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. 2024 Aug 22;27(9):110701.
doi: 10.1016/j.isci.2024.110701. eCollection 2024 Sep 20.

Tumor-associated mesenchymal stromal cells modulate macrophage phagocytosis in stromal-rich colorectal cancer via PD-1 signaling

Affiliations

Tumor-associated mesenchymal stromal cells modulate macrophage phagocytosis in stromal-rich colorectal cancer via PD-1 signaling

Niamh A Leonard et al. iScience. .

Abstract

CMS4 colorectal cancer (CRC), based on the consensus molecular subtype (CMS), stratifies patients with the poorest disease-free survival rates. It is characterized by a strong mesenchymal stromal cell (MSC) signature, wound healing-like inflammation and therapy resistance. We utilized 2D and 3D in vitro, in vivo, and ex vivo models to assess the impact of inflammation and stromal cells on immunosuppression in CMS4 CRC. RNA sequencing data from untreated stage II/III CRC patients showed enriched TNF-α signatures in CMS1 and CMS4 tumors. Secretome from TNF-α treated cancer cells induced an immunomodulatory and chemotactic phenotype in MSC and cancer-associated fibroblasts (CAFs). Macrophages in CRC tumours migrate and preferentially localise in stromal compartment. Inflammatory CRC secretome enhances expression of PD-L1 and CD47 on both human and murine stromal cells. We demonstrate that TNF-α-induced inflammation in CRC suppresses macrophage phagocytosis via stromal cells. We show that stromal cell-mediated suppression of macrophage phagocytosis is mediated in part through PD-1 signaling. These data suggest that re-stratification of CRC by CMS may reveal patient subsets with microsatellite stable tumors, particularly CMS4-like tumors, that may respond to immunotherapies.

Keywords: Cancer; Immunology; Transcriptomics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

