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Merelli I, Beretta S, Cesana D, Gennari A, Benedicenti F, Spinozzi G, Cesini D, Montini E, D’Agostino D, Calabria A. InCliniGene enables high-throughput and comprehensive in vivo clonal tracking toward clinical genomics data integration. Database (Oxford) 2023; 2023:baad069. [PMID: 37935583 PMCID: PMC10630073 DOI: 10.1093/database/baad069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/15/2023] [Accepted: 10/04/2023] [Indexed: 11/09/2023]
Abstract
High-throughput clonal tracking in patients under hematopoietic stem cell gene therapy with integrating vector is instrumental in assessing bio-safety and efficacy. Monitoring the fate of millions of transplanted clones and their progeny across differentiation and proliferation over time leverages the identification of the vector integration sites, used as surrogates of clonal identity. Although γ-tracking retroviral insertion sites (γ-TRIS) is the state-of-the-art algorithm for clonal identification, the computational drawbacks in the tracking algorithm, based on a combinatorial all-versus-all strategy, limit its use in clinical studies with several thousands of samples per patient. We developed the first clonal tracking graph database, InCliniGene (https://github.com/calabrialab/InCliniGene), that imports the output files of γ-TRIS and generates the graph of clones (nodes) connected by arches if two nodes share common genomic features as defined by the γ-TRIS rules. Embedding both clonal data and their connections in the graph, InCliniGene can track all clones longitudinally over samples through data queries that fully explore the graph. This approach resulted in being highly accurate and scalable. We validated InCliniGene using an in vitro dataset, specifically designed to mimic clinical cases, and tested the accuracy and precision. InCliniGene allows extensive use of γ-TRIS in large gene therapy clinical applications and naturally realizes the full data integration of molecular and genomics data, clinical and treatment measurements and genomic annotations. Further extensions of InCliniGene with data federation and with application programming interface will support data mining toward precision, personalized and predictive medicine in gene therapy. Database URL: https://github.com/calabrialab/InCliniGene.
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Affiliation(s)
| | - Stefano Beretta
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniela Cesana
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Alessandro Gennari
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Fabrizio Benedicenti
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Giulio Spinozzi
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniele Cesini
- Centro Nazionale Analisi Fotogrammi (CNAF), Istituto Nazionale di Fisica Nucleare, Viale Carlo Berti Pichat 6/2, Bologna 40127, Italy
| | - Eugenio Montini
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Daniele D’Agostino
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università degli Studi di Genova, Viale Causa 13, Genoa 16145, Italy
- Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, Segrate (MI) 20054, Italy
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
| | - Andrea Calabria
- San Raffaele Telethon Institute for Gene Therapy, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milano 20132, Italy
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Scott S, Hallwirth CV, Hartkopf F, Grigson S, Jain Y, Alexander IE, Bauer DC, O W Wilson L. Isling: a tool for detecting integration of wild-type viruses and clinical vectors. J Mol Biol 2021; 434:167408. [PMID: 34929203 DOI: 10.1016/j.jmb.2021.167408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 10/19/2022]
Abstract
Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.
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Affiliation(s)
- Suzanne Scott
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Claus V Hallwirth
- Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Felix Hartkopf
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Susanna Grigson
- College of Science and Engineering, Flinders University, Adelaide, Australia
| | - Yatish Jain
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia
| | - Ian E Alexander
- Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia; Discipline of Child and Adolescent Health,Faculty of Medicine and Health,The University of Sydney, Sydney, New South Wales, Australia
| | - Denis C Bauer
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Discipline of Child and Adolescent Health,Faculty of Medicine and Health,The University of Sydney, Sydney, New South Wales, Australia; Macquarie University, Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie Park, Australia.
| | - Laurence O W Wilson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Macquarie University, Applied BioSciences, Faculty of Science and Engineering, Macquarie Park, Australia.
