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中文摘要
翻译
描述(申请人提供):作为国家临床试验网络领先的学术参与网站,我们希望完成以下工作:1.在创建深思熟虑的、可行的试验以改善癌症患者健康方面提供科学领导和智力投入。我们将通过不断参与临床试验来实现这一目标,这些临床试验包括疾病特异性和放射及外科肿瘤学;通过应用北卡罗来纳大学的多学科护理文化,使NCTN试验更强大、更现代化;通过利用初级教员的承诺,他们将是未来的领导者;通过在临床试验的开发中应用卫生服务研究,包括对临床结果重要的因素,如成本效益、提供护理和政策。2.通过我们快速增长的临床事业和强劲的收益,继续成为Network Trials的坚定支持者。我们将在CALGB、GOG和ACOSOG试验以及我们资金充足和组织良好的基础设施的基础上,通过积极参与NCTN来支持这一目标。3.为NCTN临床试验中嵌入优秀的翻译科学提供机会。北卡罗来纳大学长期致力于翻译科学,并在多项试验中担任参考实验室,在下一代测序和基因组学方面投入了大量(300多万美元);我们非常适合作为未来临床试验的翻译研究合作者。
英文摘要
DESCRIPTION (provided by applicant): As a National Clinic Trials Network Lead Academic Participating Site, we hope to accomplish the following: 1. To provide scientific leadership and intellectual input in the creation of thoughtful, feasible trials to improve the health of cancer patients. We will accomplish this aim through the ongoing participation in clinical trials that spn the spectrum of disease-specific and radiation and surgical oncology; by applying the UNC culture of multidisciplinary care to make NCTN trials stronger and more modern; by leveraging the commitment of junior faculty, who will be the leaders of tomorrow; and by applying health services research in the development of clinical trials, including factors important to clinical outcomes, such as cost effectiveness, delivery of care, and policy. 2. To continue to be strong supporters of Network trials, through our rapidly growing clinical enterprise and robust accrual. We will support this aim by building on our historically strong accrual to CALGB, GOG, and ACOSOG trials and our well-funded and organized infrastructure, and through active participation across the NCTN. 3. To provide opportunities for excellent translational science embedded in NCTN clinical trials. UNC has a long commitment to translational science and has served as reference lab across multiple trials, with substantial (more than $3 million) investment in Next Generation sequencing and genomics; we are well suited to serve as a translational research collaborator for clinical trials in the future.
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Optimizing HER2-targeting using RNA- and DNA-based predictive algorithms
UNC Lead Academic Participating Site
Optimizing HER2-targeting using RNA- and DNA-based predictive algorithms
UNC Lead Academic Participating Site
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