None
Graphical abstract
Figure 1
Figure 1
TNF-α signaling in CMS4 tumors alters cancer cell secretory phenotype (A) Transcriptional profiles of stage II/III untreated colon cancer samples (GSE39582) were retrieved and CMS classified (n = 258; CMS1 = 49, CMS2 = 75, CMS3 = 35, CMS4 = 58, unknown = 41). (B) Heatmap of single sample gene set enrichment analysis (ssGSEA) scores of pathways related to TNF-α production and signaling. (C) ssGSEA analysis of pathways related to TNF-α production (top) and signaling (bottom) (Wilcoxon rank-sum test, CMS4 as reference group). (D) Experimental outline for generating tumor cell secretome (TCS) and inflammatory tumor cell secretome (iTCS). CT26 cells, a Balb/c colon cancer cell line, was cultured for 72 h in the presence (iTCS) or absence (TCS) of TNF-α. TNF-α was added 24 h prior to the collection of media to generate iTCS and TCS, respectively (top left). Bioplex analysis of secreted factors CCL2, CCL3, CCL4, CCL5 and G-CSF in the TCS or iTCS from CT26 cells. Error bars, mean ± SEM; ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001 ∗∗∗∗p < 0.0001 by Wilcoxon rank-sum test, CMS4 as reference group (C) or unpaired t-test (D). n = 2–6.
Figure 2
Figure 2
Inflammatory tumor secretome induces MSC immunomodulatory and chemotactic phenotypes (A) Experimental outline of MSC conditioning with TCS and iTCS. (B) Gene set enrichment analysis (GSEA) conducted on RNA-seq data of CT26 TCS and iTCS-treated mMSCs (n = 3). (C) Fragments per kilobase of exon per million mapped fragments (FPKM) values for CCL2, CCL5 and CXCL5 in control and tumor-conditioned MSC groups (n = 3). (D) Transcriptional profiles of laser capture microdissected CRC samples (GSE35602), were analyzed using ConfoundR application (https://confoundr.qub.ac.uk/). (E) Expression heatmap of chemoattractant gene transcripts (CCL2, CCL5 and CXCL5) in the stromal and epithelial compartments of CRC tumors (n = 13) (GSEA35602). (F) KEGG pathway analysis of chemokine signaling pathway. (G) Gene expression levels of CCL2, CCL5, and CXCL5 across CMS subtype (GSE39582) (Wilcoxon rank-sum test, CMS4 as reference group). Error bars, mean ± SD; ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by paired t-test (B) or one-way ANOVA and Tukey post hoc test (C) or Wilcoxon rank-sum test, CMS4 as reference group (G).
Figure 3
Figure 3
MSCs alter the secretory phenotype toward macrophage modulation and tumor promotion in a 3D CMS4-like culture system (A) Experimental outline depicting how the human 3D model of CMS4 CRC was generated by combining CRC cells, human bone marrow-derived MSCs and THP1 monocytes in a GelMA hydrogel for 10 days, with TNF-α addition at day 8 once complex spheroids had formed. (B) Representative scanning electron microscope image of a spheroid isolated from a triple cells 3D culture, scale bar = 50 μm. (C) Representative transmission electron microscope image of cells as part of the multicellular spheroid, scale bar = 2 μm. (D) Confocal microscopy images of Calcein AM (live) and Propidium Iodide (dead) stained gels, 10× magnification, of control and TNF-α treated samples. (E) Percentage cell viability of dissociated spheroids stained with Sytox blue and analyzed by flow cytometry (n = 3) (top). Metabolic activity of the culture systems measured by Alamar blue and displayed as relative fluorescence at day 10 relative to day 1 (n = 4) (bottom). (F) The secretome from the TNF-α treated 3D CRC culture system with or without MSCs was analyzed using a Proteome Profiler Human Cytokine Array Kit. Data from Human Cytokine arrays are expressed as pixel density of spots on the cytokine array membrane (n = 2). (G) Proliferation of HCT116, hMSCs and primary monocytes in GelMA hydrogel +/− TNF-α for 10 days analyzed using CyQuant analysis kit. (H) The secretome from the TNF-α treated 3D CRC culture system with primary monocytes with or without MSCs was analyzed using a Proteome Profiler Human Cytokine Array Kit. Data from Human Cytokine arrays are expressed as pixel density of spots on the cytokine array membrane (n = 2). Error bars, mean ± SD; ∗, p < 0.05, ∗∗, p < 0.01; ∗∗∗, p < 0.001 by two-way ANOVA and Tukey post hoc test (E) or paired t-test (F and H).
Figure 4
Figure 4
Macrophages in CRC tumors migrate and preferentially localise in the stromal compartment and positively correlate with fibroblast score (A) CD68 and CD163 staining of human CRC tissue, scale bar = 50 μm. Quantification of the percentage CD68+ and CD163+ cells in the epithelial or stromal compartment of the tumor (n = 5 patient samples) (below). (B) Pearson’s correlation between cell population estimates for fibroblasts and macrophages provided by MCPcounter and xCell, respectively (GSE39582) (colored by CMS). (C) Experimental outline. Transwell migration assay assessing THP-1 migration toward conditioned media from treated human MSCs. (D) Number of THP-1 cells migrated through the Transwell insert. (E) Experimental outline of murine tumor model. Balb/c mice were injected subcutaneously in the right flank with either CT26 cells alone, CT26 + TCS treated mMSCs or iTCS mMSCs. Tumors were harvested 13 days post-injection. (F) Flow cytometry gating strategy used to analyze single, live, CD45+, CD11b+, MHC-II+/− CD206+/− cells. (G) Percentage of CD11b+MHC-II-CD206+ (top) or CD11b+MHC-II+CD206- (bottom) cells in murine tumors. (H) ssGSEA of pathways related to macrophage activation (left), phagocytosis (middle) and cell surface receptor signaling pathways involved in phagocytosis (right) (GSE39582). Error bars, mean ± SEM; ∗, p < 0.05; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by paired t-test (A), one-way ANOVA and Tukey post hoc test (D and G) or Wilcoxon rank-sum test, CMS4 as reference group (H).
