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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Research Network of Computational and Structural Biotechnology
2020
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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. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7044647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT gendoodeenama bioinformaticsandcomputationalapproachesforanalyzingpatientderiveddiseasemodelsincancerresearch |