Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients.

Better understanding of the underlying biology of malignant gliomas is critical for the development of early detection strategies and new therapeutics. This study aimed to define genes associated with survival. We investigated whether genes selected using random survival forests model could be used to define subgroups of gliomas objectively. RNAs from 50 non-treated gliomas were analyzed using the GeneChip Human Genome U133 Plus 2.0 Expression array. We identified 82 genes whose expression was strongly and consistently related to patient survival. For practical purposes, a 15-gene set was also selected. Both the complete 82 gene signature and the 15 gene set subgroup indicated their significant predictivity in the 3 out of 4 independent external dataset. Our method was effective for objectively classifying gliomas, and provided a more accurate predictor of prognosis. We assessed the relationship between gene expressions and survival time by using the random survival forests model and this performance was a better classifier compared to significance analysis of microarrays.

[1]  Udaya B. Kogalur,et al.  High-Dimensional Variable Selection for Survival Data , 2010 .

[2]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[3]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[4]  R. Mirimanoff,et al.  Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. , 2009, The Lancet. Oncology.

[5]  Yuri Kotliarov,et al.  Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. , 2009, Cancer research.

[6]  W. Weiss,et al.  PI3K Signaling in Glioma—Animal Models and Therapeutic Challenges , 2009, Brain pathology.

[7]  Benjamin Haibe-Kains,et al.  A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? , 2008, Bioinform..

[8]  R. Britto,et al.  Novel Glioblastoma Markers with Diagnostic and Prognostic Value Identified through Transcriptome Analysis , 2008, Clinical Cancer Research.

[9]  Tom C. Freeman,et al.  Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data , 2008, Molecular Cancer Therapeutics.

[10]  Robert Weil,et al.  Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study. , 2008, Genomics.

[11]  Pedro Martínez,et al.  Identification of survival‐related genes of the phosphatidylinositol 3′‐kinase signaling pathway in glioblastoma multiforme , 2008, Cancer.

[12]  N. Hashimoto,et al.  Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis , 2007, Clinical Cancer Research.

[13]  John Sampson,et al.  Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Zora Modrusan,et al.  Identification of IGF2 signaling through phosphoinositide-3-kinase regulatory subunit 3 as a growth-promoting axis in glioblastoma , 2007, Proceedings of the National Academy of Sciences.

[15]  S. Horvath,et al.  Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target , 2006, Proceedings of the National Academy of Sciences.

[16]  R. Tibshirani,et al.  On testing the significance of sets of genes , 2006, math/0610667.

[17]  K. Nishio,et al.  Identification of expressed genes characterizing long-term survival in malignant glioma patients , 2006, Oncogene.

[18]  Thomas D. Wu,et al.  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. , 2006, Cancer cell.

[19]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[20]  R. Britto,et al.  Upregulation of ASCL1 and inhibition of Notch signaling pathway characterize progressive astrocytoma , 2005, Oncogene.

[21]  M. West,et al.  Gene expression profiling and genetic markers in glioblastoma survival. , 2005, Cancer research.

[22]  D. Botstein,et al.  Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[23]  K. Aldape,et al.  Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. , 2005, Cancer research.

[24]  K. Wong,et al.  Expression analysis of juvenile pilocytic astrocytomas by oligonucleotide microarray reveals two potential subgroups. , 2005, Cancer research.

[25]  S. Horvath,et al.  Gene Expression Profiling of Gliomas Strongly Predicts Survival , 2004, Cancer Research.

[26]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[27]  Eytan Domany,et al.  Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. , 2003, Cancer research.

[28]  Paul S Mischel,et al.  Gene expression profiling identifies molecular subtypes of gliomas , 2003, Oncogene.

[29]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Paul S Mischel,et al.  Identification of molecular subtypes of glioblastoma by gene expression profiling , 2003, Oncogene.

[31]  T. Golub,et al.  Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. , 2003, Cancer research.

[32]  E. Dougherty,et al.  Identification of combination gene sets for glioma classification. , 2002, Molecular cancer therapeutics.

[33]  L. Stewart,et al.  Chemotherapy in adult high-grade glioma: a systematic review and meta-analysis of individual patient data from 12 randomised trials , 2002, The Lancet.

[34]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[36]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[37]  David E. Misek,et al.  Distinctive molecular profiles of high-grade and low-grade gliomas based on oligonucleotide microarray analysis. , 2001, Cancer research.

[38]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[39]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[40]  O. Kallioniemi,et al.  Identification of differentially expressed genes in human gliomas by DNA microarray and tissue chip techniques. , 2000, Cancer research.

[41]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[42]  J. Uhm,et al.  The transcriptional network for mesenchymal transformation of brain tumours , 2010 .

[43]  O. Kalita,et al.  Analysis of VEGF, Flt-1, Flk-1, nestin and MMP-9 in relation to astrocytoma pathogenesis and progression. , 2009, Neoplasma.

[44]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .