Niazi Umar, Stuart Charlotte, Soares Patricia, Foure Vincent, Griffiths Gareth
Cancer Research UK Southampton Clinical Trials Unit, MP131, Southampton General Hospital, University of Southampton, Southampton, UK.
iSolutions, University of Southampton, Southampton, UK.
Clin Trials. 2025 Jun;22(3):374-377. doi: 10.1177/17407745241304331. Epub 2024 Dec 25.
Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.
解锁肿瘤学中个性化医疗的力量取决于将临床试验数据与转化数据（即生物样本衍生的分子信息）相结合。这种综合分析使研究人员能够根据患者独特的生物学构成量身定制治疗方案。然而，英国临床试验单位目前的做法存在挑战。虽然临床数据以标准化格式保存，但转化数据复杂多样，需要专门的存储。这种格式上的差异给旨在有效整理、整合和分析这些数据集的研究人员带来了重大障碍。本文提出了一种新颖的解决方案：一种专门为学术试验单位的需求设计的开源SQL数据库架构。受英国癌症研究中心对开放数据共享的承诺启发，并以南安普顿临床试验单位的CONFIRM试验（有超过15万个临床数据点）为例，这种架构在原始数据和昂贵的安全数据环境/可信研究环境之间提供了一种经济高效且实用的“中间地带”。通过充当临床和转化数据的中心枢纽，该架构促进了无缝的数据共享和分析。研究人员可以全面了解试验情况，从而探索临床观察结果与治疗反应的分子基础之间的联系。文中提供了设置数据库的详细说明。开源性质和简单的设计确保了易于实施和成本低廉，同时强大的安全措施保护了敏感数据。我们还展示了研究人员如何利用像R这样的流行统计软件直接查询数据库。这种方法促进了学术发现社区内的合作，最终加速了个性化癌症治疗的进展。
