Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

Xing Yi Woo; Anuj Srivastava; Joel H. Graber; Vinod Kumar Yadav; Vishal Sarsani; Al Simons; Glen Beane; Stephen C. Grubb; Guruprasad Ananda; Rangjiao Liu; Grace A. Stafford; Jeffrey H. Chuang; Susan D. Airhart; Radha Krishna Murthy Karuturi; Joshy George; Carol J. Bult

doi:10.1186/s12920-019-0551-2

BioinformaticsBMC Medical GenomicsJul 1, 2019

Peer-reviewed journal article

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

Xing Yi Woo iD, Anuj Srivastava iD, Joel H. Graber iD, Vinod Kumar Yadav iD, Vishal Sarsani iD, Al Simons, Glen Beane, Stephen C. Grubb iD, Guruprasad Ananda iD, Rangjiao Liu, Grace A. Stafford iD, Jeffrey H. Chuang iD, Susan D. Airhart iD, Radha Krishna Murthy Karuturi iD, Joshy George iD, Carol J. Bult iD

Abstract

Bioinformatics workflows for analyzing genomic data obtained from xenografted tumor (e.g., human tumors engrafted in a mouse host) must address several challenges, including separating mouse and human sequence reads and accurate identification of somatic mutations and copy number aberrations when paired normal DNA from the patient is not available. We report here data analysis workflows that address these challenges and result in reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from patient derived xenograft models. We validated our analytical approaches using simulated data and by assessing concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). The commands and parameters for the workflows are available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.

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