Abstract 2830: Predictive biomarker identification for response to vantictumab (OMP-18R5; anti-Frizzled) by mining gene expression data of human breast cancer xenografts

Abstract

Abstract The Wnt/β-catenin signaling pathway has been shown to play key roles in both normal development and tumorigenesis (Polakis, 2007; MacDonald et al., 2009). We have developed a monoclonal antibody, vantictumab, that blocks canonical WNT/β-catenin signaling through binding of five Fzd receptors (1, 2, 5, 7, 8) at a conserved epitope within the extracellular domain. This antibody inhibits the growth of several tumor types, including breast, pancreas, colon and lung. Furthermore, studies also showed that vantictumab reduces tumor-initiating cell frequency and exhibits synergistic activity with standard-of-care chemotherapeutic agents (Gurney et al., 2012). Predictive biomarkers are central to maximizing clinical benefit by targeting breast cancer patients most likely to respond to vantictumab. We analyzed microarray gene expression data from 8 minimally passaged breast cancer xenograft models (mostly triple-negative) with established in vivo responses to vantictumab combined with paclitaxel (4 responders, 4 non-responders). We utilized support vector machine—recursive feature elimination (SVM-RFE, Guyon et al., 2002) to identify genes that can distinguish between responder and non-responders and SVM for classification. Leave-one-out cross-validation was used to measure positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity of the models. The selected 6-gene signature achieved the best performance with PPV=NPV=sensitivity=specificity=100% in the 8 breast cancer models. In addition, we observed a strong correlation between the gene signature biomarker and the ratio of tumor volume (RTV) observed in the breast xenograft experiments. The identified 6-gene biomarker was used to predict the response to vantictumab in combination with paclitaxel in 6 additional, HER2-negative breast cancer xenograft models. The efficacy in all 6 models was predicted successfully by the biomarker. Prevalence data for the biomarker will be presented for both HER2-negative and triple-negative breast cancers. The 6-gene biomarker is currently being evaluated in a Phase 1b study of vantictumab in combination with paclitaxel in patients with locally recurrent or metastatic HER2-negative breast cancer. Citation Format: Chun Zhang, Pete Yeung, Lucia Beviglia, Belinda Cancilla, Tracy Tang, Wan-Ching Yen, Austin Gurney, John Lewicki, Timothy Hoey, Ann M. Kapoun. Predictive biomarker identification for response to vantictumab (OMP-18R5; anti-Frizzled) by mining gene expression data of human breast cancer xenografts. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2830. doi:10.1158/1538-7445.AM2014-2830