A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas

Abstract

The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.

Figure 1

Cancer barcode: In this figure we show the complete set of affected BCI interactions for each analyzed phenotype. The rows represent these BCI interactions sorted in ascending order (from top to bottom) by their MI computed over the complete set of BCGEP samples. Each column is one analyzed phenotype. These phenotypes shown include CLL-mut and CLL-unmut subsets, BL, DLCL, FL, MCL, and PEL. A ‘p' preceding a phenotype name indicates those samples were purified. Interactions are color coded in blue for LoC and red for GoC. Clearly visible from this figure is that these phenotypes all appear to have very distinct areas of the network, which define their pathologic activity.

Figure 2

BL module: A network visualization of the top 25 scoring genes in BL. Transcription factors are shown as circles, whereas other proteins are shown as squares. Protein–protein interactions are also shown in beige, protein–DNA interactions are black with an arrowhead, and transcription factor-modulated interactions are shown in blue with a circular endpoint. Red/green indicates overexpression or underexpression (P<1e−8), respectively in BL versus GC cells. There are some notable characteristics of this figure. First, all 25 genes form a connected module, which would not occur by chance. Second, MYC appears to be a central regulator of this module, as a full 21 out of the 25 members are MYC targets. MYC also appears regulated by MYC-associated zinc-finger protein (MAZ), which is also not differentially expressed. Third, there are interesting sets of genes that emerge, such as SMAD1, which is known to be associated with some NHL, and members of the NFAT family, including NFATC3, NFATC4, and NFAT5 (these proteins are members of the Wnt-signaling pathway). There also appears to be a protein complex of COL1A2, COL6A1 COL6A2, and FN1, which are all upregulated (and members of the cell signaling and ECM–receptor interaction pathway). These module diagrams can serve as a useful platform for further hypothesis generation and biochemical investigation.