A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

The high failure rate of therapeutics showing promise in mouse disease models to translate to patients is a pressing challenge in biomedical science. However, mouse models are a useful tool for evaluating mechanisms of disease and prioritizing novel therapeutic agents for clinical trials. Though retrospective studies have examined the fidelity of mouse models of inflammatory disease to their respective human in vivo conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for prospective inference of disease-associated human in vivo differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human in vivo biology from mouse model datasets. We found that a semi-supervised artificial neural network identified significantly more true human in vivo associations than interpreting mouse experiments directly (95% CI on F-score for mouse experiments [0.090, 0.175], neural network [0.278, 0.375], p = 0.00013). Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches combining mouse and human data for biological inference provides the most accurate assessment of human in vivo disease and therapeutic mechanisms. The task of translating insights from model systems to human disease contexts may therefore be better accomplished by the use of systems modeling driven approaches.