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Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted f...

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Autores principales: Petrov, Iurii, Alexeyenko, Andrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159755/
https://www.ncbi.nlm.nih.gov/pubmed/35593700
http://dx.doi.org/10.7554/eLife.74010
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author Petrov, Iurii
Alexeyenko, Andrey
author_facet Petrov, Iurii
Alexeyenko, Andrey
author_sort Petrov, Iurii
collection PubMed
description Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
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spelling pubmed-91597552022-06-02 Individualized discovery of rare cancer drivers in global network context Petrov, Iurii Alexeyenko, Andrey eLife Cancer Biology Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine. eLife Sciences Publications, Ltd 2022-05-20 /pmc/articles/PMC9159755/ /pubmed/35593700 http://dx.doi.org/10.7554/eLife.74010 Text en © 2022, Petrov and Alexeyenko https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Cancer Biology
Petrov, Iurii
Alexeyenko, Andrey
Individualized discovery of rare cancer drivers in global network context
title Individualized discovery of rare cancer drivers in global network context
title_full Individualized discovery of rare cancer drivers in global network context
title_fullStr Individualized discovery of rare cancer drivers in global network context
title_full_unstemmed Individualized discovery of rare cancer drivers in global network context
title_short Individualized discovery of rare cancer drivers in global network context
title_sort individualized discovery of rare cancer drivers in global network context
topic Cancer Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159755/
https://www.ncbi.nlm.nih.gov/pubmed/35593700
http://dx.doi.org/10.7554/eLife.74010
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