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  • 1
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 13, No. S4 ( 2012-12)
    Abstract: 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. Methods 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. Results 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. Conclusions 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.
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 2
    In: Molecular Cancer, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2010), p. 185-
    Type of Medium: Online Resource
    ISSN: 1476-4598
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 2091373-4
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  • 3
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2010
    In:  Cancer Research Vol. 70, No. 8_Supplement ( 2010-04-15), p. 2002-2002
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 2002-2002
    Abstract: Background Low oxygen tension (hypoxia) is an important determinant in tumor progression. In this study we assess the prognostic value of an in vitro derived hypoxia gene signature in neuroblastoma patients. Material and Methods l1-l2 regularization framework has been applied on gene expression profiles of 11 neuroblastoma cell lines to define the neuroblastoma hypoxia signature. We applied k-means clustering on the expression level of the signature 62 probesets to segregate 88 neuroblastoma patients and subgroups obtained by common risk factors stratification. We analyzed the classes by Kaplan-Meier curves and log-rank test for overall survival (OS) and event-free survival (EFS). Multivariate Cox analysis was performed to define the predictive power of the signature. Results The neuroblastoma hypoxia signature distinguished two groups of neuroblastoma patients classifying them as poor prognosis (21 patients), those having OS rate of 25.5% and EFS rate of 27.7%, and as good prognosis (67 patients), those having OS rate of 73.2% and EFS rate of 67.7%. The poor prognosis patients show an over-expression of the hypoxia probesets. Multivariate Cox analysis revealed that the neuroblastoma hypoxia signature is a significant independent predictor after controlling for commonly used risk factors. When applied to MYCN not amplified patients, the hypoxia signature was capable to stratify patients with OS rate of 24.2% and EFS rate of 27.3% for the patients with poor prognosis, compared with OS rate of 81.4% and EFS rate of 74.8% for the patients with good prognosis. Conclusions We demonstrate that the NB-hypo signature is a significant prognostic factor capable of stratify neuroblastoma patients. Furthermore, we obtained the proof of principle that the approach of hypoxia genes selection from in vitro controlled tumor cell lines, is a feasible method to identify specific contribution of the microenvironment to the tumors’ biology. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2002.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2010
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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