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

Abstract

BackgroundHuman 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.MethodsWe 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.ResultsFrom 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.ConclusionsThis 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-017-0112-8) contains supplementary material, which is available to authorized users.

VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites

“…We then run the simulations on other available tools for IS identification such as VISPA [ 22 ], Mavric [ 16 ], SeqMap [ 14 ] and QuickMap [ 15 ] and we evaluated their performances (Table 1 , Additional file 2 ). VISPA2 showed a precision and recall of 1.0 and 0.97 respectively, a clear improvement with respect to VISPA [ 6 ], Mavric [ 9 ], and SeqMap [ 7 ] (Fig. 4 ).…”

“…We assessed the improvements of VISPA2 in terms of computational time and space by comparing results of performances against VISPA (that was the fastest tool compared to Mavric, SeqMap and QuickMap, as reported in [ 6 ]). We used two types of Illumina sequencing runs, a MiSeq run of 14,583,450 reads (2.5GB FASTQ compressed) and a HiSeq run of 186,300,301 reads (20GB FASTQ compressed).…”

“…Beyond the GT field, integration studies have also a great importance in virology by allowing the study of the clonal composition of HTLV-1 or HIV-1 infected cells and their expansion in patients [ 6 – 9 ]. Moreover, these studies have been fundamental in retroviral and transposon based insertional mutagenesis screenings aimed to discover novel oncogenes and tumor suppressor genes in mice and human studies [ 10 – 13 ].…”

RNA sequencing identifies clonal structure of T-cell repertoires in patients with adult T-cell leukemia/lymphoma

“…Clonality data observed based on TCR variants were completely consistent with clonality analysis based on provirus integration sites. 29,30 Considering that a single RNA-seq assay can simultaneously yield gene expression and TCR profiles, it may one day be performed as a general test to capture multidimensional, clinically relevant data for patients with ATL. Deciphering the heterogeneity of the TCR repertoire in tumors may have important implications for biomarker discovery in immunotherapy studies in different types of cancers, including ATL.…”

“…26–28 High-throughput longitudinal analysis indicates that infected individuals with small clones and polyclonal patterns remain healthy over time, whereas those with large clones having an oligo- or monoclonal pattern develop ATL. 29,30 Recently, genome-wide mutational spectra of large numbers of ATL cases have been published, and a list of frequently mutated genes in ATL has been proposed. 31 Subsequently, clonal heterogeneity in ATL based on mutation profiles of cross-sectional, whole-exome sequencing samples has been monitored, and subclonal admixtures containing specific mutations have been proposed.…”