Biological systems are highly complex and dynamic, and their behavior is very difficult to predict only from knowledge of the individual parts. Any individual changes that irreversibly distort the flow of information in the network could result in pathological conditions and hence give rise to diseases such as cancer. To characterize signaling networks as a system, however, we first need technologies that allow us to rapidly and accurately quantify the abundances and post-translational modification states of many different proteins simultaneously. Working with Dr. Gavin MacBeath, I tailored the protein microarray technology for studying signaling networks in clinical samples.
The following research studies illustrate various applications of protein microarray technology, including the investigation of protein-protein interactions and signaling networks in clinical samples.
My research aims to deepen our understanding of the mechanisms that regulate cancer as well as design a method to predict and provide cancer treatment on an individual basis. The biggest problem in cancer treatment is that one kind of therapy does not work for all. Every patient arrives in the clinic at a different stage and has to be treated according to the stage or progression of cancer. I developed a general strategy to validate lysate microarray technology to accurately and reproducibly quantify the abundances and modification states of multiple proteins in very small clinical specimens. I asked whether array technology can be used to study signaling by receptor tyrosine kinases in extracts prepared from matched normal and cancer tissue from 56 breast patients. My analysis of the breast cancer signaling data shows that I can use this precise characterization of the activity status of several proteins to build “networks” and that each patient's tumor sample involves a distinct “topology”. Network maps can also serve to identify potentially new interactions. Ultimately, this approach has the potential to provide a means to map the connectivity of poorly understood signaling networks, and my findings may ultimately guide efforts to develop individualized therapies for cancer.