Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients.

Abstract

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunotherapy. Currently, patient datasets regarding immunotherapy response are scarce, while ample experiments can be performed on syngeneic mouse tumor models to generate valuable data. Therefore, how to effectively utilize mouse data to identify predictors of immunotherapy response and subsequently transfer relevant knowledge to predict human response to ICIs is a question worth studying. In this study, we propose a novel methodology to address this issue. Firstly, we identify gene modules associated with immunotherapy response from mouse tumor profiles based on cancer gene panels. Subsequently, these identified modules are employed to build prediction models for immunotherapy response based on mouse data. Furthermore, we transfer these models to predict ICIs responses of human cancer patients. Experimental results demonstrate that the gene modules identified from mouse data are reliable predictors of immunotherapy response. The mouse-based models built on these modules could be transferred to humans, effectively predicting drug responses and survival outcomes for cancer patients. Compared to conventional cancer biomarkers and existing prediction models based on mouse data, our method exhibits superior performance. These findings provide a valuable reference for further in-depth research on immunotherapy response prediction model based on mouse tumor profiles, with the potential for transfer applications in human cancer therapy.