature classifier generation
Next, we evaluated the ability of each selected signature to predict patients outcome (Figure 1). This step, although labor intense, is critical to assess performance of each of signatures and to filter out those poorly informative. Each of the 33 signatures was used to train machine learning classifiers predicting neuroblastoma patients' outcome. A panel of 22 classification paradigms implemented by the WEKA package (for ref see ) was tested for each signature to select the best possible classifier. For each signature, the expression data of the 60 patients of dataset DS1 and the associated labels ("Alive"/"Dead") were used to train a classifier in a leave-one-out cross-validation (LOOCV) framework. Thus, 726 classifiers, were generated combining 22 paradigms and 33 signatures (Figure 1).
Phase 2. Classifiers filtering on performance figures
Twenty signatures selected for NB-MuSE-classifier construction
De Preter II
response to NGF
MYCN non amplified
De Preter I
St4 vs St4s
St1 vs St4
Merging individual classifiers into the NB-MuSE classifier
Learns Bayesian nets
Learns Bayesian nets
Class is binarized and one regression model is built for each class value
Builds a complement Näive Bayes classifier
Builds a complement Näive Bayes classifier
De Preter II
De Preter I
Nearest neighbor with generalized distance function
Builds linear logistic regression models
Backpropagation neural network
Backpropagation neural network
Backpropagation neural network
Standard probabilistic Näive Bayes classifier
Builds a deciosion tree with Näive Bayes classifier at the leaves
Builds a decision tree with Näive Bayes classifier at the leaves
Constructs random forest
Builds linear logistic regression models with built-in attribute selection
Voted perceptron algorihtm
Voted perceptron algorihtm
Builds a simple decision table majority classifier
Phase 3. Neuroblastoma Multi-Signature Ensemble classifier training and validation
In the third phase of our analysis we combined the 20 predictions associated with the corresponding signature into a single outcome prediction. For this purpose, we trained a new classifier (NB-MuSE-classifier) on the previously generated dataset containing the 20 prediction applied to the DS2 patients. Similarly to the training and validation step, 22 algorithms were tested to sort out the best performing (Additional file 8). The performance of the NB-MuSE-classifier was validated on the independent dataset DS3. The classification accuracy of the classifiers that were used in the process leading to the selection of the best performing algorithm is detailed in the Additional file 8. The best performance was obtained by the Decision Table algorithm that produced a classifier characterized by an external validation accuracy of 94% (Table 3 and Additional file 8). This accuracy is greater than that shown by the individual algorithms within the context of the current framework and it represents an excellent predictor of neuroblastoma outcome based on gene expression profile. The predictions of the NB-MuSE-classifier relative to that of the individual signatures from which it was derived is shown in the Additional file 8. In conclusion, we combined the predictive power of different signatures to merge survival categories into a single classifier predicting, with high accuracy, the outcome of neuroblastoma patients.
Evaluation of multi-step classification process
Clinical impact of the results
We designed a new prognostic model based on a neuroblastoma classifier, NB-MuSE, that predicts patients' outcome by merging the biological and prognostic information of published gene expression signatures, assessed by a panel of machine learning algorithms, into a single outcome predictor. We examined every neuroblastoma-related signature described in the literature since 2002 without consideration for the purpose for which it was generated or the gene expression platform used. We took the blind screening approach to avoid biases and to include biology-driven signatures, not previously tested for patients stratification, in addition to risk-based signatures. We identified 33 signatures, complete of gene lists, suitable for our study. Patients' outcome was the final readout of the classifier and we had to develop a strategy to filter out poorly information signatures contributing to the background noise. We developed a multi-algorithm screening and an 80% accuracy filter for signature selection. This essential step was based on the overproduce-and-select approach in which a pool of classifiers are spawned and then optimally selected on-the-fly by monitoring accuracy of prediction on an external dataset. We evaluated 22 machine learning algorithms for outcome prediction on the 33 signatures generating 726 prediction to be evaluated for accuracy on an independent dataset. We selected the signatures for which we identified at least one algorithm performing with an accuracy > 80%. Exclusion of a signature from this analysis indicated that we did not identified an algorithm capable of translating those signatures into a predictor in our cohorts or that the signatures were not related to patients' outcome but it does impact on the relevance of those genes in the contest of the original publication. Eleven out of thirty three signatures were discarded. We then matched each of the remaining 20 signatures with the best performing algorithm among those with > 80% accuracy to generate signature specific outcome prediction classifier. In essence, we transformed 20 datasets each with 60 instances (patients) and numeric attributes (probesets expression value) into one dataset with 60 instances and 20 nominal "alive" or "dead" attributes (one per selected signature). The latter dataset could then be used as input to train the new NB-MuSE-classifier merging all the signature information. 