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<p>Although immunotherapy combined with chemotherapy (ICT) is the standard treatment for advanced non-small cell lung cancer (NSCLC), identification of reliable prognostic biomarkers remains challenging. In this multicenter study, we performed next-generation sequencing of tumor samples from 162 patients receiving first-line ICT at the Chinese PLA General Hospital and collected their pathological image information. First, we established a model to predict the risk of tumor progression based on genomic characteristics. Furthermore, a deep learning method was employed to recognize different cell types from pathological images, which significantly improved the accuracy of progression-free survival (PFS) and overall survival (OS) prediction. In summary, we constructed a Prognostic Multimodal Classifier for Progression (PMCP) that possesses the capability to precisely forecast PFS and OS. Patients with the PMCP1 subtype exhibit a low risk of progression and demonstrate a higher proportion of epithelial cells. PMCP highlighted the potential value of multimodal biomarkers in guiding clinical decisions regarding ICT. The area under curve (AUC) for predicting PFS was 0.807. This study revealed the importance of integrating genomic and pathological data to improve prognostic accuracy and enable personalized treatment for patients with advanced NSCLC.</p>

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Integrating genomic and pathological characteristics to enhance prognostic precision in advanced NSCLC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月2日

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  • Yingjie Han,
  • Junxun Ma,
  • Zhefeng Liu,
  • Lijie Wang,
  • Fan Zhang,
  • Di Huang,
  • Siyao Liu,
  • Jifang Hu,
  • Wenhua Xiao,
  • Hong Wang,
  • Juyi Wen,
  • Haifeng Qin,
  • Hongjun Gao,
  • Xiaosong Li,
  • Ziwei Huang,
  • Jiali Zhang,
  • Yue Zhang,
  • Dawei Sun,
  • Junyan Su,
  • Jing Chen,
  • Beifang Niu,
  • Haitao Tao,
  • Bo Yang,
  • Xiaoqing Liu,
  • Jinliang Wang,
  • Yi Hu

摘要

Although immunotherapy combined with chemotherapy (ICT) is the standard treatment for advanced non-small cell lung cancer (NSCLC), identification of reliable prognostic biomarkers remains challenging. In this multicenter study, we performed next-generation sequencing of tumor samples from 162 patients receiving first-line ICT at the Chinese PLA General Hospital and collected their pathological image information. First, we established a model to predict the risk of tumor progression based on genomic characteristics. Furthermore, a deep learning method was employed to recognize different cell types from pathological images, which significantly improved the accuracy of progression-free survival (PFS) and overall survival (OS) prediction. In summary, we constructed a Prognostic Multimodal Classifier for Progression (PMCP) that possesses the capability to precisely forecast PFS and OS. Patients with the PMCP1 subtype exhibit a low risk of progression and demonstrate a higher proportion of epithelial cells. PMCP highlighted the potential value of multimodal biomarkers in guiding clinical decisions regarding ICT. The area under curve (AUC) for predicting PFS was 0.807. This study revealed the importance of integrating genomic and pathological data to improve prognostic accuracy and enable personalized treatment for patients with advanced NSCLC.