Skip to main content
NCBI home page
Cancer Cell International logoLink to Cancer Cell International
. 2026 Feb 9;26:153. doi: 10.1186/s12935-026-04200-x

CT26.MtC3, a new preclinical model to investigate immune resistance in metastatic colorectal cancer

Célia Bienassis 1,2,3,4, Jordan Da silva 1, Nora Fournil 1, Yolanda Prezado 2,5,6, Frédérique Megnin-Chanet 3,4, Sébastien Paris 1,✉
PMCID: PMC13049813  PMID: 41664171

Abstract

Preclinical syngeneic tumor models are widely used to evaluate therapeutic responses in immunocompetent hosts, particularly for radiotherapy and radio-immunotherapy. Standard models, such as CT26.WT mouse colorectal carcinoma, have provided valuable insights into treatment efficacy and immune modulation. However, they often fail to reproduce the complexity of advanced human cancers, especially metastases, which differ from primary tumors in immunogenicity, antigen presentation, and tumor microenvironment (TME). Metastatic lesions are typically characterized by poor cytotoxic T cell infiltration and enrichment of immunosuppressive populations such as regulatory T cells and myeloid-derived suppressor cells, limiting the translational relevance of conventional approaches. To better model these features, we developed CT26.MtC3, a novel cell line derived from pulmonary metastases generated by systemic injection of CT26.WT cells into BALB/c mice. When implanted subcutaneously, CT26.MtC3 tumors displayed greater aggressiveness than parental CT26.WT. Immune profiling revealed a strongly immunosuppressive TME, with reduced CD8⁺ T cell infiltration and increased myeloid populations. Complementary in vitro analyses confirmed intrinsic differences, including higher migratory capacity, and significant upregulation of CAECAM-1a. Upon irradiation, CT26.MtC3 cells also showed altered expression of immune-regulatory molecules such as CD47, Fas, PD-L1, and PD-L2, potentially contributing to their immunoevasive phenotype in vivo. Together, these findings establish CT26.MtC3 as a clinically relevant model of metastatic, immune-resistant colorectal cancer. This model provides a robust platform for evaluating strategies to overcome tumor immunoresistance and for rigorously testing the potential of radio-immunotherapy combinations in settings that better reflect clinical reality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-026-04200-x.

Keywords: Metastasis, Radiotherapy, CT26.WT, Mouse model, Tumor microenvironment, CEACAM-1a

Introduction

Preclinical modeling of tumor responses to various therapeutic approaches, such as radiotherapy [1, 2] immunotherapies [3, 4] or combination of both [5, 6], relies heavily on syngeneic murine models [7], in which tumor cell lines derived from the same genetic background as the host are implanted into immunocompetent mice. These models offer a key advantage: they preserve the integrity of the host immune system, enabling the study of interactions between treatment and antitumor immunity, an essential aspect in the era of combination therapies involving immune checkpoint inhibitors and other immunomodulatory agents. CT26.WT [8] have thus become standard platforms for investigating treatment-induced immune responses and therapeutic efficacy. Nevertheless, despite its utility, this syngeneic model fails to fully recapitulate the biological complexity of advanced human colorectal cancers, particularly in the metastatic setting. In patients, metastatic lesions often diverge significantly from the primary tumor in terms of genetic mutations, epigenetic modifications, tumor cell phenotype, and interactions with the microenvironment. Metastases typically exhibit reduced immunogenicity, altered antigen presentation, and a profoundly immunosuppressive microenvironment, characterized by poor infiltration of cytotoxic T cells, enrichment of regulatory immune cells (e.g., Tregs, myeloid-derived suppressor cells), and local production of inhibitory cytokines and metabolic factors [9–11]. This intertumoral heterogeneity, both between primary and metastatic sites and among metastases at different anatomical locations, poses a major challenge for the development of effective therapeutic strategies [12–14]. This is particularly true for approaches aimed at inducing a systemic antitumor immune response, such as radio-immunotherapy combinations, where the goal is not only to control the irradiated tumor but also to trigger immune-mediated regression of non-irradiated lesions, a phenomenon known as the abscopal effect [15, 16]. However, most conventional models fail to mimic this immunological asymmetry between tumors. In such settings, the use of the same cells in both primary and secondary sites may lead to overestimation of the therapeutic efficacy of immune-based treatments. There is a growing need for refined preclinical models that incorporate tumors with distinct immunological profiles to better simulate the clinical reality of metastatic immune evasion and treatment resistance. Developing such models using, for instance, metastasis-derived cell lines with reduced immunogenicity and increased resistance, represents a critical step toward improving the predictive value of preclinical studies. These models allow for a more rigorous assessment of therapeutic strategies designed to overcome immune escape mechanisms, and to evaluate their ability to elicit durable, systemic immune responses across heterogeneous tumor sites. Ultimately, this approach will enhance the translational relevance of preclinical findings and accelerate the clinical advancement of next-generation radio-immunotherapies.

In this study, we describe the generation and characterization of CT26.MtC3, a novel tumor cell line derived from pulmonary metastases of parental CT26.WT colorectal carcinoma cells in BALB/c mice. When injected subcutaneously, CT26.MtC3 cells display a more aggressive phenotype, with higher tumor take. Comparative immunophenotyping of CT26.MtC3 versus CT26.WT tumor revealed a more immunosuppressive microenvironment, marked by lower CD8⁺ T-cell infiltration and increased myeloid cells. In vitro assays further showed that CT26.MtC3 cells differ intrinsically in migration and expression of immune modulatory surface molecules, particularly marked for CEACAM-1a. Together, these features establish CT26.MtC3 as a relevant preclinical model of metastatic, immunoresistant colorectal cancer for testing therapeutic strategies such as immunotherapy and radiotherapy.

Materials and methods

Cells and mice

The CT26.WT cell line (murine colorectal carcinoma) was purchased from the ATCC (CRL-2638). Cells were cultivated according to manufacturer’s recommendations and were controlled for mycoplasma by an independent laboratory (Clean Cells). For in vivo experiments, female BALB/c mice (6 weeks old, Janvier Labs) were housed under specific pathogen-free conditions at Paris Brain Institute (Paris, France). All animal procedures were conducted in accordance with French and European regulations on animal welfare (EU Directive 2010/63/EU). Experimental protocols were reviewed and approved by the local ethics committee and the French Ministry of Research (authorization no. 35858–2022102115492949).

