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Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches

The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction,...

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Detalles Bibliográficos
Autores principales: Sandmann, Sarah, Richter, Silja, Jiang, Xiaoyi, Varghese, Julian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049679/
https://www.ncbi.nlm.nih.gov/pubmed/36982036
http://dx.doi.org/10.3390/ijerph20065128
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author Sandmann, Sarah
Richter, Silja
Jiang, Xiaoyi
Varghese, Julian
author_facet Sandmann, Sarah
Richter, Silja
Jiang, Xiaoyi
Varghese, Julian
author_sort Sandmann, Sarah
collection PubMed
description The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim—an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.
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spelling pubmed-100496792023-03-29 Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches Sandmann, Sarah Richter, Silja Jiang, Xiaoyi Varghese, Julian Int J Environ Res Public Health Article The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim—an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed. MDPI 2023-03-14 /pmc/articles/PMC10049679/ /pubmed/36982036 http://dx.doi.org/10.3390/ijerph20065128 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sandmann, Sarah
Richter, Silja
Jiang, Xiaoyi
Varghese, Julian
Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title_full Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title_fullStr Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title_full_unstemmed Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title_short Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches
title_sort reconstructing clonal evolution—a systematic evaluation of current bioinformatics approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049679/
https://www.ncbi.nlm.nih.gov/pubmed/36982036
http://dx.doi.org/10.3390/ijerph20065128
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