Regulatory modules in normal and transformed B-cells
Riccardo Dalla-Favera and Andrea Califano
We attempted to identify conserved functional modules within Human B-Cell gene regulatory networks, especially in relation to the Germinal Center (a key structure for antibody-mediated immune response and a target of transformation in B-Cell lymphomas). To that end we used several MAGNet-developed bioinformatics methodologies. Specifically, we integrated (a) clues from information theoretic reverse-engineering methods applied to a large microarray expression profile set (~400 microarrays) of normal, tumor-related, and experimentally manipulated B cells, (b) clues from advanced natural language-based data-mining algorithms applied to a large literature collection, and (c) clues from protein-protein and protein-DNA interaction databases. We identified disregulated modules in a variety of Germinal Center-related tumors using expression analysis algorithms supported in geWorkbench. Candidate module control genes were identified using a combination of in-silico and in-vitro techniques.
This project was aimed at understanding regulatory modules determining B cell maturation and transformation, with focus on those regulated by the MYC and BCL6 oncogenes. Our approach was based on creating a compendium of molecular interactions in human B cells. This B-Cell interactome (hBCi) (Lefebvre 2009, Mani 2008, Lefebvre 2007) was assembled using Bayesian integration, combining evidence from multiple sources: both computationally derived (in the form of predictions made by analyzing genomic data trough MAGNet tools such as ARACNe (Margolin, Nemenman 2006; Margolin, Wang 2006; Basso 2005, Palomero 2006), MINDy (Mani 2008; Wang, Saito 2009; Wang 2006), GeneWays (Rzhetsky 2004)), as well as experimentally derived (obtained from various database sources (e.g. BIND)). The hBCi (which is accessible through the geWorkbench Cellular Network Knowledge Base component), provides the first map of the interface between signal-transduction and transcriptional networks in a eukaryotic cell (Wang, Alvarez 2009).
Two algorithms, IDEA and MARINa, both developed within MAGNet projects, were used to interrogate the hBCi. Application of the two algorithms led to the identification of regulatory modules causally related to these phenotypes (Lefebvre 2009, Mani 2008) and provided a fine-grained representation of the regulatory modules controlled by MYC (Mani 2008, Basso 2005) and BCL6 (Basso 2009, Saito 2009). The paper describing the IDEA algorithms was the fourth-most downloaded Molecular Systems Biology manuscript in 2008. A similar approach was also instrumental in demonstrating activation of the NF-kB pathway in the ABC (aggressive) subtype of Diffuse Large B Cell Lymphoma (DLBCL) as well as in identifying the specific spectrum of genetic alterations contributing to its activation. Included among these were the highly penetrant alterations for A20 and CARD11 (Compagno 2009).
When applied to additional normal and tumor-related phenotypes beyond those originally part of this project, these methods have proved very effective and have led to publication of several high-impact papers. Specifically:
- ARACNe was used to dissect tumorigenic pathways downstream of NOTCH1 (Palomero 2006).
- MINDy was used to identify drugable-targets activating or inhibiting a repertoire of transcriptional programs, followed up by experimental validation of the role of STK38 kinase (Wang, Alvarez 2009).
- ARACNe and MINDy were used to elucidate the role of HUWE1, a ubiquitin ligase, in regulating the N-MYC-DLL3 cascade. This cascade leads to brain malformation in mouse embryos and to oncogenesis (Zhao 2007).
- MARINa was used to infer the actions of C/EBPβ and Stat3 as synergistic Master Regulators of the mesenchymal subtype of Glioblastoma Multiform (Carro 2009).
- MARINa was used to identify Master Regulators of aggressive Breast Cancer (Zhao 2007). These Master Regulators were robust to inference from distinct datasets and outperformed previous signatures (van de Vijver 2002, Wang 2005) in classifying tumor types
- MARINa was used to identify a repertoire of Master Regulators of the Germinal Center reaction, including virtually all previously validated ones (i.e., BCL6, POU2F1, etc.). It also identified the novel synergistic pair MYB-FOXM1, which <>was experimentally validated (Lefebvre 2009) (in review).
- Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37(4):382-90.
- Compagno M, Lim WK, Grunn A, Nandula SV, Brahmachary M, Shen Q, et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature. 2009;459(7247):717-21.
- Lefebvre C, Rajbhandari P, Alvarez M, Lim WK, Sato M, Wang K, et al. A A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers. submitted. 2009.
- Palomero T, Lim WK, Odom DT, Sulis ML, Real PJ, Margolin A, et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc Natl Acad Sci U S A. 2006;103(48):18261-6.
- Rzhetsky A, Iossifov I, Koike T, Krauthammer M, Kra P, Morris M, et al. GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data. J Biomed Inform. 2004;37(1):43-53.
- van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999-2009.
- Wang K, Alvarez MJ, Bisikirska BC, Linding R, Basso K, Dalla Favera R, et al. Dissecting the interface between signaling and transcriptional regulation in human B cells. Pac Symp Biocomput. 2009:264-75.
- Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671-9.
- Zhao X, D DA, Lim WK, Brahmachary M, Carro MS, Ludwig T, et al. The N-Myc-DLL3 cascade is suppressed by the ubiquitin ligase Huwe1 to inhibit proliferation and promote neurogenesis in the developing brain. Dev Cell. 2009;17(2):210-21.