GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes

Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas

Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes

Correction to “Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes”

Roll 184a. Klubertanz / Ginger Lee / Chris Keeler. Image 14 of 42. (19 April, 1955) [PHO 1.184a.20]The Boleslaus Lukaszewski (Father Luke) Photographs contain more than 28,000 images of Saint Louis University people, activities, and events between 1951 and 1970. The photographs were taken by Boleslaus Lukaszewski (Father Luke), a Jesuit priest and member of the University's Philosophy Department faculty

Antitumor Activity and Prolonged Expression from a TRAIL-Expressing Adenoviral Vector

AbstractTumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induces apoptosis in a variety of transformed cell lines, but generally spares most normal cells. Transduction by an adenoviral vector expressing human TRAIL cDNA (Ad.TRAIL-GFP) resulted in both direct tumor cell killing as well as a potent bystander effect through presentation of TRAIL by transduced normal cells. Administration of Ad.TRAIL-GFP significantly prolonged survival of mice harboring either intracerebral glioblastomas or breast carcinoma-induced peritoneal carcinomatosis. Additionally, TRAIL induced prolonged transgene expression in normal tissue, presumably as a result of diminished immunemediated destruction of vector-transduced cells. Taken together, these data suggest that vector-mediated transduction of TRAIL may represent an effective strategy for cancer gene therapy

Insolubility Theorems and EPR Argument

I wish to thank in particular Arthur Fine for very perceptive comments on a previous draft of this paper. Many thanks also to Theo Nieuwenhuizen for inspiration, to Max Schlosshauer for correspondence, to two anonymous referees for shrewd observations, and to audiences at Aberdeen, Cagliari and Oxford (in particular to Harvey Brown, Elise Crull, Simon Saunders, Chris Timpson and David Wallace) for stimulating questions. This paper was written during my tenure of a Leverhulme Grant on ‘The Einstein Paradox’: The Debate on Nonlocality and Incompleteness in 1935 (Project Grant nr. F/00 152/AN), and it was revised for publication during my tenure of a Visiting Professorship in the Doctoral School of Philosophy and Epistemology, University of Cagliari (Contract nr. 268/21647).Peer reviewedPostprin

Qualitative Network Modeling of the Myc-p53 Control System of Cell Proliferation and Differentiation

AbstractA kinetic model of a molecular control system for the cellular decision to proliferate or differentiate is formulated and analyzed for the purpose of understanding how the system can break down in cancer cells. The proposed core of this control system is composed of the transcription factors Myc and p53. The network of interactions between these factors involves negative and positive feedback loops that are linked to pathways involved in differentiation, cell cycle, and apoptosis. Understanding the dynamics of the Myc-p53 control system is aided by the postulate that there exists a cancer zone defined as a range of oncogenic Myc activities where the probability of initiating cancer is high. We propose that an essential role of p53 is to prevent the system from entering or staying too long in the cancer zone by downregulating Myc or, when Myc activity somehow becomes too high, by inducing apoptosis, cell cycle arrest, or differentiation. Kinetic modeling illustrates how deletions or aberrations in PTEN, MDM2, and ARF (genes implicated in various cancers, including glioma) affect the Myc-p53 control system. In addition, computer simulations demonstrate how this control system generates different cellular phenotypes characterized by rates of cellular differentiation and proliferation

Micro-Environment Causes Reversible Changes in DNA Methylation and mRNA Expression Profiles in Patient-Derived Glioma Stem Cells

In vitro and in vivo models are widely used in cancer research. Characterizing the similarities and differences between a patient\u27s tumor and corresponding in vitro and in vivo models is important for understanding the potential clinical relevance of experimental data generated with these models. Towards this aim, we analyzed the genomic aberrations, DNA methylation and transcriptome profiles of five parental tumors and their matched in vitro isolated glioma stem cell (GSC) lines and xenografts generated from these same GSCs using high-resolution platforms. We observed that the methylation and transcriptome profiles of in vitro GSCs were significantly different from their corresponding xenografts, which were actually more similar to their original parental tumors. This points to the potentially critical role of the brain microenvironment in influencing methylation and transcriptional patterns of GSCs. Consistent with this possibility, ex vivo cultured GSCs isolated from xenografts showed a tendency to return to their initial in vitro states even after a short time in culture, supporting a rapid dynamic adaptation to the in vitro microenvironment. These results show that methylation and transcriptome profiles are highly dependent on the microenvironment and growth in orthotopic sites partially reverse the changes caused by in vitro culturing

Relational EPR

We study the EPR-type correlations from the perspective of the relational interpretation of quantum mechanics. We argue that these correlations do not entail any form of 'non-locality', when viewed in the context of this interpretation. The abandonment of strict Einstein realism implied by the relational stance permits to reconcile quantum mechanics, completeness, (operationally defined) separability, and locality.Comment: Revised, published versio