CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins.
CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins.
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翻译的非规范蛋白来源于非编码区或可选择的开放阅读框（orf），可以促进关键和多样化的细胞过程。在癌症的背景下，它们也代表了癌症免疫治疗的一个被低估的靶标来源，通过它们的肿瘤富集表达或通过携带产生新抗原的体细胞突变。在这里，我们引入了对新型肽的最大整合和蛋白质基因组学分析，以评估14种癌症类型中900多个患者蛋白质组和26个免疫肽球数据集中的非规范orf （ncorf）的患病率。全细胞蛋白质组和免疫肽组的综合蛋白质基因组学分析显示，蛋白质编码基因中有9760个非冗余的上游、下游和帧外ncorf，非编码rna中有12811个非冗余的ncorf。值得注意的是，6486个ncorf来自差异表达基因，340个ncorf在8种或更多癌症中被普遍翻译。该分析还导致在两个队列中发现34个表位和8个来自非规范蛋白的新抗原作为新的癌症免疫靶点。总的来说，我们的分析结合了自下而上的蛋白质基因组学和靶向肽验证，以说明翻译的非典型蛋白在癌症中的患病率，并为蛋白质组学、免疫肽组学、基因组学和转录组学数据支持的新蛋白的优先级提供资源，可在https://www.maherlab.com/crypticproteindb上获得。
Translated non-canonical proteins derived from noncoding regions or alternative open reading frames (ORFs) can contribute to critical and diverse cellular processes. In the context of cancer, they also represent an under-appreciated source of targets for cancer immunotherapy through their tumor-enriched expression or by harboring somatic mutations that produce neoantigens. Here, we introduce the largest integration and proteogenomic analysis of novel peptides to assess the prevalence of non-canonical ORFs (ncORFs) in more than 900 patient proteomes and 26 immunopeptidome datasets across 14 cancer types. The integrative proteogenomic analysis of whole-cell proteomes and immunopeptidomes revealed peptide support for a nonredundant set of 9760 upstream, downstream, and out-of-frame ncORFs in protein coding genes and 12811 in noncoding RNAs. Notably, 6486 ncORFs were derived from differentially expressed genes and 340 were ubiquitously translated across eight or more cancers. The analysis also led to the discovery of thirty-four epitopes and eight neoantigens from non-canonical proteins in two cohorts as novel cancer immunotargets. Collectively, our analysis integrated both bottom-up proteogenomic and targeted peptide validation to illustrate the prevalence of translated non-canonical proteins in cancer and to provide a resource for the prioritization of novel proteins supported by proteomic, immunopeptidomic, genomic and transcriptomic data, available at https://www.maherlab.com/crypticproteindb.