γ-TRIS: a graph-algorithm for comprehensive identification of vector genomic insertion sites.

SUMMARY: Retroviruses and their vector derivatives integrate semi-randomly in the genome of host cells and are inherited by their progeny as stable genetic marks. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites in gene therapy or virally infected patients, essential for monitoring the evolution of genetically modified cells in vivo. However, since ∼30% of insertions land in low complexity or repetitive regions of the host cell genome, they cannot be correctly assigned and are currently discarded, limiting the accuracy and predictive power of clonal tracking studies. Here, we present γ-TRIS, a new graph-based genome-free alignment tool for identifying insertion sites even if embedded in low complexity regions. By using γ-TRIS to reanalyze clinical studies, we observed improvements in clonal quantification and tracking. AVAILABILITY AND IMPLEMENTATION: Source code at https://bitbucket.org/bereste/g-tris. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites.

BACKGROUND: Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. RESULTS: Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). CONCLUSIONS: We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a > 6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ).

Lentiviral vector-based insertional mutagenesis identifies genes involved in the resistance to targeted anticancer therapies.

The high transduction efficiency of lentiviral vectors in a wide variety of cells makes them an ideal tool for forward genetics screenings addressing issues of cancer research. Although molecular targeted therapies have provided significant advances in tumor treatment, relapses often occur by the expansion of tumor cell clones carrying mutations that confer resistance. Identification of the culprits of anticancer drug resistance is fundamental for the achievement of long-term response. Here, we developed a new lentiviral vector-based insertional mutagenesis screening to identify genes that confer resistance to clinically relevant targeted anticancer therapies. By applying this genome-wide approach to cell lines representing two subtypes of HER2(+) breast cancer, we identified 62 candidate lapatinib resistance genes. We validated the top ranking genes, i.e., PIK3CA and PIK3CB, by showing that their forced expression confers resistance to lapatinib in vitro and found that their mutation/overexpression is associated to poor prognosis in human breast tumors. Then, we successfully applied this approach to the identification of erlotinib resistance genes in pancreatic cancer, thus showing the intrinsic versatility of the approach. The acquired knowledge can help identifying combinations of targeted drugs to overcome the occurrence of resistance, thus opening new horizons for more effective treatment of tumors.