Model development

For the establishment of a metastatic model, CT26.WT cells were suspended in sterile medium and intravenously (i.v.) injected into the tail vein of mice at a concentration of 5.105 cells/100 µL. Two weeks after injection, lungs were collected, and metastatic nodules were dissected under sterile conditions. Pulmonary metastases were placed in culture using DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (Invitrogen). Following expansion, a single clone was selected and i.v. reinjected into the tail vein of mice under the same conditions. This in vivo selection procedure, consisting of harvesting pulmonary metastases, culturing, and reinjection, was repeated for three consecutive cycles. At the end of the process, a stable clone was established and designated CT26.MtC3.

In vivo experiments

For tumor growth analysis and survival experiments, 3.105 CT26.WT or CT26.MtC3 cells were subcutaneously (s.c.) injected into the right flank of mice (n = 12 mice for CT26.WT group and n = 16 mice for CT26.MtC3 group). Tumor length (L) and width (W) were measured two to three times per week using a digital caliper. Tumor volumes were calculated using the formula:

graphic file with name d33e322.gif

The animals were euthanized by cervical dislocation without prior anesthesia as soon as they reached a threshold point comprising tumor volume ≥ 1500 mm³, significant weight loss, or tumor necrosis, or at the end of the experimental protocol.

Flow cytometry analysis of TME

To assess immune cell populations in the tumor microenvironment, 3.10⁵ CT26.WT or CT26.MtC3 (isoT) cells were s.c. injected as previously described. An additional group of mice (isoV) received 3.10⁵ CT26.MtC3 cells, injected 4 days after the initial CT26.WT and CT26.MtC3 (isoT) inoculation. This approach ensured that CT26.MtC3 tumors were harvested at a size comparable to CT26.WT tumors. Each group included five mice. Mice in the CT26.WT and CT26.MtC3 (isoV) groups were euthanized at day 11 and day 15, respectively, once tumor volumes reached 70–130 mm³. In contrast, the CT26.MtC3 (isoT) group was euthanized simultaneously with the CT26.WT group (day 15), regardless of tumor volume. Tumors were excised and were enzymatically dissociated in digestion buffer containing 0.1 mg/mL Liberase (Roche, #5401020001) and 0.1 mg/mL DNase I (Roche, #10104159001). Red blood cells were lysed using RBC lysis buffer (3 min on ice), followed by Fc receptor blockade with TruStain FcX™ PLUS anti-mouse CD16/32 (BioLegend, #156603). Cells were stained with LIVE/DEAD viability dye (1:1000 dilution; Invitrogen, #10083364) and fluorochrome-conjugated antibodies as following: Anti-mouse CD45 (BioLegend, #103108), Anti-mouse CD11b (BioLegend, #101235), Anti-mouse TCR β chain (BioLegend, #109222), Anti-mouse CD335 (NKp46) (BD Biosciences, #568630), Anti-mouse CD8A ((BioLegend, #100758), Anti-mouse CD4 (BD Biosciences, #612843), Anti-mouse CD19 (BioLegend, #115512), Anti-FOXP3 (Invitrogen, #15341190), diluted in staining buffer (PBS supplemented with 2 mM EDTA and 0.5% fetal bovine serum). Fluorochromes and dilutions used are detailed in Supplemental Table 1. For intracellular staining, cells were fixed and permeabilized with the True-Nuclear Transcription Factor Buffer Set (BioLegend, #424401) and subsequently stained with anti-FOXP3 PerCP-Cyanine5.5 (Invitrogen, #45–5773-82). Flow cytometric analysis was performed on an LSRFortessa (BD Biosciences) and data were processed using FlowJo v10.6 (BD Biosciences). A schematic representation of the procedure is provided in Figs. 1, 2A, 3, 4.

Fig. 1.

Fig. 1

Open in a new tab

CT26.MtC3 cells display accelerated growth and aggressiveness compared to CT26.WT. (A) Schematic representation of the in vivo selection cycles used to generate the CT26.MtC3 cell line. (B) Overall tumor growth of CT26.WT tumors (n = 12 mice) and CT26.MtC3 tumors (n = 16 mice). Statistical analysis: Two-way ANOVA. (C) Individual tumor growth curves. (D) Kaplan–Meier survival analysis. Statistical analysis: Mantel-Cox test. (E) Table summarizing the different endpoints and median survival measured during the whole experiment. (F) Comparison of endpoint occurrence over time

Fig. 2.

Fig. 2

Open in a new tab

CT26.MtC3 tumors exhibit a more immunosuppressive microenvironment than CT26.WT. (A) Schematic representation of the subcutaneous injection strategy used to study the tumor microenvironment (TME). (B) Tumor growth volumes (n = 5 mice). Statistical test: One-way Anova. (C) Comparison of immune cell population composition at matched tumor volumes between CT26.WT and CT26.MtC3 tumors. (D) Comparison of immune cell population composition in CT26.MtC3 tumors collected at day 11 (isoV) and day 15 (isoT). Statistical test: Student’s t-test

Fig. 3.

Fig. 3

Open in a new tab

Dynamic differences in immune cell size and granularity distinguish CT26.WT from CT26.MtC3. Comparison of (A) immune cell granularity and (B) size between volume-matched CT26.WT and CT26.MtC3 tumors (isoV), and across CT26.MtC3 tumors at days 11 and 15. Statistical test: One-way ANOVA

Fig. 4.

Fig. 4

Open in a new tab

Comparison in morphological and functional parameters between CT26.WT and CT26.MtC3. (A) Images obtained by optical microscopy, scale bar 80 μm. (B) Comparison of doubling times (h) between CT26.WT and CT26.MtC3. Flow cytometry analysis of (C) cell size (FSC-A), (D) granularity (SSC-A), (E) LysoTracker signal, and (F) MitoTracker signal. Results are expressed as means ± SEM from 3 independent experiments. (G) Comparison of plating efficiency and (H) survival fractions, expressed as percentages ± SEM. Wound healing assay performed at 0 h, 8 h, and 24 h after scratching, (I) with quantification of the scratch area (n = 3) and (J) representative images. Scale bar 500 μm. Statistical test: Student’s t-test

Widefield microscopy and wound healing assay

Bright-field images were acquired using an inverted microscope equipped with a color camera (Primovert, Zeiss; Axiocam 208 Color). For wound healing assays, 5.105 cells were seeded in 6-well plates and grown to ~ 90% confluence. A vertical scratch was introduced at the center of each well, and images were acquired immediately (0 h), 8 h, and 24 h post-scratch. Wound surface area was quantified using the ImageJ/Fiji plugin Wound_healing_size_tool_update [17].

