Transition of adult T-cell leukemia/lymphoma clones during clinical progression

An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites

The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that short-read aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden. This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a “clean-and-recover” step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals. The optimized workflow and analysis recommendations can be implemented in the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets.

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

The diversity of T-cell receptor (TCR) repertoires, as generated by somatic DNA rearrangements, is central to immune system function. High-throughput sequencing technologies now allow examination of antigen receptor repertoires at single-nucleotide and, more recently, single-cell resolution. The TCR repertoire can be altered in the context of infections, malignancies or immunological disorders. Here we examined the diversity of TCR clonality and its association with pathogenesis and prognosis in adult T-cell leukemia/lymphoma (ATL), a malignancy caused by infection with human T-cell leukemia virus type-1 (HTLV-1). We analyzed 62 sets of high-throughput RNA sequencing data from 59 samples of HTLV-1−infected individuals—asymptomatic carriers (ACs), smoldering, chronic, acute and lymphoma ATL subtypes—and three uninfected controls to evaluate TCR distribution. Based on these TCR profiles, CD4-positive cells and ACs showed polyclonal patterns, whereas ATL patients showed oligo- or monoclonal patterns (with 446 average clonotypes across samples). Expression of TCRα and TCRβ genes in the dominant clone differed among the samples. ACs, CD4-positive samples and smoldering patients showed significantly higher TCR diversity compared with chronic, acute and lymphoma subtypes. CDR3 sequence length distribution, amino acid conservation and gene usage variability for ATL patients resembled those of peripheral blood cells from ACs and healthy donors. Thus, determining monoclonal architecture and clonal diversity by RNA sequencing might be useful for prognostic purposes and for personalizing ATL diagnosis and assessment of treatments.

Mutational Intratumor Heterogeneity is a Complex and Early Event in the Development of Adult T-cell Leukemia/Lymphoma

The clonal architecture of tumors plays a vital role in their pathogenesis and invasiveness; however, it is not yet clear how this clonality contributes to different malignancies. In this study we sought to address mutational intratumor heterogeneity (ITH) in adult T-cell leukemia/lymphoma (ATL). ATL is a malignancy with an incompletely understood molecular pathogenesis caused by infection with human T-cell leukemia virus type-1 (HTLV-1). To determine the clonal structure through tumor genetic diversity profiles, we investigated 142 whole-exome sequencing data of tumor and matched normal samples from 71 ATL patients. Based on SciClone analysis, the ATL samples showed a wide spectrum of modes over clonal/subclonal frequencies ranging from one to nine clusters. The average number of clusters was six across samples, but the number of clusters differed among different samples. Of these ATL samples, 94% had more than two clusters. Aggressive ATL cases had slightly more clonal clusters than indolent types, indicating the presence of ITH during earlier stages of disease. The known significantly mutated genes in ATL were frequently clustered together and possibly coexisted in the same clone. IRF4, CCR4, TP53, and PLCG1 mutations were almost clustered in subclones with a moderate variant allele frequency (VAF), whereas HLA-B, CARD11, and NOTCH1 mutations were clustered in subclones with lower VAFs. Taken together, these results show that ATL displays a high degree of ITH and a complex subclonal structure. Our findings suggest that clonal/subclonal architecture might be a useful measure for prognostic purposes and personalized assessment of the therapeutic response.

Human T Cell Leukemia Virus Type 1: Persistence and Pathogenesis

Human T cell leukemia virus type 1 (HTLV-1), also known as human T lymphotropic virus type 1, was the first exogenous human retrovirus discovered. Unlike the distantly related lentivirus HIV-1, HTLV-1 causes disease in only 5-10% of infected people, depending on their ethnic origin. But whereas HIV-1 infection and the consequent diseases can be efficiently contained in most cases by antiretroviral drug treatment, there is no satisfactory treatment for the malignant or inflammatory diseases caused by HTLV-1. The purpose of the present article is to review recent advances in the understanding of the mechanisms by which the virus persists in vivo and causes disabling or fatal diseases.

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

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.

