Abstract 1075: Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): Challenges and guidelines
Authors: Woo Xing Yi , Srivastava Anuj , Graber Joel H. , Yadav Vinod , Sarsani Vishal Kumar , Simons Al , Beane Glen , Grubb Stephen , Ananda Guruprasad , Stafford Grace , Chuang Jeffrey H. , Airhart Susan D. , Karuturi R. Krishna , George Joshy , Bult Carol J. 
Published: August 2019

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 has over 450 models representing more than 20 different types of cancer. 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.

Here we describe bioinformatics analysis workflows and guidelines (https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows) that we developed specifically for the analysis of genomic data generated from PDX tumors. 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. Finally, to demonstrate the effectiveness of our workflows, we show the overall concordance of the genomic and transcriptomic profiles of the PDX models in the JAX PDX resource with relevant tumor types from The Cancer Genome Atlas (TCGA).

Using the reliable results obtained from the PDX genomics data analysis, we are able to compare the patient tumor with different PDX passages, perform classification analysis to verify the annotations of PDX tumors, as well as associate genomic signatures of each PDX tumor with results from dosing studies.

Acknowledgements

The data analysis workflows reported in this publication were partially supported by the National Cancer Institute of the National Institutes of Health under Award Number P30CA034196. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The public portal for JAX PDX data is supported by R01CA089713.

Citation Format: Xing Yi Woo, Anuj Srivastava, Joel H. Graber, Vinod Yadav, Vishal Kumar Sarsani, Al Simons, Glen Beane, Stephen Grubb, Guruprasad Ananda, Grace Stafford, Jeffrey H. Chuang, Susan D. Airhart, R. Krishna Karuturi, Joshy George, Carol J. Bult. Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): Challenges and guidelines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1075.