Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.
Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.
复制标题
评估癌细胞系和患者来源的异种移植物在计算机中再现肿瘤和非患病组织基因表达谱。
DOI：
10.1101/2023.04.11.536431
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Lasseigne,BrittanyN
中科院分区：
文献类型：
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作者：
Williams,AveryS;Wilk,ElizabethJ;Fisher,JenniferL;Lasseigne,BrittanyN
临床前模型，如癌细胞系和患者来源的异种移植物（PDX），对于研究疾病机制和评估治疗方案至关重要。至关重要的是，它们准确地概括了感兴趣的疾病状态，以产生将在临床中转化的结果。先前的研究已经证明，临床前模型不能概括人体组织的所有生物学方面，特别是关于组织来源的基因表达特征。因此，评估临床前模型基因表达谱与人类癌症组织的相关性，以告知临床前模型选择和数据分析决策至关重要。目的在此，我们评估了临床前模型在全基因表达谱以及最可变基因、与肿瘤纯度显著相关的基因、和组织特异性基因。MethodsBy using publicly available gene expression profile across multiple sources，we evaluated cancer cell line and patient‐derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.ResultsWe found that using full gene set improves correlations between preclinical model and tissue global gene expression profiles，证实了胶质母细胞瘤（GBM）PDX整体基因表达与GBM肿瘤整体基因表达的相关性优于GBM细胞系与GBM肿瘤整体基因表达的相关性，并证明了我们研究中的临床前模型通常无法重现组织特异性表达。虽然包括额外的基因用于细胞系和组织之间的全局基因表达比较降低了整体相关性，但与其他组织相比，它提高了细胞系和其来源组织之间的相对等级。我们的研究结果强调了在比较临床前模型和组织时使用全基因表达集测量的重要性，并证实组织特异性模式在GBM PDX模型中比在GBM细胞系中更好地保留。
BackgroundPreclinical models like cancer cell lines and patient‐derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions.AimsHere we evaluated how well preclinical models recapitulate human cancer and non‐diseased tissue gene expression patternsin silicowith respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue‐specific genes.MethodsBy using publicly available gene expression profiles across multiple sources, we evaluated cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico.ResultsWe found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that glioblastoma (GBM) PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue‐specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue‐specific patterns are better preserved in GBM PDX models than in GBM cell lines.ConclusionFuture studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.