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

IF 4.6 2区 医学 Q2 IMMUNOLOGY Cancer Immunology, Immunotherapy Pub Date : 2024-10-03 DOI:10.1007/s00262-024-03829-9
Yuxin Jiang, Yueying Chen, Qinpei Cheng, Wanjun Lu, Yu Li, Xueying Zuo, Qiuxia Wu, Xiaoxia Wang, Fang Zhang, Dong Wang, Qin Wang, Tangfeng Lv, Yong Song, Ping Zhan
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Abstract

Background: 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.

Methods: 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.

Results and conclusion: 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.

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基于随机生存森林的病理组学特征可对ES-SCLC患者的免疫疗法预后进行分类,并对TIME和基因组学进行分析。
背景: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,只有少数广泛期SCLC(ES-SCLC)患者在化疗免疫治疗下能延长生存期,因此需要探索可靠的生物标志物。在此,我们利用从苏木精和伊红(H&E)染色图像中提取的病理组学特征建立了一个基于机器学习的模型,对预后进行分类,并探索其与基因组学和TIME的潜在关联:我们回顾性地招募了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。我们采集了数字化H&E染色的全切片图像,并对部分患者进行了靶向新一代测序、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)模型来预测总生存率。通过单细胞RNA测序评估了推测基因的功能:在中位随访期12.12个月期间,共招募了118名接受一线免疫疗法的ES-SCLC患者。利用三个病理组学特征和肝转移、骨转移、吸烟状态及乳酸脱氢酶的RSF模型,可以预测ES-SCLC患者一线化疗免疫治疗的生存率,并具有良好的区分度和校准性。RSF-Score越高,表明基质中CD8+ T细胞浸润越多,MCL-1扩增和EP300突变的可能性越大。在单细胞水平上,MCL-1 与 TNFA-NFKB 信号转导和细胞凋亡相关过程有关。希望这种非侵入性模型能成为免疫疗法的生物标记物,从而促进ES-SCLC治疗的精准医疗。
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来源期刊
CiteScore
10.50
自引率
1.70%
发文量
207
审稿时长
1 months
期刊介绍: Cancer Immunology, Immunotherapy has the basic aim of keeping readers informed of the latest research results in the fields of oncology and immunology. As knowledge expands, the scope of the journal has broadened to include more of the progress being made in the areas of biology concerned with biological response modifiers. This helps keep readers up to date on the latest advances in our understanding of tumor-host interactions. The journal publishes short editorials including "position papers," general reviews, original articles, and short communications, providing a forum for the most current experimental and clinical advances in tumor immunology.
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