| Title |
Tumor immunity and survival as a function of alternative neopeptides in human cancer
|
|---|---|
| Published in |
Cancer Immunology Research, February 2018
|
| DOI | 10.1158/2326-6066.cir-17-0559 |
| Pubmed ID | |
| Authors |
Andrew J. Rech, David Balli, Alejandro Mantero, Hemant Ishwaran, Katherine L. Nathanson, Ben Z. Stanger, Robert H. Vonderheide |
| Abstract |
The immune system exerts antitumor activity via T cell-dependent recognition of tumor-specific antigens. Although the number of tumor neopeptides - peptides derived from somatic mutations - often correlates with immune activity and survival, most classically defined high-affinity neopeptides (CDNs) are not immunogenic, and only rare CDNs have been linked to tumor rejection. Thus, the rules of tumor antigen recognition remain incompletely understood. Here, we analyzed neopeptides, immune activity, and clinical outcome from 6,324 patients across 27 tumor types. We characterized a class of 'alternatively defined neopeptides' (ADNs), which are mutant peptides predicted to bind MHC (class I or II) with improved affinity relative to their non-mutated counterpart. ADNs are abundant and molecularly distinct from CDNs. The load of ADNs correlated with intratumoral T-cell responses and immune suppression, and ADNs were also strong predictors of patient survival across tumor types. These results expand the spectrum of mutation-derived tumor antigens with potential clinical relevance. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 39% |
| Spain | 3 | 13% |
| Brazil | 2 | 9% |
| China | 1 | 4% |
| Canada | 1 | 4% |
| Russia | 1 | 4% |
| Chile | 1 | 4% |
| Guinea | 1 | 4% |
| Unknown | 4 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 57% |
| Scientists | 9 | 39% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 99 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 23% |
| Researcher | 23 | 23% |
| Other | 9 | 9% |
| Student > Bachelor | 8 | 8% |
| Student > Doctoral Student | 4 | 4% |
| Other | 8 | 8% |
| Unknown | 24 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 21% |
| Immunology and Microbiology | 15 | 15% |
| Agricultural and Biological Sciences | 12 | 12% |
| Medicine and Dentistry | 12 | 12% |
| Computer Science | 7 | 7% |
| Other | 7 | 7% |
| Unknown | 25 | 25% |