Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization

Human Genomics

卷:11

Amir Farmanbar¹  Kaoru Uchimaru¹  Toshiki Watanabe¹  Kenta Nakai¹  Sanaz Firouzi¹  Masako Iwanaga²  Atae Utsunomiya³  Wojciech Makałowski⁴ 

[1] Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo;

[2] Department of Frontier Life Science, Graduate School of Biomedical Sciences, Nagasaki University;

[3] Department of Hematology, Imamura General Hospital;

[4] Institute of Bioinformatics, Faculty of Medicine, University of Muenster;

关键词: Data-driven modeling;    Adult T cell leukemia;    Human T cell leukemia virus type 1;    Integration site;    Clonal expansion;    High-throughput sequencing;   

DOI  :  10.1186/s40246-017-0112-8

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown