| Chapter title |
Gene Modification of Human Natural Killer Cells Using a Retroviral Vector.
|
|---|---|
| Chapter number | 17 |
| Book title |
Natural Killer Cells
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3684-7_17 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3682-3, 978-1-4939-3684-7
|
| Authors |
Joshua N. Kellner, Conrad R. Cruz, Catherine M. Bollard, Eric S. Yvon Ph.D., Kellner, Joshua N, Cruz, Conrad R, Bollard, Catherine M, Yvon, Eric S, Kellner, Joshua N., Cruz, Conrad R., Bollard, Catherine M., Yvon, Eric S., Eric S. Yvon |
| Editors |
Srinivas S. Somanchi |
| Abstract |
As part of the innate immune system, natural killer (NK) cells are regarded as promising effector cells for adoptive cell therapy approaches to treat patients with cancer. In some cases, genetic modification of the NK cells may be considered but such manipulation has to be integrated into the expansion method to allow the generation of clinically relevant numbers of gene-modified NK cells. Therefore, an efficient gene transfer procedure is needed.Our group developed a retrovirus-based transduction protocol capable of robust expansion of gene-modified NK cells with a high rate of transgene expression. Actively dividing cells is a prerequisite for efficient gene transfer when using a retroviral vector. In the procedure presented here, strong activation of the NK cells was provided by a combination of IL-15 and the K-562 feeder cells. Beside the interest in developing a simple procedure compliant with good manufacturing practice (GMP) for the production of therapeutic products, this approach also provides a valuable means of generating genetically modified primary NK cells for future preclinical studies. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 25% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Student > Postgraduate | 1 | 8% |
| Unknown | 6 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 17% |
| Agricultural and Biological Sciences | 1 | 8% |
| Immunology and Microbiology | 1 | 8% |
| Neuroscience | 1 | 8% |
| Engineering | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 6 | 50% |