Evidence map›Paper›PMID 42364494›Full record

ArticleTranslational oncology2026

Pan-cancer analysis identifies immunoproteasome as the predominant survival-associated component of antigen processing machinery.

Jiun-I Lai, Chun-Yu Liu, Yi-Fang Tsai, Chi-Cheng Huang, Ling-Ming Tseng, Ta-Chung Chao

Abstract read
In one paragraph

Article in Translational oncology, 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

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jiun-I LaiDivision of Medical Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Center of Immuno-Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei City, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan. Electronic address: jilai@nycu.edu.tw.
Chun-Yu LiuDivision of Medical Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; Center of Immuno-Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei City, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yi-Fang TsaiComprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of General Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
Chi-Cheng HuangComprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of General Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
Ling-Ming TsengComprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of General Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
Ta-Chung ChaoDivision of Medical Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Cancer Prevention, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan. Electronic address: tcchao@vghtpe.gov.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neoantigens are critical targets for cancer immunotherapy, yet the relationship between experimentally validated neoantigen burden and antigen processing machinery (APM) expression in determining clinical outcomes remains unclear. We mapped CEDAR-annotated neoantigens (CENs) onto mutation data from 43,980 patients across 14 cancer types using cBioPortal. APM gene expression was correlated with survival outcomes across 13 cohorts. Machine learning approaches (elastic net stability selection, random survival forest, univariable Cox regression) identified prognostically important APM genes across 11 cohorts. Findings were validated in the IMvigor210 immunotherapy trial (n=348 metastatic urothelial carcinoma patients) and single-cell RNA-sequencing data (GSE161529; n=29 breast cancers). Overall, 40.4% of patients harbored at least one CEN, with high prevalence in pancreatic (>75%) and skin cancers (>70%). CENs predominantly arose from driver oncogenes including PIK3CA, KRAS, BRAF, TP53, and EGFR. High APM expression was associated with improved survival, particularly in CEN-positive tumors. Machine learning identified immunoproteasome components (PSME1, PSMB8, PSMB9, PSMB10) as the dominant prognostic contributors within the 12-gene APM signature. A simplified 4-gene immunoproteasome score performed equivalently to the full APM score in leave-one-cohort-out cross-validation (median C-index 0.545 vs 0.545; p=0.31). In IMvigor210, immunoproteasome-high patients achieved a 3.2-fold higher response rate to atezolizumab (19.8% vs 6.2%; p=0.010). Single-cell analysis confirmed that tumor-intrinsic immunoproteasome expression correlated with increased CD8+ T cell infiltration (p=0.0014) and total immune fraction (p=0.0002). The 4-gene immunoproteasome signature demonstrates robust prognostic and predictive value across bulk sequencing, clinical trial, and single-cell platforms, warranting prospective validation as an immunotherapy biomarker.

Indexed as

Antigen processing machineryCancer immunotherapyCEDARImmunoproteasomeNeoantigens

Identifiers

PMID42364494
PMCPMC13324302

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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.