Evaluating somatic tumor mutation detection without matched normal samples
Abstract 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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2017-09-01
|
| Series: | Human Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40246-017-0118-2 |
| _version_ | 1811302120684519424 |
|---|---|
| author | Jamie K. Teer Yonghong Zhang Lu Chen Eric A. Welsh W. Douglas Cress Steven A. Eschrich Anders E. Berglund |
| author_facet | Jamie K. Teer Yonghong Zhang Lu Chen Eric A. Welsh W. Douglas Cress Steven A. Eschrich Anders E. Berglund |
| author_sort | Jamie K. Teer |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T07:22:22Z |
| format | Article |
| id | doaj.art-617587bcde17463eaf2d11a7e78d9a43 |
| institution | Directory Open Access Journal |
| issn | 1479-7364 |
| language | English |
| last_indexed | 2024-04-13T07:22:22Z |
| publishDate | 2017-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj.art-617587bcde17463eaf2d11a7e78d9a432022-12-22T02:56:34ZengBMCHuman Genomics1479-73642017-09-0111111310.1186/s40246-017-0118-2Evaluating somatic tumor mutation detection without matched normal samplesJamie K. Teer0Yonghong Zhang1Lu Chen2Eric A. Welsh3W. Douglas Cress4Steven A. Eschrich5Anders E. Berglund6Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Molecular Oncology, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Molecular Oncology, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research InstituteAbstract 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.http://link.springer.com/article/10.1186/s40246-017-0118-2Somatic mutationCancer genomicsNext-generation sequencingPrecision medicine |
| spellingShingle | Jamie K. Teer Yonghong Zhang Lu Chen Eric A. Welsh W. Douglas Cress Steven A. Eschrich Anders E. Berglund Evaluating somatic tumor mutation detection without matched normal samples Human Genomics Somatic mutation Cancer genomics Next-generation sequencing Precision medicine |
| 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 | Somatic mutation Cancer genomics Next-generation sequencing Precision medicine |
| url | http://link.springer.com/article/10.1186/s40246-017-0118-2 |
| work_keys_str_mv | AT jamiekteer evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT yonghongzhang evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT luchen evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT ericawelsh evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT wdouglascress evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT stevenaeschrich evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples AT anderseberglund evaluatingsomatictumormutationdetectionwithoutmatchednormalsamples |