Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma.

Gliomas are primary brain tumors with high mortality and heterogeneous biology that is insufficiently understood. In this study, we performed a systematic analysis of the intrinsic organization of complex glioma transcriptome to gain deeper knowledge of the tumor biology. Gene coexpression relationships were explored in 790 glioma samples from 5 published patient cohorts treated at different institutions. We identified 20 coexpression modules that were common to all the data sets and associated with proliferation, angiogenesis, hypoxia, immune response, genomic alterations, cell differentiation phenotypes, and other features inherent to glial tumors. A collection of high-quality signatures for the respective processes was obtained using cross-data set summarization of the modules' gene composition. Individual modules were found to be organized into higher order coexpression groups, the two largest of them associated with glioblastoma and oligodendroglioma, respectively. We identified a novel prognostic gene expression signature (185 genes) linked to a proastrocytic pattern of tumor cell differentiation. This "proastrocytic" signature was associated with long survival and defined a subgroup of the previously established "proneural" class of gliomas. A strong negative correlation between proastrocytic and proneural markers across differentiated tumors underscored the distinction between these subtypes of glioma. Interestingly, one further novel signature in glioma was identified that was associated with EGFR (epidermal growth factor receptor) gene amplification and suggested that EGF signaling in glioma may be a subject to regulation by Sprouty family proteins. In summary, this integrated analysis of the glioma transcriptome provided several novel insights into molecular heterogeneity and pathogenesis of glial tumors.

[1]  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.

[2]  S. Nelson,et al.  DNA-microarray analysis of brain cancer: molecular classification for therapy , 2004, Nature Reviews Neuroscience.

[3]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[4]  Y. Xing,et al.  A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function , 2008, The Journal of Neuroscience.

[5]  D. Louis WHO classification of tumours of the central nervous system , 2007 .

[6]  Jean-Yves Delattre,et al.  Anaplastic oligodendrogliomas with 1p19q codeletion have a proneural gene expression profile , 2008, Molecular Cancer.

[7]  S. Horvath,et al.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks , 2006, BMC Genomics.

[8]  Jonathan D. Licht,et al.  Mammalian Sprouty Proteins Inhibit Cell Growth and Differentiation by Preventing Ras Activation* , 2001, The Journal of Biological Chemistry.

[9]  Wenjun Guo,et al.  Integrin signalling during tumour progression , 2004, Nature Reviews Molecular Cell Biology.

[10]  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.

[11]  S. Horvath,et al.  Functional organization of the transcriptome in human brain , 2008, Nature Neuroscience.

[12]  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.

[13]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[14]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[15]  N. Hacohen,et al.  sprouty Encodes a Novel Antagonist of FGF Signaling that Patterns Apical Branching of the Drosophila Airways , 1998, Cell.

[16]  D. Bar-Sagi,et al.  The bimodal regulation of epidermal growth factor signaling by human Sprouty proteins , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  E. Raymond,et al.  Lessons learned in the development of targeted therapy for malignant gliomas , 2007, Molecular Cancer Therapeutics.

[18]  Yu Liang,et al.  Id4 and FABP7 are preferentially expressed in cells with astrocytic features in oligodendrogliomas and oligoastrocytomas , 2005, BMC clinical pathology.

[19]  A. Chinnaiyan,et al.  Integrative analysis of the cancer transcriptome , 2005, Nature Genetics.

[20]  A. Iafrate,et al.  Expression of Oligodendroglial and Astrocytic Lineage Markers in Diffuse Gliomas: Use of YKL-40, ApoE, ASCL1, and NKX2-2 , 2006, Journal of neuropathology and experimental neurology.

[21]  D. Louis,et al.  Specific genetic predictors of chemotherapeutic response and survival in patients with anaplastic oligodendrogliomas. , 1998, Journal of the National Cancer Institute.

[22]  R. Salgia,et al.  Epidermal Growth Factor Receptor–Mediated Signal Transduction in the Development and Therapy of Gliomas , 2006, Clinical Cancer Research.

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

[24]  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.

[25]  David E. Misek,et al.  Characterization of gene expression profiles associated with glioma progression using oligonucleotide-based microarray analysis and real-time reverse transcription-polymerase chain reaction. , 2003, The American journal of pathology.

[26]  Kai Wang,et al.  Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks , 2007, ISMB/ECCB.

[27]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[28]  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.

[29]  Lei Liu,et al.  A study of inter-lab and inter-platform agreement of DNA microarray data , 2005, BMC Genomics.

[30]  P. Brown,et al.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[31]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[32]  D. Louis,et al.  Glioma classification: a molecular reappraisal. , 2001, The American journal of pathology.

[33]  Hiroyuki Aburatani,et al.  Selective Expression of a Subset of Neuronal Genes in Oligodendroglioma with Chromosome 1p Loss , 2004, Brain pathology.

[34]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

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

[36]  E. Domany,et al.  Stem cell-related "self-renewal" signature and high epidermal growth factor receptor expression associated with resistance to concomitant chemoradiotherapy in glioblastoma. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Boon Chuan Low,et al.  Sprouty2 attenuates epidermal growth factor receptor ubiquitylation and endocytosis, and consequently enhances Ras/ERK signalling , 2002, The EMBO journal.

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

[40]  C. Der,et al.  Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer , 2007, Oncogene.

[41]  Andrew P. Stubbs,et al.  Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. , 2009, Cancer research.

[42]  ZhangBin,et al.  Defining clusters from a hierarchical cluster tree , 2008 .

[43]  Z. Kohutek,et al.  ADAM-10-Mediated N-Cadherin Cleavage Is Protein Kinase C-α Dependent and Promotes Glioblastoma Cell Migration , 2009, The Journal of Neuroscience.

[44]  L. Chin,et al.  Malignant astrocytic glioma: genetics, biology, and paths to treatment. , 2007, Genes & development.