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Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular...

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Autor principal: Gendoo, Deena M.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044647/
https://www.ncbi.nlm.nih.gov/pubmed/32128067
http://dx.doi.org/10.1016/j.csbj.2020.01.010
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author Gendoo, Deena M.A.
author_facet Gendoo, Deena M.A.
author_sort Gendoo, Deena M.A.
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description Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly ‘omics profiling and bioinformatics analyses, in addressing the ‘efficacy’ of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.
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spelling pubmed-70446472020-03-03 Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research Gendoo, Deena M.A. Comput Struct Biotechnol J Review Article Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly ‘omics profiling and bioinformatics analyses, in addressing the ‘efficacy’ of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models. Research Network of Computational and Structural Biotechnology 2020-02-05 /pmc/articles/PMC7044647/ /pubmed/32128067 http://dx.doi.org/10.1016/j.csbj.2020.01.010 Text en © 2020 The Author http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review Article
Gendoo, Deena M.A.
Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title_full Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title_fullStr Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title_full_unstemmed Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title_short Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
title_sort bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044647/
https://www.ncbi.nlm.nih.gov/pubmed/32128067
http://dx.doi.org/10.1016/j.csbj.2020.01.010
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