Credentialing Preclinical Pediatric Xenograft Models Using Gene Expression and Tissue Microarray Analysis

TLDR

Human tumor xenografts have been used for 35 years to rapidly screen anticancer drugs, yet model selection has been empirical and does not reflect the molecular similarity to the tumor of origin. This study aims to provide the first comprehensive transcriptomic analysis of pediatric xenografts used in preclinical drug testing. We profiled the transcriptomes of a large set of pediatric xenografts, created tissue arrays to validate mRNA–protein correlation for key genes, and built a web database to enable rapid target confirmation. The analysis identified xenograft models that faithfully represent their tumor types, showed that primary tumor expression patterns are largely preserved, and the database will aid in discovering predictive markers and new therapeutic targets. Cancer Res 2007;67(1):32–40.

Abstract

Abstract Human tumor xenografts have been used extensively for rapid screening of the efficacy of anticancer drugs for the past 35 years. The selection of appropriate xenograft models for drug testing has been largely empirical and has not incorporated a similarity to the tumor type of origin at the molecular level. This study is the first comprehensive analysis of the transcriptome of a large set of pediatric xenografts, which are currently used for preclinical drug testing. Suitable models representing the tumor type of origin were identified. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. Because a prerequisite for developing rationally designed drugs is that the target is expressed at the protein level, we developed tissue arrays from these xenografts and corroborated that high mRNA levels yielded high protein levels for two tested genes. The web database and availability of tissue arrays will allow for the rapid confirmation of the expression of potential targets at both the mRNA and the protein level for molecularly targeted agents. The database will facilitate the identification of tumor markers predictive of response to tested agents as well as the discovery of new molecular targets. [Cancer Res 2007;67(1):32–40]