Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy

This study establishes a reliable risk model that complements TMB in guiding treatment decisions for ICI therapy. By incorporating the interaction between TMB and the TME, our model provides a more accurate prediction of patient prognosis and treatment response across multiple cancer types. The risk score's association with immunosuppressive TME components underscores the importance of considering the tumor's microenvironment in treatment planning. Furthermore, our findings highlight RPLP0 as a promising therapeutic target for combination immunotherapy. The robust predictive ability of our model across various cohorts and its potential to improve therapeutic outcomes offer new insights and directions for enhancing the efficacy of ICI therapy. Future research should focus on further validating this model in larger and more diverse cohorts, refining the gene set selection process, and exploring the specific mechanisms through which the identified biomarkers influence the TME and treatment response.

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Institutions

Publication Details

Journal
Frontiers in Immunology
Published
2025-07-24
DOI
https://doi.org/10.3389/fimmu.2025.1557461
Citations
4
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
4.18

Funders

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article

Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy

Mingjie Lin, Youyan Tan, Liangmin Fu, Haoqian Feng et al.
4 citations
Frontiers in Immunology
Cancer Immunotherapy and Biomarkers
4.18
article

Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy

Mingjie Lin, Youyan Tan, Liangmin Fu, Haoqian Feng, Yinghan Wang, Minyu Chen, Hansen Lin, Wuyuan Liao, Zhiping Tan, Yuhang Chen, Jinwei Chen, Li Luo, Zihao Feng, Xinwei Zhou, Pengju Li, Jun Lu, Jinhuan Wei, Xinyan Chen, G YAO
article en
4 citations

Abstract

This study establishes a reliable risk model that complements TMB in guiding treatment decisions for ICI therapy. By incorporating the interaction between TMB and the TME, our model provides a more accurate prediction of patient prognosis and treatment response across multiple cancer types. The risk score's association with immunosuppressive TME components underscores the importance of considering the tumor's microenvironment in treatment planning. Furthermore, our findings highlight RPLP0 as a promising therapeutic target for combination immunotherapy. The robust predictive ability of our model across various cohorts and its potential to improve therapeutic outcomes offer new insights and directions for enhancing the efficacy of ICI therapy. Future research should focus on further validating this model in larger and more diverse cohorts, refining the gene set selection process, and exploring the specific mechanisms through which the identified biomarkers influence the TME and treatment response.

Frontiers in ImmunologyVol. 16
Central South University (CN), Sun Yat-sen University (CN), Guangdong Pharmaceutical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), The Seventh Affiliated Hospital of Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Second Xiangya Hospital of Central South University (CN), Ministry of Education and Child Care (CA)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Guangdong Medical Research Foundation
Good health and well-being
Openalex Percentile: Top 5%
Cancer Immunotherapy and Biomarkers
4.18
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