Jia Jia, Cui Juan, Liu Xianghui, Han Jinhua, Yang Shengyong, Wei Yuquan, Chen Yuzong
Bioinformatics and Drug Design Group, Department of Pharmacy, and Centre for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.
Mol Immunol. 2009 May;46(8-9):1824-9. doi: 10.1016/j.molimm.2009.01.019. Epub 2009 Feb 24.
Tumor-specific antigens (TSAs) are potential sources of cancer vaccines, some of which are derived from T-cell epitopes of over-expressed mutant proteins to elicit immunogenicity and overcome tolerance and evasion. The lack of effective vaccines for many cancers has prompted strong interest in improved TSA search methods. Recent progresses in profiling somatic mutations and expressions of human cancer genomes, and in predicting T-cell epitopes enable genome-scale TSA search by collectively analyzing these profiles. Such a collective approach has not been explored in spite of the availability and usage of individual methods.
Genome-scale TSA search was conducted by genome-scale search of tumor-specific mutations in differentially over-expressed genes of specific cancers based on tumor-specific somatic mutation and microarray gene expression data, followed by T-cell recognition analysis of the identified mutant and over-expressed peptides to determine if they are substrates of proteasomal cleavage, TAP mediated transport and MHC-I alleles capable of eliciting immune response. The performance of our method was tested against 12 and 4 known T-cell defined melanoma and lung cancer TSAs in the Cancer Immunity database.
Our approach identified 50% and 75% of the 12 and 4 known TSAs and predicted from the human cancer genomes additional 8-250 and 14-359 putative TSAs of 5 and 3 HLA alleles respectively. The known TSA hit rates (1.9% and 0.8%) are enriched by 29-fold and 35-fold over those of mutation analysis. The numbers of predicted TSAs are within the testing range of typical screening campaigns. Noises in expression data of small sample sizes appear to be a major factor for misidentification of known TSAs. With improved data quality and analysis methods, the collective approach is potentially useful for facilitating genome-scale TSA search.
肿瘤特异性抗原(TSA)是癌症疫苗的潜在来源,其中一些源自过表达突变蛋白的T细胞表位,以引发免疫原性并克服耐受性和逃逸。许多癌症缺乏有效的疫苗引发了人们对改进TSA搜索方法的浓厚兴趣。人类癌症基因组的体细胞突变和表达谱分析以及T细胞表位预测方面的最新进展,使得通过综合分析这些谱来进行全基因组规模的TSA搜索成为可能。尽管有个别方法可用且已被使用,但这种综合方法尚未得到探索。
基于肿瘤特异性体细胞突变和微阵列基因表达数据,通过对特定癌症差异过表达基因中的肿瘤特异性突变进行全基因组规模搜索来进行全基因组规模的TSA搜索,随后对鉴定出的突变肽和过表达肽进行T细胞识别分析,以确定它们是否是蛋白酶体切割、TAP介导转运以及能够引发免疫反应的MHC-I等位基因的底物。我们的方法针对癌症免疫数据库中12个和4个已知的T细胞定义的黑色素瘤和肺癌TSA进行了性能测试。
我们的方法分别识别出了12个和4个已知TSA中的50%和75%,并从人类癌症基因组中预测出了分别针对5个和3个HLA等位基因的另外8 - 250个和14 - 359个推定TSA。已知TSA的命中率(1.9%和0.8%)比突变分析的命中率分别提高了29倍和35倍。预测的TSA数量在典型筛选活动的测试范围内。小样本量表达数据中的噪声似乎是误识别已知TSA的主要因素。随着数据质量和分析方法的改进