Figure 5
Figure 5
Inflammatory CRC secretome induces phagocytosis inhibitory molecules PD-L1, PD-L2 and CD47 on stromal cells (A) FPKM values for CD47 and CD274 in control and tumour-conditioned MSC groups (n = 3). (B) Expression heatmap of CD47 and CD274 (PD-L1) in the stromal and epithelial compartments of human CRC samples (n = 13) created by ConfoundR (GSE35602). (C) Gene expression of CD47 and CD274 (PD-L1) (GSE39582), grouped according to CMS molecular subtypes. (D) Flow cytometry gating strategy used to analyze single, live stromal cells. (E) CD47 expression analyzed by flow cytometry on Balb/c MSCs and CT26 CRC cells. Data displayed as median fluorescent intensity (MFI) with representative histograms (right) (N = 3). (F) PD-L1 expression analyzed by flow cytometry on Balb/c MSCs and CT26 CRC cells displayed as MFI with representative histograms (right) (n = 3). (G) Experimental outline showing isolation of cancer associated fibroblasts (CAFs) from primary CRC samples. (H) CD47, PD-L1 and PD-L2 expression on CAFs isolated from human CRC tumors, treated with HCT116 TCS or iTCS, analyzed by flow cytometry and displayed as MFI (n = 4 patient samples). Error bars, mean ± SD; ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by one-way ANOVA and Tukey post hoc test (A, E, F, and H) or Wilcoxon rank-sum test, CMS4 as reference group (C).
Figure 6
Figure 6
Association of CCL2, ITGAM and PDCD1 gene expression with relapse-free survival in untreated Stage II/III CRC tumors (A) Transcriptional profiles of stage II/III untreated colon cancer samples (GSE39582) were retrieved, and CMS classified (n = 258; CMS1 = 49, CMS2 = 75, CMS3 = 35, CMS4 = 58, unknown = 41). (B) Survival analysis for CCL2, ITGAM (CD11b) and PDCD1 (PD-1) in the entire cohort (n = 258) (top) or specifically in CMS4 samples (n = 58) (bottom). Kaplan-Meier survival curves showing log rank test p value.
Figure 7
Figure 7
Tumor conditioned stromal cells regulate co-cultured macrophage phenotype (A) Experimental outline of Balb/c-derived BMDM isolation and subsequent culture and experimental setup utilized to co-culture BMDMs and MSCs. (B) Flow cytometry gating strategy used to analyze single, live macrophages following co-culture with MSCs. Flow cytometric analysis of PD-1, SIRPα, MHC-II, PD-L1 and CD206 expression on (C) naive or (D) IFN-γ+LPS activated BMDMs following co-culture with control and tumour-conditioned MSCs (n = 3). (E) Experimental outline of PBMC and CAF isolation and subsequent culture and experimental setup utilized to co-culture human PBMC derived mononuclear cells. (F) Flow cytometry gating strategy used to analyze single, live human mononuclear cells following co-culture with CAFs conditioned by human TCS. (G) Flow cytometric analysis of HLA-DR on co-cultured CD11b (left) CD14 (right) mononuclear cells. (H) Flow cytometric analysis of PD-L1 on co-cultured CD11b (left) CD14 (right) mononuclear cells (n = 4 independent CAF donors). Error bars, mean ± SD; ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by one-way ANOVA and Tukey post hoc test.
Figure 8
Figure 8
Stromal cells suppress macrophage phagocytic capacity by signaling through PD-1 (A) Experimental outline of Balb/c-derived BMDM isolation and subsequent culture and experimental setup utilized for co-culture BMDMs and MSCs. (B) Flow cytometry gating strategy used to analyze single, live macrophages which had taken up the fluorescently labeled (CFSE) cancer cells. (C) Tumor cell uptake was measured as the relative frequency of CD11b macrophages that were positive for CFSE following co-culture with conditioned or control stromal cells (relative to the macrophage alone group) (n = 7–9). (D) Data show relative frequency (relative to macrophages alone) of CFSE-expressing CD11b+ macrophages following internalization of CFSE-labelled cancer cells. Macrophages co-cultured with the iTCS MSCs treated with SIRPα blocking monoclonal antibody. Naive BMDM (Left) and LPS/IFN-γ BMDM (right) or (E) PD-1 blocking monoclonal antibody. Naive BMDM (Left) and LPS/IFN-γ BMDM (right) or relevant isotype control (n = 4–8). Note: for (D) and (A) (left) and (D) and (E) (right) the macrophage alone (Gray bar) and MSCiTCS untreated group (Pink/red bar) show the same data. Error bars, mean ± SD; ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001; ∗∗∗∗, p < 0.0001 by one-way ANOVA and Tukey post hoc test (C, D, and E).

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References

    1. Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 2018;68:394–424. doi: 10.3322/caac.21492. - DOI - PubMed
    1. Siegel R.L., Wagle N.S., Cercek A., Smith R.A., Jemal A. Colorectal cancer statistics, 2023. CA. Cancer J. Clin. 2023;73:233–254. doi: 10.3322/caac.21772. - DOI - PubMed
    1. Arnold M., Sierra M.S., Laversanne M., Soerjomataram I., Jemal A., Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66:683–691. doi: 10.1136/gutjnl-2015-310912. - DOI - PubMed
    1. Chan G.H.J., Chee C.E. Making sense of adjuvant chemotherapy in colorectal cancer. J. Gastrointest. Oncol. 2019;10:1183–1192. doi: 10.21037/jgo.2019.06.03. - DOI - PMC - PubMed
    1. Källberg J., Harrison A., March V., Berzina S., Nemazanyy I., Kepp O., Kroemer G., Mouillet-Richard S., Laurent-Puig P., Taly V., Xiao W. Intratumor heterogeneity and cell secretome promote chemotherapy resistance and progression of colorectal cancer. Cell Death Dis. 2023;14:306. doi: 10.1038/s41419-023-05806-z. - DOI - PMC - PubMed

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