Quantitative Analysis of Myeloid Cell Patterns and Immunosuppressive Enzyme (IDO, ARG1) Expression in Colorectal Cancer Pulmonary Metastases and Corresponding Primary Tumours

Quantitative Analysis of Myeloid Cell Patterns and Immunosuppressive Enzyme (IDO, ARG1) Expression in Colorectal Cancer Pulmonary Metastases and Corresponding Primary Tumours | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantitative Analysis of Myeloid Cell Patterns and Immunosuppressive Enzyme (IDO, ARG1) Expression in Colorectal Cancer Pulmonary Metastases and Corresponding Primary Tumours Topias Karjula, Hanna Elomaa, Sara A. Väyrynen, Vesa-Matti Pohjanen, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7420974/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Indoleamine-2,3-dioxygeanse (IDO) and arginase1 (ARG1) are immunosuppressive enzymes frequently overexpressed in cancer, with higher expression in colorectal cancer primary tumours paradoxically linked to favourable survival. Their significance in metastases remain unexplored. Employing multiplexed immunohistochemistry and supervised machine learning-based digital image analysis, we analysed the IDO and ARG1 expression in monocytic cells, granulocytes, mast-cells and tumour cells in 91 resected pulmonary metastases from 53 corresponding primary tumours. We also evaluated the spatial distribution of IDO + and ARG1 + cells relative to tumour cells and compared these patterns between metastases and primary tumours. In metastases, higher IDO + monocytic cell densities in the tumour centre were associated with favourable survival (adjusted HR 0.17, 95%CI 0.05–0.59, p = 0.005), while higher IDO – monocytic cell densities in the invasive margins were associated with worse survival (adjusted HR 4.19, 95%CI 1.45–2.38, p = 0.008). Moreover, higher densities of IDO + HLA-DR − immature monocytic cells and IDO + FCGR3 + monocytic cells in the invasive margin were also associated with poor survival, after adjusting for T-cell densities (adjusted HRs 26.4, 95%CI 4.95 − 140.42, p < 0.001 and 3.91 95%CI 0.97 − 15.76, p = 0.056, respectively). These findings provide detailed insights into the immunosuppressive myeloid cell landscape in colorectal cancer pulmonary metastases, highlighting key differences from primary tumours and potential implications for therapy development. Biological sciences/Cancer Health sciences/Oncology pulmonary metastases colorectal cancer cancer immunology myeloid cells IDO ARG1 Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Indoleamine 2,3-dioxygenase (IDO) is a cytosolic enzyme involved in the catabolism of tryptophan in the first and rate-limiting step of kynurenine pathway that is frequently overexpressed in cancer [ 1 ]. The catabolism of tryptophan, an essential amino-acid, leads to the functional and proliferative arrest of T-cells in the tumour microenvironment via kynurenine metabolites and tryptophan deprivation [ 2 ]. Another enzyme with immunosuppressive features is arginase-1 (ARG1), catalysing the hydrolysis of L-arginine into immunologically active metabolites [ 3 ]. ARG1 is also overexpressed in several solid tumours [ 4 ]. It takes part in the complex interplay of immunoediting and is associated with poor survival in several solid tumours [ 5 , 6 ]. IDO is expressed in tumour cells, as well as in monocytic cells, endothelial cells, and T-cells [ 6 ], whereas ARG1 is usually expressed in granulocytes [ 7 ]. Given their immunosuppressive nature, ARG1 and IDO have been targets of immunotherapeutic development in recent clinical trials [ 8 – 10 ]. Colorectal cancer (CRC) is one of the most common malignancies globally, ranking as the third leading cause of cancer mortality [ 11 ]. The prognostic significance of IDO and ARG1 expression in CRC have been contradictory [ 12 – 16 ]. This inconsistency may stem from the frequent use of qualitative, single-plex analysis methods that fail to distinguish between cell types contributing to their expression. Additionally, many studies have not differentiated the invasive margin and tumour centre in the analysis, thus overlooking the intricate complexity of the tumour microenvironment (TME). Applying multiplexed immunohistochemistry, we recently demonstrated a positive survival effect for both enzymes in CRC primary tumours [ 17 ]. Of all CRC patients, 15 − 30% have synchronous metastases at the time of diagnosis and around 20% develop metachronous metastases within 5-years following primary tumour resection [ 18 , 19 ]. While the 5-year overall survival of CRC across