Data and Statistical R Code:
Analysis of Oncogenic Signaling Networks in Glioblastoma: Identification of ASPM as a Novel Target

Horvath, S., Nelson S, Mischel PS


Method: Weighted Gene Co-Expression Network Analysis ( WGCNA )
                 Weighted Gene Coexpression Network Analysis


Here we provide statistical code and data for the paper:

Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) "Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target", PNAS | November 14, 2006 | vol. 103 | no. 46 | 17402-17407

Link to paper: PNAS Webpage

Abstract: Glioblastoma is the most common primary malignant brain tumor of adults and one of the most lethal of all cancers. Patients with this disease have a median survival of 15 months from the time of diagnosis despite surgery, radiation and chemotherapy. New treatment approaches are needed. Recent works suggest that glioblastoma patients may benefit from molecularly targeted therapies. Here, we address the compelling need for identification of new molecular targets. Leveraging global gene expression data from two independent sets of clinical tumor samples (n=55 and n=65), we identify a gene coexpression module in glioblastoma that is also present in breast cancer and significantly overlaps with the ˇ§meta-signatureˇ¨ (MS) for undifferentiated cancer. Studies in an isogenic model system demonstrate that this module is downstream of the mutant EGFR receptor, EGFRvIII and that it can be inhibited by the EGFR tyrosine kinase inhibitor Erlotinib. We identify ASPM (abnormal spindle-like microcephaly associated) as a key gene within this module and demonstrate its over-expression in glioblastoma relative to normal brain (or body tissues). Finally, we show that ASPM inhibition by siRNA-mediated knockdown inhibits tumor cell proliferation and neural stem cell proliferation, supporting ASPM as a potential molecular target in glioblastoma. Our weighted gene co-expression network analysis (WGCNA) provides a blueprint for leveraging genomic data to identify key control networks and molecular targets for glioblastoma, and the principle eluted from our work can be applied to other cancers.


Real Data

  1. Detailed Materials And Methods Section (without R code)
        Microsoft Word Version

  2. Brain cancer (GBM) data network analysis. The tutorial shows how we constructed our brain cancer networks in 2 independent datasets and how to relate the 2 networks.
    Contents Part A
        *) Weighted Brain Cancer Network Construction based on *8000* most varying genes
        *) Module Detection involving the 3600 most connected genes
        *) Gene significance and intramodular connectivity
        *) Robustness analysis with respect to the soft threshold beta

        *) Comparing the results to the unweighted network construction
        Microsoft Word version
        PDF version

        Datasets (Zipped)

    Contents part B (the beginning overlaps with part A)
        *) Weighted brain cancer network construction based on *3600* most connected genes
        *) Gene significance and intramodular connectivity in data sets I and II
        *) Module Eigengene and its relationship to individual genes
        *) Regressing survival time on individual gene expression and the module eigengene
    Microsoft Word version
        PDF version
        Datasets (Zipped)

  3. Breast Cancer Analysis. This tutorial shows how to map the Affymetrix U133A probe set IDs into Rosetta chip data. Then it uses the resulting breast cancer array data to construct a weighted network.
    Microsoft Word version
    PDF Version

    Datasets (Zipped)

  4. Cell Line Validation Data. We used dChip pm-mm normalization on the original Affymmetric Cel files. Rows correspond to probes, columns to microarrays samples.
    Dataset (Zipped)

  5. dChip and RMA Normalized measurements for all probesets on all samples
    Datasets (Zipped)

    Clinical data for all samples
    Dataset (Microsoft Excel)

Download the following R function file, which contains several R functions needed for Weighted Gene Co-Expression Network Analysis.
 Network R functions

A simulated gene co-expression network to illustrate the use of the topological overlap matrix for module detection

             Microsoft Word version (recommended)

             PDF version

The second most influential publication in the brain tumor field for the year 2006.

             A snapshot of the webpage

Other material regarding weighted gene co-expression network analysis

             Weighted Gene Co-Expression Network Page

             The weighted gene co-expression network analysis method is described in Theory Paper 1: Zhang and Horvath (2005)

             For a more mathematical description of weighted gene co-expression networks consider Theory Papers: Dong and Horvath (2007, 2008)



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