Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization
Abstract Background Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cell...
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | Human Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40246-017-0112-8 |
| _version_ | 1798038306020130816 |
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| author | Amir Farmanbar Sanaz Firouzi Wojciech Makałowski Masako Iwanaga Kaoru Uchimaru Atae Utsunomiya Toshiki Watanabe Kenta Nakai |
| author_facet | Amir Farmanbar Sanaz Firouzi Wojciech Makałowski Masako Iwanaga Kaoru Uchimaru Atae Utsunomiya Toshiki Watanabe Kenta Nakai |
| author_sort | Amir Farmanbar |
| collection | DOAJ |
| description | Abstract Background Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated. Methods We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples. Results From four size categories analyzed, we found that big clones (B; 513–2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition. Conclusions This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals. |
| first_indexed | 2024-04-11T21:38:23Z |
| format | Article |
| id | doaj.art-3e375913522541749632275b8c11dcac |
| institution | Directory Open Access Journal |
| issn | 1479-7364 |
| language | English |
| last_indexed | 2024-04-11T21:38:23Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj.art-3e375913522541749632275b8c11dcac2022-12-22T04:01:41ZengBMCHuman Genomics1479-73642017-07-0111111310.1186/s40246-017-0112-8Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualizationAmir Farmanbar0Sanaz Firouzi1Wojciech Makałowski2Masako Iwanaga3Kaoru Uchimaru4Atae Utsunomiya5Toshiki Watanabe6Kenta Nakai7Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoInstitute of Bioinformatics, Faculty of Medicine, University of MuensterDepartment of Frontier Life Science, Graduate School of Biomedical Sciences, Nagasaki UniversityDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoDepartment of Hematology, Imamura General HospitalDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoAbstract Background Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated. Methods We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples. Results From four size categories analyzed, we found that big clones (B; 513–2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition. Conclusions This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals.http://link.springer.com/article/10.1186/s40246-017-0112-8Data-driven modelingAdult T cell leukemiaHuman T cell leukemia virus type 1Integration siteClonal expansionHigh-throughput sequencing |
| spellingShingle | Amir Farmanbar Sanaz Firouzi Wojciech Makałowski Masako Iwanaga Kaoru Uchimaru Atae Utsunomiya Toshiki Watanabe Kenta Nakai Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization Human Genomics Data-driven modeling Adult T cell leukemia Human T cell leukemia virus type 1 Integration site Clonal expansion High-throughput sequencing |
| title | Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization |
| title_full | Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization |
| title_fullStr | Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization |
| title_full_unstemmed | Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization |
| title_short | Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization |
| title_sort | inferring clonal structure in htlv 1 infected individuals towards bridging the gap between analysis and visualization |
| topic | Data-driven modeling Adult T cell leukemia Human T cell leukemia virus type 1 Integration site Clonal expansion High-throughput sequencing |
| url | http://link.springer.com/article/10.1186/s40246-017-0112-8 |
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