{"title":"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。","authors":"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","doi":"10.1007/s00262-024-03829-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results and conclusion: </strong>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<sup>+</sup> 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.</p>","PeriodicalId":9595,"journal":{"name":"Cancer Immunology, Immunotherapy","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448477/pdf/","citationCount":"0","resultStr":"{\"title\":\"A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.\",\"authors\":\"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\",\"doi\":\"10.1007/s00262-024-03829-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results and conclusion: </strong>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<sup>+</sup> 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.</p>\",\"PeriodicalId\":9595,\"journal\":{\"name\":\"Cancer Immunology, Immunotherapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448477/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Immunology, Immunotherapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00262-024-03829-9\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Immunology, Immunotherapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00262-024-03829-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.
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.
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.