Abstract 2667: Molecular profiling guided human PDX tumor model selection for preclinical drug evaluation
Authors: Cai Jie , Song Xiaoming , Yang Mengmeng , Chen Dawei , Deng Jianyun , Li Henry , Chen Yiyou , Wery Jean-Pierre , Chen Taiping 
Published: August 2012

Recent advances in targeted anticancer drug discovery and development has brought about many new promising IND entities to the frontline of preclinical and clinical research. One of the major challenges is to identify and select appropriate patient population for further validation of the new drug efficacy in preclinical and clinical settings. Thus, patient stratification is a critical step in developing personalized cancer therapeutic drugs. This requires comprehensive understanding of genetic background and biomarker profiles of the patients. To meet this highly desired need, we have built up a robust preclinical oncology pharmacology platform, establishoing over 250 human primary tumor xenograft models (HuPrimeTM), including more than 20 common cancer types. All HuPrimeTM models are annotated with clinical information, diagnosis pathology, genome-wide transcription profiling, genome-wide SNP-copy number variation (CNV), hot-spot mutations of key oncogenes, key biomarker IHC information, and responses to standard-of-care (SOC) agent treatment. All the HuPrimeTM model information, along with cancer cell line genomic information, were collected into a proprietary interactive database, HuBaseTM, which offers intuitive navigation control and searchable function, enabling cancer researchers to analyze hundreds of cancer cell lines and HuPrimeTM tumor models for their need of drug development programs. With easily accessible genomic information, HuPrimeTM models or cell lines which express specific target genes of interest can be identified by transcription profiling and gene copy number variation (CNV) status. Biomarkers that are potentially predictive to drug response can then be validated in HuPrimeTM tumor models based on genomic annotation prior to pharmacological studies. Models with appropriate genomic signatures and biomarker information can be selected for more efficient drug efficacy tests with higher success rate. Using the genomic-biomarker guided approaches, we have validated standard-of-care drug responses in HuPrimeTM tumor models that showed significantly elevated expression of molecules involved in drug target related signaling pathways, conducted PD biomarker identification and discovery studies, including biomarkers in various cellular response upon drug treatment.In this presentation, examples of HuPrimeTM tumor models, application of HuBase® data and predictive biomarker IHC analysis in model selection for several targeted therapy agents, including Trastuzumab (Herceptin), Sorafenib, Cetuximab, and Tarceva, will be discussed. We believed that this genomics-biomarker guided approach can greatly improve the effectiveness of preclinical evaluation of targeted drug development.

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 2667. doi:1538-7445.AM2012-2667