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      Noncoding regions are the main source of targetable tumor-specific antigens

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          Abstract

          Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly noncoding regions and would have been missed by standard exome-based approaches. Moreover, most of these TSAs derived from nonmutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. Last, we demonstrated that, in mice, the strength of antitumor responses after TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the preimmune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.

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          Most cited references42

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          Is Open Access

          Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

          The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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            Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing.

            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.
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              Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

              Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.
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                Author and article information

                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                December 05 2018
                December 05 2018
                December 05 2018
                December 05 2018
                : 10
                : 470
                : eaau5516
                Article
                10.1126/scitranslmed.aau5516
                30518613
                bcd48e93-37a5-4ee2-b64a-58922aa71851
                © 2018

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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