ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1600265

This article is part of the Research TopicPrecision Oncology in Checkpoint Immunotherapy: Leveraging Predictive Biomarkers for Personalized TreatmentView all 18 articles

Quality-of-life scale machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer

Provisionally accepted
Juanyan ShenJuanyan Shen1,2Jian-Guo ZhouJian-Guo Zhou1,3,4*Junliang MaJunliang Ma2*Shaolin ChenShaolin Chen2,5Su-Han JinSu-Han Jin6Junzhu XuJunzhu Xu1Qisha LiQisha Li1Chi ZhangChi Zhang1Xiaojing TianXiaojing Tian1Xiaofei ChenXiaofei Chen7Fangya TanFangya Tan8Markus HechtMarkus Hecht9Benjamin FreyBenjamin Frey10Udo S GaiplUdo S Gaipl10Hu MaHu Ma1
  • 1Department of Oncology, Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou Province, China
  • 2Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou Province, China
  • 3Department of Radiotherapy and Radiation Oncology, Saarland University Medical Center, Homburg, Germany
  • 4Department of Biostat & Programming, Sanofi, Bridgewater, NJ, United States., Bridgewater, Massachusetts, United States
  • 5Zunyi Medical University, Zunyi, Guizhou Province, China
  • 6Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, Guizhou Province, China
  • 7AstraZeneca, Gaithersburg, Maryland, United States
  • 8University at Buffalo, Buffalo, New York, United States
  • 9Saarland University Hospital, Homburg, Saarland, Germany
  • 10Translational Radiobiology, Department of Radiation Oncology,Universitätsklinikum Erlangen, Friedrich-Alexander- Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany

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Background: Despite immune checkpoint inhibitors(ICIs) significantly improve clinical outcomes in patients with advanced non-small cell lung cancer (aNSCLC), disease progression is inevitable. A diverse patient-reported Quality-of-life(QoL) scales were used to predict outcomes for aNSCLC patients with atezolizumab using machine learning.Materials and Methods: This study analyzed the association between baseline QoL and clinical outcomes in aNSCLC patients with atezolizumab in 4 randomized clinical trials: the IMpower150 study (discovery cohort), the BIRCH, OAK and POPLAR study (validation cohorts). We identified quality of life subtypes (QoLS) by consensus clustering in the discovery cohort and predicted them in external validated cohorts.Results: We identified QoLS1 and QoLS2 via consensus clustering in the discovery cohort. Compared with QoLS1, QoLS2 was associated with significantly worse survival outcomes, including a shorter median overall survival (OS: 13.14 vs. 21.42 months, hazard ratio (HR) 2.07, 95% CI: 1.64 to 2.62; p < 0.0001) and progression-free survival (PFS: 5.7 vs. 8.3 months, HR 1.69, 95% CI 1.42 to 2.04; p < 0.0001). QoLS2 also was associated with lower clinical benefit rate (57% vs. 68%, p = 0.0027). In external cohorts, QoLS2 was consistently associated with unfavorable OS (p < 0.0001). Notably, QoLS1 was a positive predictive biomarker for atezolizumab efficacy: patients in QoLS1 group derived greater survival benefit from ICIs versus chemotherapy (IMpower150, p = 0.04; OAK+POPLAR, p = 0.007), while patients in QoLS2 showed no significant treatment benefit.Conclusions: Our study demonstrated the potential of integrative machine learning in effectively analyzing baseline QoL and predicting clinical outcomes in aNSCLC patients undergoing atezolizumab immunotherapy.

Keywords: Quality of Life, consensus clustering, atezolizumab, NSCLC, overall survival

Received: 23 Apr 2025; Accepted: 26 Jun 2025.

Copyright: © 2025 Shen, Zhou, Ma, Chen, Jin, Xu, Li, Zhang, Tian, Chen, Tan, Hecht, Frey, Gaipl and Ma. 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:
Jian-Guo Zhou, Department of Oncology, Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou Province, China
Junliang Ma, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, Guizhou Province, 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.

Supplementary Material