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|Title:||Genome-scale search of tumor-specific antigens by collective analysis of mutations, expressions and T-cell recognition||Authors:||Jia, J.
Human cancer genomes
|Issue Date:||May-2009||Citation:||Jia, J., Cui, J., Liu, X., Han, J., Yang, S., Wei, Y., Chen, Y. (2009-05). Genome-scale search of tumor-specific antigens by collective analysis of mutations, expressions and T-cell recognition. Molecular Immunology 46 (8-9) : 1824-1829. ScholarBank@NUS Repository. https://doi.org/10.1016/j.molimm.2009.01.019||Abstract:||Background: 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. Methodology: 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. Conclusions: 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. © 2009 Elsevier Ltd.||Source Title:||Molecular Immunology||URI:||http://scholarbank.nus.edu.sg/handle/10635/100755||ISSN:||01615890||DOI:||10.1016/j.molimm.2009.01.019|
|Appears in Collections:||Staff Publications|
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