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Computational Approaches to Cancer Therapy

29 November 2012

Leslie K. Ferrarelli, Nancy R. Gough

Rune Linding (Technical University of Denmark) stated that we need to not consider the individual “node” in the network as the drug target, but rather we should consider the "network" as the drug target. To this end, his lab has been developing computational approaches to integrating genomic data and proteomic data to understand signaling networks in normal and disease states. Recognizing that traditional tumor sequencing identifies many "passenger" mutations, which do not drive the cancer and which cloud the identification of cancer drivers, Linding and colleagues have developed algorithms and online tools, such as NetworKIN and NetPhorest. Using these tools and genomics data, researchers can analyze the massive amounts of proteomics data and make predictions about the effects of kinase mutations on the structure and dynamics of the network. The key—but also the major challenge—to understanding the dynamic changes in signaling networks that drive cancer is to analyze data from tissue early during its carcinogenic transformation, because the early changes that drive cancer initiation are difficult to detect after the accumulation of other mutations in later stages of the disease.

Douglas Lauffenburger (MIT) is using a different approach that he referred to as “cue-signal-response” analysis, to model pathway dynamics. His lab’s approach relies on principle component analysis and uses partial least-squares discriminant analysis modeling to understand complex signaling networks and how their dysregulation contributes to complex diseases, such as inflammatory bowel disease. This method can be used to investigate how specific kinase mutations or inhibitors affect multiple signaling pathways through crosstalk. In the case of cancer, this computational approach may ultimately enable the identification of effective therapeutic targets on the basis of the genetic profile of the tumor. Diseases under investigation with collaborators in multiple institutions include chronic intestinal inflammation, endometriosis, Alzheimer’s disease, and AIDS.

Related Reading

P. Creixell, E. M. Schoof, J. T. Erler, R. Linding, Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol. 30, 842-848 (2012). [PubMed]

K. S. Lau, V. Cortez-Retamozo, S. R. Philips, M. J. Pitet, D. A. Lauffenburger, K. M. Haigis, Multi-scale in vivo systems analysis reveals the influence of immune cells on TNF-α-induced apoptosis in the intestinal epithelium. PLoS Biol. 10, e1001393 (2012). [PubMed]

Related Resources in Science Signaling

M. L. Miller, L. J. Jensen, F. Diella, C. Jřrgensen, M. Tinti, L. Li, M. Hsiung, S. A. Parker, J. Bordeaux, T. Sicheritz-Ponten, M. Olhovsky, A. Pasculescu, J. Alexander, S. Knapp, N. Blom, P. Bork, S. Li, G. Cesareni, T. Pawson, B. E. Turk, M. B. Yaffe, S. Brunak, R. Linding, Linear Motif Atlas for Phosphorylation-Dependent Signaling. Sci. Signal. 1, ra2 (2008). [Abstract] [Full Text]

C. S. H. Tan, B. Bodenmiller, A. Pasculescu, M. Jovanovic, M. O. Hengartner, C. Jřrgensen, G. D. Bader, R. Aebersold, T. Pawson, R. Linding, Comparative Analysis Reveals Conserved Protein Phosphorylation Networks Implicated in Multiple Diseases. Sci. Signal. 2, ra39 (2009). [Abstract] [Full Text]

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K. S. Lau, A. M. Juchheim, K. R. Cavaliere, S. R. Philips, D. A. Lauffenburger, K. M. Haigis, In vivo systems analysis identifies spatial and temporal aspects of the modulation of TNF-α–induced apoptosis and proliferation by MAPKs. Sci. Signal. 4, ra16. (2011). [Abstract] [Full Text]

N. R. Gough, W. Wong, Focus Issue: The Evolution of Complexity. Sci. Signal. 3, eg5 (2010). [Abstract] [Full Text]

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