Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Lastly, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of pre-immunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti–PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.