Title |
Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing
|
---|---|
Published in |
Nature, November 2014
|
DOI | 10.1038/nature14001 |
Pubmed ID | |
Authors |
Mahesh Yadav, Suchit Jhunjhunwala, Qui T. Phung, Patrick Lupardus, Joshua Tanguay, Stephanie Bumbaca, Christian Franci, Tommy K. Cheung, Jens Fritsche, Toni Weinschenk, Zora Modrusan, Ira Mellman, Jennie R. Lill, Lélia Delamarre |
Abstract |
Human tumours typically harbour a remarkable number of somatic mutations. If presented on major histocompatibility complex class I molecules (MHCI), peptides containing these mutations could potentially be immunogenic as they should be recognized as 'non-self' neo-antigens by the adaptive immune system. Recent work has confirmed that mutant peptides can serve as T-cell epitopes. However, few mutant epitopes have been described because their discovery required the laborious screening of patient tumour-infiltrating lymphocytes for their ability to recognize antigen libraries constructed following tumour exome sequencing. We sought to simplify the discovery of immunogenic mutant peptides by characterizing their general properties. We developed an approach that combines whole-exome and transcriptome sequencing analysis with mass spectrometry to identify neo-epitopes in two widely used murine tumour models. Of the >1,300 amino acid changes identified, ∼13% were predicted to bind MHCI, a small fraction of which were confirmed by mass spectrometry. The peptides were then structurally modelled bound to MHCI. Mutations that were solvent-exposed and therefore accessible to T-cell antigen receptors were predicted to be immunogenic. Vaccination of mice confirmed the approach, with each predicted immunogenic peptide yielding therapeutically active T-cell responses. The predictions also enabled the generation of peptide-MHCI dextramers that could be used to monitor the kinetics and distribution of the anti-tumour T-cell response before and after vaccination. These findings indicate that a suitable prediction algorithm may provide an approach for the pharmacodynamic monitoring of T-cell responses as well as for the development of personalized vaccines in cancer patients. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 30% |
United Kingdom | 4 | 12% |
Spain | 1 | 3% |
Canada | 1 | 3% |
Brazil | 1 | 3% |
Korea, Republic of | 1 | 3% |
Belgium | 1 | 3% |
Germany | 1 | 3% |
Unknown | 13 | 39% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 23 | 70% |
Scientists | 7 | 21% |
Science communicators (journalists, bloggers, editors) | 2 | 6% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | <1% |
France | 3 | <1% |
United Kingdom | 2 | <1% |
Korea, Republic of | 1 | <1% |
Australia | 1 | <1% |
Israel | 1 | <1% |
Germany | 1 | <1% |
Canada | 1 | <1% |
Switzerland | 1 | <1% |
Other | 2 | <1% |
Unknown | 1318 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 319 | 24% |
Student > Ph. D. Student | 290 | 22% |
Student > Master | 116 | 9% |
Student > Bachelor | 106 | 8% |
Other | 100 | 7% |
Other | 187 | 14% |
Unknown | 224 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 314 | 23% |
Biochemistry, Genetics and Molecular Biology | 245 | 18% |
Immunology and Microbiology | 195 | 15% |
Medicine and Dentistry | 188 | 14% |
Chemistry | 35 | 3% |
Other | 116 | 9% |
Unknown | 249 | 19% |