Abstract 3756: Prediction of xenograft response to MM-121, an anti-ErbB3 inhibitor, using computational modeling and measurements of five biomarkers

Abstract

Abstract One of the challenges faced by targeted therapeutics currently in the clinic is the relatively small population of patients who derive significant benefit from their use. We report the development of a preclinical classifier which can correctly predict xenograft response to MM-121, an anti-ErbB3 antibody, based on the measurement of a few key biomarkers in cell lysates. Deregulation of the ErbB family receptors is common in many cancers. Using a combination of computational modeling and quantitative experiments we identified ErbB3 as a key mediator of mitogenic signaling downstream of the ErbB receptors. Based on these results, we developed MM-121, a first in class anti-ErbB3 monoclonal antibody that blocks heregulin-induced signaling and inhibits tumor growth in multiple xenograft models of human cancer. Here we present our efforts to derive a predictive biomarker signature that identifies tumors that are responsive to MM-121. Using our computational model of the ErbB signaling pathway we identified the five most critical proteins for predicting activation of phospho-AKT - a key mediator of cell survival and apoptosis. These proteins include MM-121's target, ErbB3, and its ligand, heregulin. We profiled these biomarkers in a large panel of cancer cell lines, and using the measured effect of MM-121 on inhibiting tumor growth in eight xenograft tumor models, we determined a classification rule for predicting xenograft response. We subsequently used this classification rule to correctly predict a priori MM-121 response in 11 xenograft models. These results suggest that our computationally-derived biomarker signature is sufficient for predicting response to MM-121 in xenografts, and could offer significant clinical benefit by helping select patients for MM-121 treatment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3756.