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Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Rui ChengZhao-Chun XuMeng LuoPingping WangHuimin CaoXiyun JinWenyang ZhouLixing XiaoQinghua Jiang
Published in: Briefings in bioinformatics (2022)
Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.
Keyphrases
  • papillary thyroid
  • squamous cell
  • high throughput
  • lymph node metastasis
  • childhood cancer
  • machine learning
  • drug delivery
  • young adults
  • artificial intelligence
  • big data
  • cancer therapy