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Vectors and Integration in Gene Therapy: Statistical Considerations | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Vectors and Integration in Gene Therapy: Statistical Considerations

Alessandro Ambrosi, Clelia Di Serio*
University Centre of Statistics for Biomedical Sciences (CUSSB), Università Vita-Salute San Raffaele, Via Olgettina, 58 – 20132 Milano
Corresponding Author : Dr. Clelia Di Serio
University Centre of Statistics for Biomedical Sciences (CUSSB)
Università Vita-Salute San Raffaele
Via Olgettina, 58 – 20132 Milano
Email: [email protected]
Received: February 23, 2009; Accepted: February 25, 2009; Published: February 27, 2009
Citation: Alessandro A, Di Serio C (2009) Vectors and Integration in Gene Therapy: Statistical Considerations. J Comput Sci Syst Biol 2: 117-123. doi:10.4172/jcsb.1000023
Copyright: © 2009 Alessandro A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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In gene therapy the integration process of the viral DNA genome into the host cell genome is a necessary step for virus integration. Just few years ago, retrovirus integration was believed to be random and the chance of accidentally activating a gene was considered remote. It has been seen that this process is not random and different viruses may show different preferences to integrate in some specific areas of the genome. Tumorigensis associated to some studies in gene therapy is suspected to be caused by insertion process. Depending on whether the provirus integrates into or in the vicinity of genes (Transcription Start Sites , TSS), normal trascription can be enhanced or disrupted thus inducing oncogenic mutations. This is called “insertional mutagenesis”. Investigating whether an area over the genome could be favoured by retrovirus integration is a crucial aspect in gene therapy. These area are called “Common Integration Sites”(CIS)or “hotspots”. In the paper we stressed the importance of developing statistical procedures leading to a unique definition of CIS rather than a “problem related” definition. We here propose some statistical solutions for the search of hotspots based on the “Peaksheight distribution”, which account within the null hypothesis for the possible non-random behaviour of the integrations.

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