Abstract 4524: Characterization of 28 patient derived colorectal cancer xenografts and analysis for response markers to an antiangiogenic treatment

Abstract

Abstract We have recently described an extended panel of new primary human CRC xenografts and their preclinical chemo-sensitivity to anti-cancer drugs (Poster 1584/3 AACR Annual Meeting 2010). These models consider more thoroughly the heterogeneity and genetic instability of tumors in a way ultimately enabling treatment decisions on an individualized basis. The approach is based on large scale screening for activity in new in vivo models combined with extensive characterization of these models (gene expression and mutations). Bioinformatic methods are used to further evaluate the mechanism of action of new drugs in colorectal cancer, to investigate possible resistance mechanisms and provide a rationale for combination therapies. Here we describe the evaluation of an antiangiogenic treatment in this panel of primary patient derived colorectal cancer models. Primary colon carcinoma tissue samples were collected using a standardized procedure. Tumor pieces were transplanted into immunodeficient mice immediately after surgery. A panel of 28 stably passagable colon cancer xenografts could be established as permanent tumor models. The results obtained so far, show that these patient-derived colon cancer models feature a high coincidence with the original tumor regarding histology and gene expression profiling. Response analysis in a subset of 19 xenografts evaluating the treated-to-control (T/C) ratios of tumor volumes revealed that the human specific VEGF antibody bevacizumab induces a meaningful biological effect (T/C below 50%) in 13 of 19 models. A first comparative genome-wide gene expression analyses of this subset revealed reasonable genes and pathways possibly involved in bevacizumab sensitivity. We will extend this investigation to all 28 tumor models in order to enhance the statistical robustness of the preliminary data. Our approach demonstrates that analysis of drug sensitivity together with bioinformatics correlations can provide candidate markers for further studies. An early implementation of such an approach in the drug development process could be helpful for the identification of predictive markers helping to select tumors likely to benefit from the drug as well as provide a rationale for combinations to be tested in clinical trials. These new strategies may help to better integrate research results in clinical development and may increase the success of new anti-cancer drugs in clinical studies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4524. doi:1538-7445.AM2012-4524