{"title":"Genetic analysis of primary renal cell carcinoma to determine treatment approaches","authors":"V. Stühler, S. Rausch, A. Stenzl, J. Bedke","doi":"10.1080/23808993.2021.1874822","DOIUrl":null,"url":null,"abstract":"ABSTRACT Introduction To date, there is no validated predictive biomarker available that guides treatment selection between an immune-based or an anti-VEGF-based regimen in patients with metastatic renal cell carcinoma (mRCC). Here, valid biomarkers could increase the benefit of therapy and thereby safe unnecessary toxicity. Recently, phase II and III clinical trials have shown a correlation between molecular clusters and responses to targeted therapy with tyrosine kinase inhibitors (TKIs), immune checkpoint inhibitors (ICIs) or as combination of both in patients with clear-cell mRCC. Areas covered This review discusses recent advances in the discovery of predictive biomarkers, highlighting the growing role of genetic analysis for treatment selection and its potential impact on precision medicine in mRCC. In this context, we extensively analyzed the available literature from Pubmed’s archives on this topic. Expert opinion Molecular subclassification which predicts responses to TKI, or ICI therapy is an exciting step toward personalized medicine in mRCC, but this still requires validation. However, intratumoral heterogeneity in relationship to the predictive power of molecular analysis of the primary tumor and circulating tumor DNA is challenging and requires further analysis.","PeriodicalId":12124,"journal":{"name":"Expert Review of Precision Medicine and Drug Development","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23808993.2021.1874822","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Precision Medicine and Drug Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23808993.2021.1874822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT Introduction To date, there is no validated predictive biomarker available that guides treatment selection between an immune-based or an anti-VEGF-based regimen in patients with metastatic renal cell carcinoma (mRCC). Here, valid biomarkers could increase the benefit of therapy and thereby safe unnecessary toxicity. Recently, phase II and III clinical trials have shown a correlation between molecular clusters and responses to targeted therapy with tyrosine kinase inhibitors (TKIs), immune checkpoint inhibitors (ICIs) or as combination of both in patients with clear-cell mRCC. Areas covered This review discusses recent advances in the discovery of predictive biomarkers, highlighting the growing role of genetic analysis for treatment selection and its potential impact on precision medicine in mRCC. In this context, we extensively analyzed the available literature from Pubmed’s archives on this topic. Expert opinion Molecular subclassification which predicts responses to TKI, or ICI therapy is an exciting step toward personalized medicine in mRCC, but this still requires validation. However, intratumoral heterogeneity in relationship to the predictive power of molecular analysis of the primary tumor and circulating tumor DNA is challenging and requires further analysis.
期刊介绍:
Expert Review of Precision Medicine and Drug Development publishes primarily review articles covering the development and clinical application of medicine to be used in a personalized therapy setting; in addition, the journal also publishes original research and commentary-style articles. In an era where medicine is recognizing that a one-size-fits-all approach is not always appropriate, it has become necessary to identify patients responsive to treatments and treat patient populations using a tailored approach. Areas covered include: Development and application of drugs targeted to specific genotypes and populations, as well as advanced diagnostic technologies and significant biomarkers that aid in this. Clinical trials and case studies within personalized therapy and drug development. Screening, prediction and prevention of disease, prediction of adverse events, treatment monitoring, effects of metabolomics and microbiomics on treatment. Secondary population research, genome-wide association studies, disease–gene association studies, personal genome technologies. Ethical and cost–benefit issues, the impact to healthcare and business infrastructure, and regulatory issues.