22 algorithms were tested to select the best performing which was the Decision Table which builds a simple decision table majority classifier and evaluates features subsets using best-first search and can use cross validation for evaluation (for review see ). Performance can be evaluated by many parameters and there is heterogeneity in the performance of the various algorithms tested as shown by the Additional file 8. The Decision Table algorithm was chosen because it showed maximal accuracy, but other parameters could have been selected to highlight other features like sensitivity or specificity. Ensemble learning approaches have proven to exceed average classifier performance . Our strategy utilizes such strategy to produce a flexible tool merging gene expression signatures overcoming the limitations imposed by specific environments in which they were generated. We observed that, in the absence of signature/algorithm filtering, the accuracy of our classifier fell below 82% a level that was lower than that achieved by individual classifiers. The importance of including these steps in model generation procedures to obtain a more robust and better performing classifier was recently reported . Optimization and filtering is quite labor intense and was not considered, for example, in breast cancer studies merging hundred of gene expression signatures to build classifiers . The high number of signatures available in breast cancer may balance the avoidance of filtering out poorly informative signatures. An automated implementation of this process can be envisioned if this approach was exported to larger lists of signatures.
The accuracy of NB-MuSE-classifier on external validation was 94%, a value that is very high from the biologic stand point. Although there is no logical reason why it cannot be higher, it is difficult to envision a much better precision from a biological standpoint considering the variability of the experimental and clinical data. On the other hand, there is no limit to the number of signatures that can be derived with biological questions in mind. Our model offers a reliable way to keep merging this information into an outcome classifier that will be more robust even if not much more accurate. It is noteworthy that the misclassified patients are grouped in the stage 4 category in agreement with the fact that prognosis of this stage is traditionally difficult. We can speculate that combination of the information of stage and NB-MuSE-classifier could be particularly effective in predicting outcome in patients with localized tumors (stage 1-3) or stage 4s Survival analysis if this group of patients supports this claim showing excellent outcome separation superior to that observed on the whole cohort. however, more patients will have to be tested to substantiate this claim. Similar analysis performed on patients with MYCN amplified tumors showed a significant outcome stratification although not as good as that observed with the whole cohort. We are working on strategies for comparing neuroblastoma gene expression dataset obtained with different platforms in order build a larger data set to address question on smaller groups of patients. We are among the few focusing on the question of merging heterogeneous gene expression signatures to predict outcome. To limit the variability, we considered only gene expression data generated by microarray analysis of the primary neuroblastomas using the Affymetrix platform U133plus2 and we put together 182 primary neuroblastomas, a cohort that is large for this kind of tumor. On the other hand, there was no restriction on the technology used to generate the signatures that turned out to be quite heterogeneous demonstrating that our multistep approach a is suited to work across experimental platforms. This aspect is very important particularly in the field of rare tumors, such as pediatric tumors, where it is extremely difficult to build large homogeneous gene expression datasets and where we may envision that the developing signatures will be based on new experimental platforms.
Affymetrix platform differs largely from the those used in the studies reporting the single classifiers (e.g. two-color gene-expression data from different technological platforms, QPCR analyses etc.). In addition, some of the machine learning algorithms used in the original reports of the classifiers were not part of the panel used in the present study. This may explain discrepancies between the performances of individual signatures that were previously published and that calculated in this work. The problem of downplaying the performance of some signatures is partially offset by the discovery of the prognostic ability of other signatures, a feature not previously shown in the original publications. However, the possible advantage of the MuSE-classifier over presently existing classifiers cannot be easily quantified because we took individual signatures out of their original contest. Table 3 shows that merging signatures into a single classifier results in a predictor with very high accuracy but it does not imply that this value is maximal and considerations on the relative performance of MuSE versus other signatures is valid only in the contest of this work.
The discovery of outcome prediction ability of biology-driven signatures, never tested before for patients stratification, is a spinoff of the process of NB-MuSE-classifier generation. This was true for most of the biology-driven signatures comprising about half of those in the NB-MuSE-classifier [1, 22–24, 26–29, 31] with the exception of those addressing the prognostic significance of hypoxia  and MYC pathway  that had already been validated in patients stratification. Our data bear direct evidence to the suggestion that the biology driven features, measured by the gene expression signatures, such as neuroblast transformation, apoptosis histone deacetylase etc. (Table 2) are strongly interconnected with the progression of the human disease and support the need for further research in this direction .
