Nanopore sequencing as a scalable, cost-effective platform for analyzing polyclonal vector integration sites following clinical T cell therapyPublication
Date: 10th June 2020 | Source: Journal for Immunotherapy of Cancer
Background Analysis of vector integration sites in gene-modified cells can provide critical information on clonality and potential biological impact on nearby genes. Current short-read next-generation sequencing methods require specialized instruments and large batch runs.
Methods We used nanopore sequencing to analyze the vector integration sites of T cells transduced by the gammaretroviral vector, SFG.iCasp9.2A.ΔCD19. DNA from oligoclonal cell lines and polyclonal clinical samples were restriction enzyme digested with two 6-cutters, NcoI and BspHI; and the flanking genomic DNA amplified by inverse PCR or cassette ligation PCR. Following nested PCR and barcoding, the amplicons were sequenced on the Oxford Nanopore platform. Reads were filtered for quality, trimmed, and aligned. Custom tool was developed to cluster reads and merge overlapping clusters.
Results Both inverse PCR and cassette ligation PCR could successfully amplify flanking genomic DNA, with cassette ligation PCR showing less bias. The 4.8 million raw reads were grouped into 12,186 clusters and 6410 clones. The 3′long terminal repeat (LTR)-genome junction could be resolved within a 5-nucleotide span for a majority of clusters and within one nucleotide span for clusters with ≥5 reads. The chromosomal distributions of the insertional sites and their predilection for regions proximate to transcription start sites were consistent with previous reports for gammaretroviral vector integrants as analyzed by short-read next-generation sequencing.
Conclusion Our study shows that it is feasible to use nanopore sequencing to map polyclonal vector integration sites. The assay is scalable and requires minimum capital, which together enable cost-effective and timely analysis. Further refinement is required to reduce amplification bias and improve single nucleotide resolution.