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. 2007 Aug;117(8):2225-32.
doi: 10.1172/JCI31659.

Vector integration is nonrandom and clustered and influences the fate of lymphopoiesis in SCID-X1 gene therapy

Annette Deichmann  1 , Salima Hacein-Bey-Abina, Manfred Schmidt, Alexandrine Garrigue, Martijn H Brugman, Jingqiong Hu, Hanno Glimm, Gabor Gyapay, Bernard Prum, Christopher C Fraser, Nicolas Fischer, Kerstin Schwarzwaelder, Maria-Luise Siegler, Dick de Ridder, Karin Pike-Overzet, Steven J Howe, Adrian J Thrasher, Gerard Wagemaker, Ulrich Abel, Frank J T Staal, Eric Delabesse, Jean-Luc Villeval, Bruce Aronow, Christophe Hue, Claudia Prinz, Manuela Wissler, Chuck Klanke, Jean Weissenbach, Ian Alexander, Alain Fischer, Christof von Kalle, Marina Cavazzana-Calvo
Affiliations

Vector integration is nonrandom and clustered and influences the fate of lymphopoiesis in SCID-X1 gene therapy

Annette Deichmann et al. J Clin Invest. 2007 Aug.

Abstract

Recent reports have challenged the notion that retroviruses and retroviral vectors integrate randomly into the host genome. These reports pointed to a strong bias toward integration in and near gene coding regions and, for gammaretroviral vectors, around transcription start sites. Here, we report the results obtained from a large-scale mapping of 572 retroviral integration sites (RISs) isolated from cells of 9 patients with X-linked SCID (SCID-X1) treated with a retrovirus-based gene therapy protocol. Our data showed that two-thirds of insertions occurred in or very near to genes, of which more than half were highly expressed in CD34(+) progenitor cells. Strikingly, one-fourth of all integrations were clustered as common integration sites (CISs). The highly significant incidence of CISs in circulating T cells and the nature of their locations indicate that insertion in many gene loci has an influence on cell engraftment, survival, and proliferation. Beyond the observed cases of insertional mutagenesis in 3 patients, these data help to elucidate the relationship between vector insertion and long-term in vivo selection of transduced cells in human patients with SCID-X1.

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Figures

Figure 1
Figure 1. RIS distribution analysis of engrafted cells.
(A) RIS distribution compared with chromosome size and gene content. The displayed chromosome distribution accounts for the double copy number of diploid autosomes. Black bars, size of chromosomes; gray bars, number of known genes; white bars, number of RISs. (B and C) Vector integration in and near RefSeq genes. RISs were preferentially found near the TSS (B) and within gene coding regions (C). Negative numbers denote the region upstream (Up) of a gene, positive numbers indicate the gene region downstream of the TSS (RefSeq gene) (B) or downstream (Down) of the gene (C). (C) The position of intragenic hits was mapped according to the percentage of overall gene length.
Figure 2
Figure 2. Comparison of pre- and posttransplant RIS distribution in Pt4.
(A) Percentage of RISs detected in the indicated gene regions. (B) Distribution of vector-targeted genes (including the surrounding 10-kbp genomic region) with respect to GO and CIS formation. The GO categories were chosen according to the most significantly overrepresented ones retrieved from engrafted cells from all patients. Black bars, pretransplantation samples of Pt4 (102 RISs); gray bars, posttransplantation samples of Pt4 (141 RISs).
Figure 3
Figure 3. Association between vector integration and gene expression.
(A and B) Number of RISs detected in engrafted cells (A) and in CD34+ cells prior to reinfusion (B) as a function of relative gene expression in stimulated peripheral blood CD34+ cells. For each gene, the probeset with the highest expression value was used. All 20,600 genes present on the array were sorted on expression and divided in 10 percentile categories according to their expression level, so that each category contains 10% of the genes. Values represent the average number of genes in each category based on 3 individual arrays (see Methods).
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