Abstract 2701: Combining whole-exome and RNA-Seq data improves the quality of PDX mutation profiles
Authors: Landesfeind Manuel , Zeitouni Bruno , Peille Anne-Lise , Vuaroqueaux Vincent 
Published: July 2016

Patient-derived xenograft tumor models (PDX) are of increasing interest for anti-cancer agent testing due to their close resemblance to patient tumors. An accurate molecular characterization of the models is essential 1) to select the PDX that best fit the genetic requirements for a successful cancer therapy investigation and 2) to identify potential predictive biomarkers of response. In this study, we evaluated the quality of mutation profiles from whole-exome sequencing (WES) in terms of concordance with previously acquired mutation data in a large collection of PDX. Further, we analyzed the persistence of disclosed mutations at the transcript level with RNA-Seq.

From 339 PDX, DNA was extracted and enriched in exonic regions with Agilent SureSelect kits before Illumina HiSeq 2000 sequencing with a minimum expected average-of-coverage of 100X. Raw paired-end reads were analyzed by a PDX-specific bioinformatics pipeline to identify the human mutation profile. Sequenom Oncocarta and Sanger sequencing data acquired for 29 cancer genes in 272 PDX was used to evaluate the WES mutation profiles. In parallel, 92 PDX were profiled with RNA-Seq (100M sequencing reads required) and we investigated the expressed mutation profiles by comparing with mutations from WES data.

Among 502 point mutations found with classical methods, 95% were retrieved by WES analyses, revealing the very high sensitivity of the PDX-specific bioinformatics pipeline. 5% of mutations were missed because of a low coverage, particularly in the STK11 gene and in the KRAS gene of pancreatic models, possibly due to poor gene enrichment and high mouse stroma content, respectively. Deeper sequencing could potentially overcome this lack of coverage. Additionally, the WES analysis pipeline displays a high specificity, reporting only 1 additional mutation at gene positions covered with the classical methods. Finally, 507 mutations were detected by WES at positions not interrogated by classical methods emphasizing the necessity for next-generation sequencing (NGS) to obtain a comprehensive mutational spectrum. The number of mutations found using RNA-Seq data was on average two times lower and covered 15% of the mutations detected in WES. This was mainly due to the non-expression of genes or isoforms (40%), the mono-allelic expression of genes (30%), and low coverage data (15%). RNA-Seq analysis restricted to expressed genes represents a substantial complement to WES mutation data and enhances understanding of actual gene alterations in cancer cells.

This study demonstrated the high quality of mutation profiles obtained by WES and highlights the importance of integrating expression data to accurately predict the impact of a mutation at the protein level. An accurate molecular characterization of models is crucial for the selection of PDX with a specific genetic background for the evaluation of anticancer agents.

Citation Format: Manuel Landesfeind, Bruno Zeitouni, Anne-Lise Peille, Vincent Vuaroqueaux. Combining whole-exome and RNA-Seq data improves the quality of PDX mutation profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2701.