Cargando…
Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers
Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to d...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665576/ https://www.ncbi.nlm.nih.gov/pubmed/23819556 http://dx.doi.org/10.1186/1471-2164-14-S3-S8 |
_version_ | 1782271275370020864 |
---|---|
author | Reva, B |
author_facet | Reva, B |
author_sort | Reva, B |
collection | PubMed |
description | Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations. Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations. |
format | Online Article Text |
id | pubmed-3665576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36655762013-06-05 Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers Reva, B BMC Genomics Research Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations. Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations. BioMed Central 2013-05-28 /pmc/articles/PMC3665576/ /pubmed/23819556 http://dx.doi.org/10.1186/1471-2164-14-S3-S8 Text en Copyright © 2013 Reva; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Reva, B Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title | Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title_full | Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title_fullStr | Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title_full_unstemmed | Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title_short | Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers |
title_sort | revealing selection in cancer using the predicted functional impact of cancer mutations. application to nomination of cancer drivers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665576/ https://www.ncbi.nlm.nih.gov/pubmed/23819556 http://dx.doi.org/10.1186/1471-2164-14-S3-S8 |
work_keys_str_mv | AT revab revealingselectionincancerusingthepredictedfunctionalimpactofcancermutationsapplicationtonominationofcancerdrivers |