一个集成的TCGA全癌症临床数据资源,以推动高质量的生存结果分析
Jianfang Liu1, Tara Lichtenberg2, Katherine A Hoadley3
1Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA 15963, USA.
Cell
|April 7, 2018
概括
癌症基因组图谱 (TCGA) 计划创建了一个标准化的临床数据资源 (TCGA-CDR),来自33种癌症的11000多种瘤. 这项资源通过将临床数据与基因组特征联系起来来帮助癌症生物学研究.
科学领域:
- 癌症学
- 基因组学
- 生物信息学
背景情况:
- 癌症基因组图谱 (TCGA) 计划收集了33种癌症的11000多种瘤的广泛临床和分子数据.
- 虽然TCGA的临床数据丰富,但在标准化和整合大规模分析方面存在挑战.
研究的目的:
- 从TCGA开发一个标准化的临床数据集,命名为TCGA全癌症临床数据资源 (TCGA-CDR).
- 通过为每个癌症类型提供终点使用建议,确保适当使用TCGA临床数据.
- 通过将临床相关性与基因组数据整合起来,促进对癌症生物学的大规模调查.
主要方法:
- 从TCGA程序收集和整合临床病理学注释数据.
- 开发了一个标准化数据集 (TCGA-CDR),包含四个主要的临床结果终点.
- 确定并详细说明数据整合的挑战和统计局限性.
- 提供癌症类型特定的终点使用建议.
主要成果:
- 建立了TCGA全癌症临床数据资源 (TCGA-CDR),这是一个标准化的临床数据集.
- 在数据整合过程中遇到的详细挑战和局限性.
- 对不同癌症类型的临床终点的使用提出了建议.
- 证实TCGA-CDR发现与独立的癌症基因组学研究一致.
结论:
- TCGA-CDR为癌症研究提供了有价值的标准化资源.
- 这种资源使得通过临床相关物来研究癌症生物学具有前所未有的规模.
- 终点使用建议加强TCGA临床数据的适当应用.
- 这些发现支持综合临床和基因组数据在促进癌症理解方面的有用性.
更多相关视频
07:41Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
9.6K
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
926
相关概念视频
Censoring Survival Data
564
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
564
Cancer Survival Analysis
774
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
774
Statistical Software for Data Analysis and Clinical Trials
1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Short-distance Transport of Resources
17.8K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
17.8K
Survival Curves
732
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
732
Survival Tree
436
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
436
