High-resolution mutational profiling suggests the genetic validity of glioblastoma patient-derived pre-clinical models

• Published in: PloS one 2013-02, Vol.8 (2), p.e56185-e56185
• Main Authors: Yost, Shawn E, Pastorino, Sandra, Rozenzhak, Sophie, Smith, Erin N, Chao, Ying S, Jiang, Pengfei, Kesari, Santosh, Frazer, Kelly A, Harismendy, Olivier
• Format: Article
• Language: English
• Subjects: Aberration; Animal models; Animals; Bioindicators; Bioinformatics; Biology; Biomarkers; Brain cancer; Brain Neoplasms - genetics; Cancer therapies; Cell culture; Clinical trials; Cluster Analysis; Coding; Conservation; Copy number; Deoxyribonucleic acid; Disease Models, Animal; DNA; Drug development; Exome; Gene expression; Genes; Genetic aspects; Genetics; Genomes; Genomics; Glioblastoma; Glioblastoma - genetics; Glioblastomas; Glioma; Humans; In vivo methods and tests; Information management; Information science; Laboratories; Mathematics; Medical research; Medicine; Mice; Model matching; Mutation; Neural coding; Neurosciences; Neurospheres; Oligonucleotide Array Sequence Analysis; Oncology; p53 Protein; Pediatrics; Polymorphism, Single Nucleotide; PTEN protein; Reproducibility of Results; Stochasticity; Transplantation, Heterologous; Tumor proteins; Tumors; Validity; Xenografts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0056185

Recent advances in the ability to efficiently characterize tumor genomes is enabling targeted drug development, which requires rigorous biomarker-based patient selection to increase effectiveness. Consequently, representative DNA biomarkers become equally important in pre-clinical studies. However, it is still unclear how well these markers are maintained between the primary tumor and the patient-derived tumor models. Here, we report the comprehensive identification of somatic coding mutations and copy number aberrations in four glioblastoma (GBM) primary tumors and their matched pre-clinical models: serum-free neurospheres, adherent cell cultures, and mouse xenografts. We developed innovative methods to improve the data quality and allow a strict comparison of matched tumor samples. Our analysis identifies known GBM mutations altering PTEN and TP53 genes, and new actionable mutations such as the loss of PIK3R1, and reveals clear patient-to-patient differences. In contrast, for each patient, we do not observe any significant remodeling of the mutational profile between primary to model tumors and the few discrepancies can be attributed to stochastic errors or differences in sample purity. Similarly, we observe ∼96% primary-to-model concordance in copy number calls in the high-cellularity samples. In contrast to previous reports based on gene expression profiles, we do not observe significant differences at the DNA level between in vitro compared to in vivo models. This study suggests, at a remarkable resolution, the genome-wide conservation of a patient's tumor genetics in various pre-clinical models, and therefore supports their use for the development and testing of personalized targeted therapies.