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Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm

BACKGROUND: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, t...

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Autores principales: Masecchia, Salvatore, Coco, Simona, Barla, Annalisa, Verri, Alessandro, Tonini, Gian Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566396/
https://www.ncbi.nlm.nih.gov/pubmed/26358114
http://dx.doi.org/10.1186/s12920-015-0132-y
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author Masecchia, Salvatore
Coco, Simona
Barla, Annalisa
Verri, Alessandro
Tonini, Gian Paolo
author_facet Masecchia, Salvatore
Coco, Simona
Barla, Annalisa
Verri, Alessandro
Tonini, Gian Paolo
author_sort Masecchia, Salvatore
collection PubMed
description BACKGROUND: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis. METHOD: We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary. RESULTS: The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases. CONCLUSION: These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-015-0132-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-45663962015-09-12 Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm Masecchia, Salvatore Coco, Simona Barla, Annalisa Verri, Alessandro Tonini, Gian Paolo BMC Med Genomics Research Article BACKGROUND: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis. METHOD: We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary. RESULTS: The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases. CONCLUSION: These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-015-0132-y) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-10 /pmc/articles/PMC4566396/ /pubmed/26358114 http://dx.doi.org/10.1186/s12920-015-0132-y Text en © Masecchia et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Masecchia, Salvatore
Coco, Simona
Barla, Annalisa
Verri, Alessandro
Tonini, Gian Paolo
Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title_full Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title_fullStr Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title_full_unstemmed Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title_short Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
title_sort genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566396/
https://www.ncbi.nlm.nih.gov/pubmed/26358114
http://dx.doi.org/10.1186/s12920-015-0132-y
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