Clonality of HTLV-1-infected T cells as a risk indicator for development and progression of adult T-cell leukemia

Adult T-cell leukemia (ATL) is an aggressive T-cell malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) that develops along a carcinogenic process involving 5 or more genetic events in infected cells. The lifetime incidence of ATL among HTLV-1-infected individuals is approximately 5%. Although epidemiologic studies have revealed risk factors for ATL, the molecular mechanisms that determine the fates of carriers remain unclear. A better understanding of clonal composition and related longitudinal dynamics would clarify the process of ATL leukemogenesis and provide insights into the mechanisms underlying the proliferation of a malignant clone. Genomic DNA samples and clinical information were obtained from individuals enrolled in the Joint Study for Predisposing Factors for ATL Development, a Japanese prospective cohort study. Forty-seven longitudinal samples from 20 individuals (9 asymptomatic carriers and 11 patients with ATL at enrollment) were subjected to a clonality analysis. A method based on next-generation sequencing was used to characterize clones on the basis of integration sites. Relationships were analyzed among clonal patterns, clone sizes, and clinical status, including ATL onset and progression. Among carriers, those exhibiting an oligoclonal or monoclonal pattern with largely expanded clones subsequently progressed to ATL. All indolent patients who progressed to acute-type ATL exhibited monoclonal expansion. In both situations, the major expanded clone after progression was derived from the largest pre-existing clone. This study has provided the first detailed information regarding the dynamics of HTLV-1-infected T-cell clones and collectively suggests that the clonality of HTLV-1-infected cells could be a useful predictive marker of ATL onset and progression.

Adult T-cell leukemia: molecular basis for clonal expansion and transformation of HTLV-1-infected T cells

Adult T-cell leukemia (ATL) is an aggressive T-cell malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) that develops through a multistep carcinogenesis process involving 5 or more genetic events. We provide a comprehensive overview of recently uncovered information on the molecular basis of leukemogenesis in ATL. Broadly, the landscape of genetic abnormalities in ATL that include alterations highly enriched in genes for T-cell receptor-NF-κB signaling such as PLCG1, PRKCB, and CARD11 and gain-of function mutations in CCR4 and CCR7 Conversely, the epigenetic landscape of ATL can be summarized as polycomb repressive complex 2 hyperactivation with genome-wide H3K27 me3 accumulation as the basis of the unique transcriptome of ATL cells. Expression of H3K27 methyltransferase enhancer of zeste 2 was shown to be induced by HTLV-1 Tax and NF-κB. Furthermore, provirus integration site analysis with high-throughput sequencing enabled the analysis of clonal composition and cell number of each clone in vivo, whereas multicolor flow cytometric analysis with CD7 and cell adhesion molecule 1 enabled the identification of HTLV-1-infected CD4+ T cells in vivo. Sorted immortalized but untransformed cells displayed epigenetic changes closely overlapping those observed in terminally transformed ATL cells, suggesting that epigenetic abnormalities are likely earlier events in leukemogenesis. These new findings broaden the scope of conceptualization of the molecular mechanisms of leukemogenesis, dissecting them into immortalization and clonal progression. These recent findings also open a new direction of drug development for ATL prevention and treatment because epigenetic marks can be reprogrammed. Mechanisms underlying initial immortalization and progressive accumulation of these abnormalities remain to be elucidated.

Multidisciplinary insight into clonal expansion of HTLV-1-infected cells in adult T-cell leukemia via modeling by deterministic finite automata coupled with high-throughput sequencing

Background

Clonal expansion of leukemic cells leads to onset of adult T-cell leukemia (ATL), an aggressive lymphoid malignancy with a very poor prognosis. Infection with human T-cell leukemia virus type-1 (HTLV-1) is the direct cause of ATL onset, and integration of HTLV-1 into the human genome is essential for clonal expansion of leukemic cells. Therefore, monitoring clonal expansion of HTLV-1–infected cells via isolation of integration sites assists in analyzing infected individuals from early infection to the final stage of ATL development. However, because of the complex nature of clonal expansion, the underlying mechanisms have yet to be clarified. Combining computational/mathematical modeling with experimental and clinical data of integration site–based clonality analysis derived from next generation sequencing technologies provides an appropriate strategy to achieve a better understanding of ATL development.

Methods

As a comprehensively interdisciplinary project, this study combined three main aspects: wet laboratory experiments, in silico analysis and empirical modeling.

Results

We analyzed clinical samples from HTLV-1–infected individuals with a broad range of proviral loads using a high-throughput methodology that enables isolation of HTLV-1 integration sites and accurate measurement of the size of infected clones. We categorized clones into four size groups, “very small”, “small”, “big”, and “very big”, based on the patterns of clonal growth and observed clone sizes. We propose an empirical formal model based on deterministic finite state automata (DFA) analysis of real clinical samples to illustrate patterns of clonal expansion.

Conclusions

Through the developed model, we have translated biological data of clonal expansion into the formal language of mathematics and represented the observed clonality data with DFA. Our data suggest that combining experimental data (absolute size of clones) with DFA can describe the clonality status of patients. This kind of modeling provides a basic understanding as well as a unique perspective for clarifying the mechanisms of clonal expansion in ATL.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0241-2) contains supplementary material, which is available to authorized users.