Mitsuaki Shirahata

Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis

Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Endogenous tenascin‐C enhances glioblastoma invasion with reactive change of surrounding brain tissue

Tenascin‐C is an extracellular matrix glycoprotein implicated in embryogenesis, wound healing and tumor progression. We previously revealed that tenascin‐C expression is correlated with the prognosis of patients with glioblastoma. However, the exact role of endogenous tenascin‐C in regulation of glioblastoma proliferation and invasion remains to be established. We show here that endogenous tenascin‐C facilitates glioblastoma invasion, followed by reactive change of the surrounding brain tissue. Although shRNA‐mediated knockdown of endogenous tenascin‐C does not affect proliferation of glioblastoma cells, it abolishes cell migration on a two‐dimensional substrate and tumor invasion with brain tissue changes in a xenograft model. The tyrosine phosphorylation of focal adhesion kinase, a cytoplasmic tyrosine kinase that associates with integrins, was decreased in tenascin‐C‐knockdown cells. In the analysis of clinical samples, tenascin‐C expression correlates with the volume of peritumoral reactive change detected by magnetic resonance imaging. Interestingly, glioblastoma cells with high tenascin‐C expression infiltrate brain tissue in an autocrine manner. Our results suggest that endogenous tenascin‐C contributes the invasive nature of glioblastoma and the compositional change of brain tissue, which renders tenascin‐C as a prime candidate for anti‐invasion therapy for glioblastoma. (Cancer Sci 2009)

Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

BackgroundThe advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR.MethodsPreviously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression.ResultsWe selected UBL5 as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis.ConclusionWe successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.

A new gene expression-based diagnostic test to predict prognosis of gliomas for the support of histopathologic diagnosis.

2085 Background: Histopathologic classification of gliomas is often clinically inadequate due to the diversity of tumors that fall within the same class. Based on the strong correlation between mal...