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. 2024 Jan 8:14:1320614.
doi: 10.3389/fimmu.2023.1320614. eCollection 2023.

Dissecting tumor microenvironment heterogeneity in syngeneic mouse models: insights on cancer-associated fibroblast phenotypes shaped by infiltrating T cells

Marco Carretta #  1 , Marie-Louise Thorseth #  1 , Aimilia Schina  1 , Dennis Alexander Agardy  1 , Astrid Zedlitz Johansen  1 , Kevin James Baker  1 , Shawez Khan  1 , Anne Mette Askehøj Rømer  1 , Klaire Yixin Fjæstad  1 , Hannes Linder  1 , Dorota Ewa Kuczek  1 , Marco Donia  1 , Lars Grøntved  2 , Daniel Hargbøl Madsen  1   3
Affiliations

Dissecting tumor microenvironment heterogeneity in syngeneic mouse models: insights on cancer-associated fibroblast phenotypes shaped by infiltrating T cells

Marco Carretta et al. Front Immunol. .

Abstract

Murine syngeneic tumor models have been used extensively for cancer research for several decades and have been instrumental in driving the discovery and development of cancer immunotherapies. These tumor models are very simplistic cancer models, but recent reports have, however, indicated that the different inoculated cancer cell lines can lead to the formation of unique tumor microenvironments (TMEs). To gain more knowledge from studies based on syngeneic tumor models, it is essential to obtain an in-depth understanding of the cellular and molecular composition of the TME in the different models. Additionally, other parameters that are important for cancer progression, such as collagen content and mechanical tissue stiffness across syngeneic tumor models have not previously been reported. Here, we compare the TME of tumors derived from six common syngeneic tumor models. Using flow cytometry and transcriptomic analyses, we show that strikingly unique TMEs are formed by the different cancer cell lines. The differences are reflected as changes in abundance and phenotype of myeloid, lymphoid, and stromal cells in the tumors. Gene expression analyses support the different cellular composition of the TMEs and indicate that distinct immunosuppressive mechanisms are employed depending on the tumor model. Cancer-associated fibroblasts (CAFs) also acquire very different phenotypes across the tumor models. These differences include differential expression of genes encoding extracellular matrix (ECM) proteins, matrix metalloproteinases (MMPs), and immunosuppressive factors. The gene expression profiles suggest that CAFs can contribute to the formation of an immunosuppressive TME, and flow cytometry analyses show increased PD-L1 expression by CAFs in the immunogenic tumor models, MC38 and CT26. Comparison with CAF subsets identified in other studies shows that CAFs are skewed towards specific subsets depending on the model. In athymic mice lacking tumor-infiltrating cytotoxic T cells, CAFs express lower levels of PD-L1 and lower levels of fibroblast activation markers. Our data underscores that CAFs can be involved in the formation of an immunosuppressive TME.

Keywords: PD-L1; cancer-associated fibroblasts; immunosuppressive mechanisms; immunotherapy; stroma; syngeneic mouse cancer models; tissue stiffness; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Six commonly used murine syngeneic tumor models show distinct gene expression profiles. (A) Table summarizing the tumor models, cancer type, origin, and rate of ulceration. (B) Principal component analysis (PCA) plot of RNA isolated from tumor fragments derived from the indicated cell lines. C-F) Heatmaps of normalized (Z-score) RNAseq read counts of genes encoding myeloid factors (C), lymphoid factors (D), stromal factors (E), and immunorsuppressive factors (F).
Figure 2
Figure 2
Flow cytometry analysis of the tumor microenvironment unveils different composition of immune populations across tumor models. (A) Histogram summarizing the median abundance (% of live cells) of cancer cells, CAFs, and immune cells; (B-R) PD-L1 median fluorescence intensity (MFI) expression on cancer cells (B), CD45+ cells (C), and FAP+ CAFs (D); percentage of M-MDSCs (E), PMN-MDSCs (F), and dendritic cells (G) out of living cells; percentage of CD103+ dendritic cells out of all dendritic cells (H); percentage of macrophages out of living cells (I); percentage of M2-like macrophages out of all macrophages (J); percentage of CD8+ T cells out of living cells (K); percentage of PD-1+ CD8+ T cells out of all CD8+ T cells (L); percentage of CD4+ T cells out of living cells (M); percentage of PD-1+ CD4+ T cells out of all CD4+ T cells (N); CD4/CD8 ratio (O); percentage of TRegs (P), NK cells (Q), and B cells (R) out of living cells. (A-D) are based on the general flow cytometry panel, (E-J) are based on the myeloid flow cytometry panel, and (K-R) are based on the lymphoid flow cytometry panel.
Figure 3
Figure 3
Transcriptomic analysis of whole tumors shows differences in immune cell composition between models. (A) Absolute amount of immune infiltration across six tumor models based on RNA isolated from tumor fragments analyzed using the CIBERSORT tool. (B) Ratio of lymphoid to myeloid cells based on RNAseq data analyzed using the CIBERSORT tool. (C) Comparison of immune cell population abundancies estimated from CIBERSORT and flow cytometry. (D) Abundancies of specific T cell subsets across the tumor models. (E) Ratio of CD8 to Tregs based on absolute infiltration score.
Figure 4
Figure 4
Relative extracellular matrix stiffness measurements by shear rheology varies profoundly between models. (A) Measurements of mechanical stiffness (storage modulus) for all the tumor models (n = 7-12). (B) Quantification of collagen based on picrosirius red (PSR) staining of paraffin-embedded tissue sections (n = 3-6). (C) Representative images of PSR staining in B16 (left) and Pan02 (right) tumor sections. (D) Correlation between storage modules (stiffness) and percentage of collagen positive area, analyzed by Pearson rank correlation (R = 0.8710). (E) Correlation between percentage of FAP+ CAFs and percentage of collagen positive area analyzed by Pearson rank correlation (R = 0.02857).
Figure 5
Figure 5
Transcriptomic analysis of isolated CAFs reveal model-specific transcriptional programs. (A) 3D PCA analysis based on RNAseq of FACS-isolated CAFs from tumors from five tumor models. (B) Fuzzy clustering analysis of RNA from isolated CAFs. (C-F) Heatmaps of normalized (Z-score) RNAseq read counts of genes encoding collagens (C), core matrisome proteins (D), MMPs (E), and immunosuppressive factors (F). (G, H) Comparison of RNAseq data from isolated CAFs from the indicated models with previously described CAF subsets from murine 4T1 breast cancer42 (G) and murine pancreatic cancer41 (H).
Figure 6
Figure 6
The immunosuppressive phenotype of CAFs is induced by tumor-infiltrating T cells. Flow cytometry analysis of CT26 tumors from athymic BALB/c nude (BALB/c nu/nu) mice (filled circles) and BALB/c mice (empty circles). (A) Percentage of CD8+ T cells out of live cells. (B) Percentage of TAMs (CD11b+F4/80+) out of live cells. (C) Percentage of six immune cell populations out of live cells. (D) Percentage of CAFs (CD45-FAP+) out of live cells. (E) PD-L1 MFI expression of CAFs. n = 6. (F) qRT-PCR analysis of a panel of genes associated with activation or immunosuppression in sorted FAP+ CAFs. Error bars indicate SEM. Statistical analysis was performed by two-tailed Student’s t-test. *** = p ≤ 0.001, ** = p ≤ 0.01, * = p ≤ 0.05, not significant when p > 0.05. In review
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The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Lundbeck Foundation (R307-2018-3326) (DM), the Danish Cancer Society (R231-A14035) (DM), and the Department of Oncology, Copenhagen University Hospital -Herlev & Gentofte.