Fox Chase Cancer Center Background
Signal transduction pathways are often extremely complex and have multiple redundancies, with the consequence that resistance to targeted anti-cancer agents can develop through use of alternative pathways. A thorough understanding of the full extent of such signalling networks would enable the identification of potential drug combinations that would prevent the emergence of resistance.
Researchers at Fox Chase Cancer Center (FCCC) have used a network-based bioinformatics methodology, incorporating known pathways and protein interactions around a central core drug target, coupled with a network-guided screening and analysis approach, to identify potential interacting or parallel pathways. This allows a selective, knowledge-driven approach to elucidating potentially complementary approaches to inhibition of complex pathways. The method uses highly enriched sets of gene targets in the form of siRNA to identify targets that have a synergistic effect with a known anticancer drug.
Researchers have developed a method for identifying putative interacting components of complex, redundant pathways, such as those involved in activation and control of cell division. A bioinformatic approach allows selection of a manageable number of probes (e.g. siRNA) to determine the interconnectivity and interaction between potential members of a signal transduction pathway and allows the discovery of new pathway components.
In this way, it is possible to design new combinations of protein-targeted inhibitors that will have increased efficacy and reduce the potential for emergence of drug resistant tumours: a common problem with new, targeted agents such as Gleevec®.
Astsaturov I et al. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci Signal. 2010 Sep 21;3(140):ra67.
The method enables the identification of secondary targets for the development of novel chemotherapeutics, particularly for use in combination with other targeted chemotherapeutic agents to ensure a low probability of emergent resistance.
FCCC researchers have demonstrated the value of this approach by using it to identify novel targets for drugs to be used in combination with anti-EGFR drugs for treatment of cancer. This study identified a number of targets for which inhibition of target genes using siRNA was synergistic (synthetically lethal) with the EGFR inhibitor erlotinib (Tarceva).
Springboarding forward, further network analysis led to the discovery that a known inhibitor of Aurora-A kinase (PHA-680632), was strongly synergistic with erlotinib.
The technology is available for licensing or for partnering/collaboration in drug target identification.
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