Using cancer models to validate drug targets, evaluate drug candidates, and support clinical trial design has been important parts of preclinical studies in cancer drug research. To translate cancer model studies into clinical studies, great efforts have been made to generate a large number of patient derived xenograft (PDX) tumor models in certain cancer types and to demonstrate their similarities to cancer patients in tumor growth, histopathology, tumor complexity, molecular features and drug responses. Recently, focus has been shifted to use cancer model populations to mimic clinical trial design and predict drug responses in clinical trials.

We have developed over 1200 PDX models in multiple cancer types from naive or relapse tumor samples. Genomic profile and hotspot mutation analyses were performed to characterize drug targets and biomarkers used in clinical settings. Chemotherapies such as taxane and platinum, and targeted drugs such as cabozantinib, olaparib or sorafenib were tested at different doses and durations in PDX models such as lung cancer, gastric cancer or liver cancer. Drug response results from different regimens in PDX studies were analyzed by mRECIST method and compared with the corresponding results from clinical trials.

Our results demonstrated that selection of PDX models with histopathology and genetic features matched to the corresponding patient population in clinical trials is important for treatment result prediction. Some widely used doses for chemos in preclinical studies need to be reduced to achieve consistency with clinical results. Longer treatment time and more models than those normally used in preclinical efficacy studies also improve prediction value especially in cancer types with higher heterogeneity. Overall benefits of a targeted drug combined with one chemo over its combination with another chemo can be more accurately reflected in a large PDX population. In contrast PDX models derived from naive patient samples showed not much difference from models derived from chemo resistant tumors in their responses to new targeted treatments. Drugs targeting RAS/RAF signaling, PI3K/AKT signaling or cell cycle showed more uncertainty in PDX models if single biomarkers were used for drug response prediction.

In summary, a sufficient number of PDX models with pathological and molecular features similar to compositions of human cancer patients in clinical trials are necessary for using PDX mouse trial in predicting clinical outcome. Considerations should be given to mouse trial design similar to clinical trial design rather than traditional preclinical studies for targeting validation or proof-of-concept efficacy tests.

Citation Format: Jingjing Jiang, Ying Yan, Tingting Tan, Wei Du, Jiali Gu, Ling Qiu, Katherine Ye, Zhenyu Gu. Considerations in PDX mouse trial design and their relevance to human clinical trial outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2175.