Frontiers in Immunology (Nov 2024)

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

  • Jiaqing Liu,
  • Jiaqing Liu,
  • Jiaqing Liu,
  • Jiaqing Liu,
  • Dongchen Sun,
  • Dongchen Sun,
  • Dongchen Sun,
  • Dongchen Sun,
  • Shuoyu Xu,
  • Jiayi Shen,
  • Jiayi Shen,
  • Jiayi Shen,
  • Jiayi Shen,
  • Wenjuan Ma,
  • Wenjuan Ma,
  • Wenjuan Ma,
  • Wenjuan Ma,
  • Huaqiang Zhou,
  • Huaqiang Zhou,
  • Huaqiang Zhou,
  • Huaqiang Zhou,
  • Yuxiang Ma,
  • Yuxiang Ma,
  • Yuxiang Ma,
  • Yuxiang Ma,
  • Yaxiong Zhang,
  • Yaxiong Zhang,
  • Yaxiong Zhang,
  • Yaxiong Zhang,
  • Wenfeng Fang,
  • Wenfeng Fang,
  • Wenfeng Fang,
  • Wenfeng Fang,
  • Yuanyuan Zhao,
  • Yuanyuan Zhao,
  • Yuanyuan Zhao,
  • Yuanyuan Zhao,
  • Shaodong Hong,
  • Shaodong Hong,
  • Shaodong Hong,
  • Shaodong Hong,
  • Jianhua Zhan,
  • Jianhua Zhan,
  • Jianhua Zhan,
  • Jianhua Zhan,
  • Xue Hou,
  • Xue Hou,
  • Xue Hou,
  • Xue Hou,
  • Hongyun Zhao,
  • Hongyun Zhao,
  • Hongyun Zhao,
  • Hongyun Zhao,
  • Yan Huang,
  • Yan Huang,
  • Yan Huang,
  • Yan Huang,
  • Bingdou He,
  • Yunpeng Yang,
  • Yunpeng Yang,
  • Yunpeng Yang,
  • Yunpeng Yang,
  • Li Zhang,
  • Li Zhang,
  • Li Zhang,
  • Li Zhang

DOI
https://doi.org/10.3389/fimmu.2024.1485703
Journal volume & issue
Vol. 15

Abstract

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PurposeCurrently, 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 non-squamous non-small cell lung cancer (NSCLC).MethodsWe 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.ResultsOur 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 patho-immunoscore (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.ConclusionOur 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.

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