The Health Equity Leadership, Science, and Community Research Laboratory, Genome Center, University of California, Davis, California.
The Jackson Laboratory for Mammalian Genetics, Bar Harbor, Maine.
Cancer Res Commun. 2024 Aug 1;4(8):2147-2152. doi: 10.1158/2767-9764.CRC-23-0417.
Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), are developed and for whom treatments are optimized. In this study, we developed a genetic ancestry pipeline for the Cancer Genomics Cloud, which we used to assess the diversity of models currently available in the National Cancer Institute-supported PDX Development and Trial Centers Research Network (PDXNet). We showed that there is an under-representation of models derived from patients of non-European ancestry, consistent with other cancer model resources. We discussed these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US, as well as power analyses for biomarker discovery, to highlight the immediate need for developing models from minority populations to address cancer health equity in precision medicine. Our analyses identified key priority disparity-associated cancer types for which new models should be developed.
Understanding whether and how tumor genetic factors drive differences in outcomes among U.S. minority groups is critical to addressing cancer health disparities. Our findings suggest that many additional models will be necessary to understand the genome-driven sources of these disparities.
精准医学在改善癌症治疗结果方面具有巨大的潜力。然而,在开发基因组数据和模型(包括患者来源的异种移植物[PDX])的患者人群的人口统计学特征以及为其优化治疗方案方面存在很大的不平等。在这项研究中,我们开发了一个用于 Cancer Genomics Cloud 的遗传起源分析工具,我们用它来评估目前在 NCI 支持的 PDX 开发和试验中心研究网络(PDXNet)中可用的模型的多样性。我们发现,源自非欧洲裔患者的模型代表性不足,这与其他癌症模型资源一致。我们在美国癌症发病率和不同人群治疗结果的差异背景下讨论了这些发现,以及生物标志物发现的功效分析,以强调从少数人群中开发模型以解决精准医学中的癌症健康公平问题的迫切需要。我们的分析确定了需要开发新模型的关键优先差异相关癌症类型。
了解肿瘤遗传因素是否以及如何导致美国少数群体之间的治疗结果差异,对于解决癌症健康差异至关重要。我们的研究结果表明,为了了解这些差异的基因组驱动因素,还需要许多额外的模型。