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Potential shared neoantigens from pan-cancer transcript isoforms.

作者信息

Techachakrit Jirapat, Malik Aijaz Ahmad, Pisitkun Trairak, Sriswasdi Sira

机构信息

Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.

Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.

出版信息

Sci Rep. 2025 May 7;15(1):15886. doi: 10.1038/s41598-025-00817-6.


DOI:10.1038/s41598-025-00817-6
PMID:40335513
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12059137/
Abstract

Isoform switching in cancer is a prevalent phenomenon with significant implications for immunotherapy, as actionable neoantigens derived from these cancer-specific events would be applicable to broad categories of patients, reducing the necessity for personalized treatments. By integrating five large-scale transcriptomic datasets comprising over 19,500 samples across 29 cancer and 54 normal tissue types, we identified cancer-associated isoform switching events common to multiple cancer types, several of which involve genes with established mechanistic roles in oncogenesis. The presence of neoantigen-containing peptides derived from these transcripts was confirmed in broad cancer and normal tissue proteome datasets and the binding affinity of predicted neoantigens to the human leukocyte antigen (HLA) complex via molecular dynamics simulations. The study presents strong evidence that isoform switching in cancer is a significant source of actionable neoantigens that have the capability to trigger an immune response. These findings suggest that isoform switching events could potentially be leveraged for broad immunotherapeutic strategies across various cancer types.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/d4558c384d79/41598_2025_817_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/6ede343bb41c/41598_2025_817_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/b5ee407756c7/41598_2025_817_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/f90077d0638b/41598_2025_817_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/4b4824da3502/41598_2025_817_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/36da1f8944c4/41598_2025_817_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/908c96a97470/41598_2025_817_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/d4558c384d79/41598_2025_817_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/6ede343bb41c/41598_2025_817_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/b5ee407756c7/41598_2025_817_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/f90077d0638b/41598_2025_817_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/4b4824da3502/41598_2025_817_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/36da1f8944c4/41598_2025_817_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/908c96a97470/41598_2025_817_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b862/12059137/d4558c384d79/41598_2025_817_Fig2_HTML.jpg

相似文献

[1]
Potential shared neoantigens from pan-cancer transcript isoforms.

Sci Rep. 2025-5-7

[2]
Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review).

Int J Oncol. 2025-6

[3]
Comprehensive profiling of cancer neoantigens from aberrant RNA splicing.

J Immunother Cancer. 2024-5-15

[4]
Best practices for bioinformatic characterization of neoantigens for clinical utility.

Genome Med. 2019-8-28

[5]
Identifying Strong Neoantigen MHC-I/II Binding Candidates for Targeted Immunotherapy with SINE.

Int J Mol Sci. 2024-12-29

[6]
Identification of shared neoantigens in esophageal carcinoma by the combination of comprehensive analysis of genomic data and in silico neoantigen prediction.

Cell Immunol. 2022-7

[7]
NEPdb: A Database of T-Cell Experimentally-Validated Neoantigens and Pan-Cancer Predicted Neoepitopes for Cancer Immunotherapy.

Front Immunol. 2021

[8]
DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Front Immunol. 2019-11-1

[9]
TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis.

Genomics Proteomics Bioinformatics. 2018-9-15

[10]
Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors.

J Clin Invest. 2019-3-5

本文引用的文献

[1]
Comprehensive profiling of cancer neoantigens from aberrant RNA splicing.

J Immunother Cancer. 2024-5-15

[2]
Cancer testis antigens: Emerging therapeutic targets leveraging genomic instability in cancer.

Mol Ther Oncol. 2024-1-26

[3]
Lnc-LRRTM4 promotes proliferation, metastasis and EMT of colorectal cancer through activating LRRTM4 transcription.

Cancer Cell Int. 2023-7-19

[4]
WD repeat protein 54-mediator of ErbB2-driven cell motility 1 axis promotes bladder cancer tumorigenesis and metastasis and impairs chemosensitivity.

Cancer Lett. 2023-3-1

[5]
Neoantigens: promising targets for cancer therapy.

Signal Transduct Target Ther. 2023-1-6

[6]
HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells.

Respir Res. 2022-12-19

[7]
PSMA2 knockdown impacts expression of proteins involved in immune and cellular stress responses in human lung cells.

Biochim Biophys Acta Mol Basis Dis. 2023-2

[8]
The mechanical regulation of RNA binding protein hnRNPC in the failing heart.

Sci Transl Med. 2022-11-23

[9]
SELENBP1 inhibits progression of colorectal cancer by suppressing epithelial-mesenchymal transition.

Open Med (Wars). 2022-9-1

[10]
PSMB5 overexpression is correlated with tumor proliferation and poor prognosis in hepatocellular carcinoma.

FEBS Open Bio. 2022-11

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