Improving the bioinformatics analysis of HTS clonality data in virus-induced leukemia

Proviral integration into the host genome is one of the main hallmarks of infection by oncogenic retroviruses. This event creates a life-long signature, each infected cell being characterized by a specific integration site (IS). Monitoring of the clonal architecture over time (clone: population of cells sharing an identical IS) has significantly contributed to a better understanding of HIV persistence, gene therapy vector mediated treatment and deltaretrovirus-induced leukemia.
Our lab recently developed an optimized high-throughput sequencing (HTS) based clonality method. It enables the identification of proviral integration sites genome-wide while simultaneously quantifying the abundance of the corresponding clones. The method is superior to any of the previously available protocols, mainly in terms of sensitivity, cost-effectiveness and hands-on time, making it suitable for routine clinical observation of infected individuals. Using this method, we recently showed that longitudinal monitoring of the dominant leukemic clone in patients infected by Human T-cell Leukemia Virus-1 (HTLV-1) better predicts therapeutic response (Artesi et al, Leukemia, 2017).
We applied the method to biological samples isolated from HTLV-1 infected patients and Bovine Leukemia Virus (BLV) infected animals (bovine and sheep). This resulted in the generation of an unprecedented volume of raw sequence data. In this study we developed a novel clonality analysis pipeline that better exploits the potential of the method, improving previously published protocols.