Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis.

PURPOSE: To determine the difference in gene expression between completely versus incompletely enhancing glioblastoma multiforme (GBM). MATERIALS AND METHODS: Gene expression was determined for 52 newly diagnosed GBMs by using DNA microarrays, and the relationship to enhancement pattern and survival was analyzed. This study was approved by the institutional review board and was HIPAA compliant; informed consent was obtained. RESULTS: Thirty-eight percent (20 of 52) of GBMs were incompletely enhancing (IE). The expression of eight genes was increased more than twofold in IE GBM when compared with completely enhancing (CE) GBM. Among these were tight junction protein-2 (2.2-fold increase, P = .019), and the oligodendroglioma markers oligodendrocyte lineage transcription factor 2 (2.4-fold increase, P = .029) and Achaete-scute complex-like 1 (ASCL1; 2.7-fold increase, P = .023). The expression of 71 genes showed relative overexpression in CE when compared with IE GBM. These included several proangiogenic and edema-related genes, including vascular endothelial growth factor (2.1-fold, P = .005) and neuronal pentraxin-2 (3.0-fold, P = .029). Several genes associated with primary GBM were overexpressed in CE tumors, whereas ASCL1, which is associated with secondary GBM, was overexpressed in IE tumors. Many genes overexpressed in IE GBM were associated with longer survival, whereas several genes overexpressed in CE GBM correlated with shortened survival. CONCLUSION: The enhancement pattern divides GBM in two groups with differing prognoses. By comparing gene expression between IE and CE GBMs, it was possible to identify genes that may affect magnetic resonance imaging features of edema and enhancement, and genes whose expression levels are predictive of both improved and shortened survival.

MR imaging correlates of survival in patients with high-grade gliomas.

BACKGROUND AND PURPOSE: For patients with malignant gliomas, clinical data-including age, perioperative Karnofsky Performance Status (KPS), and tumor resection-and tumor imaging features-including necrosis and edema-have been found to correlate with survival. The purpose of this study was to assess the validity of these results and determine whether other imaging features are useful in predicting survival. METHODS: We analyzed the relationship between 15 imaging variables obtained from contrast-enhanced MR imaging scans and survival in patients with grade III (n = 43) and grade IV (n = 110) glioblastoma multiforme (GBM) gliomas. Image analysis was performed by 2 neuroradiologists who were blinded to clinical data. The Kaplan-Meier method was used to estimate survival probabilities. Univariable Cox models were used to assess the impact of imaging features on survival. A recursive partitioning analysis also was performed. RESULTS: As expected, age and KPS scores had significant prognostic value for both tumor grades. The extent of resection was not a statistically meaningful predictor of survival. For GBM, univariable analysis revealed the following imaging features to be significant, (hazard ratios in parentheses): noncontrast-enhancing tumor (nCET, 0.55), edema (1.62), satellites (1.74), and multifocality (4.34). For grade III tumors, the Cox hazard ratio for necrosis was 4.43 (P = .014) and correlated with a poor outcome and survival rates comparable to GBM patients. Lack of nCET, multifocality, and satellite lesions also were correlated with shortened survival. CONCLUSION: Of 15 tumor imaging features in GBM patients, only nCET, edema, and multifocality/satellites are statistically significant prognostic indicators. The survival advantage of nCET is a novel finding.

Nutrigenomics: concepts and applications to pharmacogenomics and clinical medicine.

The maintenance of health and the prevention and treatment of chronic diseases are influenced by naturally occurring chemicals in foods. In addition to supplying the substrates for producing energy, a large number of dietary chemicals are bioactive--that is, they alter the regulation of biological processes and, either directly or indirectly, the expression of genetic information. Nutrients and bioactives may produce different physiological phenotypes among individuals because of genetic variability and not only alter health, but also disease initiation, progression and severity. The study and application of gene-nutrient interactions is called nutritional genomics or nutrigenomics. Nutrigenomic concepts, research strategies and clinical implementation are similar to and overlap those of pharmacogenomics, and both are fundamental to the treatment of disease and maintenance of optimal health.

Positive selection detection in 40,000 human immunodeficiency virus (HIV) type 1 sequences automatically identifies drug resistance and positive fitness mutations in HIV protease and reverse transcriptase.

Drug resistance is a major problem in the treatment of AIDS, due to the very high mutation rate of human immunodeficiency virus (HIV) and subsequent rapid development of resistance to new drugs. Identification of mutations associated with drug resistance is critical for both individualized treatment selection and new drug design. We have performed an automated mutation analysis of HIV Type 1 (HIV-1) protease and reverse transcriptase (RT) from approximately 40,000 AIDS patient plasma samples sequenced by Specialty Laboratories Inc. from 1999 to mid-2002. This data set provides a nearly complete mutagenesis of HIV protease and enables the calculation of statistically significant K(a)/K(s) values for each individual amino acid mutation in protease and RT. Positive selection (i.e., a K(a)/K(s) ratio of >1, indicating increased reproductive fitness) detected 19 of 23 known drug-resistant mutation positions in protease and 20 of 34 such positions in RT. We also discovered 163 new amino acid mutations in HIV protease and RT that are strong candidates for drug resistance or fitness. Our results match available independent data on protease mutations associated with specific drug treatments and mutations with positive reproductive fitness, with high statistical significance (the P values for the observed matches to occur by random chance are 10(-5.2) and 10(-16.6), respectively). Our mutation analysis provides a valuable resource for AIDS research and will be available to academic researchers upon publication at http://www.bioinformatics.ucla.edu/HIV. Our data indicate that positive selection mapping is an analysis that can yield powerful insights from high-throughput sequencing of rapidly mutating pathogens.