Coordinating and Data Management Center for Translational and Basic Science Research in Early Lesions

早期病变转化和基础科学研究协调和数据管理中心

• 批准号: 10517004
• 负责人: Liang Li
• 金额: $ 81万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10517004
• 关键词: Adopted; Age; Area; Automobile Driving; Basic Science; Bioinformatics; Biological Markers; Biological Specimen Banks; Biometry; Breast; Budgets; Center for Translational Science Activities; Cervical; Clinical; Clinical Management; Clinical Trials; Clinical Trials Data Monitoring Committees; Collaborations; Colon; Communication; Communities; Complex; Computer software; Consent Forms; DNA Sequence Alteration; Data; Data Analytics; Data Science; Detection; Development; Diabetes Mellitus; Diagnosis; Early Diagnosis; Epidemiologic Factors; Faculty; Funding; Goals; Healthcare; Human Resources; Image; Image Analysis; Immunocompetence; Indolent; Industry; Inflammation; Information Technology; Infrastructure; Knowledge; Leadership; Lesion; Link; Logistics; Machine Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Mediating; Methods; Molecular; Molecular Profiling; Monitor; Multicenter Studies; Online Systems; Pathology; Patient risk; Patients; Preparation; Procedures; Process; Protocols documentation; Research; Research Design; Research Methodology; Research Personnel; Screening for cancer; Software Tools; Specialist; Stress; Technology; Testing; Therapeutic Effect; Time; Tissues; Translational Research; Translations; United States National Institutes of Health; University of Texas M D Anderson Cancer Center; Visualization; Work; biobank; biomarker development; cancer diagnosis; cancer type; care burden; chronic pancreatitis; cost; data curation; data management; data tools; density; experience; image archival system; improved; innovation; mortality; novel; operation; overtreatment; patient stratification; personalized medicine; programs; protocol development; repository; response; risk prediction; single cell analysis; statistics; tool

Project Summary Early diagnosis of cancer can improve therapeutic effect and prolong patient survival. The increasingly sensitive and widely adopted early cancer screening technologies have led to significantly more detection of early lesions that may or may not progress to cancer. Elucidating the mechanisms that drive or restrain early cancer would allow differentiation of aggressive cancer versus indolent types, improving personalized treatment and avoiding over-diagnosis and over-treatment. Whether an early lesion progress to cancer or not is not solely decided by the molecular profile of the lesion but also is impacted by the surrounding microenvironment and mediated by other epidemiologic factors. Meanwhile, the progression of an early lesion to malignancy is a complex process that may take years to occur. The complexities of the problem highlight the unmet needs for researchers from basic to translational science to collaborate and coordinate in the research of the underlying mechanisms between early lesion and cancer development. In response to RFA-CA-21-055, we propose a Coordinating and Data Management Center (CDMC) for the Translational and Basic Science Research in Early Lesions (TBEL) Program. The CDMC interacts closely with other entities of the Program, including the Steering Committee, the Research Centers, biospecimen and image repository, pathology centers, sequencing facilities, Data and Safety Monitoring Board (DSMB), and NCI, and provides critical scientific, administrative, regulatory, managerial, logistic, and data-analytic support to the TBEL Program. Our proposed CDMC infrastructure and operating procedures have been time-tested in an ongoing NIH-funded Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreatic Cancer. Specifically, the proposed work includes the three aspects of required responsibilities: consortium coordination (Aim 1), statistical and computational support (Aim 2), and data management, study protocol development and implementation (Aim 3). Our team of experts include information technology specialists who have been supporting and developing innovative software tools for numerous basic and translation cancer studies, experienced research coordinators who have worked on both NIH- and industry-funded multicenter studies, and faculty statisticians and bioinformaticians who have led CDMC work for large NIH consortiums and are well- known experts in biostatistics and bioinformatics methodological research areas closely related to biomarker development, risk prediction, single cell analysis, image analysis, machine learning, and clinical trials.

项目摘要 癌症的早期诊断可以提高治疗效果，延长患者生存期。日益 敏感和广泛采用的早期癌症筛查技术已导致显著更多的检测 早期病变可能会发展成癌症也可能不会。阐明驱动或约束的机制 早期癌症将允许区分侵袭性癌症与惰性类型， 个性化治疗，避免过度诊断和过度治疗。早期病变是否 发展成癌症与否不仅取决于病变的分子特征， 由周围微环境和其他流行病学因素介导。同时 早期病变发展为恶性肿瘤是一个复杂的过程，可能需要数年才能发生。的 问题的复杂性突出了从基础科学到转化科学的研究人员的未满足的需求 在早期病变和早期病变之间的潜在机制的研究中进行合作和协调， 癌症发展作为对RFA-CA-21-055的回应，我们提出了协调和数据管理 中心（CDMC）的转化和基础科学研究在早期病变（TBEL）计划。的 CDMC与该计划的其他实体密切互动，包括指导委员会、 研究中心、生物标本和图像库、病理学中心、测序设施、数据和 安全监测委员会（DSMB）和NCI，并提供关键的科学，行政，监管， 管理，后勤和数据分析支持TBEL计划。我们建议的CDMC基础设施 和操作程序已经过时间的考验，在一个正在进行的NIH资助的财团的研究， 慢性胰腺炎、糖尿病和胰腺癌。具体而言，建议的工作包括三个方面 所需责任的各个方面：联合体协调（目标1）、统计和计算 支持（目标2）以及数据管理、研究方案开发和实施（目标3）。我们 专家组包括信息技术专家，他们一直在支持和开发 创新的软件工具，用于许多基础和翻译癌症研究，经验丰富的研究 曾参与NIH和行业资助的多中心研究的协调员，以及 领导CDMC的统计学家和生物信息学家为大型NIH财团工作， 生物统计学和生物信息学方法研究领域的知名专家， 生物标志物开发、风险预测、单细胞分析、图像分析、机器学习，以及 临床试验