Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction.

Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction.
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DOI:
10.1038/s42003-023-04529-3
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发表时间:
2023-02-16
影响因子:
5.9
通讯作者:
--
中科院分区:
生物学2区
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--
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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,但有可能不准确地代表人类生物学。这种不准确性可能导致不确定的实验或误导性的结果,迫切需要一个改进的过程,将模型系统的发现转化为人类相关的数据。我们提出了一个应用联合降维(jDR)来水平整合模型系统和人类肿瘤队列的基因表达数据的过程。然后,我们使用这种方法将联合收割机人类TCGA基因表达数据与来自人类癌细胞系和小鼠模型肿瘤的数据相结合。通过识别跨队列联合作用的基因组变异的方面,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。联合降维方法整合了来自人类肿瘤队列、人类癌细胞系和小鼠模型肿瘤的基因表达数据,以改善模型系统对临床生物标志物的预测。
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. A joint dimension reduction approach integrates gene expression data from human tumor cohorts, human cancer cell lines, and mouse model tumors to improve prediction of clinical biomarkers from model systems.
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