We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA).
The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .
我们在此报告数据分析工作流程和指南,以应对这些挑战并实现可靠的体细胞突变鉴定。拷贝数更改,和来自PDX模型的肿瘤的转录组学图谱,缺乏来自配对非肿瘤组织的基因组数据进行比较。我们的工作流程结合了常用的软件和公共数据库,但通过参数调整和定制数据过滤器来解决PDX基因组学数据分析的具体挑战,并提高了PDX模型中体细胞改变检测的准确性。我们还报道了一种基于基因表达的分类器,可以识别EBV转化的肿瘤。我们使用数据模拟验证了我们的分析方法,并证明了异种移植肿瘤的基因组特性与癌症基因组图谱(TCGA)中原发性人类肿瘤的数据的总体一致性。
我们开发的分析工作流程用于从缺乏正常组织的PDX模型中准确预测肿瘤的体细胞谱,从而能够识别关键的致癌基因组和表达特征,以支持治疗研究中的模型选择和/或生物标志物开发。我们的分析建议的参考实施可在https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows上获得。