Determining the Genetic Mutations that Contribute to the Development of Pancreatic Cancer and Those that Cause It to Spread
Drs. Dominic J. Smiraglia, Norma J. Nowak and Kenneth W. Gross have created what appears to be a remarkably useful new lab model for a rare pancreatic islet cell cancer called glucagonoma. The model was created by deleting genes for two major tumor suppressor pathways specifically within a subset of islet cells that produce glucagon. Pancreatic tumors are the chief cause of mortality in the models experiencing the gene deletion events. The tumors arise at high frequency with several independent cancers forming per pancreas. As the disease progresses, the models demonstrate symptoms and profound metastatic spread to sites representative of the human disease, resulting in death by 5-6 months.
In the human disease the primary metastatic sites are regional lymph nodes and liver, which is mimicked by our model. Not all islets become cancerous in this model. Rather, a limited number of tumors arise, apparently randomly, relative to the total number of glucagon-expressing cells, which have all deleted the two major tumor suppressor genes. This indicates that additional randomly occurring genetic mutations are require for development of the cancer; only in the rare cells where the RIGHT additional mutations have occurred, does the cancer develop.
Furthermore, only a fraction of the primary tumors, about 30%, appear to exhibit metastasis suggesting that additional genetic mutations specific to the metastatic process are required to cooperate with the two tumor suppressor pathways. This model therefore gives us the unprecedented ability to discover what are the additional mutations needed to develop the primary cancer and, perhaps more importantly, what specific mutations are needed for the cancer to progress to metastasis, which is the form of the disease that kills. The current proposal will use state-of-the-art Next Generation Sequencing technology to identify candidate additional mutations occurring within the genome of the cells becoming cancerous and characterize the associated changes in the gene expression pattern.
The analyses will be performed on matched sets of stochastically arising primary tumors and their corresponding liver metastases taking advantage of a novel new fluorescent cell reporter system which will uniquely color tag the cells that are related by descent, allowing correct pairing of the metastases with the primary tumor that spawned them. This approach will increase the power of the pair-wise comparisons between primary and metastatic tumors and should ultimately allow for dissection of multiple molecular routes of metastatic spread.
In sum, this model exhibits unique features which will allow us to foster better understanding of what genes need to become mutated to develop primary tumors and what additional genes contribute to metastatic disease. Such findings will greatly increase our ability to design therapeutic approaches that are ‘smart’ enough to target the novel molecular pathways our study will discover. Moreover, the model will provide an excellent system in which to initially test those new therapies.