Friday, May 3, 2024
5/3/2024
PROJECT SUMMARY Immunotherapy has become one of the central pillars of cancer treatment. The deployment of antibody-based therapies has transformed the lives of thousands of patients. A molecular understanding of how antibodies interact with their targets is invaluable for the development of new successful antibody drugs and immunotherapy products. One of the most important targets for the discovery of new cancer immunotherapies is the mucin family of glycoproteins. Mucin proteins are frequently overexpressed and display altered abnormal glycosylation in several types of adenocarcimona. However, despite the importance of mucin proteins as immunotherapy targets, little is known regarding how antibodies bind these proteins. To address this gap in our knowledge, we propose using a panel of antibodies that bind the mucins MUC1 and MUC16 as models to understand molecular recognition of these important therapeutic targets. Preliminary studies in our lab have demonstrated that glycosylation of MUC1 influences the conformational dynamics of epitopes which in turn influence antibody binding. We will expand our understanding of this phenomenon using a combination of structural biology, computational modeling and binding studies on a panel of MUC1 specific antibodies. Specifically, we aim to determine the role of cancer associated mucin glycosylation in antibody recognition of MUC1 (AIM 1). Previously published results suggest that MUC16 antibodies bind non-linear epitopes localized within the tandem-repeat region of the protein. This region is heavily glycosylated, and the role of MUC16 glycosylation on antibody binding is unknown. Preliminary studies in our lab have determined that a humanized MUC16 antibody binds to an epitope with a SEA domain. Using several therapeutic antibody candidates as models, we propose to employ structural and binding studies to characterize the nature of non-linear MUC16 epitopes recognized by therapeutic antibodies (AIM 2).
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