Abstract 3842: Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts
Authors: Xing Yi Woo, Vinod Yadav, Al Simons, Anuj Srivastava, Guruprasad Ananda, Vishal Kumar Sarsani, Roger Liu, Grace Stafford, Joel Graber, Krishna Karuturi, Susie Airhart, Joshy George, Carol Bult
Journal: Cancer Research (2017)

Abstract The Jackson Laboratory has established more than 400 unique patient-derived xenograft (PDX) cancer models from patient tumors in the immunocompromised NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (aka, NSGTM) mouse strain, spanning across more than 30 tumor types. At low passages, these engrafted models are known to retain similar molecular characteristics and heterogeneity to the originating human tumor. As such, PDX models offer an excellent preclinical platform to test drug responses of novel cancer therapeutics and a powerful resource for conducting preclinical cancer pharmacogenomic studies. To aid the selection of suitable PDX models for preclinical studies and for the research purpose to understand tumor biology and response or resistance to a given treatment, we have characterized the PDX models for their transcriptomic, mutational and copy number profiles using sequencing and array approaches. We have established a compendium of PDX-tailored computational pipelines as the analysis of genomic data from PDX models could be challenging due to a) the contamination of PDX sample with mouse stroma, which complicates downstream bioinformatics analyses as mouse genome is almost 90% homologous to the human genome, and b) the lack of matched normal material to call somatic events. Our pipelines incorporate various filters to identify tumor specific single nucleotide variants, indels, copy number changes and expression profile in the PDX model. For the purpose of validating the accuracy of our analysis pipelines and demonstrating that the JAX PDX models are indeed representative of patient tumors, we compared JAX’s PDX cohort with patient cohorts in TCGA for mutations, copy number aberrations and RNA expression concordance. Using gene sets representative of each tumor type, we found that the overall genomic profile of each PDX tumor type is more correlated to the same tumor type in TCGA than other tumor types. In addition, an integrative analysis across all data types reveals that there are more common affected pathways between the same tumor type in PDX and TCGA. This comprehensive analysis revealed that the PDX and patient cohorts exhibit similar molecular characteristics, hence establishing the suitability of JAX PDX models as in vivo models to study fundamental tumor biology as well as to carry out preclinical studies of cancer drugs, including identification of biomarkers of response or resistance. Citation Format: Xing Yi Woo, Vinod Yadav, Al Simons, Anuj Srivastava, Guruprasad Ananda, Vishal Kumar Sarsani, Roger Liu, Grace Stafford, Joel Graber, Krishna Karuturi, Susie Airhart, Joshy George, Carol Bult. Comprehensive genomic analysis demonstrates concordance of PDX models and patient tumor cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3842. doi:10.1158/1538-7445.AM2017-3842