Abstract 3129: Predictive biomarker identification for response to vantictumab (OMP-18R5; anti-Frizzled) using primary patient-derived human pancreatic tumor xenografts
Authors: ZHANG CHUN , Cattaruzza Fiore , Yeung Pete , Yen Wan-Ching , Fischer Marcus , Guo Claire , Brunner Alayne , Wang Min , Cancilla Belinda , Gurney Austin , Brachmann Rainer , Lewicki John , Hoey Tim , Kapoun Ann M. 
Published: July 2016

Abstract
Background: The WNT/ β-catenin signaling pathway has been shown to play a key role 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). This antibody inhibits the growth of several tumor types, including pancreas, breast, colon and lung. Furthermore, our studies showed that vantictumab reduces tumor-initiating cell frequency and exhibits synergistic activity with standard-of-care (SOC) chemotherapeutic agents (Gurney et al., 2012).
Material and methods: We set out to identify a predictive biomarker for the response to vantictumab in pancreatic cancer patients by analyzing mRNA-seq gene expression data from 14 patient-derived xenograft (PDX) models. These 14 minimally passaged pancreatic xenograft tumors were tested in vivo and their responses to vantictumab, in combination with the current SOC gemcitabine and nab-paclitaxel were established. Samples from these experiments were collected for Pharmacodynamic (PD) biomarker analysis. We utilized a two-sample Welch's t-test to identify genes that can distinguish between responders and non-responders and the K-nearest neighbor (KNN, Altman 1992) algorithm for classification. A leave-one-out cross-validation was used to measure area under the ROC curve (Fawcett et al., 2006, AUC), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity of the model. Results: PD biomarker analysis confirmed inhibition of genes in Wnt and stem cell pathways by vantictumab in combination with gemcitabine as well as gemcitabine plus nab-paclitaxel. The selected 3-gene signature comprising TGFB3, IGF2 and SMO achieved the best performance (AUC = 0.875, ACC = 0.93, PPV = 0.91, NPV = 1, sensitivity = 1, specificity = 0.75) in the 14 PDX pancreatic tumor models. In addition, a strong correlation between the gene signature biomarker and the ratio of tumor inhibition (RTI) in the pancreatic xenograft experiments was observed. The identified 3-gene biomarker was used to predict the response to vantictumab in combination with gemcitabine and nab-paclitaxel in three additional pancreatic PDX tumor models. The efficacy in the three models was successfully predicted by the biomarker. Conclusions: The 3-gene biomarker is being evaluated in a Phase 1b study of vantictumab in combination with gemcitabine and nab-paclitaxel in previously untreated stage IV pancreatic cancer (NCT02005315).
Citation Format: CHUN ZHANG, Fiore Cattaruzza, Pete Yeung, Wan-Ching Yen, Marcus Fischer, Claire Guo, Alayne Brunner, Min Wang, Belinda Cancilla, Austin Gurney, Rainer Brachmann, John Lewicki, Tim Hoey, Ann M. Kapoun. Predictive biomarker identification for response to vantictumab (OMP-18R5; anti-Frizzled) using primary patient-derived human pancreatic tumor xenografts. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3129.