Immunogenicity of murine solid tumor models as a defining feature of in vivo behavior and response to immunotherapy - Lechner, Melissa G; Karimi, Saman S; Barry-Holson, Keegan; Angell, Trevor E; Murphy, Katherine A; Church, Connor H; Ohlfest, John R; Hu, Peisheng; Epstein, Alan L (Journal of immunotherapy (1997), 2013)
Immune profiling has been widely used to probe mechanisms of immune escape in cancer and identify novel targets for therapy. Two emerging uses of immune signatures are to identify likely responders to immunotherapy regimens among individuals with cancer and to understand the variable responses seen among subjects with cancer in immunotherapy trials. Here, the immune profiles of 6 murine solid tumor models (CT26, 4T1, MAD109, RENCA, LLC, and B16) were correlated to tumor regression and survival in response to 2 immunotherapy regimens. Comprehensive profiles for each model were generated using quantitative reverse transcriptase polymerase chain reaction, immunohistochemistry, and flow cytometry techniques, as well as functional studies of suppressor cell populations (regulatory T cells and myeloid-derived suppressor cells), to analyze intratumoral and draining lymphoid tissues. Tumors were stratified as highly or poorly immunogenic, with highly immunogenic tumors showing a significantly greater presence of T-cell costimulatory molecules and immune suppression in the tumor microenvironment. An absence of tumor-infiltrating cytotoxic T lymphocytes and mature dendritic cells was seen across all models. Delayed tumor growth and increased survival with suppressor cell inhibition and tumor-targeted chemokine+/-dendritic cells vaccine immunotherapy were associated with high tumor immunogenicity in these models. Tumor MHC class I expression correlated with the overall tumor immunogenicity level and was a singular marker to predict immunotherapy response with these regimens. By using experimental tumor models as surrogates for human cancers, these studies demonstrate how select features of an immune profile may be utilized to identify patients most likely to respond to immunotherapy regimens.