Mutational signature-based biomarker to predict the response of immune checkpoint inhibitors therapy in cancers

Yu-E Huang; Shunheng Zhou; Sina Chen; Jiahao Chen; Xu Zhou; Fei Hou; Haizhou Liu; Mengqin Yuan; Wei Jiang

doi:10.1016/j.ijbiomac.2025.142585

Mutational signature-based biomarker to predict the response of immune checkpoint inhibitors therapy in cancers

Int J Biol Macromol. 2025 May;308(Pt 4):142585. doi: 10.1016/j.ijbiomac.2025.142585. Epub 2025 Mar 26.

Authors

Yu-E Huang¹, Shunheng Zhou², Sina Chen³, Jiahao Chen⁴, Xu Zhou³, Fei Hou³, Haizhou Liu⁵, Mengqin Yuan³, Wei Jiang⁶

Affiliations

¹ Fujian Provincial Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Guizhou Institute of Precision Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
² School of Computer Sciences, University of South China, Hengyang 421001, China.
³ Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
⁴ Fujian Provincial Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
⁵ Fujian Provincial Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.
⁶ Fujian Provincial Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China. Electronic address: jiangwei@fjmu.edu.cn.

PMID: 40154701
DOI: 10.1016/j.ijbiomac.2025.142585

Abstract

Patients have a limited response rate to immune checkpoint inhibitors (ICIs) therapy. Although several biomarkers have been proposed, their ability to accurately predict the response to ICIs therapy remains unsatisfactory. In addition, mutational signatures were validated to be associated with ICIs therapy. Therefore, we developed a mutational signature-based biomarker (MS-bio) to predict the response to ICIs therapy. Based on differentially mutated genes, we extracted six mutational signatures (single-base substitution (SBS)-A, SBS-B, SBS-C, SBS-D, double-base substitution (DBS)-A, and DBS-B) as MS-bio, and constructed a random forest (RF) model to predict the response. Internal and external validations consistently demonstrated the excellent predictive capability of MS-bio, with an accuracy reaching up to 0.82. Moreover, MS-bio exhibited superior performance compared to existing biomarkers. To further validate the accuracy of MS-bio, we explored its performance in The Cancer Genome Atlas (TCGA) cohort and found that the predicted responders were immunologically "hot". Finally, we found that SBS-C had the highest importance in prediction and was related to T cell differentiation. Overall, here we introduced MS-bio as a novel biomarker for accurately predicting the response to ICIs therapy, thereby contributing to the advancement of precision medicine.

Keywords: Biomarker; ICIs therapy; Immune; Mutational signature; Precision medicine.

MeSH terms

Biomarkers, Tumor* / genetics
Humans
Immune Checkpoint Inhibitors* / pharmacology
Immune Checkpoint Inhibitors* / therapeutic use
Mutation*
Neoplasms* / drug therapy
Neoplasms* / genetics
Neoplasms* / immunology

Substances

Immune Checkpoint Inhibitors
Biomarkers, Tumor