ArticleComputational and structural biotechnology journal2024
DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- Regulation of gene expression by alternative polyadenylation in health and disease.Nature reviews. Genetics · 2026Review
- PolyA-GLM: A comprehensive framework forComputational and structural biotechnology journal · 2026Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- Neoantigen-Driven Immunotherapy in Triple-Negative Breast Cancer: Emerging Strategies and Clinical Potential.Biomedicines · 2025Review
- The new era of immunotherapy for breast cancer: challenges and coping strategies of CAR-T cell therapy.Frontiers in immunology · 2025Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients.
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Registered trials
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