all stages exceeds 60%, patients with stage IV disease have a 5-year survival of only 14% [ 20 ]. In pulmonary metastases, the myeloid cell distributions and the prognostic impact of the myeloid cell subsets remain largely unexplored. The aim of this study was to evaluate the prognostic effect of myeloid cell densities, monocytic cell maturity (HLA-DR), myeloid cell FCGR3 expression, and immunosuppressive potential (IDO and ARG1) in resected CRC pulmonary metastases with a comparison to primary tumours. We hypothesized that higher expression of IDO and ARG1 would be associated with adverse survival outcomes, and the effect would be dependent on HLA-DR and FCGR3 expression. Additionally, we aimed to evaluate the spatial distribution of the myeloid cells in metastases and primary tumours according to their IDO/ARG1 expression. MATERIAL AND METHODS Study design This retrospective population-based study included all patients who underwent pulmonary metastasectomy for histologically confirmed CRC metastases with curative intent at Oulu University Hospital and Central Finland Central Hospital between 2000 and 2020. These hospitals are the sole providers of thoracic surgical services within their respective regions. Patient identification was based on surgical and pathology records, and relevant clinical data were retrospectively gathered from electronic patient records. Tumour staging was updated to conform to the 8th edition of the Union for International Cancer Control (UICC) tumour-node-metastasis (TNM) classification system [ 21 ]. Survival data, 100% complete through December 31, 2021, were obtained from Statistics Finland. Haematoxylin and eosin (H&E) stained slides of primary tumours and pulmonary metastases were retrieved from pathology archives of Northern Finland Biobank Borealis and Central-Finland Biobank, and reviewed by a pathologist (V-M.P). For cases involving multiple metastatic nodules resected in a single procedure, a representative nodule was arbitrarily selected for further analysis. Digitalization of the slides was performed using either a NanoZoomer XR (Hamamatsu Photonics, Hamamatsu City, Japan) or Aperio AT2 (Leica Biosystems Imaging Inc., Wetzlar, Germany) scanner equipped with a 20× objective. Immunohistochemical screening for DNA mismatch repair (MMR) deficiency (MLH1, MSH2, MSH6 and PMS2) and BRAF V600E mutation status (VE1) was conducted as described previously [ 22 ]. All patients were MMR proficient and only two exhibited BRAF V600E mutations. The densities of CD3 + and CD8 + T-cells in the invasive margin and tumour centre were analysed from whole section slides using methods outlined in prior studies [ 22 ]. A three-tiered immune cell score was generated based on CD3 + and CD8 + T-cell densities, following the principles of the consensus Immunoscore [ 23 ]. Multiplex immunohistochemistry Formalin-fixed paraffin-embedded tissue samples were used to construct tissue microarrays (TMAs) with 1-mm core diameter. Core selection was guided by H&E-stained slides, with two cores taken from the invasive margin and two from the tumour centre for both primary tumours and metastases. TMA-blocks were cut into 3.5-µm sections for staining. A 10-plex multiplex immunohistochemistry assay was designed to characterize myeloid cell densities and maturity and expression of immunosuppressive enzymes. The panel included markers for myeloid cells (ITGAM [CD11b], CD33), monocytic cells (CD14), granulocytes (CEACAM8 [CD66b]), mast cells (TPSAB1 [mast cell tryptase]), and tumour cells (KRT [keratin]), along with HLA-DR (human leukocyte antigen, DR-isotype), FCGR3 (Fcγ receptor 3A, [CD16]), IDO and ARG1. Haematoxylin was used to align cell nuclei. Examples of IDO and ARG1 expression in TMA cores are presented in Figure S1 . Standardized nomenclature were used for protein names [ 24 ]. Antibody optimization and assay validation have been described previously [ 17 ]. Staining was performed sequentially on a Bond-III automated stainer using a Bond Refine Detection kit (DS9800, Leica Biosystems), with 3,3'-dia-minobenzidine replaced with 3-amino-9-ethylcarbazole (AEC). All slides were stained in one batch ensuring uniform staining. The procedure included staining each marker individually, followed by slide scanning, heat-mediated antibody removal, and ethanol-based AEC removal to enable sequential staining of multiple markers. Image analysis Tissue microarray cores were identified and extracted into single-core images using the TMA dearrayer function in QuPath [ 25 ]. Non-representative cores (e.g., those with minimal tumour cell content, necrosis, or procedural damage) were excluded. Representative cores were combined into pseudo-coloured multiplex immunohistochemistry images using Fiji ImageJ [ 26 ]. Staining patterns for each marker in a representative image are presented in Fig. 1 A. The cell detection function in QuPath was used to identify cells, and the object classifier function to identify myeloid cells (ITGAM + /CD33 + ) and further phenotype them into 3 main categories—monocytic cells (CD14 + CEACAM8 − TBSAB1 − KRT − ), granulocytes (CD14 − CEACAM8 + TBSAB1 − KRT − ), and mast cells (CD14 − CEACAM8 − TBSAB1 + KRT − )—with the remaining cells categorized into tumour cells CD14 − CEACAM8 − TBSAB1 − KRT + ) and other cells (CD14 − CEACAM8 − TBSAB1 − KRT − ) (Fig. 1 B) [ 17 ]. For tissue categorization, the pixel classifier function in QuPath was trained to identify tumour epithelium, stroma, and other regions (Fig. 1 B), with other excluded from further analysis. Further data processing, quantification and spatial analysis was performed using packages tidyverse (2.0.0), cutpointr (1.1.2), purrr (1.0.2), ggplot2 (3.4.2), gtsummary (2.0.1), corrplot (0.92), ggpubr (0.6.0), patchwork (1.2.0) spatstat (3.0.5), survival (3.5-5) and surv-miner (0.4.9) in RStudio (version 2024.09.1). Cells were categorized according to their cytoplasmic staining intensities of FCGR3, HLA-DR, IDO and ARG1 by setting fixed cut-off values (positive/negative staining). Cell densities were calculated for each marker in regions of interest, with mean densities used for statistical evaluation. IDO expression in tumour cells was assessed by calculating the percentage of IDO + tumour cells relative to all tumour cells; tumours with less than 1% of IDO + tumour cells were categorized as negative. Myeloid cell densities were categorized into two categories (high vs. low) based on receiver-operating characteristics (ROC) curve analysis. The point closest to (0,1) coordinate was used as cut-point as suggested previous literature [ 27 ]. The density cutoffs based on ROC analysis are presented in Table S1 . To analyse spatial interactions between myeloid cells and tumour cells, we used the spatstat (3.0.5) package to calculate nearest neighbour distances (NNDs) from myeloid cells to their closest neighbour points of specific categories (e.g., tumour cells) (Fig. 2 A-B). To visualize the results, scaled intensities of myeloid cell markers were plotted against NNDs from tumour cells using the ggplot2 (3.3.3) package and generalized additive model smoothing [formula y ~ s(x)] (Fig. 2 C-E). Outcomes and definitions Comorbidity was classified using the Royal College of Surgeons Comorbidity Score (RCSCC) [ 28 ]. Disease-free interval (DFI) was defined as the time between primary tumour resection and pulmonary metastasis detection. None of the patients had a lung-first approach. Pulmonary metastases diagnosed within 6 months post-primary treatment were classified as synchronous, while those detected later were labelled metachronous. The primary endpoint was 5-year overall survival following pulmonary metastasectomy. Given that only one death was unrelated to cancer, cancer-specific survival was not analysed. Tumour regression grade (TRG) following neoadjuvant treatment was assessed by a pathologist (V-M.P) using a modified Dworak grading system [ 29 ]. Statistical analysis Categorical variables were compared using Chi-square or Fisher’s exact tests, while continuous variables were analysed using Student’s T-test or Mann-Whitney U-test. Normality and skewness of data distribution was visually inspected and confirmed with z-scores of skewness values for adequate use of statistical tests. Spearman coefficients were calculated for correlation analyses. Kaplan-Meier curves were used to visualize survival rates, and hazard ratios (HRs) with 95% confidence intervals (CIs) were computed using Cox proportional hazards regression models. The multivariable models were adjusted for sex (male/female), age (continuous variable), comorbidity (RCSCC 1/≥2), neoadjuvant therapy (no/yes), number of pulmonary metastases at diagnosis (1/≥2), size of the largest resected metastasis (continuous variable), former liver metastases (no/yes), synchronicity of pulmonary metastases (synchronous/metachronous), laterality of metastases (unilateral/bilateral), adjuvant therapy (no/yes), and time interval of surgery (2000–2010/2011–2020). The selection of adjustable variables was based on a recent meta-analysis [ 30 ]. Of all adjustable variables, missing values were detected in 7.7% (4 patients) of adjuvant therapy data and were imputed using the mode value (chemotherapy). The Schoenfeld residuals were analysed to ensure the proportionality of hazards assumption; no violations occurred (Figure S2). Patients with an incomplete R1 resection or death within 30 days post-surgery were excluded from survival analysis. All statistical tests were two-tailed and the statistical significance was set at p < 0.05. Statistical analysis was performed using RStudio (version 2024.09.1) and R statistical programming (version 4.0.3). Ethical aspects The Oulu University Hospital Ethics Committee (EETMK 152/2008) approved the study. The Finnish National Authority of Medicolegal Affairs (VALVIRA) waived the need for informed consent due to the retrospective nature of the study (D.no 3916/06.01.03.01/2016). The study was performed in accordance with the Declaration of Helsinki. RESULTS Baseline characteristics This study examined 106 pulmonary metastasectomies performed on 74 patients, including 36 repeat procedures on 21 individuals. The median DFI following primary CRC surgery was 337 days (IQR 0-783). Bilateral pulmonary metastases were observed in 16% of patients (n = 12), and 34% (n = 25) presented with more than one pulmonary metastasis. One patient had microscopic tumour residuals in the surgical margin, and three underwent metastasis enucleation, all classified as R1 resections and excluded from survival analysis. A history of CRC liver metastasis treatment was reported in 43% of patients (n = 33). The median follow-up period was 26.5 months (IQR 18.6-48.48, range 1-209 months). An overall 5-year survival rate was 32%. Neoadjuvant chemotherapy was given to 42% of patients (n = 45) for pulmonary metastases. For primary tumours, preoperative radiotherapy was given to 11% (n = 7), chemotherapy to 4.6% (n = 3), and chemoradiotherapy 23% (n = 15). Post-metastasectomy, only two patients did not receive any adjuvant treatment, while 93% (n = 54) received chemotherapy and 3% (n = 2) underwent radiotherapy. Tumour regression grading revealed minimal regression (TRG = 1) in 94% of neoadjuvant treated pulmonary metastases. Myeloid cell densities Analysis was successful for 291 TMA cores from 91 pulmonary metastases and 153 TMA cores from 54 primary tumours. Myeloid cells were predominantly located in the stromal areas of tumours, with higher densities in the invasive margins than in tumour centres (Table S2). Metastases had higher densities of invasive margin granulocytes and mast cells, as well as higher tumour centre granulocyte densities, compared to the primary tumours (Figure S3). In metastases, monocytic cells in the invasive margin were more mature (HLA-DR + ) compared to those in the tumour centre, a pattern not observed in primary tumours (Table S3). Monocytic cell maturity, based on HLA-DR expression, did not significantly differ between metastatic and primary tumours, though FCGR3 expression in monocytic cells was significantly higher in the invasive margin of metastases ( p < 0.001) (Fig. 3 A). No significant correlations were identified among different myeloid cell lineages or between their densities in primary tumours and metastases (Figures S4 and S5). Neoadjuvant chemotherapy was also not associated with myeloid cell densities or IDO/ARG1 expression of monocytic cells/granulocytes (Table S4). IDO and ARG1 expression and distribution Of all CD14 + monocytic cells, 2.4% expressed IDO in metastases, and 1.8% in primary tumours. 2.9% of tumour cells also expressed IDO in the metastases. The cell density ranges of IDO expressing monocytic cell subtypes in metastases are presented in Figure S6. IDO expression was higher in mature (HLA-DR + 2.6% vs. HLA-DR − 1.6%) and FCGR3 + monocytic cells (FCGR3 + 3.1% vs. FCGR3 − 1.2%) in the metastases, a same pattern also noticed in the primary tumours. In terms of cell densities, HLA-DR + IDO + and FCGR3 + IDO + monocytic cells in the invasive margin in the metastases had the highest densities up to 184 cells/mm 2 and 182 cells/mm 2 , respectively (Figure S6). Densities of IDO expressing immature HLA-DR − monocytic cells ranged from 0 to 63 cells/mm 2 , with a median cell density of 0 cells/mm 2 , indicating their sparse presence. All IDO + monocytic cell subtypes were significantly correlated with each other (invasive margin: r s =0.45–0.95, p < 0.001; Figure S7) and with the proportion of IDO + tumour cells, as well as with CD3 + and CD8 + cell densities. Invasive