Evidence map›Paper›PMID 42481156›Full record

ArticleJournal for immunotherapy of cancer2026

Decoding neoantigen-encoding tumor-specific transcripts unveils a shared target reservoir for immunotherapy in hepatocellular carcinoma.

Peng Lin, Yifan Wen, Jingjing Zhao, Feifei Zhang, Yaoming Su, Huiyi He, Hongwu Yu, Qiaojuan Li, Chengye Liu, Zhixiang Hu and 4 more

Abstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Peng Lin *Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Yifan Wen *Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Jingjing ZhaoDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Feifei ZhangDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Yaoming SuDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Huiyi HeDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Hongwu YuDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Qiaojuan LiDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Chengye LiuDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Zhixiang HuDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Yan LiDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Zhuting FangDepartment of Oncology and Vascular Interventional Therapy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, China slhuang@fudan.edu.cn ztfang@fjzlhospital.com lianglinhui@fudan.edu.cn.
Linhui LiangDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China slhuang@fudan.edu.cn ztfang@fjzlhospital.com lianglinhui@fudan.edu.cn.
Shenglin HuangDepartment of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China slhuang@fudan.edu.cn ztfang@fjzlhospital.com lianglinhui@fudan.edu.cn.ORCID http://orcid.org/0000-0003-1279-0794

Funding

BIOLOGICAL FOUNDATIONS OF VOCAL LEARNINGR01DC000198 · NIDCD · UNIV OF MARYLAND, COLLEGE PARK · PI DOOLING, ROBERT J · 1989 to 2008
$1.9M
SINGLE UNIT AND BEHAVIORAL RESPONSES TO ODORANT SERIESF32DC000198 · NIDCD · TUFTS UNIVERSITY BOSTON · PI DORRIES, KATHLEEN M · 1995 to 1997
–
NIDCD NIH HHS F32 DC000198NIDCD NIH HHS R01 DC000198
6 · The paper itself

Abstract

backgroundPrimary 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.

methodsWe 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.

resultsWe 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

conclusionsNeoTSTs 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.

Indexed as

Antigens, NeoplasmCarcinoma, HepatocellularImmunotherapyLiver NeoplasmsAnimalsHumansMiceAntigens, NeoplasmHepatocellular CarcinomaHuman leukocyte antigen - HLAImmunotherapy

Identifiers

PMID42481156
PMCPMC13405042

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.