Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies - PubMed (original) (raw)

doi: 10.1186/1471-2164-15-393.

Luke H Stockwin, Sergio Y Alcoser, Dianne L Newton, Benjamin C Orsburn, Carrie A Bonomi, Suzanne D Borgel, Raymond Divelbiss, Kelly M Dougherty, Elizabeth J Hager, Susan L Holbeck, Gurmeet Kaur, David J Kimmel, Mark W Kunkel, Angelena Millione, Michael E Mullendore, Howard Stotler, Jerry Collins

Affiliations

Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies

Melinda G Hollingshead et al. BMC Genomics. 2014.

Abstract

Background: Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial.

Results: Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment.

Conclusions: This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments.

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Figures

Figure 1

Figure 1

Study design and quality control. A) Control probe signal profiles were generated for 844 Affymetrix HG-U133 Plus 2.0 array .CEL files. The single outlier (PC-3 P10 100913) is highlighted. B) 3D principal component analysis (PCA) was performed on all .CEL files; a population of outliers representing P4 and P10 passages for SF-268 and SF-539 glioma lines is shown in red. Control probe profiles and 3D PCA were generated using Genespring GX11 (Agilent, Santa Clara, CA). C) Endpoint PCR of genomic DNA from SF-539 and SF-268 tumors at P1, P4 and P10 using mouse or human-specific PTGER2 primers [see Methods]. Genomic DNA from B16F10 [B16, Mouse] and LnCAP [LC, human] cell lines were included as positive controls, NTC = no template control. Data is representative of all tumors processed from these xenografts.

Figure 2

Figure 2

Transcript expression relative to other models and with passage. Log2 normalized gene expression values at passage 1, 4 and 10 plotted for four probe sets; 205225_at (ESR1, estrogen receptor alpha), 206426_at (MLANA, melan-A), 201839_s_at (EPCAM, epithelial cell adhesion molecule), and 201746_at (TP53, p53). The p53 status is shown for 201746_at as wild type (WT), mutant (MUT) or absent (NULL).

Figure 3

Figure 3

Change in expression from P1 to P10 for a subset of transcripts involved in drug resistance. A) Log2 normalized expression values for each transcript in all models at P1. B) Change in log2 normalized expression for each transcript in all models from P1 to P10. For each probe set, entries are formatted where red is the highest value, green is the lowest and the median is black.

Figure 4

Figure 4

Change in expression from P1 to P10 for a subset of transcripts coding for receptor tyrosine kinases (RTKs). A) Log2 normalized expression values for each transcript in all models at P1. B) Change in log2 normalized expression for each transcript in all models from P1 to P10. For each probe set, entries are formatted where red is the highest value, green is the lowest and the median is black.

Figure 5

Figure 5

Ranking model stability. For each model, Genespring GX11 was used to generate a list of differentially expressed transcripts for P1 to P4 and P1 to P10 [3-fold cut-off, p < 0.05]. The number of differentially regulated transcripts at P1 to P4 and P1 to P10 was then plotted for each model and results sorted in terms of P1 to P10 [lowest to highest] to generate a measure of model stability.

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