Expression levels and activation of a PXR variant are directly related to drug resistance in osteosarcoma cell lines.

BACKGROUND: Approximately 30% to 40% of all patients with osteosarcomas ultimately experience recurrence. The study investigated the hypothesis that the resistance of osteosarcoma to chemotherapy may be related to the expression of a pregnane xenobiotic receptor (PXR) variant protein and its role as the major inducer of P450 3A4 in these tumors. METHODS: Polymerase chain reaction (PCR) and Western blot analysis were used to determine PXR mRNA and protein expression, respectively. Real-time PCR and CYP3A catalytic activity using 7-benzyl-trifluoromethyl coumarin (BFC) as the probe substrate were used to measure the induction of P450 3A4 or MDR1. siRNA transfections were performed for PXR and cytotoxicity determined by a colorimetric based assay or Annexin v-Fitc staining. RESULTS: Differences were observed in the molecular size of the PXR protein expressed in sarcoma cell lines when compared with the wildtype PXR expressed in normal liver, kidney, or small intestine. A polyclonal PXR antibody raised against the N-terminus of the wildtype PXR did not detect PXR expressed in these sarcoma cell lines. In the osteosarcoma cell lines, etoposide and doxorubicin were better inducers of P450 3A4 and MDR1 than rifampin. siRNA against PXR down-regulated P450 3A4 expression only in the osteosarcoma cell line. Cytotoxicity assays showed that the resistance of the osteosarcoma cell lines to etoposide correlated with PXR protein expression levels and activation of P450 3A4 and could be prevented by ketoconazole. CONCLUSION: The results suggest that PXR plays a critical role in the regulation of P450 3A4 expression in osteosarcoma and that its expression and activation in these tumors may influence the effect of chemotherapeutic agents on the induction of target genes implicated in drug resistance.

Distinct transcriptional profiles of adrenocortical tumors uncovered by DNA microarray analysis.

Comprehensive expression profiling of tumors using DNA microarrays has been used recently for molecular classification and biomarker discovery, as well as a tool to identify and investigate genes involved in tumorigenesis. Application of this approach to a cohort of benign and malignant adrenocortical tissues would be potentially informative in all of these aspects. In this study, we generated transcriptional profiles of 11 adrenocortical carcinomas (ACCs), 4 adrenocortical adenomas (ACAs), 3 normal adrenal cortices (NCs), and 1 macronodular hyperplasia (MNH) using Affymetrix HG_U95Av2 oligonucleotide arrays representing approximately 10,500 unique genes. The expression data set was used for unsupervised hierarchical cluster analysis as well as principal component analysis to visually represent the expression data. An analysis of variance on the three classes (NC, ACA plus MNH, and ACC) revealed 91 genes that displayed at least threefold differential expression between the ACC cohort and both the NC and ACA cohorts at a significance level of P < 0.01. Included in these 91 genes were those known to be up-regulated in adrenocortical tumors, such as insulin-like growth factor (IGF2), as well as novel differentially expressed genes such as osteopontin (SPP) and serine threonine kinase 15 (STK15). Increased expression of IGF2 was identified in 10 of 11 ACCs (90.9%) and was verified by quantitative reverse transcriptase-polymerase chain reaction. Select proliferation-related genes (TOP2A and Ki-67) were validated at the protein level using immunohistochemistry and adrenocortical tissue microarrays. Our results demonstrated significant and consistent gene expression changes in ACCs compared to benign adrenocortical lesions. Moreover, we identified several genes that represent potential diagnostic markers and may play a role in the pathogenesis of ACC.

Gene-expression profiles predict survival of patients with lung adenocarcinoma.

Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.