Abstract A6: RNAseq and immune profiling analysis of syngeneic mouse models treated with immune checkpoint inhibitors enable biomarker discovery and model selection for cancer immunotherapy

Abstract

Abstract Background: Syngeneic tumor models have long been used in cancer research. Recently, the clinical success of anti-CTLA4 and anti-PD1 antibodies resulted in increased interests in using syngeneic models to evaluate cancer immunotherapeutics. Furthermore, as researchers discovered many of the existing or developing inhibitors of the canonical signaling pathways may interact with immune-environment, thus may synergize with cancer immunotherapies, they are looking for suitable models that may evaluate the combination of those therapies. More importantly, in the clinic it is still unknown why some patients respond to certain immunotherapies while others do not. We set out to utilize syngenetic models to address those questions. Material and methods: Syngeneic cell lines models, such as B16, CT26, MC38, 4T1, were used to evaluate anti-PD1, PD-L1 and CTLA4 antibodies efficacies. Tumors were collected before the treatment for RNAseq analysis. Biomarkers were analyzed with the RNAseq data to predict treatment response to different immune checkpoint inhibitors. Results: Crown has established a large collection of syngeneic models that covers most of the tumor types. Those models have been extensively profiled in vivo using anti-PD1, anti-CTLA4 antibodies, providing information necessary for selecting models and doses for combination therapy. Mostly recently, we have generated detailed maps of the expressional and mutational profiles of those models, as well as identified the alternative gene spliced transcripts, and gene fusion using RNA-seq. Mutational analysis indicated a number of syngeneic models harbor mutations that may be useful for combination studies of targeted and immuno-therapy. Using proprietary algorithms, we have also identified a set of biomarkers that may be useful to predict response to different immunotherapies in mouse models. Conclusions: These data will enable selection of models based on researchers' specific targets for combination studies with immunotherapy. In addition, predictive biomarkers obtained from the analysis may be useful in understanding patient response in the clinic. Citation Format: Lan Zhang, Juan Zhang, Qian Shi. RNAseq and immune profiling analysis of syngeneic mouse models treated with immune checkpoint inhibitors enable biomarker discovery and model selection for cancer immunotherapy. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A6.