We describe the design, generation and properties of the NB-MuSE-classifier based on an ensemble approach that merges heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than their numeric values, into a single, highly accurate, patients' outcome predictor. The key of our method is merging several datasets with numeric attributes into one dataset with nominal "alive" or "dead" attributes. The latter dataset could then be used as input to train the new single classifier merging all of the prognostic information of individual signatures through a process which combines individual models into an ensemble of learned models. Inevitably, the framework leading to the MuSE-classifier implied taking the signatures out of the original contest and matching, for example, the genes with the Affymetrix platform probsets. Therefore, the performances calculated by us may be different from that originally reported and considerations on the relative performance should be limited to our framework. On the other hand, our approach showed that signatures can be successfully taken out of their contest retaining their prognostic value. Moreover, the process of NB-MuSE-classifier generation lead to the discovery of the effectiveness of several biology-driven published, signatures to predict outcome suggesting that the biological features measured by such signatures could be mechanistically related to the progression of the human disease.
The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This approach was developed on a Neuroblastoma dataset. However, this model can be exported to other cancer types and to other diseases for which dedicated databases exist.
A total of 182 neuroblastoma patients belonging to four independent cohorts were enrolled on the bases of the availability of gene expression profile by Affymetrix GeneChip HG-U133plus2.0 and clinical and molecular information. Eighty-eight patients were collected by the Academic Medical Center (AMC; Amsterdam, Netherlands) ; 21 patients were collected by the University Children's Hospital, Essen, Germany and were treated according to the German Neuroblastoma trials, either NB97 or NB2004; 51 patients were collected at Hiroshima University Hospital or affiliated hospitals and were treated according to the Japanese neuroblastoma protocols ; 22 patients were collected at Gaslini Institute(Genoa, Italy) and were treated according to Italian AIEOP or European SIOPEN protocols. We utilized the gene expression profiles and associated clinical parameters available at the R2 repository  (AMC and Essen patients), at the BIT-neuroblastoma Biobank of the Gaslini Institute  of which Dr. Varesio coordinates the tumor molecular classification (Genova patients). The instigators who deposited the data in the R2 repository agree to use the data for this work. In addition, we utilized the data present on the public database at the Gene Expression Omnibus number GSE16237) for Hiroshima patients . Informed consent was obtained in accordance with institutional policies in use in each country. In every dataset, median follow-up was longer than 5 years and tumor stage was defined according to the International Neuroblastoma Staging System. The clinical characteristics of the 182 neuroblastoma tumors are listed in Table 1. Good and poor outcome were defined as patient's status (alive or dead) 5 years after diagnosis. The 182 patients dataset was randomized and divided into three subsets (DS1, DS2, and DS3) consisting of 60, 60, and 62 patients respectively. The characteristics of the composition of these datasets are detailed in the Additional file 9. DS1 has been used to train the signatures, DS2 to externally validate the single-signature classifiers and to train the NB-MuSE-classifier, and DS3 for external validation of the NB-MuSE-classifier.
Gene expression analysis
Gene expression profiles for the 182 tumors were obtained by microarray experiment using Affymetrix GeneChip HG-U133plus2.0 and the data were processed by MAS5.0 software according Affymetrix's guideline. For Gaslini's patients specimens total RNA was extracted using Trizol (Invitrogen Life technologies, Irvine, CA) according to the manufacturer's instructions. RNA was resuspended in diethyl pyrocarbonate-treated H2O (DEPC water), the physical quality control of RNA integrity was carried out by electrophoresis using Agilent Bioanalyzer 2100 (Agilent Technologies Waldbronn, Germany) and quantified by NanoDrop (NanoDrop Technologies Wilmington, Delawere USA). Total RNA was reverse transcribed into cDNA and biotin labeled according to the Affymetrix instructions (Affymetrix, SantaClara, CA). Fragmented cRNA was used for hybridization to Affymetrix HG-U133 Plus 2.0 arrays. Expression values were quantified, and array quality control was performed using the statistical algorithms implemented in Affymetrix Microarray Suite 5.0. The scale factors (SF) for all the hybridizations were within 1 SD of the mean (SF 1-3). To asses RNA integrity quality control and RNA digestion plot were used as implemented in the R package "Affy". Data of all datasets were processed by MAS5.0 software according Affymetrix's guideline.
