The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 |
doi: 10.3389/fimmu.2025.1479550
This article is part of the Research Topic Community Series in Personalized Immunotherapy: Advancing Processes to Extend Patient Collectives, Volume II View all 6 articles
Multi-omics analysis reveals the sensitivity of immunotherapy for unresectable non-small cell lung cancer
Provisionally accepted- 1 Department of Thoracic Surgery, Beijing Cancer Hospital, Peking University, Beijing, China
- 2 Shanghai Changzheng Hospital, Huangpu, Shanghai Municipality, China
- 3 School of Medicine, Shanghai University, Shanghai, China
Background: To construct a prediction model consisting of metabolites and proteins in peripheral blood plasma to predict whether patients with unresectable stage III and IV non-small cell lung cancer can benefit from immunotherapy before it is administered. Methods: Peripheral blood plasma was collected from unresectable stage III and IV non-small cell lung cancer patients who were negative for driver mutations before receiving immunotherapy. Then we classified samples according to the follow-up results after two courses of immunotherapy and non-targeted metabolomics and proteomics analyses were performed to select different metabolites and proteins. Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.Results: The presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. A total of 2 differential metabolites including 5-sulfooxymethylfurfural and Anthranilic acid and 2 differential proteins including Immunoglobulin heavy variable 1-45 and Microfibril-associated glycoprotein 4 were selected as reliable biomarkers.The area under the curve (AUC) of the prediction model built on clinical risks was merely 0.659. The AUC of metabolomics prediction model was 0.977 and the AUC of proteomics was 0.875 while the AUC of the integrative-omics prediction model was 0.955.Conclusions: Metabolic and protein biomarkers in peripheral blood both have high efficacy and reliability in the prediction of immunotherapy sensitivity in unresectable stage III and IV non-small cell lung cancer, but validation in larger population-based cohorts is still needed.
Keywords: Non-small cell lung cancer, immune checkpoint inhibitors, Metabolomics, Proteomics, Prediction models
Received: 12 Aug 2024; Accepted: 20 Jan 2025.
Copyright: © 2025 Wu, Wei, Huang, Zhou, Feng, Dong and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xingshuai Huang, Shanghai Changzheng Hospital, Huangpu, Shanghai Municipality, China
Yinge Zhou, School of Medicine, Shanghai University, Shanghai, 200444, China
Xiao Feng, Shanghai Changzheng Hospital, Huangpu, Shanghai Municipality, China
Xin Dong, School of Medicine, Shanghai University, Shanghai, 200444, China
Hao Tang, Shanghai Changzheng Hospital, Huangpu, Shanghai Municipality, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.