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<p>Transposable elements (TEs) represent an abundant and important source of HLA-presented antigens, but their immunopeptidomic characterization remains challenging due to the inflated search space. We present TIPs (TE-derived Immunopeptidomic Search), a deep learning-guided proteogenomic framework that integrates de novo sequencing, database refinement, multiple search engines and stringent FDR controls. Across various cell lines and cancer types, TIPs identified 20-fold more TE-derived peptides on average than conventional approaches. It further revealed many recurrent, tumor-specific antigens from TEs, including candidates induced by epigenetic therapy. These findings highlight the potential of TIPs to expand the antigenic landscape beyond canonical sources.</p>

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TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月11日

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  • Qian Wu,
  • Xinyue Zhou,
  • Qizhen Feng,
  • Zixiang Shang,
  • Jiayi Shen,
  • Xiaoxiang Huang,
  • Xiaobin Liu,
  • Wenguang Shao

摘要

Transposable elements (TEs) represent an abundant and important source of HLA-presented antigens, but their immunopeptidomic characterization remains challenging due to the inflated search space. We present TIPs (TE-derived Immunopeptidomic Search), a deep learning-guided proteogenomic framework that integrates de novo sequencing, database refinement, multiple search engines and stringent FDR controls. Across various cell lines and cancer types, TIPs identified 20-fold more TE-derived peptides on average than conventional approaches. It further revealed many recurrent, tumor-specific antigens from TEs, including candidates induced by epigenetic therapy. These findings highlight the potential of TIPs to expand the antigenic landscape beyond canonical sources.