Analyzing the Number of Common Integration Sites of Viral Vectors – New Methods and Computer Programs

Vectors based on γ-retroviruses or lentiviruses have been shown to stably express therapeutical transgenes and effectively cure different hematological diseases. Molecular follow up of the insertional repertoire of gene corrected cells in patients and preclinical animal models revealed different integration preferences in the host genome including clusters of integrations in small genomic areas (CIS; common integrations sites). In the majority, these CIS were found in or near genes, with the potential to influence the clonal fate of the affected cell. To determine whether the observed degree of clustering is statistically compatible with an assumed standard model of spatial distribution of integrants, we have developed various methods and computer programs for γ-retroviral and lentiviral integration site distribution. In particular, we have devised and implemented mathematical and statistical approaches for comparing two experimental samples with different numbers of integration sites with respect to the propensity to form CIS as well as for the analysis of coincidences of integration sites obtained from different blood compartments. The programs and statistical tools described here are available as workspaces in R code and allow the fast detection of excessive clustering of integration sites from any retrovirally transduced sample and thus contribute to the assessment of potential treatment-related risks in preclinical and clinical retroviral gene therapy studies

Methodology and software to detect viral integration site hot-spots

<p>Abstract</p> <p>Background</p> <p>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. 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.</p> <p>Results</p> <p>We 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.</p> <p>Conclusions</p> <p>The 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.</p

Real-Time Definition of Non-Randomness in the Distribution of Genomic Events

Features such as mutations or structural characteristics can be non-randomly or non-uniformly distributed within a genome. So far, computer simulations were required for statistical inferences on the distribution of sequence motifs. Here, we show that these analyses are possible using an analytical, mathematical approach. For the assessment of non-randomness, our calculations only require information including genome size, number of (sampled) sequence motifs and distance parameters. We have developed computer programs evaluating our analytical formulas for the real-time determination of expected values and p-values. This approach permits a flexible cluster definition that can be applied to most effectively identify non-random or non-uniform sequence motif distribution. As an example, we show the effectivity and reliability of our mathematical approach in clinical retroviral vector integration site distribution

Dual-energy CT-cholangiography in potential donors for living-related liver transplantation: Improved biliary visualization by intravenous morphine co-medication

PURPOSE: To prospectively evaluate whether intravenous morphine co-medication improves bile duct visualization of dual-energy CT-cholangiography. MATERIALS AND METHODS: Forty potential donors for living-related liver transplantation underwent CT-cholangiography with infusion of a hepatobiliary contrast agent over 40min. Twenty minutes after the beginning of the contrast agent infusion, either normal saline (n=20 patients; control group [CG]) or morphine sulfate (n=20 patients; morphine group [MG]) was injected. Forty-five minutes after initiation of the contrast agent, a dual-energy CT acquisition of the liver was performed. Applying dual-energy post-processing, pure iodine images were generated. Primary study goals were determination of bile duct diameters and visualization scores (on a scale of 0 to 3: 0-not visualized; 3-excellent visualization). RESULTS: Bile duct visualization scores for second-order and third-order branch ducts were significantly higher in the MG compared to the CG (2.9±0.1 versus 2.6±0.2 [P<0.001] and 2.7±0.3 versus 2.1±0.6 [P<0.01], respectively). Bile duct diameters for the common duct and main ducts were significantly higher in the MG compared to the CG (5.9±1.3mm versus 4.9±1.3mm [P<0.05] and 3.7±1.3mm versus 2.6±0.5mm [P<0.01], respectively). CONCLUSION: Intravenous morphine co-medication significantly improved biliary visualization on dual-energy CT-cholangiography in potential donors for living-related liver transplantation

Effect of intravenous morphine comedication on bile duct visualization, diameter and volume applying intravenous CT cholangiography in a porcine liver model

To determine whether intravenous morphine comedication improves bile duct visualization, diameter and/or volume applying intravenous CT cholangiography in a porcine liver model

Iodine removal in intravenous dual-energy CT-cholangiography: Is virtual non-enhanced imaging effective to replace true non-enhanced imaging?

To evaluate whether virtual non-enhanced imaging (VNI) is effective to replace true non-enhanced imaging (TNI) applying iodine removal in intravenous dual-energy CT-cholangiography

Dual-energy computed-tomography cholangiography in potential donors for living-related liver transplantation: initial experience

To report our initial experience with dual-energy computed-tomography (CT) cholangiography in potential donors for living-related liver transplantation

Insertion Sites in Engrafted Cells Cluster Within a Limited Repertoire of Genomic Areas After Gammaretroviral Vector Gene Therapy

Vector-associated side effects in clinical gene therapy have provided insights into the molecular mechanisms of hematopoietic regulation in vivo. Surprisingly, many retrovirus insertion sites (RIS) present in engrafted cells have been found to cluster nonrandomly in close association with specific genes. Our data demonstrate that these genes directly influence the in vivo fate of hematopoietic cell clones. Analysis of insertions thus far has been limited to individual clinical studies. Here, we studied >7,000 insertions retrieved from various studies. More than 40% of all insertions found in engrafted gene-modified cells were clustered in the same genomic areas covering only 0.36% of the genome. Gene classification analyses displayed significant overrepresentation of genes associated with hematopoietic functions and relevance for cell growth and survival in vivo. The similarity of insertion distributions indicates that vector insertions in repopulating cells cluster in predictable patterns. Thus, insertion analyses of preclinical in vitro and murine in vivo studies as well as vector insertion repertoires in clinical trials yielded concerted results and mark a small number of interesting genomic loci and genes that warrants further investigation of the biological consequences of vector insertions