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

Abstract

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.

Figure 1. Mutational Landscape of the primary tumors.

(A) The cumulative distribution of the somatic mutations identified on the targeted exons of the four patients primary tumors is reported as a function of their class and predicted protein changes. (B) Circular diagram representing all 23 chromosomes and their cytogenetic map (outer circle, grey scale bands and red centromeres). The logR tumor/normal coverage ratios (black dots) and the inferred CNA (red: amplification, blue: deletion, blue bars: Loss of Heterozygosity) identified in the 4 primary tumors (from outer to inner circle: SK01600, SK00115, SK00102, SK00072) using whole exome sequencing data are represented. (C) Chromosome-arm level copy number aberrations are observed in the 22 autosomes when >20% of a chromosome arm is reported as deleted (blue) or amplified (red). (D) A focal deletion of ∼10 Mb (set of blue segments) including a large (4.3 Mb) CNA segment affects PIK3R1 gene in SK00115 primary tumor. The LogR ratio of tumor/normal coverage (x axis) at each exon capture probe (grey dots) allows the identification of DNA segments deleted (blue bars) or amplified (red bars). (E) Similar to (D), a focal amplification of EFGR containing segment (red) is identified in addition to the chromosome 7 trisomy in patient SK01600 primary tumor. Some segments may appear to overlap as a result of the plotting resolution.

Figure 2. Comparative evaluation of the somatic mutations between primary and model tumors.

(A) The cumulative distribution of the somatic mutations identified on the targeted exons of the four patients’ primary tumors (P) as well as tumor models (N: Neurospheres, C: Cell culture, X: Xenograft) is reported as a function of their class and predicted protein changes. The mutations were identified after excluding mouse reads from patients’ SK00102 and SK00072 data. (B) A statistical comparison of the somatic mutations called between primary and model identifies shared mutations at constant mutant allele frequencies (black), shared mutations with changing mutant allele frequency (red) as well mutations specific to the primary (green) or the tumor model (blue). (C–F) Mutant Allele frequency differences between the primary tumor (x axis) and the model tumor (y axis) of patient SK01600 (C), SK00115 (D), SK00102 (E), SK00072 (F) at all positions identified as somatically mutated in either sample and covered by ≥30 reads. Mutations are classified as shared with constant frequency (black), with changing frequencies (red), specific to the primary tumor (green) or to the tumor model (blue).

Figure 3. Comparative evaluation of the CNAs between primary and model tumors.

(A) The evaluation of the copy number status at all base pairs called in high-confidence CNA segments in both primary and model tumors identifies positions with a consistent (grey), lower (blue) or higher (red) copy number call in the model when compared to the primary tumor (Table S10). (B) Average copy number status (blue-red color scale, log2 ratio) at 72 genes of the cancer gene census showing more than 2 fold copy number difference in one or more sample. (C) Euclidian distance based dendrogram classifying the 8 tumor samples using the logR ratio of high-confidence CNA called in one or more sample.