儿童癌症数据倡议:利用数据的力量,为患儿癌症的每个儿童和年轻成年人学习和改善结果
Joseph A Flores-Toro1, Subhashini Jagu1, Gregory T Armstrong2
1National Cancer Institute, Bethesda, MD.
概括
儿童癌症数据倡议 (CCDI) 通过创建共享和分析数据生态系统来增强儿科癌症研究. 该倡议旨在通过数据驱动的洞察力改善患有癌症的儿童和年轻人的结果.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 公共卫生 公共卫生
背景情况:
- 儿科癌症研究改善了结果,但数据共享的挑战阻碍了儿童和AYA特定癌症的进展.
- 由于数据收集碎片化和机构障碍,儿科瘤学仍然存在未得到满足的需求.
研究的目的:
- 通过儿童癌症数据倡议 (CCDI) 为儿科癌症研究建立一个全面的数据生态系统.
- 促进数据收集,共享和分析,以提高对儿科癌症的理解,生存率和治疗方法.
- 创建可持续的数据资源和工作流程,以实现儿童癌症研究的长期进步.
主要方法:
- 2019年启动了儿童癌症数据倡议 (CCDI),每年联邦投资5000万美元.
- 开发一个协作数据生态系统,以支持研究人员,临床医生和患者.
- 实施诸如分子表征计划等倡议,以获得全面的患者数据.
主要成果:
- 儿童癌症研究中心 (CCDI) 促进了儿童癌症研究社区的系统数据收集和共享.
- 该计划为新诊断的儿科癌症提供了全面的分子特征.
- 在建立可扩展和可持续的数据资源方面取得了进展.
结论:
- 通过CCDI系统地共享数据,可以显著推进儿科癌症研究,改善患者的治疗结果.
- 如果在儿童癌症中取得成功,CCDI模型有可能改变所有癌症患者的临床研究和治疗.
- 持续的投资和合作对于数据驱动的癌症研究的长期成功和可持续性至关重要.
相关概念视频
Cancer Survival Analysis
402
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...
402
Treatment Resistant Cancers
3.4K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.4K
Combination Therapies and Personalized Medicine
5.0K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.0K
Cancer Therapies
7.8K
Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
7.8K
Targeted Cancer Therapies
7.7K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
There are several types of targeted therapies against...
7.7K
Kaplan-Meier Approach
197
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
197


