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Sci. Signal., 24 September 2013
Vol. 6, Issue 294, p. ra85
Predicting Synergistic Therapies
Identifying oncogenic targets has improved therapeutic outcomes for cancer patients, but cancers notoriously show primary or acquired resistance to single-agent therapies. Using computational modeling derived from cell viability and high-throughput proteomics data, Miller et al. identified several synergistic pairs of targets in dedifferentiated liposarcoma (DDLS). In two patient-derived DDLS cell lines, combined inhibition of CDK4 (cyclin-dependent kinase 4) and IGF1R (insulin-like growth factor 1 receptor) induced a synergistic decrease in cell proliferation by repressing two pathways: that of retinoblastoma by CDK4 inhibitors and that of AKT and mTOR (mammalian target of rapamycin) by IGF1R inhibitors. The findings suggest that dual inhibition of CDK4 and IGF1R may be a treatment strategy for DDLS and that computational modeling may be applied to various cancers to predict improved combination therapies.
Citation: M. L. Miller, E. J. Molinelli, J. S. Nair, T. Sheikh, R. Samy, X. Jing, Q. He, A. Korkut, A. M. Crago, S. Singer, G. K. Schwartz, C. Sander, Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets. Sci. Signal.6, ra85 (2013).
Diane C. Fingar and Ken Inoki (27 March 2012) Sci. Signal.5 (217), pe12.
[DOI: 10.1126/scisignal.2003026] |Abstract »|Full Text »|PDF »
Birgit Schoeberl, Emily A. Pace, Jonathan B. Fitzgerald, Brian D. Harms, Lihui Xu, Lin Nie, Bryan Linggi, Ashish Kalra, Violette Paragas, Raghida Bukhalid, Viara Grantcharova, Neeraj Kohli, Kip A. West, Magdalena Leszczyniecka, Michael J. Feldhaus, Arthur J. Kudla, and Ulrik B. Nielsen (30 June 2009) Sci. Signal.2 (77), ra31.
[DOI: 10.1126/scisignal.2000352] |Editor's Summary »|Abstract »|Full Text »|PDF »|Supplementary Materials »