Jiang Yuxin, Chen Yueying, Cheng Qinpei, Lu Wanjun, Li Yu, Zuo Xueying, Wu Qiuxia, Wang Xiaoxia, Zhang Fang, Wang Dong, Wang Qin, Lv Tangfeng, Song Yong, Zhan Ping
School of Medicine, Southeast University, Nanjing, 210000, China.
Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China.
Cancer Immunol Immunother. 2024 Oct 3;73(12):241. doi: 10.1007/s00262-024-03829-9.
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 potentially indicated more infiltration of CD8 T cells in the stroma as well as a greater probability of MCL-1 amplification and EP300 mutation. At the single-cell level, MCL-1 was associated with TNFA-NFKB signaling and apoptosis-related processes. Hopefully, this noninvasive model could act as a biomarker for immunotherapy, potentially facilitating precision medicine in the management of ES-SCLC.
小细胞肺癌（SCLC）是一种侵袭性很强的神经内分泌肿瘤，死亡率很高，只有一小部分广泛期小细胞肺癌（ES-SCLC）患者在接受化疗免疫治疗后生存期延长，这就需要探索可靠的生物标志物。在此，我们利用苏木精和伊红（H&E）染色图像提取的病理组学特征构建了一个基于机器学习的模型，用于对预后进行分类，并探索其与基因组学和肿瘤免疫微环境（TIME）的潜在关联。
我们回顾性招募了2020年4月至2023年8月期间在南京金陵医院接受一线化疗免疫治疗的ES-SCLC患者。采集了数字H&E染色的全切片图像，并对部分患者进行了靶向二代测序、程序性死亡配体-1染色和免疫细胞多重免疫组化染色。建立了一个包含临床和病理组学特征的随机生存森林（RSF）模型来预测总生存期。通过单细胞RNA测序评估推定基因的功能。
在12.12个月的中位随访期内，招募了118例接受一线免疫治疗的ES-SCLC患者。利用三个病理组学特征以及肝转移、骨转移、吸烟状态和乳酸脱氢酶的RSF模型，能够对ES-SCLC患者一线化疗免疫治疗的生存情况进行预测，具有良好的区分度和校准度。从根本上来说，较高的RSF评分可能表明基质中CD8 T细胞浸润更多，以及MCL-蛋白1扩增和EP300突变的可能性更大。在单细胞水平上，MCL-蛋白1与肿瘤坏死因子α-核因子κB信号传导和凋亡相关过程有关。有望这个非侵入性模型可以作为免疫治疗的生物标志物，潜在地促进ES-SCLC管理中的精准医学。
