Dataset Information

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer.

ABSTRACT: A-to-I RNA editing can contribute to the transcriptomic and proteomic diversity of many diseases including cancer. It has been reported that peptides generated from RNA editing could be naturally presented by human leukocyte antigen (HLA) molecules and elicit CD8+ T cell activation. However, a systematical characterization of A-to-I RNA editing neoantigens in cancer is still lacking. Here, an integrated RNA-editing based neoantigen identification pipeline PREP (Prioritizing of RNA Editing-based Peptides) was presented. A comprehensive RNA editing neoantigen profile analysis on 12 cancer types from The Cancer Genome Atlas (TCGA) cohorts was performed. PREP was also applied to 14 ovarian tumor samples and two clinical melanoma cohorts treated with immunotherapy. We finally proposed an RNA editing neoantigen immunogenicity score scheme, i.e. REscore, which takes RNA editing level and infiltrating immune cell population into consideration. We reported variant peptide from protein IFI30 in breast cancer which was confirmed expressed and presented in two samples with mass spectrometry data support. We showed that RNA editing neoantigen could be identified from RNA-seq data and could be validated with mass spectrometry data in ovarian tumor samples. Furthermore, we characterized the RNA editing neoantigen profile of clinical melanoma cohorts treated with immunotherapy. Finally, REscore showed significant associations with improved overall survival in melanoma cohorts treated with immunotherapy. These findings provided novel insights of cancer biomarker and enhance our understanding of neoantigen derived from A-to-I RNA editing as well as more types of candidates for personalized cancer vaccines design in the context of cancer immunotherapy.

SUBMITTER: Zhou C

PROVIDER: S-EPMC7758481 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer.

Zhou Chi C Wei Zhiting Z Zhang Liye L Yang Zhaoyi Z Liu Qi Q

Frontiers in oncology 20201210

A-to-I RNA editing can contribute to the transcriptomic and proteomic diversity of many diseases including cancer. It has been reported that peptides generated from RNA editing could be naturally presented by human leukocyte antigen (HLA) molecules and elicit CD8+ T cell activation. However, a systematical characterization of A-to-I RNA editing neoantigens in cancer is still lacking. Here, an integrated RNA-editing based neoantigen identification pipeline <i>PREP</i> (Prioritizing of RNA Editing ...[more]

PMID: 33363023

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Project description:BackgroundThe treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.MethodsThree databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.ResultsThe top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.ConclusionsTogether, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.

Dataset Information

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer.

Publications

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer.

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