Ovarian Cancer is the number one cause of death among gynecological malignancies and fifth among cancer deaths in women. Advances such as novel chemotherapeutic agents, advanced surgical practice and optimized drug delivery techniques have prolonged the overall survival time in these women, but mortality rates have fluctuated little over the last twenty years.

One of the strategies used to study cancer today is through the use of mass analysis techniques. At the forefront of these techniques is the use of microarrays. Microarrays are platforms that contain information about all of the genes that are turned on or off in specific cancer cells. Determining which genes are turned on in chemotherapy resistant cells for example, would allow researchers to target these genes in order to make them sensitive once again, or to design drugs that might work on alternate gene targets. The advent of array based technology in combination with the completion of the human genome project has enabled researchers to compile tremendous amounts of data on different tumor types. More recently, it has been identified that cancer is more complex than simply a number of genes turned on or off, but rather the answer may lie in collections of genes (metagene signatures) that may be turned on or off together.

The research being performed by our group tackles the problem from several angles. First, specific genes will be sequenced to determine if subtle changes in their sequence combine with specific array patterns identified. The idea here is that whether a group of genes may be turned on or off together might be dependent on subtle changes in these specific genes. The first of these genes we are going to characterize is p53, the most commonly mutated gene in ovarian cancer. The next approach is to use the most up-to-date, sophisticated software to link gene data that has already been collected with groups of genes known to work mechanistically together (i.e. A group of genes that decides whether a cell replicates or not). Finally, once these metagene signatures have been identified, preliminary work will be performed to test cells with drugs against the specific gene collections to determine if a calculable response can be achieved.

Cancer research today has no shortage of data to analyze. The key in the coming years will be to determine how the data fit together creating specific tumor types and their varied characteristics. The research at this institution is at the forefront of this type of analysis and will continue to work at unlocking the key to ovarian cancer genetics.