| 研究実績の概要 |
We took advantage of NGS technology, a unique tag system, and an original in- silico analysis pipeline to develop and internally validate a new high-throughput methodology, which enables specific isolation of HTLV-1 integration sites, and an accurate measurement of the absolute size of clones from various kinds of clinical samples even those with very low PVLs [Firouzi et al. Genome medicine 2014]. In addition, we have started analyzing clinical samples with different disease status and PVLs. We could find differing clonality patterns specific to asymptomatic carriers (ACs) and different subtypes of ATL. Hence we proposed that HTLV-1-infected individuals may be classifiable into different groups, based on their clonality patterns, ultimately affecting their diagnosis and choice of therapy. Moreover, in a cohort study, we monitored rare sets of sequential clinical samples including: samples with no change in their clinical status, and those who progressed to a more advance clinical state, as well as patients before and after medical therapy. Our initial data showed that the changes in the clonal composition of infected individuals appear to be a beneficial prognostic indicator for early detection of ATL onset. Taken together, the realization of potential applications of our methodology may have far-reaching impacts on the diagnosis, prognosis, and clinical decision-making for treatment of infected individuals. Thus, a larger scale cohort study to evaluate the clonal composition of infected cells is currently in progress.
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