Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

Background Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome. Results We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo) taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification. Conclusions Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.Foundation KiKaChildren's Neuroblastoma Cancer FoundationSKK FoundationDutch Cancer Societ

Induction of Epithelial Mesenchimal Transition and Vasculogenesis in the Lenses of Dbl Oncogene Transgenic Mice

BACKGROUND: The Dbl family of proteins represents a large group of proto-oncogenes involved in cell growth regulation. The numerous domains that are present in many Dbl family proteins suggest that they act to integrate multiple inputs in complicated signaling networks involving the Rho GTPases. Alterations of the normal function of these proteins lead to pathological processes such as developmental disorders and neoplastic transformation. We generated transgenic mice introducing the cDNA of Dbl oncogene linked to the metallothionein promoter into the germ line of FVB mice and found that onco-Dbl expression in mouse lenses affected proliferation, migration and differentiation of lens epithelial cells. RESULTS: We used high density oligonucleotide microarray to define the transcriptional profile induced by Dbl in the lenses of 2 days, 2 weeks, and 6 weeks old transgenic mice. We observed modulation of genes encoding proteins promoting epithelial-mesenchymal transition (EMT), such as down-regulation of epithelial cell markers and up-regulation of fibroblast markers. Genes encoding proteins involved in the positive regulation of apoptosis were markedly down regulated while anti-apoptotic genes were strongly up-regulated. Finally, several genes encoding proteins involved in the process of angiogenesis were up-regulated. These observations were validated by histological and immunohistochemical examination of the transgenic lenses where vascularization can be readily observed. CONCLUSION: Onco-Dbl expression in mouse lens correlated with modulation of genes involved in the regulation of EMT, apoptosis and vasculogenesis leading to disruption of the lens architecture, epithelial cell proliferation, and aberrant angiogenesis. We conclude that onco-Dbl has a potentially important, previously unreported, capacity to dramatically alter epithelial cell migration, replication, polarization and differentiation and to induce vascularization of an epithelial tissue

Normalization of low-density microarray using external spike-in controls: analysis of macrophage cell lines expression profile

<p>Abstract</p> <p>Background</p> <p>The normalization of DNA microarrays allows comparison among samples by adjusting for individual hybridization intensities. The approaches most commonly used are global normalization methods that are based on the expression of all genes on the slide and on the modulation of a small proportion of genes. Alternative approaches must be developed for microarrays where the proportion of modulated genes and their distribution are unknown and they may be biased towards up- or down-modulated trends.</p> <p>Results</p> <p>The aim of the work is to study the use of spike-in controls to normalize low-density microarrays. Our test-array was designed to analyze gene modulation in response to hypoxia (a condition of low oxygen tension) in a macrophage cell line. RNA was extracted from controls and cells exposed to hypoxia, mixed with spike RNA, labeled and hybridized to our test-array. We used eight bacterial RNAs as source of spikes. The test-array contained the oligonucleotides specific for 178 mouse genes and those specific for the eight spikes. We assessed the quality of the spike signals, the reproducibility of the results and, in general, the nature of the variability. The small values of the coefficients of variation revealed high reproducibility of our platform either in replicated spots or in technical replicates. We demonstrated that the spike-in system was suitable for normalizing our platform and determining the threshold for discriminating the hypoxia modulated genes. We assessed the application of the spike-in normalization method to microarrays in which the distribution of the expression values was symmetric or asymmetric. We found that this system is accurate, reproducible and comparable to other normalization methods when the distribution of the expression values is symmetric. In contrast, we found that the use of the spike-in normalization method is superior and necessary when the distribution of the gene expression is asymmetric and biased towards up-regulated genes.</p> <p>Conclusion</p> <p>We demonstrate that spike-in controls based normalization is a reliable and reproducible method that has the major advantage to be applicable also to biased platform where the distribution of the up- and down-regulated genes is asymmetric as it may occur in diagnostic chips.</p

