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Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

BACKGROUND: Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vas...

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Autores principales: Cangelosi, Davide, Blengio, Fabiola, Versteeg, Rogier, Eggert, Angelika, Garaventa, Alberto, Gambini, Claudio, Conte, Massimo, Eva, Alessandra, Muselli, Marco, Varesio, Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3633028/
https://www.ncbi.nlm.nih.gov/pubmed/23815266
http://dx.doi.org/10.1186/1471-2105-14-S7-S12
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author Cangelosi, Davide
Blengio, Fabiola
Versteeg, Rogier
Eggert, Angelika
Garaventa, Alberto
Gambini, Claudio
Conte, Massimo
Eva, Alessandra
Muselli, Marco
Varesio, Luigi
author_facet Cangelosi, Davide
Blengio, Fabiola
Versteeg, Rogier
Eggert, Angelika
Garaventa, Alberto
Gambini, Claudio
Conte, Massimo
Eva, Alessandra
Muselli, Marco
Varesio, Luigi
author_sort Cangelosi, Davide
collection PubMed
description BACKGROUND: Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. RESULTS: Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. CONCLUSIONS: The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.
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spelling pubmed-36330282013-04-25 Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients Cangelosi, Davide Blengio, Fabiola Versteeg, Rogier Eggert, Angelika Garaventa, Alberto Gambini, Claudio Conte, Massimo Eva, Alessandra Muselli, Marco Varesio, Luigi BMC Bioinformatics Research BACKGROUND: Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. RESULTS: Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. CONCLUSIONS: The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences. BioMed Central 2013-04-22 /pmc/articles/PMC3633028/ /pubmed/23815266 http://dx.doi.org/10.1186/1471-2105-14-S7-S12 Text en Copyright © 2013 Cangelosi et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Cangelosi, Davide
Blengio, Fabiola
Versteeg, Rogier
Eggert, Angelika
Garaventa, Alberto
Gambini, Claudio
Conte, Massimo
Eva, Alessandra
Muselli, Marco
Varesio, Luigi
Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title_full Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title_fullStr Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title_full_unstemmed Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title_short Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
title_sort logic learning machine creates explicit and stable rules stratifying neuroblastoma patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3633028/
https://www.ncbi.nlm.nih.gov/pubmed/23815266
http://dx.doi.org/10.1186/1471-2105-14-S7-S12
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