Cell doubling time

Cells were seeded in 6-well plates in triplicate at a density of 1.5.105 cells per well in complete growth medium. At defined time points, the number of viable cells per well was determined using an automated cell counter (Countess 3FL, Invitrogen). Based on the collected data, a cell growth curve has been generated by plotting the number of cells over time to analyze cell proliferation dynamics and to calculate the doubling time using a calculator [18, 19]. Curve fitting equations were used to fit the data to a theoretical curve by directly applying the least squares method to the data. This method was used because it also weighs the data points [20].

In vitro flow cytometry and radiotherapy

For LysoTracker and MitoTracker analysis, 5.105 cells were seeded in 12-well plates. The following day, cells were stained with 75 nM LysoTracker Green DND-26 (Invitrogen, #L7526) for 5 min at room temperature in the dark, followed by MitoTracker Deep Red FM (Invitrogen, #M22426) at a 1:1000 dilution for 30 min. Lysosomal and mitochondrial signals, as well as forward scatter (FSC-A) and side scatter (SSC-A) parameters, were measured by flow cytometry (Accuri C6 Plus, BD Biosciences) to assess organelle staining, cell size, and granularity.

For cell surface biomarker analysis, cells were seeded in 6-well plates and irradiated the following day with 4–6 Gy of X-rays, using a 160 kV X-ray source (CP-160, Faxitron). Twenty-four hours post-irradiation, cells were detached using Accutase and stained with a Zombie Aqua Live/Dead (1:1000 dilution, #423101, Biolegend) for 30 min at 4 °C. Cells were then incubated for 30 min at 4 °C with the following fluorochrome-conjugated antibodies (all from BioLegend, 1:100 dilution): PE anti-mouse CEACAM1a (#134506), FITC anti-mouse CD47 (#127504), PE/Cy7 anti-mouse Fas (#152618), Brilliant Violet 421™ anti-mouse PD-L2 (#107219), APC anti-mouse FasL (#106610), and PerCP/Cy5.5 anti-mouse PD-L1 (#124334). Cells were subsequently fixed with 2% PFA and analyzed by flow cytometry (CytoFLEX S, Beckman Coulter).

Immunofluorescence

Cells were seeded in IBIDI µ-Dish plates. After 24 h, they were fixed with 4% paraformaldehyde for 10 min. Non-specific binding sites were blocked with 10% BSA for 1 h at room temperature. Cells were then incubated overnight at 4 °C with PE-conjugated anti-mouse CEACAM-1a antibody (#134506, Biolegend) diluted 1:100. Mounting was performed using ProLong™ Diamond Antifade Mountant with DAPI (#P36962, Invitrogen). Images were acquired using a Zeiss AXIO Observer.D1 fluorescence microscope, and image processing was carried out with Fiji software (v1.54p).

Clonogenic assay

Cells were seeded in 6-well plates in triplicate at 100–1300 cells well. The following day, cells were exposed to a single dose of X-rays (2, 4, or 6 Gy) or left unirradiated. Cells were then cultured in the presence of 1% penicillin-streptomycin for 9 days at 37 °C in a humidified 5% CO₂ atmosphere to allow colony formation. Colonies were fixed and stained with crystal violet (Sigma-Aldrich). All colonies of ≥ 50 cells were then counted. Surviving Fraction (SF) was calculated as the ratio of the cell/colony count relative to mock-treated cells. Survival curves were generated by fitting the SF to a linear-quadratic model: SF = exp (−αD −βD2), where D is the dose, and α and β adjustable parameters characterizing the radiation response. Data from three independent experiments performed in triplicate were pooled to determine the mean surviving fraction (n = 3).

Statistics

In vitro experiments were independently repeated at least three times. Results are expressed as mean ± SEM. For in vivo studies, the mean tumor volume was calculated for each group and used to generate growth curves. Overall survival was assessed using Kaplan-Meier survival curves and median survival was determined. Statistical analyses of mean tumor growth curves and Kaplan-Meier plots were performed using two-way ANOVA and the log-rank (Mantel-Cox) test, respectively.

Normality of data distribution was assessed using the Shapiro-Wilk normality test. Experiments with normally distributed data were analyzed using one-way ANOVA, two-way ANOVA, or Student’s t-test, depending on the experimental design (as specified in the figure legends). Experiments with non-normal distributions were analyzed using the Mann-Whitney test. A p-value < 0.05 was considered statistically significant. Graphical representations and biostatistical analyses were performed using GraphPad Prism 10® v.10.6.0 (890).

Results

Increased tumorigenicity of CT26.MtC3 cells compared to parental CT26.WT cells

The CT26.MtC3 cell line was established through three successive rounds of in vivo selection for pulmonary metastases, starting from CT26.WT cells injected intravenously, as depicted in Fig. 1A. This iterative selection was intended to enrich subpopulations with enhanced tumorigenic and metastatic potential. We first evaluated CT26.MtC3 tumorigenic potential compared with their parental CT26.WT counterpart (Fig. 1B). In vivo, CT26.MtC3 cells formed tumors more rapidly and consistently than CT26.WT cells, indicating increased aggressiveness of these cells. Indeed, 7 days after subcutaneous injection, 100% of mice (n = 16) injected with CT26.MtC3 cells developed tumors, whereas no tumors were detectable in mice injected with CT26.WT cells (n = 12) (Fig. 1C). This accelerated tumor engraftment was associated with a significant reduction in survival (Fig. 1D), with a median of 24 days for CT26.MtC3-bearing mice versus 28 days for CT26.WT-bearing mice (Fig. 1E). Notably, this difference was not due to intrinsic proliferative capacity, as both cell lines exhibited similar in vitro doubling times (Fig. 4B).

Endpoint analysis highlights distinct progression patterns between CT26.WT and CT26.MtC3 tumors

Considering all endpoint criteria that led to mouse euthanasia (Fig. 1E), a marked difference was observed between the two cell lines. Tumor necrosis was the predominant cause of euthanasia in CT26.MtC3-bearing mice (62.5%), markedly higher than in the CT26.WT group (41.7%), representing an increase of ~ 50%. However, the proportion of animals euthanized due to tumors exceeding the maximal permitted volume was comparable between groups (33% for CT26.WT and 37.5% for CT26.MtC3). Analysis of endpoint kinetics (Fig. 1F) further highlights this difference: in CT26.MtC3-bearing animals, euthanasia criteria were often reached earlier compared to CT26.WT-bearing mice, with necrosis emerging as the predominant initial limiting factor. Notably, these differences occurred in the absence of any significant variation in average body weight between groups (Supplemental Fig. 1). Post-mortem analysis of animals revealed no evidence of spontaneous lung, liver, intestine or rate metastasis in either CT26.WT- or CT26.MtC3-bearing mice, indicating that the differences observed between the two models are confined to primary tumor growth and local progression.