The l1-l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines

Background: Gene expression signatures are clusters of genes discriminating different statuses of the cells and their definition is critical for understanding the molecular bases of diseases. The identification of a gene signature is complicated by the high dimensional nature of the data and by the genetic heterogeneity of the responding cells. The l[subscript 1]-l[subscript 2] regularization is an embedded feature selection technique that fulfills all the desirable properties of a variable selection algorithm and has the potential to generate a specific signature even in biologically complex settings. We studied the application of this algorithm to detect the signature characterizing the transcriptional response of neuroblastoma tumor cell lines to hypoxia, a condition of low oxygen tension that occurs in the tumor microenvironment. Results: We determined the gene expression profile of 9 neuroblastoma cell lines cultured under normoxic and hypoxic conditions. We studied a heterogeneous set of neuroblastoma cell lines to mimic the in vivo situation and to test the robustness and validity of the l[subscript 1]-l[subscript 2] regularization with double optimization. Analysis by hierarchical, spectral, and k-means clustering or supervised approach based on t-test analysis divided the cell lines on the bases of genetic differences. However, the disturbance of this strong transcriptional response completely masked the detection of the more subtle response to hypoxia. Different results were obtained when we applied the l[subscript 1]-l[subscript 2] regularization framework. The algorithm distinguished the normoxic and hypoxic statuses defining signatures comprising 3 to 38 probesets, with a leave-one-out error of 17%. A consensus hypoxia signature was established setting the frequency score at 50% and the correlation parameter ε equal to 100. This signature is composed by 11 probesets representing 8 well characterized genes known to be modulated by hypoxia. Conclusion: We demonstrate that l[subscript 1]-l[subscript 2] regularization outperforms more conventional approaches allowing the identification and definition of a gene expression signature under complex experimental conditions. The l[subscript 1]-l[subscript 2] regularization and the cross validation generates an unbiased and objective output with a low classification error. We feel that the application of this algorithm to tumor biology will be instrumental to analyze gene expression signatures hidden in the transcriptome that, like hypoxia, may be major determinant of the course of the disease.EU Integrated Project Health-e-ChildFondazione Italiana per la Lotta al NeuroblastomaForeign Investment Review Board (project RBIN04PARL

Methodology Report Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l 1 -l 2 Regularization and Data Reduction

Hypoxia is a condition of low oxygen tension occurring in the tumor and negatively correlated with the progression of the disease. We studied the gene expression profiles of nine neuroblastoma cell lines grown under hypoxic conditions to define gene signatures that characterize hypoxic neuroblastoma. The l 1 -l 2 regularization applied to the entire transcriptome identified a single signature of 11 probesets discriminating the hypoxic state. We demonstrate that new hypoxia signatures, with similar discriminatory power, can be generated by a prior knowledge-based filtering in which a much smaller number of probesets, characterizing hypoxia-related biochemical pathways, are analyzed. l 1 -l 2 regularization identified novel and robust hypoxia signatures within apoptosis, glycolysis, and oxidative phosphorylation Gene Ontology classes. We conclude that the filtering approach overcomes the noisy nature of the microarray data and allows generating robust signatures suitable for biomarker discovery and patients risk assessment in a fraction of computer time

Growth arrest-inducing genes are activated in Dbl-transformed mouse fibroblasts

The Dbl oncogene is a guanine nucleotide exchange factor for Rho GTPases and its activity has been linked to the regulation of gene transcription. Dbl oncogene expression in NIH3T3 cells leads to changes in morphological and proliferative properties of these cells, inducing a highly transformed phenotype. To gain insights into Dbl oncogene-induced transformation we compared gene expression profiles between Dbl oncogene-transformed and parental NIH3T3 cells by cDNA microarray. We found that Dbl oncogene expression is associated with gene expression modulation involving upregulation of 51 genes and downregulation of 49 genes. Five of the overexpressed genes identified are known to exert antiproliferative functions. Our observations suggest that the expression of Dbl oncogene in NIH3T3 may lead to the induction of genes associated with cell cycle arrest, possibly through the activation of stress-induced kinases

Identification of Multiple Hypoxia Signatures in Neuroblastoma Cell Lines by l [subscript 1]-l subscript 2] Regularization and Data Reduction

Hypoxia is a condition of low oxygen tension occurring in the tumor and negatively correlated with the progression of the disease. We studied the gene expression profiles of nine neuroblastoma cell lines grown under hypoxic conditions to define gene signatures that characterize hypoxic neuroblastoma. The l[subscript 1]-l[subscript 2] regularization applied to the entire transcriptome identified a single signature of 11 probesets discriminating the hypoxic state. We demonstrate that new hypoxia signatures, with similar discriminatory power, can be generated by a prior knowledge-based filtering in which a much smaller number of probesets, characterizing hypoxia-related biochemical pathways, are analyzed. l[subscript 1]-l[subscript 2] regularization identified novel and robust hypoxia signatures within apoptosis, glycolysis, and oxidative phosphorylation Gene Ontology classes. We conclude that the filtering approach overcomes the noisy nature of the microarray data and allows generating robust signatures suitable for biomarker discovery and patients risk assessment in a fraction of computer time.Fondazione Italiana per la Lotta al NeuroblastomaItalian Association for Cancer Research (AIRC)Italian Ministry of HealthEU Integrated Project Health-e-Child (IST-2004-027749)Compagnia di San Paolo (Foundation) (Project 4998- ID/CV 2007.0887