Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples
Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the c...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2021.596194/full |
| _version_ | 1818738612024377344 |
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| author | Thomas Stiehl Thomas Stiehl Anna Marciniak-Czochra |
| author_facet | Thomas Stiehl Thomas Stiehl Anna Marciniak-Czochra |
| author_sort | Thomas Stiehl |
| collection | DOAJ |
| description | Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution. |
| first_indexed | 2024-12-18T01:11:42Z |
| format | Article |
| id | doaj.art-bfd2051418994ecd814c52deeb85a924 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-18T01:11:42Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-bfd2051418994ecd814c52deeb85a9242022-12-21T21:26:05ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2021-08-011210.3389/fphys.2021.596194596194Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia SamplesThomas Stiehl0Thomas Stiehl1Anna Marciniak-Czochra2Institute for Computational Biomedicine – Disease Modeling, RWTH Aachen University, Aachen, GermanyInstitute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, GermanyInstitute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, GermanyAcute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.https://www.frontiersin.org/articles/10.3389/fphys.2021.596194/fullcomputational algorithmacute myeloid leukemiaclonal evolutionclonal hierarchyclonal pedigreephylogenetic tree |
| spellingShingle | Thomas Stiehl Thomas Stiehl Anna Marciniak-Czochra Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples Frontiers in Physiology computational algorithm acute myeloid leukemia clonal evolution clonal hierarchy clonal pedigree phylogenetic tree |
| title | Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples |
| title_full | Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples |
| title_fullStr | Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples |
| title_full_unstemmed | Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples |
| title_short | Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples |
| title_sort | computational reconstruction of clonal hierarchies from bulk sequencing data of acute myeloid leukemia samples |
| topic | computational algorithm acute myeloid leukemia clonal evolution clonal hierarchy clonal pedigree phylogenetic tree |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2021.596194/full |
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