患者来源的异种移植物(PDX)模型是人癌症的体内模型,其已用于个体患者的转化癌症研究和治疗选择。杰克逊实验室（JAX）PDX资源包括来自34个不同主要站点的455个模型（截至2019年5月8日）。这些模型经过严格的质量控制，并具有基因组特征以识别体细胞突变，拷贝数更改，和转录谱。用于分析从移植到小鼠宿主中的人类肿瘤获得的基因组数据的生物信息学工作流程（即，患者来源的异种移植物；PDX）必须应对挑战，例如区分小鼠和人类序列读数，以及当患者的配对非肿瘤DNA无法用于比较时，准确识别体细胞突变和拷贝数改变。
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。
Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison.
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 .
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