bims-micpro Biomed News
on Discovery and characterization of microproteins
Issue of 2026–03–22
two papers selected by
Thomas Farid Martínez, University of California, Irvine
  1. Mass spectrometry-based de novo sequencing reveals non-canonical neoantigens with antitumor efficacy in hepatocellular carcinoma.
  2. ncProFormer: A CNN-enhanced Transformer for ncRNA Coding-Potential Prediction.
  1. JHEP Rep. 2026 Feb 13. pii: S2589-5559(26)00046-7. [Epub ahead of print]8(4): 101775
    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.
       BACKGROUND & AIMS: Tumor 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.
    METHODS: Using 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.
    RESULTS: We 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).
    CONCLUSIONS: MS-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.
    Keywords:  T cell immunogenicity; cryptic translation; immunopeptidomics; peptide vaccine; targeted mass spectrometry
    DOI:  https://doi.org/10.1016/j.jhepr.2026.101775
  2. J Chem Inf Model. 2026 Mar 14.
    ncProFormer: A CNN-enhanced Transformer for ncRNA Coding-Potential Prediction.
    Mengyu Tong, Yuanting Chen, Long Chen, Jingnan Xu, Yingjie Yang, Guixia Liu, Weihua Li, Yun Tang.
      Recent studies have revealed that some noncoding RNAs (ncRNAs) bear translational potential, and their encoded micropeptides have essential functions in multiple biological processes. However, accurate identification of coding-capable ncRNAs remains challenging due to weak translation signals, low conservation, and heterogeneous data distributions. Herein, we propose ncProFormer, a deep learning framework tailored for ncRNA coding-potential prediction. ncProFormer integrates the nucleic-acid language model GENA-LM to obtain contextual sequence embeddings, adopts an all-token representation strategy, and employs a convolutional neural network (CNN)-enhanced transformer encoder to jointly capture local nucleotide patterns and long-range dependencies. ncProFormer consistently outperformed the existing methods across the in-house human data set, the external validation data set, the public CPPred benchmark data set. More importantly, this study presents the first cross-species evaluation in ncRNA coding-potential prediction. Without retraining, ncProFormer maintained its strong predictive performance on mouse and rat data sets, showing that the learned biological representations are transferable and it is robust under the distributional shift and cross-species conditions. Collectively, these findings establish ncProFormer as an effective and generalizable framework for uncovering the coding potential of ncRNAs, thus offering a promising computational tool for characterizing ncRNA functions across diverse transcriptomic contexts.
    DOI:  https://doi.org/10.1021/acs.jcim.6c00056
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