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Advances in Human Induced Pluripotent Stem Cell-Derived Chimeric Antigen Receptor-Expressing Natural Killer Cells
Published on: February 14, 2025
Decoding neoantigen-encoding tumor-specific transcripts unveils a shared target reservoir for immunotherapy in
Peng Lin1,2, Yifan Wen1,2, Jingjing Zhao1,2
1Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Background:
Primary liver cancer, predominantly hepatocellular carcinoma (HCC), has limited therapeutic options. While mutation-derived neoantigen vaccine holds promise, its success is hindered by low antigen availability. This study explores transcriptome-derived neoantigens (neoantigen-encoding tumor-specific transcripts, neoTSTs) in HCC, characterizing their features, generation mechanisms, and therapeutic potential.
Methods:
We developed a computational pipeline integrating STAR/StringTie-based transcript assembly with multiexon/single-exon reference datasets (23,972 human control samples) for tumor-specific transcripts (TSTs) identification. A custom sliding-window algorithm compared TST-encoded peptides against UniProt, with neoTSTs predicted using netMHCPan. This framework was applied to 1,013 patients with liver cancer. NeoTSTs were validated through proteomics, immunopeptidomics, and HLA-transgenic models. Multiomics analyses characterized splicing patterns, transposable elements, and transcription factor regulation. Single-cell RNA-seq and Hep53.4 murine models assessed tumor coverage and immunotherapeutic efficacy.
Results:
We analyzed RNA-seq data from 1,013 patients with liver cancer and constructed a multilayered reference dataset. Using a customized pipeline, we identified an average of 60 neoTSTs per patient, significantly surpassing mutation-derived neoantigens (neoMuts). NeoTSTs exhibited higher population frequencies, with 73.1% providing multiple epitopes, and were validated through mass spectrometry and HLA transgenic mouse models. Mechanistically, neoTSTs were generated via retained introns, transposable element activation, HNF4A-regulated alternative promoters, and de novo transmembrane domain generation. Single-cell analysis revealed neoTSTs cover >75% of tumor cells and identified antigen-presenting cancer-associated fibroblasts that enriched in immunotherapy responders and amplified CD4+ T-cell responses. In murine HCC models, neoTST vaccination outperformed neoMuts, inducing dual major histocompatibility complex-I/II activation and significant tumor growth inhibition.
Conclusions:
NeoTSTs represent a superior neoantigen source in HCC, compensating for the limitations of mutation-derived targets. The remarkable abundance and patient-to-patient sharedness of neoTSTs underscore their dual potential: (1) as personalized immunotherapeutic targets, and (2) as broadly applicable antigens for low-TMB tumors. These findings provide a transformative framework for expanding treatment options in HCC immunotherapy.
Insights
Transcriptome-derived neoantigens (neoTSTs) offer a promising new avenue for hepatocellular carcinoma (HCC) immunotherapy, providing a more abundant and shared source of targets than mutation-derived neoantigens. These neoTSTs demonstrate significant therapeutic potential in preclinical models, paving the way for expanded HCC treatment options.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Hepatocellular carcinoma (HCC) presents limited therapeutic options, with existing neoantigen vaccines hampered by low antigen availability.
- This study investigates transcriptome-derived neoantigens (neoTSTs) as a potential solution for HCC immunotherapy.
Purpose of the Study:
- To characterize the features, generation mechanisms, and therapeutic potential of neoTSTs in HCC.
- To compare neoTSTs with mutation-derived neoantigens (neoMuts) in terms of abundance, frequency, and immunotherapeutic efficacy.
Main Methods:
- Developed a computational pipeline to identify tumor-specific transcripts (TSTs) and predict neoTSTs from RNA-seq data of 1,013 HCC patients.
- Validated neoTSTs using proteomics, immunopeptidomics, and HLA-transgenic mouse models.
- Assessed neoTST tumor coverage and immunotherapeutic efficacy using single-cell RNA-seq and murine HCC models.
Main Results:
- Identified an average of 60 neoTSTs per patient, significantly more than neoMuts, with high population frequencies and validation via mass spectrometry.
- Discovered neoTST generation mechanisms including retained introns, transposable element activation, and alternative promoter usage.
- Demonstrated that neoTSTs cover >75% of tumor cells, are amplified by antigen-presenting cancer-associated fibroblasts, and outperform neoMuts in preclinical HCC models.
Conclusions:
- NeoTSTs represent a superior and more abundant neoantigen source in HCC compared to neoMuts.
- NeoTSTs hold dual potential as personalized immunotherapeutic targets and broadly applicable antigens for low-TMB tumors.
- These findings offer a transformative framework for advancing HCC immunotherapy.
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lncRNA - Long Non-coding RNAs

