原发性肾细胞癌的遗传分析以确定治疗方法

V. Stühler, S. Rausch, A. Stenzl, J. Bedke
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引用次数: 1

摘要

摘要简介到目前为止,还没有经过验证的预测性生物标志物可用于指导转移性肾细胞癌(mRCC)患者在基于免疫或抗VEGF的方案之间的治疗选择。在这里,有效的生物标志物可以增加治疗的益处,从而增加安全的不必要的毒性。最近,II期和III期临床试验表明,在透明细胞mRCC患者中,分子簇与酪氨酸激酶抑制剂(TKIs)、免疫检查点抑制剂(ICIs)或两者结合的靶向治疗反应之间存在相关性。本综述讨论了预测性生物标志物发现的最新进展,强调了基因分析在治疗选择中日益增长的作用及其对mRCC精准医学的潜在影响。在此背景下,我们广泛分析了Pubmed档案中关于这一主题的可用文献。专家意见预测TKI或ICI治疗反应的分子亚类化是mRCC个性化药物的令人兴奋的一步,但这仍需要验证。然而,肿瘤内异质性与原发肿瘤和循环肿瘤DNA的分子分析的预测能力之间的关系是具有挑战性的,需要进一步分析。
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Genetic analysis of primary renal cell carcinoma to determine treatment approaches
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.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
9
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.
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