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Identification of Tumor Antigens Using Subtraction and Microarrays

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Part of the book series: Cancer Drug Discovery and Development ((CDD&D))

Abstract

Recent advances in genomic discovery approaches have led to the identification of a new generation of tumor antigens. In this chapter, we discuss the application of nucleic acid subtraction methods combined with cDNA microarrays to identify potential tumor antigen candidates. Subtraction techniques form an effective means of enriching for tissue- and tumor-specific genes whereas microarray technology provides us with an efficient high-throughput screening approach that can simultaneously determine the gene expression of thousands of genes in a single experiment. The combination of these two powerful new discovery tools allows a systematic comparison of the cancer genome with the normal tissue genome to identify differentially expressed genes. This process has been optimized for rapid, thorough, and effective gene discovery and is not dependent on the availability of immunological reagents such as antibody and T cells from cancer patients. Genes identified via this integrated approach have the advantage of being tumoror tissue-specific and broadly expressed in cancers. Numerous tissue- and tumor-specific genes have been identified in prostate, breast, lung, and colon cancers using these approaches. Immunological validation strategies have been employed to determine the immunogenicity of these antigens and to identify naturally processed epitopes that can be used as immunogens or reagents for monitoring antigen-specific immune responses in subsequent vaccine strategies.

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© 2004 Humana Press Inc.

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Xu, J. (2004). Identification of Tumor Antigens Using Subtraction and Microarrays. In: Morse, M.A., Clay, T.M., Lyerly, H.K. (eds) Handbook of Cancer Vaccines. Cancer Drug Discovery and Development. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-59259-680-5_3

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  • DOI: https://doi.org/10.1007/978-1-59259-680-5_3

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-4684-9818-9

  • Online ISBN: 978-1-59259-680-5

  • eBook Packages: Springer Book Archive

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