Neuroblastoma gene signatures selection
The Neuroblastoma related signatures were obtained by searching the relevant literature. Specifically, the papers were selected from the literature by Medline search using "neuroblastoma signatures" and "neuroblastoma expression profile" as keywords and limiting the search to articles published after year 2002. The process of selecting out the signatures is part of the workflow and it will be detailed in the results' section. As a result, 33 gene signatures were selected for NB-MuSE-classifier development [1, 4–10, 12, 20–43]. To handle signatures not based on Affymetrix platform or lacking probesets values information, we associated, one Affymetrix GeneChip HG-U133plus2.0 probesets value to each gene of the signature by unpaired t-test on the entire dataset. The best probesets discriminating between the "alive" and "dead" class was picked to represent the unique gene names (Additional file 7). This selection criterion was preferred to other methods, such as mean, median or highest value, which can cause loss of information at the single probesets level and the relevance of the specific signature.
The NB-MuSE-classifier framework, is summarized in Figure 1. The WEKA package  has been chosen to perform all the training and validation steps in our analysis. Gene expression data have been used in linear scale in all the computations. In the first phase, each of the selected 33 gene signatures was used to generate a classifier trained to predict the neuroblastoma patients 'outcome. For each signature, the expression data of the 60 patients of dataset DS1 and the associated 60 "true" outcome labels ("Alive"/"Dead") was provided to WEKA to train a classifier in a leave-one-out cross-validation (LOOCV) framework. A panel of 22 classification algorithms available in Weka  was tested for each signature. The best performing algorithm was selected for each signature according to the prediction accuracy score obtained during LOOCV. As a cut-off, we chose the top three accuracy values for each gene signature. The next step consisted in the external validation of the selected classifiers for each signature by the application of the models to the patients gene expression values included in DS2. The best-performing model for each signature was selected. If the best-performing model had a prediction accuracy < 80%, the associated signature was discarded and no longer considered. The decision was made considering that relevant published classifiers have an accuracy that is generally > 80%. Some algorithms (Bagging, BayesLogisticRegression, ClassificationViaCluster, DecisionTable, FT, IB1, J48, LWL, RandomTree, ZeroR) did did not perform with sufficient accuracy in any of the signatures. The remaining 12 were utilized in the study (BayesNet, ClassificationViaRegression, ComplementNaiveBayes, IBk, KStar, Logistic, MultiLayerPerceptron, NaiveBayes, NBTree, RandomForest, SimpleLogistic, Voted Perceptron). Finally, we trained, tested, and validated the new MuSE-classifier that, during the training phase, takes into account the prediction produced by the application of the selected models to DS2 to produce a single prediction for each patient tested. A training dataset consisting of 60 patients has been assembled from the predictions generated by the 20 classifiers obtained in the previous phase on DS2 (Additional file 1) and, likewise, a validation dataset of 62 patients was assembled from the predictions performed by the same classifiers on DS3 (Additional file 2). Similarly to the training and validation steps performed for each gene signature during the first phase, 22 were tested to select optimal performance. The resulting best performing classifier is our NB-MuSE-classifier.
The probability of overall survival (OS) and event-free survival (EFS) was calculated using Kaplan-Meier method, and the significance of the difference between Kaplan-Meier curves was calculated by the log-rank test using Prism 4.03 (GraphPad Software, Inc.). Accuracy, specificity, and sensitivity were computed to estimate the performance of the predictions performed in the various steps of the study.
List of abbreviations
event free survival
The work was supported by the Fondazione Italiana per la Lotta al Neuroblastoma, the Associazione Italiana per la Ricerca sul Cancro and the Ministero della Salute Italiano and the Associazione Italiana Glicogenosi. Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Analysis", project C1.
The authors would like to thank the Italian Association of Pediatric Hematology/Oncology (AIEOP) for tumor samples collection and Drs. A.Garaventa and C. Gambini for the stimulating discussion. P.F, A.C, and F.B. are recipients of fellowship from the Fondazione Italiana per la Lotta al Neuroblastoma.
This article has been published as part of BMC Bioinformatics Volume 13 Supplement 4, 2012: Italian Society of Bioinformatics (BITS): Annual Meeting 2011. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/13/S4.
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