High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Novel robust hepatitis C virus mouse efficacy model.

The lack of a robust small-animal model for hepatitis C virus (HCV) has hindered the discovery and development of novel drug treatments for HCV infections. We developed a reproducible and easily accessible xenograft mouse efficacy model in which HCV RNA replication is accurately monitored in vivo by real-time, noninvasive whole-body imaging of gamma-irradiated SCID mice implanted with a mouse-adapted luciferase replicon-containing Huh-7 cell line (T7-11). The model was validated by demonstrating that both a small-molecule NS3/4A protease inhibitor (BILN 2061) and human alpha interferon (IFN-alpha) decreased HCV RNA replication and that treatment withdrawal resulted in a rebound in replication, which paralleled clinical outcomes in humans. We further showed that protease inhibitor and IFN-alpha combination therapy was more effective in reducing HCV RNA replication than treatment with each compound alone and supports testing in humans. This robust mouse efficacy model provides a powerful tool for rapid evaluation of potential anti-HCV compounds in vivo as part of aggressive drug discovery efforts.