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.

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.

Identification of CBX3 and ABCA5 as putative biomarkers for tumor stem cells in osteosarcoma.

Recently, there has been renewed interest in the role of tumor stem cells (TSCs) in tumorigenesis, chemoresistance, and relapse of malignant tumors including osteosarcoma. The potential exists to improve osteosarcoma treatment through characterization of TSCs and identification of therapeutic targets. Using transcriptome, proteome, immunophenotyping for cell-surface markers, and bioinformatic analyses, heterogeneous expression of previously reported TSC or osteosarcoma markers, such as CD133, nestin, POU5F1 (OCT3/4), NANOG, SOX2, and aldehyde dehydrogenase, among others, was observed in vitro. However, consistently significantly lower CD326, CD24, CD44, and higher ABCG2 expression in TSC-enriched as compared with un-enriched osteosarcoma cultures was observed. In addition, consistently higher CBX3 expression in TSC-enriched osteosarcoma cultures was identified. ABCA5 was identified as a putative biomarker of TSCs and/or osteosarcoma. Lastly, in a high-throughput screen we identified epigenetic (5-azacytidine), anti-microtubule (vincristine), and anti-telomerase (3,11-difluoro-6,8,13-trimethyl- 8H-quino [4,3,2-kl] acridinium methosulfate; RHPS4)-targeted therapeutic agents as candidates for TSC ablation in osteosarcoma.