Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Trends Pharmacol Sci. 2021 Apr;42(4):268-282. doi: 10.1016/j.tips.2021.01.006.
Cancer transcriptomes frequently exhibit RNA dysregulation. As the resulting aberrant transcripts may be translated into cancer-specific proteins, there is growing interest in exploiting RNA dysregulation as a source of tumor antigens (TAs) and thus novel immunotherapy targets. Recent advances in high-throughput technologies and rapid accumulation of multiomic cancer profiling data in public repositories have provided opportunities to systematically characterize RNA dysregulation in cancer and identify antigen targets for immunotherapy. However, given the complexity of cancer transcriptomes and proteomes, important conceptual and technological challenges exist. Here, we highlight the expanding repertoire of TAs arising from RNA dysregulation and introduce multiomic and big data strategies for identifying optimal immunotherapy targets. We discuss extant barriers for translating these targets into effective therapies as well as the implications for future research.
癌症转录组经常表现出 RNA 失调。由于由此产生的异常转录本可能被翻译成癌症特异性蛋白质，因此人们越来越感兴趣地将 RNA 失调作为肿瘤抗原 (TA) 的来源，并因此作为新型免疫治疗靶标。高通量技术的最新进展和公共存储库中多组学癌症分析数据的快速积累为系统地描述癌症中的 RNA 失调并鉴定免疫治疗的抗原靶标提供了机会。然而，鉴于癌症转录组和蛋白质组的复杂性，存在重要的概念和技术挑战。在这里，我们强调了由 RNA 失调引起的 TA 的不断扩大的范围，并介绍了用于识别最佳免疫治疗靶标的多组学和大数据策略。我们讨论了将这些靶标转化为有效治疗方法的现存障碍以及对未来研究的影响。
