Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
Abstract A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration o...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2017-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-16813-4 |
| _version_ | 1818993851119960064 |
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| author | Jie Liu John T. Halloran Jeffrey A. Bilmes Riza M. Daza Choli Lee Elisabeth M. Mahen Donna Prunkard Chaozhong Song Sibel Blau Michael O. Dorschner Vijayakrishna K. Gadi Jay Shendure C. Anthony Blau William S. Noble |
| author_facet | Jie Liu John T. Halloran Jeffrey A. Bilmes Riza M. Daza Choli Lee Elisabeth M. Mahen Donna Prunkard Chaozhong Song Sibel Blau Michael O. Dorschner Vijayakrishna K. Gadi Jay Shendure C. Anthony Blau William S. Noble |
| author_sort | Jie Liu |
| collection | DOAJ |
| description | Abstract A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend. |
| first_indexed | 2024-12-20T20:48:37Z |
| format | Article |
| id | doaj.art-c58b18b457184aafaf83bba3e97a5fdf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-20T20:48:37Z |
| publishDate | 2017-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c58b18b457184aafaf83bba3e97a5fdf2022-12-21T19:26:59ZengNature PortfolioScientific Reports2045-23222017-12-017111310.1038/s41598-017-16813-4Comprehensive statistical inference of the clonal structure of cancer from multiple biopsiesJie Liu0John T. Halloran1Jeffrey A. Bilmes2Riza M. Daza3Choli Lee4Elisabeth M. Mahen5Donna Prunkard6Chaozhong Song7Sibel Blau8Michael O. Dorschner9Vijayakrishna K. Gadi10Jay Shendure11C. Anthony Blau12William S. Noble13Department of Genome Sciences, University of WashingtonDepartment of Electrical Engineering, University of WashingtonDepartment of Electrical Engineering, University of WashingtonDepartment of Genome Sciences, University of WashingtonDepartment of Genome Sciences, University of WashingtonCenter for Cancer Innovation, University of WashingtonDepartment of Pathology, University of WashingtonCenter for Cancer Innovation, University of WashingtonCenter for Cancer Innovation, University of WashingtonCenter for Cancer Innovation, University of WashingtonDepartment of Medicine/Oncology, University of WashingtonDepartment of Genome Sciences, University of WashingtonCenter for Cancer Innovation, University of WashingtonDepartment of Genome Sciences, University of WashingtonAbstract A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.https://doi.org/10.1038/s41598-017-16813-4 |
| spellingShingle | Jie Liu John T. Halloran Jeffrey A. Bilmes Riza M. Daza Choli Lee Elisabeth M. Mahen Donna Prunkard Chaozhong Song Sibel Blau Michael O. Dorschner Vijayakrishna K. Gadi Jay Shendure C. Anthony Blau William S. Noble Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies Scientific Reports |
| title | Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| title_full | Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| title_fullStr | Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| title_full_unstemmed | Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| title_short | Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| title_sort | comprehensive statistical inference of the clonal structure of cancer from multiple biopsies |
| url | https://doi.org/10.1038/s41598-017-16813-4 |
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