Tumor-immune profiling of murine syngeneic tumor models as a framework to guide mechanistic studies and predict therapy response in distinct tumor microenvironments

Abstract

Mouse syngeneic tumor models are widely used tools to demonstrate activity of novel anti-cancer immunotherapies. Despite their widespread use, a comprehensive view of their tumor-immune compositions and their relevance to human tumors has only begun to emerge. We propose each model possesses a unique tumor-immune infiltrate profile that can be probed with immunotherapies to inform on anti-tumor mechanisms and treatment strategies in human tumors with similar profiles. In support of this endeavor, we characterized the tumor microenvironment of four commonly used models and demonstrate they encompass a range of immunogenicities, from highly immune infiltrated RENCA tumors to poorly infiltrated B16F10 tumors. Tumor cell lines for each model exhibit different intrinsic factors in vitro that likely influence immune infiltration upon subcutaneous implantation. Similarly, solid tumors in vivo for each model are unique, each enriched in distinct features ranging from pathogen response elements to antigen presentation machinery. As RENCA tumors progress in size, all major T cell populations diminish while myeloid-derived suppressor cells become more enriched, possibly driving immune suppression and tumor progression. In CT26 tumors, CD8 T cells paradoxically increase in density yet are restrained as tumor volume increases. Finally, immunotherapy treatment across these different tumor-immune landscapes segregate into responders and non-responders based on features partially dependent on pre-existing immune infiltrates. Overall, these studies provide an important resource to enhance our translation of syngeneic models to human tumors. Future mechanistic studies paired with this resource will help identify responsive patient populations and improve strategies where immunotherapies are predicted to be ineffective.

Fig 1. Murine tumor cell lines exhibit distinct immune characteristics in vitro and different growth rates post-implantation in vivo.

(A) Murine tumor cell line background, mutational load pre-implantation, and mouse strain inoculation information. (B) RNA-seq analysis of genes involved in MHC-I presentation and chemokines/cytokines for all cell lines prior to implantation. Heatmaps were generated using log2 transformed data, with the low cutoff set at FPKM = 0.01. (I) indicates MHC-I or related genes. (C) Syngeneic tumor growth rates were assessed after reaching 100mm³ using 10 to 11 mice per model. Dashed lines indicate tumor collection points for three tumor sizes (100mm³ pretreatment, 500mm³, and 2000mm³). Error bars represent SEM.

Fig 2. Syngeneic tumors at 100mm³ exhibit different immune cell and complement function profiles based on RNA expression.

Heat map views of basal RNA expression levels from total tumor samples using log2 transformed data. Five tumor samples per model were analyzed. The functions of genes in grey (from the T cell costimulatory and coinhibitory receptors set) are not well defined. Genes highlighted in blue are referred to in the Results section. Filled blue squares represent transcripts from RENCA overexpressed relative to EMT6, while open blue squares represent transcripts from EMT6 overexpressed relative to RENCA (FDR < 0.1). Filled orange circles represent transcripts from RENCA overexpressed relative to CT26, while open orange circles represent transcripts from CT26 overexpressed relative to RENCA (FDR < 0.1). Filled cyan diamonds represent transcripts from EMT6 overexpressed relative to CT26, while open cyan diamonds represent transcripts from CT26 overexpressed relative to EMT6 (FDR < 0.1). Statiscially significant differences with B16F10 are not shown here.

Fig 3. Pretreatment tumor expression profile for genes involved in innate and adaptive immune functions.

Heat map views of total tumor (100mm³) RNA expression using log2 transformed data. Five tumor samples per model were analyzed. Genes highlighted in blue are referred to in the Results section. Filled blue squares represent transcripts from RENCA overexpressed relative to EMT6, while open blue squares represent transcripts from EMT6 overexpressed relative to RENCA (FDR < 0.1). Filled orange circles represent transcripts from RENCA overexpressed relative to CT26, while open orange circles represent transcripts from CT26 overexpressed relative to RENCA (FDR < 0.1). Filled cyan diamonds represent transcripts from EMT6 overexpressed relative to CT26, while open cyan diamonds represent transcripts from CT26 overexpressed relative to EMT6 (FDR < 0.1). Statiscially significant differences with B16F10 are not shown here.

Fig 4. Pretreatment tumor expression profile of genes for cytokines, chemokines, and their cognate receptors.