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Micklethwaite KP, Gowrishankar K, Gloss BS, Li Z, Street JA, Moezzi L, Mach MA, Sutrave G, Clancy LE, Bishop DC, Louie RHY, Cai C, Foox J, MacKay M, Sedlazeck FJ, Blombery P, Mason CE, Luciani F, Gottlieb DJ, Blyth E. Investigation of product-derived lymphoma following infusion of piggyBac-modified CD19 chimeric antigen receptor T cells. Blood 2021; 138:1391-1405. [PMID: 33974080 PMCID: PMC8532197 DOI: 10.1182/blood.2021010858] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/24/2021] [Indexed: 11/20/2022] Open
Abstract
We performed a phase 1 clinical trial to evaluate outcomes in patients receiving donor-derived CD19-specific chimeric antigen receptor (CAR) T cells for B-cell malignancy that relapsed or persisted after matched related allogeneic hemopoietic stem cell transplant. To overcome the cost and transgene-capacity limitations of traditional viral vectors, CAR T cells were produced using the piggyBac transposon system of genetic modification. Following CAR T-cell infusion, 1 patient developed a gradually enlarging retroperitoneal tumor due to a CAR-expressing CD4+ T-cell lymphoma. Screening of other patients led to the detection, in an asymptomatic patient, of a second CAR T-cell tumor in thoracic para-aortic lymph nodes. Analysis of the first lymphoma showed a high transgene copy number, but no insertion into typical oncogenes. There were also structural changes such as altered genomic copy number and point mutations unrelated to the insertion sites. Transcriptome analysis showed transgene promoter-driven upregulation of transcription of surrounding regions despite insulator sequences surrounding the transgene. However, marked global changes in transcription predominantly correlated with gene copy number rather than insertion sites. In both patients, the CAR T-cell-derived lymphoma progressed and 1 patient died. We describe the first 2 cases of malignant lymphoma derived from CAR gene-modified T cells. Although CAR T cells have an enviable record of safety to date, our results emphasize the need for caution and regular follow-up of CAR T recipients, especially when novel methods of gene transfer are used to create genetically modified immune therapies. This trial was registered at www.anzctr.org.au as ACTRN12617001579381.
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MESH Headings
- Aged
- DNA Transposable Elements
- Gene Expression Regulation, Neoplastic
- Gene Transfer Techniques
- Humans
- Immunotherapy, Adoptive/adverse effects
- Immunotherapy, Adoptive/methods
- Leukemia, B-Cell/genetics
- Leukemia, B-Cell/therapy
- Lymphoma/etiology
- Lymphoma/genetics
- Lymphoma, B-Cell/genetics
- Lymphoma, B-Cell/therapy
- Male
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/therapeutic use
- T-Lymphocytes/metabolism
- Transcriptome
- Transgenes
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Affiliation(s)
- Kenneth P Micklethwaite
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Kavitha Gowrishankar
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Brian S Gloss
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Ziduo Li
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Janine A Street
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Leili Moezzi
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Melanie A Mach
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Gaurav Sutrave
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Leighton E Clancy
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - David C Bishop
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Raymond H Y Louie
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - Curtis Cai
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - Jonathan Foox
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
| | - Matthew MacKay
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, College of Medicine, Baylor University, Houston, TX
| | - Piers Blombery
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Clinical Haematology, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher E Mason
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY
- The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY
- The Feil Family Brain and Mind Research Institute, New York, NY; and
- The WorldQuant Initiative for Quantitative Prediction, New York, NY
| | - Fabio Luciani
- Kirby Institute, University of New South Wales, Sydney. NSW, Australia
| | - David J Gottlieb
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Emily Blyth
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology-ICPMR Westmead, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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Kamboj A, Hallwirth CV, Alexander IE, McCowage GB, Kramer B. Ub-ISAP: a streamlined UNIX pipeline for mining unique viral vector integration sites from next generation sequencing data. BMC Bioinformatics 2017. [PMID: 28623888 PMCID: PMC5474025 DOI: 10.1186/s12859-017-1719-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The analysis of viral vector genomic integration sites is an important component in assessing the safety and efficiency of patient treatment using gene therapy. Alongside this clinical application, integration site identification is a key step in the genetic mapping of viral elements in mutagenesis screens that aim to elucidate gene function. RESULTS We have developed a UNIX-based vector integration site analysis pipeline (Ub-ISAP) that utilises a UNIX-based workflow for automated integration site identification and annotation of both single and paired-end sequencing reads. Reads that contain viral sequences of interest are selected and aligned to the host genome, and unique integration sites are then classified as transcription start site-proximal, intragenic or intergenic. CONCLUSION Ub-ISAP provides a reliable and efficient pipeline to generate large datasets for assessing the safety and efficiency of integrating vectors in clinical settings, with broader applications in cancer research. Ub-ISAP is available as an open source software package at https://sourceforge.net/projects/ub-isap/ .
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Affiliation(s)
- Atul Kamboj
- Children's Cancer Research Unit, Kids' Research Institute, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, NSW, 2145, Australia.
| | - Claus V Hallwirth
- Gene Therapy Research Unit, Children's Medical Research Institute and The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Ian E Alexander
- Gene Therapy Research Unit, Children's Medical Research Institute and The Children's Hospital at Westmead, Westmead, NSW, Australia.,The University of Sydney, Discipline of Paediatrics and Child Health, Westmead, NSW, Australia
| | - Geoffrey B McCowage
- Cancer Centre for Children, The Children's Hospital, Westmead, NSW, Australia
| | - Belinda Kramer
- Children's Cancer Research Unit, Kids' Research Institute, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, NSW, 2145, Australia
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