Abstract LB-006: Oncology model fidelity scores

Animal models remain a cornerstone of research efforts in Oncology to model the complexity of cancer progression and to discover new therapeutic approaches to disease management. With the advent of new genomic manipulation techniques such as CRISPR, and advances in mouse modeling with genetically engineered mice (GEM) and patient-derived xenografts (PDX), we can expect the development of novel and powerful animal models in the near future. These techniques are expanding our capability for pre-clinical testing of novel therapeutic agents or for n-of-1 patient-specific disease modeling. However, the reliability of animal models to faithfully recapitulate human disease, especially cancer, remains controversial, as evidenced by numerous therapeutic agents that show promise in animal models but fail in clinical trials. We propose a new scoring system called the Oncology Model Fidelity Scores , which allow researchers and clinicians to compare animal models and select those most suited for the question at hand, whether in basic science, for translational research, or within clinical applications. The formalism and tools we are developing for the analysis of mouse models and other model organisms are based on the Hallmarks of Cancer and therapeutic pathways as well as The Cancer Genome Atlas (TCGA) and other comprehensive systems. The scoring system begins with RNASEQ expression data and DNA variation analysis, and is designed for use in mapping animal models both to individual patients and to a TCGA cohort. We are applying this scoring system to mouse models available through NCI’s Oncology Model Forum and we are analyzing how these animal models compare to human cancers based on TCGA data. Ultimately, the development of an Oncology Model Fidelity Score will help to advance patient care through efficient identification and validation of animal models for a variety of applications, from pre-clinical testing of novel therapeutics to the use of patient-specific animal models. Citation Format: Debajyoti Datta, Theodore Goldstein, Zhiping Gu, Atul Butte. Oncology model fidelity scores [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-006. doi:10.1158/1538-7445.AM2017-LB-006