Jan-Niklas May, Jennifer I. Moss, Florian Mueller, Susanne K. Golombek, Ilaria Biancacci, Larissa Rizzo, Asmaa Said Elshafei, Felix Gremse, Robert Pola, Michal Pechar, Tomáš Etrych, Svea Becker, Christian Trautwein, Roman D. Bülow, Peter Boor, Ruth Knuechel, Saskia von Stillfried, Gert Storm, Sanyogitta Puri, Simon T. Barry, Volkmar Schulz, Fabian Kiessling, Marianne B. Ashford, Twan Lammers
{"title":"预测纳米药物肿瘤蓄积的组织病理学生物标志物","authors":"Jan-Niklas May, Jennifer I. Moss, Florian Mueller, Susanne K. Golombek, Ilaria Biancacci, Larissa Rizzo, Asmaa Said Elshafei, Felix Gremse, Robert Pola, Michal Pechar, Tomáš Etrych, Svea Becker, Christian Trautwein, Roman D. Bülow, Peter Boor, Ruth Knuechel, Saskia von Stillfried, Gert Storm, Sanyogitta Puri, Simon T. Barry, Volkmar Schulz, Fabian Kiessling, Marianne B. Ashford, Twan Lammers","doi":"10.1038/s41551-024-01197-4","DOIUrl":null,"url":null,"abstract":"<p>The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score’s effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":26.8000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Histopathological biomarkers for predicting the tumour accumulation of nanomedicines\",\"authors\":\"Jan-Niklas May, Jennifer I. Moss, Florian Mueller, Susanne K. Golombek, Ilaria Biancacci, Larissa Rizzo, Asmaa Said Elshafei, Felix Gremse, Robert Pola, Michal Pechar, Tomáš Etrych, Svea Becker, Christian Trautwein, Roman D. Bülow, Peter Boor, Ruth Knuechel, Saskia von Stillfried, Gert Storm, Sanyogitta Puri, Simon T. Barry, Volkmar Schulz, Fabian Kiessling, Marianne B. Ashford, Twan Lammers\",\"doi\":\"10.1038/s41551-024-01197-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score’s effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.</p>\",\"PeriodicalId\":19063,\"journal\":{\"name\":\"Nature Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":26.8000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1038/s41551-024-01197-4\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1038/s41551-024-01197-4","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Histopathological biomarkers for predicting the tumour accumulation of nanomedicines
The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score’s effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.
期刊介绍:
Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.