CT26.MtC3 tumors exhibit a more immunosuppressive TME compared to CT26.WT

Given the superior tumor-forming capacity of CT26.MtC3 cells compared to the parental CT26.WT cells, we hypothesized that CT26.MtC3 tumors might harbor a distinct immune cell composition, contributing to a more immunosuppressive tumor microenvironment. To evaluate this, we first compared the abundance of myeloid and lymphoid immune cell populations in CT26.WT and CT26.MtC3 tumors, according to the workflow illustrated in Fig. 2A, at equivalent volume (isoV, Fig. 2B). Because CT26.MtC3 tumors engraftment occur earlier than for CT26.WT, injections of these cells were delayed by 4 days to ensure comparable tumor size at analysis, allowing iso-volume flow cytometry comparison of their tumor microenvironments (Fig. 2C). Our analysis shows that the TME of CT26.WT tumors contains a relatively high proportion of CD45 + immune cells (median, 28.7%±6.31). Although CT26.MtC3 tumors have a higher percentage of immune cells (median, 35.4%±5.42), this increase is not statistically significant (Fig. 2C, immune cells). When examining the proportions of different immune cell types, myeloid cells represent a large fraction of the immune cell population in both tumor lines (Fig. 2C, Myeloid cells). CT26.MtC3 tumors show a higher, but still not significant, proportion of myeloid cells compared to CT26.WT tumors (median, 65.3%±4.25 vs. 58%±7.10, respectively). Despite some variations, there are no significant differences between the two cell lines regarding the populations of NK cells, B cells, CD4 + T cells and Treg. However, we observed a highly significant decrease in the proportion of CD8 + T cells within the immune cell population in CT26.MtC3 tumors (median, 4.6%±1.71) compared to CT26.WT tumors (median, 10.3%±1.29), representing a roughly 2-fold reduction.

To investigate how the immune microenvironment of CT26.MtC3 tumors evolves during tumor progression, we analyzed the composition of immune cell subsets at two distinct timepoints after subcutaneous implantation: day 11 (corresponding to isoV group) and day 15 (corresponding to isoT condition). These timepoints were selected to capture differences between an early growth phase and a more advanced stage of tumor development. As expected, tumor volumes increased significantly between these intervals (100 mm3±19.5 vs. 245 mm3±14.6, for 11 and 15 days, respectively) (Fig. 2B), raising the concern that variations in immune cell frequencies might be confounded by differences in tumor size. To minimize this bias, we normalized all immune cell counts to a fixed tumor volume of 100 mm³. This normalization allowed us to directly compare immune infiltration patterns across conditions and to identify genuine shifts in the tumor immune landscape over time (Fig. 2D). Such an approach is particularly relevant in this model, as temporal changes in immune composition may reflect the transition from an initial immune-responsive state toward a more immunosuppressive microenvironment, which is critical for understanding mechanisms of tumor progression and therapeutic resistance. This analysis demonstrates a pronounced temporal decrease in the proportion of CD45⁺ immune cells within CT26.MtC3 tumors, dropping from 36%±7.7 at day 11 (isoV) to 11%±2.1 at day 15 (isoT), corresponding to an approximate 3.3-fold reduction. Importantly, this decline appears to affect most immune cell subsets, with the notable exception of CD8⁺ T lymphocytes, which remain relatively stable across timepoints.

We next assessed potential changes in immune cell granularity between the two tumor cell lines at day 11, as well as longitudinally between days 11 and 15 for the CT26.MtC3 line (Fig. 3A). Compared with CT26.WT tumors, our analyses revealed an increase in granularity on both day 11 and day 15, reaching statistical significance at the latter timepoint. This tendency was consistent across myeloid and lymphoid lineages, including NK cells, whereas CD4⁺ T cells and Tregs exhibited granularity that remained stable or modestly decreased. Although B cells were scarce within the tumor microenvironment of these tumors, we nevertheless observed a significant reduction in their granularity in CT26.MtC3 tumors compared with CT26.WT.

Finally, we compared the mean size (FSC-A) of immune cells across the different conditions (Fig. 3B). Overall, this parameter remained relatively stable regardless of the context. However, a significant reduction in myeloid cell size was consistently observed at both time points in CT26.MtC3 tumors. In addition, lymphoblasts displayed a transient decrease in size on day 11, followed by a return to baseline levels.

CT26.MtC3 and CT26.WT display distinct cellular characteristics

To further characterize CT26.MtC3 cells vs. CT26.WT and explore whether intrinsic cellular properties could contribute to their enhanced aggressiveness and immunosuppressive profile in vivo, we next analyzed several morphological and functional parameters. At the morphological level, light microscopy examination did not reveal major differences between CT26.WT and CT26.MtC3 cells, as both cell lines appeared similar in overall shape (Fig. 4A). Consistent with these observations, quantitative flow cytometry analysis revealed no significant differences in average cell size (Fig. 4C). The doubling times of the two cell lines were highly comparable, with median values of 20.36 h for CT26.WT and 20.45 h for CT26.MtC3, respectively (Fig. 4B). In contrast, our analyses revealed a significant increase in cell granularity, with mean SSC values elevated by 12.3% ± 1.6 in the CT26.MtC3 line compared to the parental line (Fig. 4D). In addition, CT26.MtC3 cells displayed a significant elevation in lysosomal content, reflected by a 29.4%±12.3 increase in LysoTracker mean fluorescence intensity (MFI), suggesting a potential enrichment in lysosomal activity within these cells (Fig. 4E). In contrast, mitochondrial mass, as assessed by MitoTracker staining, showed a downward trend (− 20.6%±7.7, MFI), although this difference did not reach statistical significance (Fig. 4F).

We then compared the motility of the two cell lines using a wound-healing assay (Fig. 4I and J). Migratory capacity was assessed by quantifying the wound area on microscopy images at 8 h and 24 h post-scratch [17]. The analysis showed that CT26.MtC3 cells closed the wound faster than CT26.WT cells, with differences at both time points, especially evident at 24 h (Fig. 4I). A representative example is shown in Fig. 4J. These results point toward an enhanced motility in CT26.MtC3 cells, consistent with their selection through iterative in vivo passages designed to enrich for metastatic traits.

CT26.MtC3 and CT26.WT exhibit equivalent in vitro response to radiation

We next examined the response of both cell lines to radiotherapy (RT), one of the main therapeutic modalities to treat solid tumors, by clonogenic assay. We first examined plating efficiency (PE), defined as the ability of cells to attach, survive, and form colonies after low-density seeding. PE was comparable between CT26.WT and CT26.MtC3 cells, although CT26.MtC3 showed a slight, non-significant increase of approximately ~ 10%, indicating a similar basal clonogenic potential in vitro (Fig. 4G). Then, to directly test radiosensitivity, we performed clonogenic survival assays following single-fraction irradiation at 2 Gy, 4 Gy, and 6 Gy, or no irradiation (0 Gy, control condition). Both cell lines displayed overlapping survival curves, with no significant difference in colony-forming ability at any dose tested, despite a slight trend for CT26.MtC3 to be more sensitive (Fig. 4H).

Enhanced expression of immunosuppressive surface markers in CT26.MtC3 cells

To determine whether the heightened in vivo aggressiveness of CT26.MtC3 cells and their pro-tumoral tumor microenvironment could be associated with immune evasion, we compared the expression of key immunosuppressive surface markers (CEACAM-1a, CD47, Fas/FasL, PD-L1, and PD-L2) in CT26.WT and CT26.MtC3 cells by flow-cytometry, with or without radiotherapy (Fig. 5A). Our analyses first assessed the basal expression of key immunosuppressive surface markers in untreated cells (0 Gy). In this context, CD47, Fas/FasL, PD-L1, and PD-L2 showed overall comparable expression levels between CT26.WT and CT26.MtC3 cells. In stark contrast, CEACAM-1a was markedly upregulated in CT26.MtC3 cells. At baseline, its expression was increased by approximately 25-fold, relative to the parental CT26.WT cells. A representative flow cytometry profile illustrating this difference is shown in Fig. 5B. This significant difference in CEACAM-1a expression was further confirmed by immunofluorescence microscopy, which revealed a strong enrichment in CT26.MtC3 cells (Fig. 5C).

Fig. 5.

Fig. 5

Open in a new tab

Variation in the expression of immunosuppressive surface markers between CT26.WT and CT26.MtC3. (A) Flow cytometry analysis (n = 3) of CD47, PD-L1, PD-L2, CEACAM-1a, Fas, and FasL expression without irradiation and after irradiation at doses of 4 Gy and 6 Gy. Results are expressed as means ± SEM. (B) Representative evolution of CEACAM-1a signal MFI. (C) Immunofluorescence microscopy analysis of CEACAM-1a signal. Statistical analysis: Two-way ANOVA

We next investigated the effect of radiotherapy on the expression of these immunoregulatory markers (Fig. 5A). If we consider only CT26.WT cells, radiotherapy induced a dose-dependent increase in the expression of all markers, most prominently CD47, PD-L2, and Fas/FasL, and to a lesser extend PD-L1 and CEACAM-1a. In these conditions of irradiation, CT26.MtC3 cells exhibited a trend toward increased expression of several immunosuppressive surface molecules, including CD47, PD-L1, and PD-L2, thereby widening the expression gap relative to CT26.WT cells, although these changes did not reach statistical significance. A modest, non-significant dose-dependent upregulation of CEACAM-1a was also measured in CT26.MtC3 cells, compared to untreated condition. Interestingly, Fas expression increased in a dose-dependent manner in both cell lines, but the magnitude of induction was lower in CT26.MtC3 compared to CT26.WT cells. In the other hand, FasL expression was similarly induced in a dose-dependent fashion in both lines. Collectively, these results indicate that while irradiation triggers the upregulation of multiple immune-regulatory markers in CT26.MtC3 cells, the amplitude and significance of these changes differ from those observed in the parental CT26.WT line.

Discussion

The CT26.MtC3 cell line, established through iterative in vivo selection for enhanced pulmonary metastatic potential. A similar approach was previously employed by Corti et al. [21] to derive the H460M cell line, which exhibits significantly enhanced metastatic potential compared to its parental line, NCI-H460 (human lung carcinoma). To support future radiotherapy experiments, CT26.MtC3 cells were subcutaneously injected to mice. This approach ensures the reproducible irradiation geometry and stable positioning necessary for precise dose delivery—conditions difficult to achieve orthotopically due to surgical complexity and inconsistent tumor takes. Furthermore, it avoids lethal intestinal toxicity. CT26.MtC3 cells clearly exhibits increased aggressiveness in tumor formation and reduced survival in murine models compared to the parental CT26.WT cells. This enhanced tumorigenic phenotype, despite comparable in vitro proliferation rates, suggests that its behavior is driven by factors beyond intrinsic growth rate, including improved resistance to immune surveillance, more efficient early colonization and vascularization, and/or enhanced interactions with the host microenvironment. This possibility is consistent with the generation of metastatic variants through repeated in vivo selection found in other tumor models, such as Lewis lung carcinoma or UCP3 cells, where metastatic sublines display worsened host outcomes [22, 23]. In addition, the predominance of necrosis in CT26.MtC3 tumors suggests that rapid growth outpaces vascular supply, leading to hypoxic stress that not only limits tumor expansion but also fosters a more malignant microenvironment through inflammation, angiogenic signaling, and selection of hypoxia-adapted clones [24].

It is now well established that the TME of metastases differs significantly from that of primary tumors, particularly regarding the immune cell infiltrate [25]. Metastases establish themselves in a distinct environment, which promotes specific immune profiles that contrast with those of the primary tumor. For example, metastatic sites typically show a reduction in CD8+ cytotoxic T lymphocytes and an increase in immunosuppressive populations such as tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), facilitating immune evasion in secondary sites [26]. Furthermore, the metastatic TME adapts to the target tissue, with tumor cells and local microenvironmental components interacting to modulate immune responses and tumor progression specific to that site [27]. Understanding these differences is critical for developing therapeutic approaches, as immunotherapies often need to be tailored to the unique microenvironment of metastases. In this study, we demonstrate that CT26.MtC3 tumors exhibit a markedly distinct immune landscape compared to their parental CT26.WT counterparts, characterized by altered lymphoid and myeloid cell dynamics, temporal remodeling of immune cell infiltration, and changes in cellular granularity and size. The increased granularity (SSC) observed across multiple immune subsets in CT26.MtC3 tumors, including myeloid cells, NK cells, and lymphocytes, suggests greater cytoplasmic complexity associated with activation and effector functionality. High SSC has been linked to terminal effector states, whereas SSC low populations tend to retain a naïve or stem-like profile with superior proliferative potential in immunotherapy settings [28]. The stable or slightly reduced SSC observed in CD4⁺ T cells and Tregs is consistent with their regulatory or quiescent nature, typically lacking abundant lytic granules. However, in the tumor microenvironment, increased granularity among myeloid cells often reflects immunosuppressive phenotypes, while NK-cell granularity may accompany both activation and dysfunctional or exhausted states [29], but further investigations beyond scatter properties are requested to validate this hypothesis. In parallel, the consistent reduction in myeloid cell size (FSC) may reflect metabolic stress or early apoptotic events, while the transient shrinkage of lymphoblasts at day 11 followed by normalization likely corresponds to proliferation and activation cycles induced by tumor-driven cues [30]. These findings collectively suggest that CT26.MtC3 tumors have evolved toward a more suppressive TME, which likely underlies their superior tumor-forming capacity.