Heat map views of total tumor (100mm³) RNA expression using log2 transformed data. Five tumor samples per model were analyzed. Genes highlighted in blue are referred to in the Results section. Filled blue squares represent transcripts from RENCA overexpressed relative to EMT6, while open blue squares represent transcripts from EMT6 overexpressed relative to RENCA (FDR < 0.1). Filled orange circles represent transcripts from RENCA overexpressed relative to CT26, while open orange circles represent transcripts from CT26 overexpressed relative to RENCA (FDR < 0.1). Filled cyan diamonds represent transcripts from EMT6 overexpressed relative to CT26, while open cyan diamonds represent transcripts from CT26 overexpressed relative to EMT6 (FDR < 0.1). Statistically significant differences with B16F10 are not shown here.

Fig 5. Flow cytometry analysis of key immune cell populations in 100mm³ tumors stratifies each syngeneic model by immune cell content.

All flow cytometry data are represented as percent total live cells, which include tumor cells and immune infiltrates. (A) Pie charts summarizing the median abundance (% total live cells) of eight different immune populations in 100mm³ tumors from each syngeneic model. Percent values in pie charts refer to “other cells,” which includes tumor and other immune cells not captured in this analysis. Plots showing abundance of (B) T cell populations, (C) NK and B cell populations, and (D) myeloid cell populations in 100mm³ tumors. Medians of each immune population are indicated as bars and these values were used in the pie charts shown in (A). Statistical significance between groups: * 0.01 < p < 0.05, ** 0.001 < p < 0.01, *** p < 0.001.

Fig 6. Flow cytometry analysis of lymphocyte (T/B/NK) changes within the tumor of each model as size increases.

All data are represented as percent total live cells, which include tumor cells as well as immune infiltrates. (A) Pie charts summarizing the median abundance (% total live cells) of only lymphocyte (T, B, NK) populations in RENCA tumors at different sizes. Myeloid populations are excluded from this figure. Percent values in pie charts refer to “other cells,” which includes tumor and other immune cells not captured in this analysis. Plots showing abundance of T, B, and NK populations in (B) RENCA tumors, (C) CT26 tumors, (D) EMT6 tumors, and (E) B16F10 tumors. The green box highlights CD8 T cell increase with tumor volume increase in the CT26 model. Medians of each immune population are indicated as bars. Statistical significance between groups: * 0.01 < p < 0.05, ** 0.001< p < 0.01, *** p < 0.001.

Fig 7. Flow cytometry analysis of myeloid population changes within the tumor of each model as size increases.

All data are represented as percent total live cells, which include tumor cells as well as immune infiltrates. (A) Pie charts summarizing the median abundance (% total live cells) of myeloid populations in RENCA tumors at different sizes. Lymphocyte populations are excluded from this figure. Percent values in pie charts refer to “other cells,” which includes tumor and other immune cells not captured in this analysis. Plots showing abundance of myeloid populations in (B) RENCA tumors, (C) CT26 tumors, (D) EMT6 tumors, and (E) B16F10 tumors. The green box highlights MDSC increase with tumor volume increase in the RENCA model. Medians of each immune population are indicated as bars. Statistical significance between groups: * 0.01 < p < 0.05, ** 0.001 < p < 0.01, *** p < 0.001.

Fig 8. Ki67 staining of tumor samples reveals proliferation at the invasive margin.

IHC was performed on fixed and paraffin embedded tumor samples across the different models and across all tumor sizes. Five mice per model at each tumor size were used for this analysis. A representative image for each is shown.

Fig 9. CD3⁺ cells are confined predominantly to the invasive margin in untreated tumors.

IHC was performed on fixed and paraffin embedded tumor samples across the different models and across all tumor sizes. Five mice per model at each tumor size were used for this analysis. A representative image for each is shown.

Fig 10. Immunotherapy treatment enhances survival in models with distinct tumor microenvironments.

For each model, mice were dosed with either 1μg α-OX40 antibody or 400μg isotype control antibody every three or four days (six doses in total). Ten mice were used per treatment group. Mice were categorized as dead when the tumor volume reached 2000mm³. Statistical significance between groups: * 0.01 < p < 0.05, ** 0.001 < p < 0.01, *** p < 0.001.