Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines

BMC Medical Genomics

  ^{1    2    2    2    2    2    2    3    4    5    5    5    5    5    5    6} 

[1] 0000 0001 2194 4033, grid.250230.6, MDI Biological Laboratory, 04609, Bar Harbor, ME, USA;0000 0004 0374 0039, grid.249880.f, The Jackson Laboratory for Genomic Medicine, 06030, Farmington, CT, USA;0000 0004 0374 0039, grid.249880.f, The Jackson Laboratory for Genomic Medicine, 06030, Farmington, CT, USA;0000 0001 2341 2786, grid.116068.8, Present Address: Massachusetts Institute of Technology, 02139, Cambridge, MA, USA;0000 0004 0374 0039, grid.249880.f, The Jackson Laboratory for Genomic Medicine, 06030, Farmington, CT, USA;Present Address: Novogene Corporation, 20850, Rockville, MD, USA;0000 0004 0374 0039, grid.249880.f, The Jackson Laboratory for Mammalian Genetics, 04609, Bar Harbor, ME, USA;0000 0004 0374 0039, grid.249880.f, The Jackson Laboratory for Mammalian Genetics, 04609, Bar Harbor, ME, USA;0000 0001 2184 9220, grid.266683.f, Present Address: University of Massachusetts, 01003, Amherst, MA, USA;

关键词: Patient-derived xenografts;    DNA sequencing;    RNA sequencing;    SNP array;    Somatic mutation;    Gene expression;    Copy number alterations;    Mouse stroma;    Bioinformatic analysis;   

DOI  :  10.1186/s12920-019-0551-2

来源: publisher

【 摘 要 】

BackgroundPatient-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.ResultsWe 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).ConclusionsThe 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.

【 授权许可】

CC BY