UCLA

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.