Cargando…

Evaluating somatic tumor mutation detection without matched normal samples

BACKGROUND: Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutation...

Descripción completa

Detalles Bibliográficos
Autores principales: Teer, Jamie K., Zhang, Yonghong, Chen, Lu, Welsh, Eric A., Cress, W. Douglas, Eschrich, Steven A., Berglund, Anders E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584341/
https://www.ncbi.nlm.nih.gov/pubmed/28870239
http://dx.doi.org/10.1186/s40246-017-0118-2
_version_ 1783261461780365312
author Teer, Jamie K.
Zhang, Yonghong
Chen, Lu
Welsh, Eric A.
Cress, W. Douglas
Eschrich, Steven A.
Berglund, Anders E.
author_facet Teer, Jamie K.
Zhang, Yonghong
Chen, Lu
Welsh, Eric A.
Cress, W. Douglas
Eschrich, Steven A.
Berglund, Anders E.
author_sort Teer, Jamie K.
collection PubMed
description BACKGROUND: Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a “normal” sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual. RESULTS: We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43–60%) but lesser precision (20–21%) to current matched tumor/normal approaches (recall 43–73%, precision 30–82%) when compared to a “gold-standard” tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis. CONCLUSIONS: A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40246-017-0118-2) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5584341
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-55843412017-09-06 Evaluating somatic tumor mutation detection without matched normal samples Teer, Jamie K. Zhang, Yonghong Chen, Lu Welsh, Eric A. Cress, W. Douglas Eschrich, Steven A. Berglund, Anders E. Hum Genomics Primary Research BACKGROUND: Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a “normal” sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual. RESULTS: We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43–60%) but lesser precision (20–21%) to current matched tumor/normal approaches (recall 43–73%, precision 30–82%) when compared to a “gold-standard” tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis. CONCLUSIONS: A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40246-017-0118-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-04 /pmc/articles/PMC5584341/ /pubmed/28870239 http://dx.doi.org/10.1186/s40246-017-0118-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Primary Research
Teer, Jamie K.
Zhang, Yonghong
Chen, Lu
Welsh, Eric A.
Cress, W. Douglas
Eschrich, Steven A.
Berglund, Anders E.
Evaluating somatic tumor mutation detection without matched normal samples
title Evaluating somatic tumor mutation detection without matched normal samples
title_full Evaluating somatic tumor mutation detection without matched normal samples
title_fullStr Evaluating somatic tumor mutation detection without matched normal samples
title_full_unstemmed Evaluating somatic tumor mutation detection without matched normal samples
title_short Evaluating somatic tumor mutation detection without matched normal samples
title_sort evaluating somatic tumor mutation detection without matched normal samples
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584341/
https://www.ncbi.nlm.nih.gov/pubmed/28870239
http://dx.doi.org/10.1186/s40246-017-0118-2
work_keys_str_mv AT teerjamiek evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT zhangyonghong evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT chenlu evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT welsherica evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT cresswdouglas evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT eschrichstevena evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples
AT berglundanderse evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples