Network Signatures of Survival in Glioblastoma Multiforme

<div><p>To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.</p></div

Proteomic detection and dysregulation of biomarkers discovered using various pipelines.

<p>(A) Comparison of the number of proteomic targets identified using a network-based algorithm for identifying combinatorial gene markers (“CRANE”) versus one using individual differentially expressed genes (“Individual Gene Markers”). (B) Comparison of the number of proteomic targets identified using the subtypes identified by Verhaak et al. We plot the total number of classifier targets detected in the proteomic experiment (“Identification”), as well as the subset of classifier genes showing evidence for differential expression (<i>p</i>-value≤0.05) at the protein level (“D.E.”).</p

Survival curves comparing various classifiers when tested on the dataset of Lee et al.

<p>(GEO ID: GSE13041). While the Verhaak subtypes – Proneural, Classical, Neural, and Mesenchymal – do not show statistically significant differences in survival, the top 5 CRANE subnetworks clearly distinguish short-term from long-term survivor groups.</p

The top five CRANE subnetworks representing a signature of survival in glioblastoma.

<p>Gene names are indicated within the nodes; edges represent either protein-protein interactions (turquoise), or proteins found together as partners within a complex (violet). Subnetworks are added into the classifier in clockwise fashion (from 1 to 5); after the addition of each subnetwork, an updated positive predictive value (PPV) is calculated, as shown along the periphery for prediction of both short-term (pink) and long-term (purple) survival.</p

Workflow of the CRANE algorithm for detecting combinatorially dysregulated subnetworks.

<p>We begin by mapping patient-specific, binarized mRNA expression data onto a protein interaction network. Then, we identify subnetworks whose pattern of expression – the subnetwork state function – can separate short-term and long-term survivors. Measures of separation are the support (the fraction of samples containing a particular subnetwork state), the fraction of long/short-term survivors, and the <i>J</i>-value (see text for description). In the table (bottom), the top ten states of the first TCGA subnetwork are shown. Each row represents a different state of the subnetwork. Each character in the state function (first column) represents the expression state of a particular gene in the subnetwork, where “L” and “H” stand for “low” and “high” expression, respectively.</p

Dysregulated proteins identified within the 50-gene subnetwork signature.

<p>Proteins with p-values<0.05 are in bold. Ratios (LTS-to-STS) were calculated from the raw data.</p

Additional file 1: Table S1. of ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

Cohorts, ethnicity, and tumor stage of samples used for WES, SNP array, and qPCR. Table S2. Regions with significant copy-number alterations, as detected by ENVE, in the 30 AA CRC WES cases. Table S3. Regions with significant copy-number alterations, as detected by ENVE, in the 30 predominantly late-stage Caucasian TCGA CRC WES cases. Table S4. Recurrent somatic copy-number altered regions in the 30 AA CRC WES cases estimated by GISTIC. Table S5. Recurrent focal copy-number amplifications and deletions in the 30 AA CRC WES cases estimated by GISTIC. Table S6. Chromosomal arm-level sCNA frequencies in AA and TCGA CRCs estimated by GISTIC. (XLSX 535 kb

Additional file 2: Figure S1. of ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

Fraction of chromosomal coverage across segmental LogRatio thresholds in AA normal–normal comparisons. Figure S2. Entropy of chromosomal coverage across segmental LogRatio thresholds in AA normal–normal comparisons. Figure S3. Concordance assessment of ENVE/Control-FREEC sCNA segments with SNP array. Figure S4. Performance evaluation of Control-FREEC with contamination-correction against ENVE. Figure S5. Performance evaluation of ENVE against Control-FREEC across SNP array Segment-Mean cutoffs in the TCGA dataset. Figure S6. Impact of sequencing read depth on ENVE versus Control-FREEC performance in the TCGA WES dataset. Figure S7. Concordance analysis of Control-FREEC-based and qPCR-based sCNA estimates. Figure S8. Relationship between the tumor/normal Segmental LogRatios and ENVE P-value. Figure S9. Effect of the number of normal samples on ENVE noise-threshold estimates. Figure S10. Performance evaluation of ENVE in matched- versus pooled normal analysis scenarios. (PDF 2411 kb

Distinguishing germline variants from somatic variants.

<p>(A) For each SNP, the non-reference allele frequencies for samples (out of seven) with smallest (black) and largest (gray) such frequencies are shown. In all samples, these frequencies do not deviate substantially from the germline frequencies. All SNPs in all samples have minor allele frequencies near the expected 50% or 100% and are therefore clearly and consistently distinguishable as either homozygotes (leftmost three) or heterozygotes (the remainder). (B) In contrast, the somatic variants display much wider ranges in allele frequencies across samples. Note that the absence of black bars is indicative complete absence of the mutation in some samples.</p

Computational classifications of mutations.

<p>Computational classifications of mutations.</p