One of the most striking observations is the significant reduction in intratumoral CD8⁺ T cell frequencies in CT26.MtC3 tumors relative to CT26.WT. CD8⁺ T lymphocytes are well-established effectors of antitumor immunity and their abundance within tumors strongly correlates with immune-mediated tumor control and improved responses to immunotherapy [31, 32]. The approximate 2.2-fold reduction observed here suggests that CT26.MtC3 tumors acquire mechanisms either to exclude CD8⁺ T cells from the TME or to impair their survival. Such phenomena have been linked to the recruitment of immunosuppressive myeloid populations, including myeloid-derived suppressor cells (MDSCs) and TAMs, which inhibit T cell activity via metabolic depletion, inhibitory cytokines, and immune checkpoint ligands [33, 34]. Alterations in chemokine networks, such as reduced CXCL9/10–CXCR3 signaling, may also contribute to defective T cell trafficking in aggressive tumors [35].

Longitudinal analysis revealed a sharp decline in total CD45⁺ immune cell infiltration in CT26.MtC3 tumors as they progressed from day 11 to day 15, despite tumor volume normalization. This suggests a dynamic process of progressive immune exclusion that is not explained merely by tumor size. Similar reductions in immune content have been described as hallmarks of “immune desert” phenotypes, which are associated with resistance to checkpoint blockade [36]. Stromal remodeling and the accumulation of fibroblasts within the TME have been implicated in such restricted immune access [37], and could represent relevant mechanisms in this context. Importantly, the relative stability of CD8⁺ T cell proportions over time—despite the global immune contraction—suggests that the principal effect of tumor progression in CT26.MtC3 is broad immune cell attrition rather than selective lymphocyte elimination. Interestingly, CT26.MtC3 tumors did not display a significant accumulation of regulatory T cells (Treg), a common hallmark of immunosuppressive environments [38]. This observation argues that in this model, immunosuppression is more likely mediated by the myeloid compartment rather than regulatory T cells, reinforcing evidence that multiple redundant suppressive pathways can operate across different tumor types.

The distinct cellular characteristics observed between CT26.MtC3 and CT26.WT cells provide insights into the mechanisms underlying the enhanced aggressiveness and immunosuppressive phenotype of the CT26.MtC3 variant. Although morphological parameters such as shape, size, and proliferation rates were largely comparable between the two cell lines, the significant increase in cell granularity and lysosomal content in CT26.MtC3 cells suggests altered intracellular trafficking and lysosomal activity, which have been implicated in cancer progression and immune modulation [39]. Lysosomes are not merely degradative organelles but play major roles in nutrient sensing, metabolic adaptation, and secretion of pro-tumorigenic factors, potentially contributing to the immunosuppressive microenvironment observed in vivo [40]. In addition, the trend toward reduced mitochondrial content in CT26.MtC3 cells, although not statistically significant, could reflect metabolic reprogramming, favoring glycolysis over oxidative phosphorylation, a common feature of aggressive and metastatic cancer cells. This metabolic shift supports tumor growth and immune evasion by altering the tumor microenvironment and modulating immune cell function [41, 42]. Furthermore, the observed enhanced motility of CT26.MtC3 cells aligns with data showing that acquisition of migratory and invasive properties is linked to metastatic potential. Increased cell motility entails cytoskeletal reorganization and altered cell–matrix interactions, features often associated with epithelial-to-mesenchymal transition (EMT), which not only promotes metastasis but also influences the immune landscape of tumors. This EMT-associated immunomodulation can facilitate immune escape by creating an immunosuppressive niche [43].

In this study, we demonstrate that CT26.MtC3 cells exhibit a strong upregulation of CEACAM-1a compared with their parental CT26.WT counterparts, while other immunosuppressive markers such as CD47, Fas/FasL, PD-L1, and PD-L2 showed comparable basal expression. This striking induction of CEACAM-1a, suggests that this protein plays a central role in shaping the immune-evasive phenotype of this aggressive tumor subline. The observed 25-fold upregulation of CEACAM-1a in CT26.MtC3 cells may be biologically meaningful, since CEACAM-1 is increasingly recognized as a multifunctional immune checkpoint molecule in cancer. Beyond its role as an adhesion molecule, CEACAM-1 suppresses T-cell activity through binding to TIM-3 and modulates NK cell cytotoxicity, thereby promoting tumor immune escape [44, 45]. Importantly, these findings align with our observation that CEACAM-1a constitutes the most discriminant immunosuppressive marker induced in CT26.MtC3 at baseline. When assessing the impact of radiotherapy, our data show that irradiation induces a global upregulation of immunoregulatory markers in both CT26.WT and CT26.MtC3 cells, albeit with different amplitudes. Interestingly, CEACAM-1a, already high at baseline in CT26.MtC3, was only modestly increased after irradiation. This may reflect a ceiling effect, consistent with the hypothesis that CEACAM-1 expression is already maximized in this highly metastatic subline. By contrast, CD47, PD-L1, and PD-L2 levels were further enhanced after irradiation, confirming previous reports that radiotherapy remodels the tumor immune landscape [46] and can paradoxically favor immunosuppression [47].

From a clinical perspective, the marked enrichment of CEACAM-1a in CT26.MtC3 is particularly relevant to colorectal cancer (CRC) progression and metastasis [48]. Recent cohort studies have provided clinical evidence that increased CEACAM-1 expression in CRC patients correlates with enhanced metastatic dissemination, poor prognosis, and reduced survival [49, 50]. CEACAM-1 expression was detected at significantly higher levels in primary CRC tumors that gave rise to liver metastases, the most common metastatic site in this malignancy. Mechanistically, CEACAM-1 supports epithelial-to-mesenchymal transition (EMT), tumor cell intravasation, and resistance to T-cell–mediated killing [45, 51]. These findings highlight a potential link between our experimental data and the clinical reality of CRC aggressiveness.

