Person:

Johnson, Mark

Background: Anaplastic astrocytoma (AA) and its more aggressive counterpart, glioblastoma multiforme (GBM), are the most common intrinsic brain tumors in adults and are almost universally fatal. A deeper understanding of the molecular relationship of these tumor types is necessary to derive insights into the diagnosis, prognosis, and treatment of gliomas. Although genomewide profiling of expression levels with microarrays can be used to identify differentially expressed genes between these tumor types, comparative studies so far have resulted in gene lists that show little overlap. Results: To achieve a more accurate and stable list of the differentially expressed genes and pathways between primary GBM and AA, we performed a meta-analysis using publicly available genome-scale mRNA data sets. There were four data sets with sufficiently large sample sizes of both GBMs and AAs, all of which coincidentally used human U133 platforms from Affymetrix, allowing for easier and more precise integration of data. After scoring genes and pathways within each data set, we combined the statistics across studies using the nonparametric rank sum method to identify the features that differentiate GBMs and AAs. We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested. We also used the rank sum approach to select >20 significant Biocarta pathways after correction for multiple testing out of >175 pathways examined. The most significant pathway was the hypoxia-inducible factor (HIF) pathway. Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA. Conclusion: We have performed a meta-analysis of genome-scale mRNA expression data for 289 human malignant gliomas and have identified a list of >900 probe sets and >20 pathways that are significantly different between GBM and AA. These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology. More generally, this approach suggests that combined analysis of existing data sets can reveal new insights and that the large amount of publicly available cancer data sets should be further utilized in a similar manner.

Extracellular signaling is commonly mediated through post-translational protein modifications that propagate messages from membrane-bound receptors to ultimately regulate gene expression. Signaling cascades are ubiquitously intertwined, and a full understanding of function can only be gleaned by observing dynamics across multiple key signaling nodes. Importantly, targets within signaling cascades often represent opportunities for therapeutic development or can serve as diagnostic biomarkers. Protein phosphorylation is a particularly important post-translational modification that controls many essential cellular signaling pathways. Not surprisingly, aberrant phosphorylation is found in many human diseases, including cancer, and phosphoprotein-based biomarker signatures hold unrealized promise for disease monitoring. Moreover, phosphoprotein analysis has wide-ranging applications across fundamental chemical biology, as many drug discovery efforts seek to target nodes within kinase signaling pathways. For both fundamental and translational applications, the analysis of phosphoprotein biomarker targets is limited by a reliance on labor-intensive and/or technically challenging methods, particularly when considering the simultaneous monitoring of multiplexed panels of phosphoprotein biomarkers. We have developed a technology based upon arrays of silicon photonic microring resonator sensors that fills this void, facilitating the rapid and automated analysis of multiple phosphoprotein levels from both cell lines and primary human tumor samples requiring only minimal sample preparation.