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Cancer predictive studies
The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood t...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557058/ https://www.ncbi.nlm.nih.gov/pubmed/33054808 http://dx.doi.org/10.1186/s13062-020-00274-3 |
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author | Amelio, Ivano Bertolo, Riccardo Bove, Pierluigi Candi, Eleonora Chiocchi, Marcello Cipriani, Chiara Di Daniele, Nicola Ganini, Carlo Juhl, Hartmut Mauriello, Alessandro Marani, Carla Marshall, John Montanaro, Manuela Palmieri, Giampiero Piacentini, Mauro Sica, Giuseppe Tesauro, Manfredi Rovella, Valentina Tisone, Giuseppe Shi, Yufang Wang, Ying Melino, Gerry |
author_facet | Amelio, Ivano Bertolo, Riccardo Bove, Pierluigi Candi, Eleonora Chiocchi, Marcello Cipriani, Chiara Di Daniele, Nicola Ganini, Carlo Juhl, Hartmut Mauriello, Alessandro Marani, Carla Marshall, John Montanaro, Manuela Palmieri, Giampiero Piacentini, Mauro Sica, Giuseppe Tesauro, Manfredi Rovella, Valentina Tisone, Giuseppe Shi, Yufang Wang, Ying Melino, Gerry |
author_sort | Amelio, Ivano |
collection | PubMed |
description | The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients. |
format | Online Article Text |
id | pubmed-7557058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75570582020-10-15 Cancer predictive studies Amelio, Ivano Bertolo, Riccardo Bove, Pierluigi Candi, Eleonora Chiocchi, Marcello Cipriani, Chiara Di Daniele, Nicola Ganini, Carlo Juhl, Hartmut Mauriello, Alessandro Marani, Carla Marshall, John Montanaro, Manuela Palmieri, Giampiero Piacentini, Mauro Sica, Giuseppe Tesauro, Manfredi Rovella, Valentina Tisone, Giuseppe Shi, Yufang Wang, Ying Melino, Gerry Biol Direct Review The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients. BioMed Central 2020-10-14 /pmc/articles/PMC7557058/ /pubmed/33054808 http://dx.doi.org/10.1186/s13062-020-00274-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Review Amelio, Ivano Bertolo, Riccardo Bove, Pierluigi Candi, Eleonora Chiocchi, Marcello Cipriani, Chiara Di Daniele, Nicola Ganini, Carlo Juhl, Hartmut Mauriello, Alessandro Marani, Carla Marshall, John Montanaro, Manuela Palmieri, Giampiero Piacentini, Mauro Sica, Giuseppe Tesauro, Manfredi Rovella, Valentina Tisone, Giuseppe Shi, Yufang Wang, Ying Melino, Gerry Cancer predictive studies |
title | Cancer predictive studies |
title_full | Cancer predictive studies |
title_fullStr | Cancer predictive studies |
title_full_unstemmed | Cancer predictive studies |
title_short | Cancer predictive studies |
title_sort | cancer predictive studies |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557058/ https://www.ncbi.nlm.nih.gov/pubmed/33054808 http://dx.doi.org/10.1186/s13062-020-00274-3 |
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