Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis

Cancer Research Institute of Medical Science, The Catholic University of Korea, Seoul, Korea, (Y.-W.K.), Brain Tumor Center, Department of Neuro-Oncology (D.K., S.H.K., A.K.L.-E., J.Y., K.A., W.K.A.Y.), and Department of Bioinformatics and Computational Biology (P.R.F., J.W., J.S.A.), The University of Texas MD Anderson Cancer Center, Houston, Texas.
Neuro-Oncology (Impact Factor: 5.29). 03/2013; 15(7). DOI: 10.1093/neuonc/not024
Source: PubMed

ABSTRACT Background
The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM).Methods
Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant.ResultsTo identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables.Conclusions
By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.

1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Antineoplastons are peptide and amino acid derivatives that occur naturally in the human body. They inhibit the growth of neoplastic cells without growth inhibition of normal cells. Phenylace-tylglutaminate (PG) is an active ingredient of antineoplastons A10 and AS2-1 (ANP) and is also a metabolic by-product of phenylbutyrate (PB). The formulation of antineoplaston AS2-1 is a 4:1 mixture of phenylacetate (PN) and PG. Antineoplaston A10 is a 4:1 mixture of PG and isoPG. This study investigates the molecular mechanism of action of PG and PN. The Human U87 glioblastoma (GBM) cell line was used as the model system in this study. A total human gene array screen using the Affymetrix Human Genome plus 2.0 oligonucleotide arrays was performed using mRNA de-rived from U87 cells exposed to PG and PN. Pathway analysis was performed to allow the visuali-zation of effect on metabolic pathways and gene interaction networks. Our preliminary results in-dicate that PG and PN interrupt signal transduction in RAS/MAPK/ERK and PI3K/AKT/PTEN pathways, interfere with cell cycle, decrease metabolism and promote apoptosis in human U87 GBM cells. The effect on multiple cellular pathways and targets, suggests that ANP and PB are pro-mising candidates for clinical studies in GBM.
    Journal of Cancer Therapy 01/2014; 5(5):929-945. DOI:10.4236/jct.2014.510099
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The US National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) created the Cancer Genome Atlas (TCGA) Project in 2006. The TCGA’s goal was to sequence the genomes of 10, 000 tumors to identify common genetic changes among different types of tumors for developing genetic-based treatments. TCGA had some potential for cancer patients, but in reality has had an insignificant impact in clinical applications. Recent reports place the past TCGA approach of testing a small tumor mass at a single timepoint at a crossroads. This crossroads presents us with the conundrum of whether we should sequence more tumors or obtain multiple biopsies from each individual tumor at different time points. Sequencing more tumors with the past TCGA approach of single time-point sampling can neither capture the heterogeneity between different parts of the same tumor nor catch the heterogeneity that occurs as a function of time, error rates, and random drift. Obtaining multiple biopsies from each individual tumor presents a logistical challenge because usually a tumor is sampled only in its periphery and yet more tumorigenic clones may exist at its core, which is rarely sampled in a biopsy. Here, we review current literature and rethink the utility and application of the TCGA approach. We discuss that the TCGA-led catalogue may provide insights into studying the functional significance of oncogenic genes in reference to non-cancer genetic background. Different methods to enhance identifying cancer targets, such as single cell technology, real time imaging of cancer cells with a biological global positioning system, and cross-referencing big data sets, are offered as ways to address sampling discrepancies in the face of tumor heterogeneity. We predict that TCGA landmarks may prove far more useful for cancer prevention rather than cancer diagnosis and treatment when considering the effect of the non-cancer genes – the normal genetic background on tumor microenvironment. Understanding how therapy affects the genetic makeup of cancer over time in a clinical setting may help create novel therapies for gene mutations that arise during a tumor’s evolution from onset of treatment and for which may surface.
    Cancer Cell International 11/2014; 2014, 14:115 doi:10.1186/s12935-014-0115-7(1):1-28. DOI:10.1186/s12935-014-0115-7 · 1.99 Impact Factor