As a future development, mutational and transcriptomic analyses of CT26.MtC3, compared to CT26.WT cells, could provide important insights into the mechanisms driving its increased aggressiveness and immunosuppressive capacity. CT26.MtC3 is particularly valuable for studying the molecular basis of this transition as well as for testing therapeutic strategies, including radiotherapy and novel immune checkpoint inhibitors, in the context of a dynamic tumor microenvironment. Nevertheless, some caveats should be considered: although derived from an experimental metastasis, CT26.MtC3 does not generate spontaneous metastases following subcutaneous implantation. This limitation may in part reflect the model’s rapid local tumor growth, which likely precludes sufficient time for metastatic dissemination. Future work assessing orthotopic transplantation could help overcome this limitation and provide a more physiologically relevant framework to investigate both tumor progression and therapeutic responses.

Supplementary Information

Supplementary Material 1 (209.8KB, pdf)

Acknowledgements

The authors warmly thank Charlène Lasgi (Cytometry Platform CYTPIC, CurieCoretech, Institut Curie, 91400 Orsay, France) for her support in flow cytometry experiments.

Author contributions

C.B., J.D.A. and N.F. performed experiments.C.B., J.D.A., N.F. and S.P prepared figures.C.B., S.P., Y.P., F.M.C. and S.P. wrote the main manuscript text.

