Evidence map›Paper›PMID 41861674›Full record

ArticleJHEP reports : innovation in hepatology2026

Mass spectrometry-based de novo sequencing reveals non-canonical neoantigens with antitumor efficacy in hepatocellular carcinoma.

Xiaohua Xing, Mingxin Liu, Jiahe Ouyang, Yaxin Tang, Baozhen Shan, Ruijing Tang, En Hu, Ming Li, Xiaolong Liu

Abstract read
In one paragraph

Article in JHEP reports : innovation in hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

9 authors.

Xiaohua XingThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China; School of Computer Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
Mingxin LiuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Jiahe OuyangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Yaxin TangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Baozhen ShanDepartment of Pathology, Institute of Oncology, School of Basic Medical Science, Fujian Medical University, Fuzhou 350025, China.
Ruijing TangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
En HuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Ming LiBioinformatics Solutions Inc, Waterloo, Ontario N2L 3K8, Canada. Electronic address: mli@uwaterloo.ca.
Xiaolong LiuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China; Department of Pathology, Institute of Oncology, School of Basic Medical Science, Fujian Medical University, Fuzhou 350025, China. Electronic address: xiaoloong.liu@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND &

aimsTumor neoantigens, especially cryptic antigens from non-canonical translation, are vital for cancer immunotherapy. Mass spectrometry (MS)-based de novo sequencing identifies candidates, but unverified immunogenicity and antitumor efficacy limit clinical applicability. This study aimed to identify novel non-canonical neoantigens in hepatocellular carcinoma (HCC) using MS-based de novo sequencing and rigorously validate their immunogenicity and antitumor efficacy.

methodsUsing a C57BL/6 subcutaneous HCC mouse model, immunopeptides were comprehensively profiled via MHC-I immunoprecipitation combined with MS-based de novo sequencing. Identified high-immunogenicity peptides predicted by deep learning were validated using ex vivo ELISpot assays. Endogenous peptide expression was confirmed using parallel reaction monitoring-targeted quantification. The antitumor efficacy of therapeutic peptide vaccines comprising the seven most immunogenic peptides combined with the adjuvant poly(I:C) was evaluated in vivo in the subcutaneous and orthotopic HCC models.

resultsWe identified 5,576 immunopeptides, with sequence motifs consistent with prior reports. Remarkably, 95% of deep learning-predicted high-immunogenicity peptides were successfully validated by ELISpot (p <0.05). Parallel reaction monitoring confirmed endogenous expression of these peptides. Most significantly, the peptide vaccines (7 peptides + poly(I:C)) demonstrated potent antitumor efficacy in vivo compared to controls (p <0.05).

conclusionsMS-based de novo sequencing combined with computational prioritization enables identification of non-canonical, immunogenic neoantigens in HCC. Selected peptides demonstrated endogenous presentation and measurable antitumor activity in preclinical models. IMPACT AND IMPLICATIONS: This study provides robust experimental validation that mass spectrometry-based de novo sequencing effectively identifies novel, highly immunogenic non-canonical neoantigens in hepatocellular carcinoma, overcoming a key limitation of prior predictive methods and opening avenues for exploring this understudied neoantigen class in other cancers. The findings are critical for cancer immunologists and oncologists developing next-generation immunotherapies, demonstrating a viable discovery-to-validation pipeline for novel therapeutic targets. The validated neoantigens and successful peptide vaccine strategy offer a direct pathway towards developing personalized hepatocellular carcinoma immunotherapies, enabling clinicians to adopt similar integrated approaches for patient-specific neoantigen discovery; however, clinical translation beyond this preclinical murine model requires confirmation in human settings due to potential differences in HLA presentation and the tumor microenvironment.

Indexed as

cryptic translationimmunopeptidomicspeptide vaccinetargeted mass spectrometryT cell immunogenicity

Identifiers

PMID41861674
PMCPMC13019551

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.