Analysis of self-inactivating lentiviral vector integration sites and flanking gene expression in human peripheral blood progenitor cells after alkylator chemotherapy.

Abstract Hematotoxicity is a major and frequently dose-limiting side effect of chemotherapy. Retroviral methylguanine-DNA-methyltransferase (MGMT; EC 2.1.1.63) gene transfer to primitive hematopoietic progenitor cells (CD34(+) cells) might allow the application of high-dose alkylator chemotherapy with almost mild to absent myelosuppression. Because gammaretroviral vector integration was found in association with malignant or increased proliferation, novel lentiviral vectors with self-inactivating (SIN) capacity might display a safer option for future gene transfer studies. We assessed the influence of chemoselection on integration patterns in 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU)-treated and untreated human CD34(+) cells transduced with an SIN lentiviral vector carrying the MGMT(P140K) transgene, using ligation-mediated PCR (LM-PCR) and next-generation sequencing. In addition, for the first time, the local influence of the lentiviral provirus on the expression of hit and flanking genes in human CD34(+) cells was analyzed at a clonal level. For each colony, the integration site was detected (LM-PCR) and analyzed (QuickMap), and the expression of hit and flanking genes was measured (quantitative RT-PCR). Analyses of both treated and untreated CD34(+) cells revealed preferential integration into genes. Integration patterns in BCNU-treated cells showed mild, but not significant, differences compared with those found in untreated CD34(+) cells. Most importantly, when analyzing the local influence of the provirus, we saw no significant deregulation of the integration-flanking genes. These findings demonstrate that SIN vector-mediated gene transfer might display a feasible and possibly safe option for MGMT(P140K)-mediated chemoprotection of CD34(+) cells.

QuickMap: a public tool for large-scale gene therapy vector insertion site mapping and analysis.

Several events of insertional mutagenesis in pre-clinical and clinical gene therapy studies have created intense interest in assessing the genomic insertion profiles of gene therapy vectors. For the construction of such profiles, vector-flanking sequences detected by inverse PCR, linear amplification-mediated-PCR or ligation-mediated-PCR need to be mapped to the host cell's genome and compared to a reference set. Although remarkable progress has been achieved in mapping gene therapy vector insertion sites, public reference sets are lacking, as are the possibilities to quickly detect non-random patterns in experimental data. We developed a tool termed QuickMap, which uniformly maps and analyzes human and murine vector-flanking sequences within seconds (available at www.gtsg.org). Besides information about hits in chromosomes and fragile sites, QuickMap automatically determines insertion frequencies in +/- 250 kb adjacency to genes, cancer genes, pseudogenes, transcription factor and (post-transcriptional) miRNA binding sites, CpG islands and repetitive elements (short interspersed nuclear elements (SINE), long interspersed nuclear elements (LINE), Type II elements and LTR elements). Additionally, all experimental frequencies are compared with the data obtained from a reference set, containing 1 000 000 random integrations ('random set'). Thus, for the first time a tool allowing high-throughput profiling of gene therapy vector insertion sites is available. It provides a basis for large-scale insertion site analyses, which is now urgently needed to discover novel gene therapy vectors with 'safe' insertion profiles.

New bioinformatic strategies to rapidly characterize retroviral integration sites of gene therapy vectors.

OBJECTIVE: Increasing use of retroviral vector-mediated gene transfer created intense interest to characterize vector integrations on the genomic level. Techniques to determine insertion sites, mainly based on time-consuming manual data processing, are commonly applied. Since a high variability in processing methods hampers further data comparison, there is an urgent need to systematically process the data arising from such analysis. METHODS: To allow large-scale and standardized comparison of insertion sites of viral vectors we developed two programs, IntegrationSeq and IntegrationMap. IntegrationSeq can trim sequences, and valid integration sequences get further processed with IntegrationMap for automatic genomic mapping. IntegrationMap retrieves detailed information about whether integrations are located in or close to genes, the name of the gene, the exact localization in the transcriptional units, and further parameters like the distance from the transcription start site to the integration. RESULTS: We validated the method using 259 files originating from integration site analysis (LM-PCR). Sequences processed by IntegrationSeq led to an increased yield of valid integration sequence detection, which were shown to be more sensitive than conventional analysis and 15 times faster, while the specificities are equal. Output files generated by IntegrationMap were found to be 99.8% identical with results retrieved by much slower conventional mapping with the ENSEMBL alignment tool. CONCLUSION: Using IntegrationSeq and IntegrationMap, a validated, fast and standardized high-throughput analysis of insertion sites can be achieved for the first time.

Retroviral vector insertions in T-lymphocytes used for suicide gene therapy occur in gene groups with specific molecular functions.

Graft-versus-host disease (GvHD) is a severe complication in the context of allogeneic stem cell transplantation and adoptive immunotherapy. The transfer of a suicide gene into donor T-lymphocytes (TLCs) allows selective elimination of GvHD-causing cells. As retroviral gene transfer into hematopoietic stem cells can induce leukaemia, there is an urgent need also to analyze retroviral integration sites in TLCs. We examined suicide gene-transduced TLCs in four grafts and from four transplanted patients. One-hundred and fifteen integration sites were detected in vitro. Of these 90 could be mapped to the human genome; 50% (45) were located in genes and 32% (29) were detected 10 kb upstream or downstream of transcription start sites. We found a significant overrepresentation of genes encoding for proteins with receptor activity, signal transducer activity, transcription regulator activity, nucleic acid binding activity and translation regulator activity. Similar data were obtained from patient samples. Our results point to preferred vector integration patterns, which are specific for the target cell population and probably independent of selection processes. Thus, future preclinical analysis of the integration repertoire with abundant amounts of transduced cells could allow a prediction also for the in vivo situation, where target cells are scarce.