Cancer Cell Signaling
The Cancer Cell Signaling Program (SIG) is comprised of investigators dedicated to providing fundamental knowledge of the intra- and inter-cellular signaling pathways that control tumor cell proliferation, migration, and survival and to understanding how this information can be used to improve the diagnosis, prevention, and treatment of cancer. Program members utilize this fundamental knowledge to study tumor cell responses to the microenvironment and to drug treatments, using in vitro cell culture models and in vivo genetically engineered mouse models and xenograft models of individual cancers. Studying cell signaling in the context of pre-clinical cancer models provides relevant translation of cell signaling to the practical context of therapeutic intervention.
SIG is led by David Brautigan, PhD who has extensive experience in cell signaling research and programmatic and administrative leadership. The Program leader catalyze advances in signaling research by organizing retreats to foster new and innovative approaches, dispense “mini” pilot grants to stimulate the development of new ideas and technologies with an emphasis on using Cancer Center Shared Resources, and contribute to the overall intellectual environment of the Cancer Center by participating in seminars, journal clubs, research in progress and graduate and postgraduate education.
The research of the members of SIG is organized around three themes:
- Understanding fundamental properties of cancer cell signaling networks
- Identifying pathways that govern cell responses to the microenvironment
- Defining in vivo systems to study signaling networks and test preclinical therapeutic strategies for cancer treatment
The future goals of SIG are to foster discovery science focused on the complex integration of signaling networks, on how tumors respond to the microenvironment, and how tumors evolve to survive anticancer therapies. In addition, SIG will engage oncologists and surgeons within the program and Cancer Center to seize opportunities for translation of information on signaling networks to clinical applications.