Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD.
Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
JCO Clin Cancer Inform. 2023 Apr;7:e2200108. doi: 10.1200/CCI.22.00108.
Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies.
We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM).
Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%).
Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.
精准肿瘤学需要开发标准化的通用数据模型（CDMs），以促进分析并为临床决策提供支持。以专家意见为基础的精准肿瘤学计划以分子肿瘤委员会（MTB）为代表，该委员会处理大量的临床基因组数据，以将基因型与分子指导的治疗方法相匹配。
我们以约翰霍普金斯大学 MTB 为例，开发了一个精准肿瘤学核心数据模型（Precision-DM），以捕获关键的临床基因组数据元素。我们利用现有的 CDMs，以最小共同肿瘤学数据元素模型（mCODE）为基础进行构建。我们的模型被定义为一组具有多个数据元素的配置文件，重点是下一代测序和变体注释。大多数元素都映射到术语或代码集以及快速医疗保健互操作性资源（FHIR）。随后，我们将 Precision-DM 与现有的 CDMs 进行了比较，包括美国国立癌症研究所的基因组数据公共库（NCI GDC）、mCODE、OSIRIS、临床基因组数据模型（cGDM）和基因组 CDM（gCDM）。
Precision-DM 包含 16 个配置文件和 355 个数据元素。39%的元素从选定的术语或代码集中派生值，61%映射到 FHIR。虽然我们使用了 mCODE 中包含的大多数元素，但我们显著扩展了配置文件以包括基因组注释，因此我们的核心模型与 mCODE 之间存在部分重叠，重叠率为 50.7%。我们还发现 Precision-DM 与 OSIRIS（33.2%）、NCI GDC（21.4%）、cGDM（9.3%）和 gCDM（7.9%）之间的重叠有限。Precision-DM 涵盖了 mCODE 元素的大部分（87.7%），而对 OSIRIS（35.8%）、NCI GDC（11%）、cGDM（26%）和 gCDM（33.3%）的覆盖范围较小。
Precision-DM 支持临床基因组数据标准化，以支持 MTB 用例，并允许在医疗保健系统、学术机构和社区医疗中心之间进行协调的数据提取。
