Olsen Lars Rønn, Campos Benito, Barnkob Mike Stein, Winther Ole, Brusic Vladimir, Andersen Mads Hald
Department of Biology, Bioinformatics Centre, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark,
Cancer Immunol Immunother. 2014 Dec;63(12):1235-49. doi: 10.1007/s00262-014-1627-7. Epub 2014 Oct 26.
The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.
针对癌症的免疫反应机制已得到广泛研究，并且人们已投入巨大努力来利用免疫系统的治疗潜力。在过去20年中，免疫疗法取得了重大进展，但保护性和治疗性癌症免疫疗法的全部潜力尚未实现。现有治疗方法疗效不足可归因于一些生物学和技术问题。在本综述中，我们详细阐述了免疫疗法靶点选择和设计的当前局限性，并回顾了在生物信息学分析流程中简化治疗靶点发现的计算方法。我们描述了针对免疫疗法靶点发现中的三个主要瓶颈的专门生物信息学工具和数据库：潜在抗原性蛋白质的编目、潜在HLA结合物的鉴定以及单表位和多表位策略的表位和共靶点选择。我们提供了应用于著名肿瘤抗原HER2的示例，并提出了生物信息学方法来改善治疗抗性并确保对肿瘤进行有效和持久的控制。
