Abstract
BackgroundModern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.ResultsWe develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.ConclusionsThe z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.
Highlights
Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome
Using simulated and real data sets we show that the z-threshold and BCP methods improve over a conventional common insertion sites’ (CIS) method by defining hot-spots relatively independent of data set size
The z-threshold and BCP methods are useful for studying hot-spot conservation across vector integration site (VIS) data sets of varying size, for example data collected on multiple subjects, time-points or cell types
Summary
Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30100 kb region), which depends on the total number of observed VIS This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary. The successes of gene therapy have been somewhat offset by the accompanying risk of ‘insertional mutagenesis’, or activation of local gene expression near the integration site. It is important to study genes and DNA features located within or near vector integration site (VIS) hot-spots to gain insight into the mechanism of vector integration and predict potential long-term toxicity of gene therapy vectors [13,14,15,16]
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