Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Commun Biol. 2023 Feb 16;6(1):179. doi: 10.1038/s42003-023-04529-3.
Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.
模型系统是癌症研究的重要资源。它们模拟了我们可以推断到人类身上的效果，但存在着不准确地代表人类生物学的风险。这种不准确性可能导致实验结果不确定或产生误导，因此需要改进将模型系统研究结果转化为人类相关数据的过程。我们提出了一种应用联合降维（jDR）的方法，以在模型系统和人类肿瘤队列之间水平整合基因表达数据。然后，我们使用这种方法将人类 TCGA 基因表达数据与人类癌细胞系和小鼠模型肿瘤的数据相结合。通过确定跨队列共同作用的基因组变异方面，我们展示了如何改进模型系统的预测建模和临床生物标志物。
