A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients

作者：Yuxin Jiang, Yueying Chen, Qinpei Cheng, Wanjun Lu, Yu Hong Li, Xueying Zuo, Qiuxia Wu, Xiaoxia Wang, Fang Zhang, Dong Wang, Qing K. Wang, Tangfeng Lv, Yong Song, Ping Zhan · 发表于：Cancer Immunology Immunotherapy · 年份：2024 · DOI：10.1007/s00262-024-03829-9 · 被引用次数：7 · 研究领域：Lung Cancer Research Studies、Cancer Immunotherapy and Biomarkers、Lung Cancer Treatments and Mutations

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME. We retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing. During the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score pot...