EORTC 2015: Molecular fidelity of patient-derived xenograft (PDX) models to original human tumor and to The Cancer Genome Atlas (TCGA)

Title
Molecular fidelity of patient-derived xenograft (PDX) models to original human tumor and to The Cancer Genome Atlas (TCGA)

Authors
Ido Ben-Zvi1, Ido Sloma1, Tim Khor1, Daniel Ciznadija1, Amanda Katz1, David Vasquez1, David Sidransky2, Keren Paz11Champions Oncology, Baltimore, MD; 2Johns Hopkins University School of Medicine, Baltimore, MD

First Presented
EORTC 2015

Abstract
Patient-derived xenograft (PDX) models, also know as Champions TumorGraft® models, maintain the complex intra-tumoral biology of the primary tumor.  Over 250 of the Champions models, ranging over a wide variety of solid tumors and passaging generation, have been analyzed using whole exome sequencing (WES) and RNA sequencing (RNAseq).  SNPs, InDels and copy number alterations (CNAs) data have been generated for each models, following the Genome Analysis Toolkit (GATK).  While several publications compare small numbers of PDX models and human tumors on the molecular level, this is the first known comprehensive analysis whereby the molecular fidelity of the PDX platform is corroborated across several cancer types and throughout different mouse generations.

First we compared PDXs to their original human counterparts using a preliminary group of four PDX models with available matching human paitent WES data.  Patient tumor source included dedifferentiated liposarcoma, synovial sarcoma, renal cell carcinoma and squamous cell carcinoma of the lung.  PDX passages ranged from 2 to 4.  We compared called mutation and a high percentage of identified human tumor mutations were present in the PDX models (42-84%), with the lowest scoring model also showing signs of normal contamination in the human tumor sample.  For CNAs in oncogenic sites, we saw an average of 65% of human tumor alterations recurring in the PDX models.  This was observed, despite inherent difficulties due to exome based CNA analysis methods.

Encouraged by the individual results, we subjected our largest (per cancer types) PDX cohorts to a molecular comparison with the equivalent TCGA cohorts.  More than 200 of the sequenced models, grouped into colorectal adenocarcinoma (COADREAD), lung adenocarcinoma (LUAD) breast carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC) and ovarian serous carcinoma (OV) cohorts were compared.  We applied mutation category (MC) and significantly mutated genes (SMG) analysis, as well as comparison of mutation population frequencies for TCGA SMG.  Results showed high correlation between the TCGA and the Champions PDX cohorts, although the level of matching varied between cancer types.  For instance, COADREAD was highly correlative, while other cancer types such as BRCA, showed bias toward CpG site mutations.  In SMG analysis and population frequency analysis, major SMGs recur across the cohorts, while, as expected, weaker signals from the TCGA were often missed in the smaller cohorts.

Detailed comparison of several PDX models to the human tumor counterpart demonstrated high fidelity, no only at the gene level but also the mutation and CNA level.  Cohort comparisons were correlative as well, but a certain bias was discerned in both MC and SMG analyses.  There could be several causes fro this, including statistical artifacts due to small cohort sizes, clinical and demographic differences between the Champions and the TCGA patient profiles, or biological factors such as clonal selection and engraftment pressure.  Further analysis is ongoing to better understand the model at the molecular level and maximize its utility as a robust translational research tool.

Champions Oncology - Specializing in | Personalized Cancer Therapy | Translational Oncology Solutions | Oncology drug development | Oncology drug discovery | Translational oncology | Translation oncology research | Predictive oncology | Patient-derived xenograft model | Tumorgrafts | Alternative/new cancer treatment | Anticancer therapies | Personalized cancer treatment | Personalized cancer therapy | Personalized oncology