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Comparative Study
. 2013 Jun 19;8(6):e66003.
doi: 10.1371/journal.pone.0066003. Print 2013.

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib

James R Bradford  1 , Matthew Farren, Steve J Powell, Sarah Runswick, Susie L Weston, Helen Brown, Oona Delpuech, Mark Wappett, Neil R Smith, T Hedley Carr, Jonathan R Dry, Neil J Gibson, Simon T Barry
Affiliations
Comparative Study

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib

James R Bradford et al. PLoS One. .

Abstract

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers.

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Conflict of interest statement

Competing Interests: All authors have either a current or past affiliation to the commercial funders of this research (AstraZeneca). This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Schematic of the species-specific mapping workflow applicable to RNA-Seq data from xenografts.
(1) Human tumour cells originating from the Calu-6 non-small cell lung carcinoma cell line were grown in mouse, and RNA extracted and sequenced using standard protocols. (2) Sequenced reads were then mapped to human and mouse genomes separately, and reads mapping to both species discarded. The remaining reads were used to quantify and delineate tumour (human) from host (mouse) gene expression.
Figure 2
Figure 2. RNA-Seq gene quantification and detection closely corresponds to values obtained by species-specific RT-qPCR.
Comparison between RNA-Seq and RT-qPCR across 170 human and 174 mouse genes from a Calu-6 human xenograft sample (Control_2). Correlation in gene expression in (A) human (r2 = 0.72) and (B) mouse (r2 = 0.60). r2 values are calculated only from genes detected on both platforms. Overlap in number of (C) human and (D) mouse genes detected in both RNA-Seq and RT-qPCR, and genes detected on one platform only. The transcript mapped to by the highest number of reads was chosen to represent the expression of its parent gene. Any gene containing at least one mappable read was classed as detected in RNA-Seq. The genes included in this comparison are listed in Table S5. Details of gene expression and detection correspondence across both Control_1 and Control_2 samples are given in Table S8.
Figure 3
Figure 3. RNA-Seq versus microarray gene expression correspondence.
Comparison between RNA-Seq and microarray platforms across 8621 human and 5467 mouse genes from a Calu-6 human xenograft sample (Control_2) and detected on both platforms. Correlation in gene expression in (A) human (r2 = 0.50) and (B) mouse (r2 = 0.35). r 2 values are calculated only from genes detected on both platforms. The transcript mapped by the highest number of reads was chosen to represent the expression of its parent gene. Any gene containing at least one mappable read was classed as detected in RNA-Seq. A gene was called “present” on the array if the signals of all probesets assigned unambiguously to that gene were separable from the general background with a p-value<0.01. Correlation increases between (C) human and (D) mouse gene expression values across both samples when probes on the array at high risk of cross-species hybridization are removed. Only genes detected on both platforms were considered. A mean of ∼8400 human and ∼5300 mouse genes were used in the comparison of which ∼6800 and ∼4400 genes were deemed at high risk respectively. Genes at high risk were defined as those corresponding to a probe with up to three mismatches to the transcriptome of the alternative species.
Figure 4
Figure 4. RNA-Seq differentiates the tumour (human) transcriptional response to cediranib from the host (mouse).
Gene Set Enrichment Analysis (GSEA) reveals significant enrichment of (A) hypoxia and (B) cell cycle associated signatures in tumour genes differentially regulated in response to cediranib dosed at 6 mg/kg once daily for 4 days (** indicates gene sets enriched with p<0.001, FDR q<0.05 and FWER p<0.1; * indicates gene sets enriched with p<0.001 and FDR q<0.05). GSEA-defined “Leading Edge” genes most frequently included in (C) hypoxia-associated gene sets and up-regulated in response to cediranib include CA9, HK2 and VEGFA. Leading edge cell cycle associated genes are given in (D). (E) Ingenuity Pathway Analysis (IPA) highlights functions significantly enriched (p<1×10−5) amongst host genes differentially regulated in response to cediranib. For both GSEA and IPA, genes achieving a log2 fold change magnitude>1 and p<0.1 were defined as differentially regulated. n = 2 in treated and control groups; a positive fold change indicates genes up-regulated in response to cediranib, and vice versa.
Figure 5
Figure 5. The phenotypic effects of cediranib observed by immunohistochemical analysis of supporting vasculature in cediranib treated tumours.
Calu-6 xenografts were established and dosed for 4 days with cediranib at 6mg/kg once daily and fixed in formalin. Micro Vessel Density was then quantified by histological staining for CD31 as previously described . Images are representative for CD31 staining in control and cediranib treated tumours, and arrows indicate blood vessels. Graph depicts the significant (p-value<0.001; student t-test) reduction of the supporting vasculature in cediranib treated tumours; 8 control tumours and 6 cediranib tumours, error bars are standard error of the mean.
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Grants and funding

The work was funded by AstraZeneca (www.astrazeneca.com) who are current employers of JRB, SJP, SR, SLW, HB, OD, MW, NRS, THC, JRD and STB, and past employers of MF and NJG. Other than the named authors, the funder had no direct role in study design, data collection and analysis, decision to publish, and preparation of the manuscript.