Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating Unit

用于早期癌症评估的液体活检的竞争前合作：数据管理和协调单位

• 批准号: 10838127
• 负责人: Wei Sun
• 金额: $ 79.2万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10838127
• 关键词: Address; Adoption; Area; Artificial Intelligence; Benign; Bioinformatics; Biological Assay; Biological Markers; Biometry; Biopsy; Blood; Body Fluids; Cancer Center; Cancer Detection; Cancer Patient; Cancerous; Caring; Cerebrospinal Fluid; Clinical; Collaborations; Collection; Common Data Element; Communication; Communities; Community Outreach; Computational Biology; Computer Analysis; Confidence Intervals; Data; Data Analyses; Data Coordinating Center; Data Element; Data Science; Data Storage and Retrieval; Detection; Development; Disease; Early Detection Research Network; Educational workshop; Goals; Image; Indolent; Informatics; Leadership; Lesion; Machine Learning; Malignant Neoplasms; Manuals; Methods; Molecular; Morbidity - disease rate; National Cancer Institute; Neoplasm Circulating Cells; Patients; Performance; Population; Positioning Attribute; Procedures; Proteins; RNA; Reproducibility; Reproducibility of Results; Research; Research Design; Research Methodology; Research Personnel; Research Support; Resources; Saliva; Science; Scientific Advances and Accomplishments; Screening for cancer; Secure; Sensitivity and Specificity; Somatotype; Specimen; Sputum; Standardization; Statistical Data Interpretation; Statistical Methods; System; Techniques; Technology; Tissues; Translational Research; Urine; Validation; Work; big data management; biomarker development; biomarker validation; cancer diagnosis; cancer type; cell free DNA; cloud storage; curative treatments; data centers; data management; data sharing; data standards; deep learning; design; exosome; experience; extracellular vesicles; federated learning; improved; innovation; laboratory experience; learning strategy; liquid biopsy; machine learning model; machine learning prediction; meetings; member; minimally invasive; molecular imaging; mortality; multidisciplinary; notch protein; novel; operation; outreach; pre-clinical; premalignant; programs; prospective; protocol development; research clinical testing; response; service coordination; statistics; symposium; tumor; tumor DNA; validation studies; web site

PROJECT SUMMARY Liquid biopsy is a clinical test that involves the analysis of various biomarkers present in the blood or other types of body fluids. It enables the detection of cancer at an earlier stage than traditional imaging or tissue biopsy methods, and thus can lead to reduction of cancer mobility and mortality. As one of the most promising solutions for cancer early detection, liquid biopsy has the potential to revolutionize cancer diagnosis, providing patients with more effective and less invasive care. The liquid biopsy analytes include circulating tumor cells (CTCs), circulating tumor DNA, which is part of cell-free DNA (cfDNA), extracellular vesicles, as well other tumor associated RNAs, proteins and metabolites. These analytes typically have very low levels in blood or other types of body fluid of early-stage cancer patients, leading to difficulties in obtaining reproducible results and low sensitivity and specificity for cancer early detection. While many new assays and techniques have been developed to tackle these challenges, validation of these assays/techniques in different populations and/or cancer types poses many challenges, and these are the main areas of research supported by the liquid biopsy consortium (LBC). As Data Management and Coordinating Unit (DMCU) of LBC, we will support state- of-the-art statistical methods for study design and data analysis, the coordination, implementation, and conduct of collaborative studies across the consortium and work closely with LBC investigators on data analysis by statistical methods, machine Learning modeling and Artificial Intelligence. We will also support consortium coordination and data management, including identification and adoption of standard data elements for liquid biopsy studies. We seek to establish the DMCU as a leading center for innovation and resources in liquid biopsy research, and to enhance, improve and maintain LBC network integration and coordination.

项目摘要 液体活检是一种临床试验，涉及分析血液或其他组织中存在的各种生物标志物。 体液的种类它能够在比传统成像或组织更早的阶段检测癌症 活检方法，因此可以导致癌症的流动性和死亡率的降低。作为最有前途的 作为癌症早期检测的解决方案，液体活检有可能彻底改变癌症诊断， 更有效和更少侵入性的护理。液体活检分析物包括循环肿瘤细胞 细胞外基质包括细胞毒性T细胞（CTC）、循环肿瘤DNA（其是无细胞DNA（cfDNA）的一部分）、细胞外囊泡以及其他细胞外基质。 肿瘤相关RNA、蛋白质和代谢物。这些分析物通常在血液中具有非常低的水平， 其他类型的早期癌症患者的体液，导致难以获得可重复的结果 以及癌症早期检测的低灵敏度和特异性。虽然许多新的分析和技术已经 为应对这些挑战而开发，在不同人群中验证这些测定/技术 和/或癌症类型提出了许多挑战，这些是液体支持的主要研究领域 活检联盟（LBC）。作为LBC的数据管理和协调单位（DMCU），我们将支持国家- 研究设计和数据分析的最先进的统计方法，协调，实施和进行 在整个联盟的合作研究，并与LBC研究人员密切合作，进行数据分析， 统计方法，机器学习建模和人工智能。我们还将支持财团 协调和数据管理，包括确定和采用液体 活检研究。我们寻求建立DMCU作为创新和流动资源的领先中心 活检研究，并加强，改善和维护LBC网络的整合和协调。