Sushant Patkar,Joshua Mannheimer,Stephanie A Harmon,Christina J Ramirez,Christina N Mazcko,Peter L Choyke,G Thomas Brown,Baris Turkbey,Amy K LeBlanc,Jessica A Beck
{"title":"对犬骨肉瘤和人类骨肉瘤的大规模比较分析发现了保守的临床相关肿瘤微环境亚型。","authors":"Sushant Patkar,Joshua Mannheimer,Stephanie A Harmon,Christina J Ramirez,Christina N Mazcko,Peter L Choyke,G Thomas Brown,Baris Turkbey,Amy K LeBlanc,Jessica A Beck","doi":"10.1158/1078-0432.ccr-24-1854","DOIUrl":null,"url":null,"abstract":"PURPOSE\r\nOsteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression.\r\n\r\nEXPERIMENTAL DESIGN\r\nBulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value.\r\n\r\nRESULTS\r\nThis study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis.\r\n\r\nCONCLUSIONS\r\nThese findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.","PeriodicalId":10279,"journal":{"name":"Clinical Cancer Research","volume":null,"pages":null},"PeriodicalIF":10.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.\",\"authors\":\"Sushant Patkar,Joshua Mannheimer,Stephanie A Harmon,Christina J Ramirez,Christina N Mazcko,Peter L Choyke,G Thomas Brown,Baris Turkbey,Amy K LeBlanc,Jessica A Beck\",\"doi\":\"10.1158/1078-0432.ccr-24-1854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PURPOSE\\r\\nOsteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression.\\r\\n\\r\\nEXPERIMENTAL DESIGN\\r\\nBulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value.\\r\\n\\r\\nRESULTS\\r\\nThis study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis.\\r\\n\\r\\nCONCLUSIONS\\r\\nThese findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.\",\"PeriodicalId\":10279,\"journal\":{\"name\":\"Clinical Cancer Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.0000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/1078-0432.ccr-24-1854\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1078-0432.ccr-24-1854","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Large Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.
PURPOSE
Osteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression.
EXPERIMENTAL DESIGN
Bulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value.
RESULTS
This study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis.
CONCLUSIONS
These findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.
期刊介绍:
Clinical Cancer Research is a journal focusing on groundbreaking research in cancer, specifically in the areas where the laboratory and the clinic intersect. Our primary interest lies in clinical trials that investigate novel treatments, accompanied by research on pharmacology, molecular alterations, and biomarkers that can predict response or resistance to these treatments. Furthermore, we prioritize laboratory and animal studies that explore new drugs and targeted agents with the potential to advance to clinical trials. We also encourage research on targetable mechanisms of cancer development, progression, and metastasis.