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Progression inference for somatic mutations in cancer

Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mut...

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Autores principales: Peterson, Leif E., Kovyrshina, Tatiana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415494/
https://www.ncbi.nlm.nih.gov/pubmed/28492066
http://dx.doi.org/10.1016/j.heliyon.2017.e00277
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author Peterson, Leif E.
Kovyrshina, Tatiana
author_facet Peterson, Leif E.
Kovyrshina, Tatiana
author_sort Peterson, Leif E.
collection PubMed
description Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.
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spelling pubmed-54154942017-05-10 Progression inference for somatic mutations in cancer Peterson, Leif E. Kovyrshina, Tatiana Heliyon Article Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer. Elsevier 2017-04-11 /pmc/articles/PMC5415494/ /pubmed/28492066 http://dx.doi.org/10.1016/j.heliyon.2017.e00277 Text en © 2017 The Authors 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 Article
Peterson, Leif E.
Kovyrshina, Tatiana
Progression inference for somatic mutations in cancer
title Progression inference for somatic mutations in cancer
title_full Progression inference for somatic mutations in cancer
title_fullStr Progression inference for somatic mutations in cancer
title_full_unstemmed Progression inference for somatic mutations in cancer
title_short Progression inference for somatic mutations in cancer
title_sort progression inference for somatic mutations in cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415494/
https://www.ncbi.nlm.nih.gov/pubmed/28492066
http://dx.doi.org/10.1016/j.heliyon.2017.e00277
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