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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2017
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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. |
format | Online Article Text |
id | pubmed-5415494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT petersonleife progressioninferenceforsomaticmutationsincancer AT kovyrshinatatiana progressioninferenceforsomaticmutationsincancer |