This project has as its overall goals the identification of the molecular pathogenesis of lymphoproliferative diseases and the development of objective criteria for lymphoma diagnosis and prognosis, using the specific molecular and immunohistochemical characteristics identified in different lymphoma subtypes. There are several ongoing studies within this project. These include studies of the cell cycle machinery in lymphomas, studies of the molecular genetic features of ALCL and mantle cell lymphoma, and a recent initiative using cDNA and tissue microarray technologies to accelerate the discovery of molecular targets in lymphomas. Over the last year we have focused on investigating abnormalities of the CDK-I p27 in the non-Hodgkin's lymphomas. We reported an inverse correlation between the levels of immunohistochemically detectable p27 and proliferation rate, in the majority of lymphoma subtypes, with the notable exception of mantle cell lymphomas. These lymphomas which are unique their expression of high levels of cyclin D1 as a result of the t(11;14) translocation, show little or no expression of p27 regardless of their proliferation rate. We found that the high levels of cyclin D1 in these lymphomas are responsible for binding and physically sequestering the p27 protein in the cell. As a result of its sequestration p27 protein is rendered non-functional. This appears to be an important mechanism through which high levels of cyclin D1 exert its oncogenic effect. In the past year we have begun to use a combination of cDNA microarrays, tissue microarrays and immunohistochemistry to accelerate the discovery of molecular targets for clinically related purposes. We have identified several unique gene products with diagnostic potential using this approach. In a recently published study, we reported the first diagnostic marker to be identified by gene microarray technology for cancer diagnosis. A protein, clusterin, was found to be expressed only in a specific subtype of lymphoma, so called large cell anaplastic lymphoma. This study illustrates the feasibility of using gene expression array technology not only to identify biologically significant gene expression patterns in lymphomas, but also to accelerate the discovery of molecular markers in cancer, generally.
