Chapter title |
T-Cell Epitope Discovery for Therapeutic Cancer Vaccines.
|
---|---|
Chapter number | 45 |
Book title |
Vaccine Design
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3387-7_45 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3385-3, 978-1-4939-3387-7
|
Authors |
Sri Krishna, Karen S. Anderson M.D., Ph.D., Karen S. Anderson |
Editors |
Sunil Thomas |
Abstract |
The success of recent immune checkpoint blockade trials in solid tumors has demonstrated the tremendous potential of immune-mediated treatment strategies for cancer therapy. These immune therapies activate preexisting cytotoxic CD8(+) T cells (CTL) to selectively target and eradicate malignant cells. In vitro models suggest that these therapies may be more effective in combination with priming of CTL using cancer vaccines. CTL-mediated tumor targeting is achieved by its recognition of tumor antigenic epitopes presented on human leukocyte antigen (HLA) class I molecules by tumor cells. Discovering CTL-antigenic epitopes is therefore central to the design of therapeutic T-cell vaccines and immune monitoring of these complex immunotherapies. However, selecting and monitoring T-cell epitopes remains difficult due to the extensive polymorphism of HLA alleles and the presence of confounding non-immunogenic self-peptides. To overcome these challenges, this chapter presents methodologies for the design of CTL-targeted vaccines using selection of target HLA alleles, novel integrated computational strategies to predict HLA-class I CTL epitopes, and epitope validation methods using short-term ex vivo T-cell stimulation. This strategy results in the improved efficiency for selecting antigenic epitopes for CTL-mediated vaccines and for immune monitoring of tumor antigens. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 147 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Doctoral Student | 3 | 2% |
Student > Master | 3 | 2% |
Other | 2 | 1% |
Researcher | 2 | 1% |
Student > Ph. D. Student | 2 | 1% |
Other | 2 | 1% |
Unknown | 133 | 90% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 4 | 3% |
Biochemistry, Genetics and Molecular Biology | 4 | 3% |
Medicine and Dentistry | 2 | 1% |
Immunology and Microbiology | 2 | 1% |
Arts and Humanities | 1 | <1% |
Other | 1 | <1% |
Unknown | 133 | 90% |