Funding

No funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

C.B., J.D.A., N.F. and S.P. are employees of Nanobiotix.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Zhang P, Darmon A, Marill J, Mohamed Anesary N, Paris S. Radiotherapy-activated hafnium oxide nanoparticles produce abscopal effect in a mouse colorectal cancer model. Int J Nanomed. 2020;15:3843–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Demaria S, Golden EB, Formenti SC. Role of local radiation therapy in cancer immunotherapy. JAMA Oncol. 2015;1(9):1325–32. [DOI] [PubMed] [Google Scholar]
  • 3.Hu Y, Paris S, Barsoumian H, Abana CO, He K, Sezen D, et al. A radioenhancing nanoparticle mediated immunoradiation improves survival and generates long-term antitumor immune memory in an anti-PD1-resistant murine lung cancer model. J Nanobiotechnology. 2021;19(1):416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Demaria S, Kawashima N, Yang AM, Devitt ML, Babb JS, Allison JP, et al. Immune-mediated inhibition of metastases after treatment with local radiation and CTLA-4 blockade in a mouse model of breast cancer. Clin Cancer Res. 2005;11(2 Pt 1):728–34. [PubMed] [Google Scholar]
  • 5.Hu Y, Paris S, Bertolet G, Barsoumian HB, He K, Sezen D, et al. Combining a nanoparticle-mediated immunoradiotherapy with dual blockade of LAG3 and TIGIT improves the treatment efficacy in anti-PD1 resistant lung cancer. J Nanobiotechnology. 2022;20(1):417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Vanpouille-Box C, Formenti SC, Demaria S. Toward precision radiotherapy for use with immune checkpoint blockers. Clin Cancer Res. 2018;24(2):259–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Mosely SI, Prime JE, Sainson RC, Koopmann JO, Wang DY, Greenawalt DM, et al. Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery. Cancer Immunol Res. 2017;5(1):29–41. [DOI] [PubMed] [Google Scholar]
  • 8.Wang M, Bronte V, Chen PW, Gritz L, Panicali D, Rosenberg SA, et al. Active immunotherapy of cancer with a nonreplicating recombinant fowlpox virus encoding a model tumor-associated antigen. J Immunol. 1995;154(9):4685–92. [PMC free article] [PubMed] [Google Scholar]
  • 9.El-Kenawi A, Hänggi K, Ruffell B. he Immune Microenvironment and Cancer Metastasis. Cold Spring Harb Perspect Med. 2020;10(4):a037424. [DOI] [PMC free article] [PubMed]
  • 10.Liu Y, Cao X. Immunosuppressive cells in tumor immune escape and metastasis. J Mol Med. 2016;94(5):509–22. [DOI] [PubMed] [Google Scholar]
  • 11.Zhang S, Fang W, Zhou S, Zhu D, Chen R, Gao X, et al. Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis. Nat Commun. 2023;14(1):5123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Qin SS, Han BJ, Williams A, Jackson KM, Jewell R, Chacon AC, et al. Intertumoral Genetic Heterogeneity Generates Distinct Tumor Microenvironments in a Novel Murine Synchronous Melanoma Model. Cancers (Basel). 2021;13(10):2293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Lin W, Beasley AB, Ardakani NM, Denisenko E, Calapre L, Jones M, et al. Intra- and intertumoral heterogeneity of liver metastases in a patient with uveal melanoma revealed by single-cell RNA sequencing. Cold Spring Harb Mol Case Stud. 2021;7(5):a006111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Fu YC, Liang SB, Luo M, Wang XP. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell Int. 2025;25(1):103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Janopaul-Naylor JR, Shen Y, Qian DC, Buchwald ZS. The Abscopal Effect: A Review of Pre-Clinical and Clinical Advances. Int J Mol Sci. 2021;22(20):11061. [DOI] [PMC free article] [PubMed]
  • 16.Rodríguez-Ruiz ME, Vanpouille-Box C, Melero I, Formenti SC, Demaria S. Immunological mechanisms responsible for radiation-induced abscopal effect. Trends Immunol. 2018;39(8):644–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Suarez-Arnedo A, Torres Figueroa F, Clavijo C, Arbeláez P, Cruz JC, Muñoz-Camargo C. An image J plugin for the high throughput image analysis of in vitro scratch wound healing assays. PLoS One. 2020;15(7):e0232565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.V. R. Doubling Time computed with several time points. 2006.
  • 19.Roth V. Doubling time computing. 2006.
  • 20.Weisstein EW. Least Squares Fitting–Exponential.
  • 21.Corti C, Pratesi G, DeCesare M, Pellegrini R, Giardini R, Supino R, et al. Spontaneous lung metastases in a human lung tumor xenograft: a new experimental model. J Cancer Res Clin Oncol. 1996;122(3):154–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Brodt P. Characterization of two highly metastatic variants of Lewis lung carcinoma with different organ specificities. Cancer Res. 1986;46(5):2442–8. [PubMed] [Google Scholar]
  • 23.Varki NM, Roome L, Sparkes RS, Miller JE. Microscopic metastasis of a human lung carcinoma cell line in athymic nude mice: isolation of a metastatic variant. Int J Cancer. 1987;40(1):46–52. [DOI] [PubMed] [Google Scholar]
  • 24.Bredholt G, Mannelqvist M, Stefansson IM, Birkeland E, Bø TH, Øyan AM, et al. Tumor necrosis is an important hallmark of aggressive endometrial cancer and associates with hypoxia, angiogenesis and inflammation responses. Oncotarget. 2015;6(37):39676–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kim DH, Ahn JS, Kang M, Park G, Lim Y, Hwang S, et al. Comparison of tumor microenvironments between primary tumors and lymph node metastases in head and neck squamous cell carcinoma and their predictive role in immune checkpoint inhibitor treatment. Cells. 2024. 10.3390/cells13181557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Kitamura T, Qian BZ, Pollard JW. Immune cell promotion of metastasis. Nat Rev Immunol. 2015;15(2):73–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Erez N. Location, location: understanding the metastatic microenvironment. Mol Oncol. 2023;17(9):1695–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wu T, Tan JHL, Sin WX, Luah YH, Tan SY, Goh M, et al. Cell granularity reflects immune cell function and enables selection of lymphocytes with superior attributes for immunotherapy. Adv Sci. 2023;10(28):e2302175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Dean I, Lee CYC, Tuong ZK, Li Z, Tibbitt CA, Willis C, et al. Rapid functional impairment of natural killer cells following tumor entry limits anti-tumor immunity. Nat Commun. 2024;15(1):683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Xiong J, Wang H, Wang Q. Suppressive myeloid cells shape the tumor immune microenvironment. Adv Biol. 2021;5(3):e1900311. [DOI] [PubMed] [Google Scholar]
  • 31.Giles JR, Globig AM, Kaech SM, Wherry EJ. CD8(+) t cells in the cancer-immunity cycle. Immunity. 2023;56(10):2231–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.CCasalegno Garduño R, Spitschak A, Pannek T, Pützer BM. CD8+ T Cell Subsets as Biomarkers for Predicting Checkpoint Therapy Outcomes in Cancer Immunotherapy. Biomedicines. 2025;13(4):930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Gabrilovich DI. Myeloid-derived suppressor cells. Cancer Immunol Res. 2017;5(1):3–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Halama N, Zoernig I, Berthel A, Kahlert C, Klupp F, Suarez-Carmona M, et al. Tumoral immune cell exploitation in colorectal cancer metastases can be targeted effectively by anti-CCR5 therapy in cancer patients. Cancer Cell. 2016;29(4):587–601. [DOI] [PubMed] [Google Scholar]
  • 35.Spranger S, Gajewski TF. Impact of oncogenic pathways on evasion of antitumour immune responses. Nat Rev Cancer. 2018;18(3):139–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–30. [DOI] [PubMed] [Google Scholar]
  • 37.Bonneau C, Eliès A, Kieffer Y, Bourachot B, Ladoire S, Pelon F, et al. A subset of activated fibroblasts is associated with distant relapse in early luminal breast cancer. Breast Cancer Res. 2020;22(1):76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Imianowski CJ, Chen Q, Workman CJ, Vignali DAA. Regulatory T cells in the tumour microenvironment. Nat Rev Cancer. 2025;25(9):703–22. [DOI] [PubMed] [Google Scholar]
  • 39.Machado ER, Annunziata I, van de Vlekkert D, Grosveld GC, d’Azzo A. Lysosomes and cancer progression: a malignant liaison. Front Cell Dev Biol. 2021;9:642494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Chen XQ, Yang Q, Chen WM, Chen ZW, Guo GH, Zhang X, et al. Dual role of lysosome in cancer development and progression. Front Bioscience (Landmark edition). 2024;29(11):393. [DOI] [PubMed] [Google Scholar]
  • 41.Zhang H, Li S, Wang D, Liu S, Xiao T, Gu W, et al. Metabolic reprogramming and immune evasion: the interplay in the tumor microenvironment. Biomark Res. 2024;12(1):96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Ganapathy-Kanniappan S. Linking tumor glycolysis and immune evasion in cancer: emerging concepts and therapeutic opportunities. Biochimica et Biophysica Acta (BBA). 2017;1868(1):212–20. [DOI] [PubMed] [Google Scholar]
  • 43.Yuan J, Yang L, Zhang H, Beeraka NM, Zhang D, Wang Q, et al. Decoding tumor microenvironment: EMT modulation in breast cancer metastasis and therapeutic resistance, and implications of novel immune checkpoint blockers. Biomed Pharmacother. 2024;181:117714. [DOI] [PubMed] [Google Scholar]
  • 44.Dankner M, Gray-Owen SD, Huang YH, Blumberg RS, Beauchemin N. CEACAM1 as a multi-purpose target for cancer immunotherapy. Oncoimmunology. 2017;6(7):e1328336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Huang YH, Zhu C, Kondo Y, Anderson AC, Gandhi A, Russell A, et al. CEACAM1 regulates TIM-3-mediated tolerance and exhaustion. Nature. 2015;517(7534):386–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Lv X, Wang L, Qin S, Jiao Y. Effects and mechanisms of fractionated radiotherapy combined with CD47/PD-L1 inhibitors in the treatment of esophageal cancer. Int J Radiat Oncol Biol Phys. 2024;120(2):e371–2. [Google Scholar]
  • 47.Iliadi C, Verset L, Bouchart C, Martinive P, Van Gestel D, Krayem M. The current understanding of the immune landscape relative to radiotherapy across tumor types. Front Immunol. 2023;14:1148692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Arabzadeh A, Beauchemin N. Stromal CEACAM1 expression regulates colorectal cancer metastasis. Oncoimmunology. 2012;1(7):1205–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ieda J, Yokoyama S, Tamura K, Takifuji K, Hotta T, Matsuda K, et al. Re-expression of CEACAM1 long cytoplasmic domain isoform is associated with invasion and migration of colorectal cancer. Int J Cancer. 2011;129(6):1351–61. [DOI] [PubMed] [Google Scholar]
  • 50.Yamaguchi S, Yokoyama S, Ueno M, Hayami S, Mitani Y, Takeuchi A, et al. CEACAM1 is associated with recurrence after hepatectomy for colorectal liver metastasis. J Surg Res. 2017;220:353–62. [DOI] [PubMed] [Google Scholar]
  • 51.Wicklein D, Otto B, Suling A, Elies E, Lüers G, Lange T, et al. CEACAM1 promotes melanoma metastasis and is involved in the regulation of the EMT associated gene network in melanoma cells. Sci Rep. 2018;8(1):11893. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (209.8KB, pdf)

Data Availability Statement

No datasets were generated or analysed during the current study.

Articles from Cancer Cell International are provided here courtesy of BMC

RESOURCES