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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1485703
This article is part of the Research Topic Investigating tumor immunotherapy responses in lung cancer using deep learning View all 7 articles

Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced nonsquamous non-small cell lung cancer (NSCLC): a multicentre retrospective study

Provisionally accepted
Jiaqing Liu Jiaqing Liu 1Dongchen Sun Dongchen Sun 1Shuoyu Xu Shuoyu Xu 2Jiayi Shen Jiayi Shen 1Wenjuan Ma Wenjuan Ma 1Huaqiang Zhou Huaqiang Zhou 1Yuxiang Ma Yuxiang Ma 1Yaxiong Zhang Yaxiong Zhang 1Wenfeng Fang Wenfeng Fang 1Yuanyuan Zhao Yuanyuan Zhao 1SHAODONG HONG SHAODONG HONG 1Jianhua Zhan Jianhua Zhan 1Xue Hou Xue Hou 1Hong-Yun Zhao Hong-Yun Zhao 1YAN HUANG YAN HUANG 1Bingdou He Bingdou He 2Yunpeng Yang Yunpeng Yang 1*Li Zhang Li Zhang 1*
  • 1 Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
  • 2 Bio-totem Pte Ltd, Suzhou, China, Suzhou, China

The final, formatted version of the article will be published soon.

    PURPOSE Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would benefit from chemoimmunotherapy. The primary objective of our study was to develop an artificial intelligence-based immunoscore and to evaluate the value of patho-immunoscore in predicting clinical outcomes in patients with advanced nonsquamous non-small cell lung cancer (NSCLC).We have developed an artificial intelligence-powered immunoscore analyzer based on 1,333 whole-slide images from TCGA-LUAD. The predictive efficacy of the model was further validated in the CPTAC-LUAD cohort and the biomarker cohort of the ORIENT-11 study, a randomized, double-blind, phase 3 study. Finally, the clinical significance of the patho-immunoscore was evaluated using the ORIENT-11 study cohort.Our immunoscore analyzer achieved good accuracy in all the three cohort mentioned above (TCGA-LUAD, mean AUC: 0.783; ORIENT-11 cohort, AUC: 0.741; CPTAC-LUAD cohort, AUC: 0.769). In the 259 patients treated with chemoimmunotherapy, those with high patho-immunoscore (n = 146) showed significantly longer median progression-free survival than those with low pathoimmunoscore (n = 113) (13.8 months vs 7.13 months, hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.38 -0.73; p < 0.001). In contrast, no significant difference was observed in patients who were treated with chemotherapy only (5.07 months vs 5.07 months, HR: 1.04, 95% CI: 0.71 -1.54; p = 0.83). Similar trends were observed in overall survival.CONCLUSION Our study indicates that AI-powered immunoscore applied on LUAD digital slides can serve as a biomarker for survival outcomes in patients with advanced non-squamous NSCLC who received chemoimmunotherapy. This methodology could be applied to other cancers and facilitate cancer immunotherapy.

    Keywords: NSCLC, Immunotherapy, artificial intelligence, Pathology, Immunoscore

    Received: 24 Aug 2024; Accepted: 17 Oct 2024.

    Copyright: © 2024 Liu, Sun, Xu, Shen, Ma, Zhou, Ma, Zhang, Fang, Zhao, HONG, Zhan, Hou, Zhao, HUANG, He, Yang and Zhang. 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:
    Yunpeng Yang, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
    Li Zhang, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, 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.