Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping
Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping
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DOI：
10.1016/j.jbi.2019.103308
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发表时间：
2019-12-01
影响因子：
4.5
通讯作者：
Burgun, Anita
中科院分区：
文献类型：
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作者：
Chen, Xiaoyi;Garcelon, Nicolas;Burgun, Anita
罕见疾病通常很难准确诊断，而且大多数缺乏批准的治疗方法。对于一些复杂的罕见疾病，需要进一步采用精准医学方法，根据临床、生物学或分子特征将患者分为同质亚组。在这种情况下，对这些患者进行深入的表型分析，并基于主题相似性比较他们的特征，对于帮助快速准确的诊断和更好地理解病理生理过程以开发治疗方案至关重要。在这篇文章中，我们开发了一种新的管道，使用深度表型来定义患者的相似性，并将其应用于睫状体病，一组由睫状体功能障碍引起的罕见和严重的疾病。作为法国罕见和未确诊疾病的国家参考中心，Necker儿童医院（Necker Children's Hospital）拥有Imagine Institute，这是一家专注于遗传疾病的研究机构。临床数据仓库一方面包含EHR数据，另一方面包含临床研究数据。计算两个数据源的相似性度量，并与两个任务进行了评估：诊断与EHR和亚型与睫状体病的具体研究数据。我们在前30名最相似的确诊为纤毛病变的患者中获得了0.767的精确度。表型相似的纤毛病变患者的亚型与专家知识一致。应用于罕见疾病的相似性指标在翻译背景下提供了新的视角，可能有助于招募患者进行研究，缩短诊断旅程的长度，并更好地了解疾病的机制。
Rare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with two tasks: diagnoses with EHRs and subtyping with ciliopathy specific research data. We obtained a precision of 0.767 in the top 30 most similar patients with diagnosed ciliopathies. Subtyping ciliopathy patients with phenotypic similarity showed concordances with expert knowledge. Similarity metrics applied to rare disease offer new perspectives in a translational context that may help to recruit patients for research, reduce the length of the diagnostic journey, and better understand the mechanisms of the disease.