margin monocytic cell IDO expression was significantly higher in metastases compared to the primary tumours (Fig. 3 B). In spatial analysis, IDO + monocytic cells were located closer to the tumour cells compared to the IDO − counterparts (Fig. 2 E). Most of the granulocytes expressed ARG1 (72.3% in the metastases; 71.5% in the primary tumours). The expression was higher in FCGR3 + granulocytes compared to FCGR3 − granulocytes (Figure S8), with invasive margin median densities of 46 cells/mm 2 and 8 cells/mm 2 , respectively (Table S3, Figure S8). FCGR3 + ARG1 + and FCGR3 − ARG1 + granulocyte subtypes were significantly correlated (invasive margin: r s =0.63, p < 0.001; Figure S7). The granulocyte ARG1 expression and monocytic cell IDO expression, however, were not intercorrelated, suggesting varying mechanisms of immunosuppression in the metastases. ARG1 + granulocytes, irrespective of FCGR3 expression status, had a weaker correlation with CD3 + and CD8 + T-cells compared to IDO + monocytic cells (Figure S7). The invasive margin ARG1 expression was higher in the metastases compared to the primary tumours (Fig. 3 C). In the spatial analysis, ARG1 − granulocytes were located closer to the tumour cells compared to the ARG1 + counterparts (Fig. 2 E). The proportion of IDO + tumour cells was significantly correlated with IDO + monocytic cells in the invasive margin and tumour centre, but not with ARG1 + granulocytes or mast cells (Figure S7). The proportion of IDO + tumour cells also positively correlated with the densities of CD8 + (tumour centre r s =0.49, p < 0.001; invasive margin r s =0.66, p < 0.001, Figure S7) and CD3 + T-cells (tumour centre r s =0.42, p < 0.01; invasive margin r s =0.64, p < 0.001, Figure S7). Survival analysis The prognostic impact of IDO expression differed between the invasive margin and tumour centre. Higher densities of IDO + monocytic cells in the tumour centre were associated with longer 5-year overall survival (low 12% vs. high 63%, p < 0.001, Fig. 4 ; adjusted HR 0.17, 95% CI 0.05–0.59, p = 0.005, Table 1 ). This prognostic association was limited to mature and FCGR3 + monocytic cells (Figure S9, Table S5). Conversely, in the invasive margin, overall IDO + monocytic cell population was not prognostic, but higher densities of immature (HLA-DR − ) IDO + monocytic cells were associated with shorter survival (adjusted HR 5.60, 95% CI 1.70-18.48, p = 0.005, Table S5, Model 2). Similarly, IDO − monocytic cell densities in the invasive margin were associated with poorer outcomes (Figure S10; adjusted HR 4.19, 95% CI 1.45–12.05, p = 0.008, Table 1 ) as did overall monocytic cell densities (Figure S11, adjusted HR 2.87, 95% CI 1.10–7.50, p = 0.031, Table S5). The proportion of IDO expression in the tumour cells was not associated with prognosis (Table 1 ). Table 1 Hazard ratios with 95% confidence intervals for 5-year all-cause mortality according to myeloid cell densities and tumour cell IDO expression. Characteristic Cases Events Univariable HR (95% CI) Multivariable 1 HR (95% CI) Tumour centre 52 30 IDO + monocytic cell density Low 32 24 1 (referent) 1 (referent) High 20 6 0.23 (0.09 − 0.58; p = 0.002) 0.17 (0.05 − 0.59; p = 0.005) IDO − monocytic cell density Low 29 17 1 (referent) 1 (referent) High 23 13 1.02 (0.49 − 2.09; p = 0.966) 1.21 (0.51 − 2.89; p = 0.666) ARG1 + granulocyte density Low 21 11 1 (referent) 1 (referent) High 31 19 1.58 (0.75 − 3.35; p = 0.225) 2.31 (0.89 − 5.96; p = 0.082) ARG1 − granulocyte density Low 26 13 1 (referent) 1 (referent) High 26 17 1.44 (0.70 − 2.99; p = 0.317) 1.59 (0.69 − 3.67; p = 0.281) IDO + tumour cell percentage Low 36 23 1 (referent) 1 (referent) High 16 7 0.43 (0.18 − 1.03; p = 0.057) 0.43 (0.15 − 1.27; p = 0.128) Invasive margin 50 28 IDO + monocytic cell density Low 29 19 1 (referent) 1 (referent) High 21 9 0.59 (0.27 − 1.3; p = 0.19) 1.06 (0.47 − 2.38; p = 0.881) IDO − monocytic cell density Low 29 13 1 (referent) 1 (referent) High 21 15 2.52 (1.19 − 5.34; p = 0.016) 4.19 (1.45 − 12.05; p = 0.008) ARG1 + granulocyte density Low 27 17 1 (referent) 1 (referent) High 23 11 0.88 (0.41 − 1.88; p = 0.74) 1.09 (0.50 − 2.35; p = 0.831) ARG1 − granulocyte density Low 27 18 1 (referent) 1 (referent) High 23 10 0.61 (0.28 − 1.32; p = 0.212) 0.54 (0.21 − 1.39; p = 0.206) IDO + tumour cell percentage Low 29 18 1 (referent) 1 (referent) High 21 10 0.77 (0.35 − 1.66; p = 0.501) 1.01 (0.36 − 2.80; p = 0.987) 1 Cox proportional hazards regression model adjusted for sex (female/male), age (continuous), RCSCC (1/2/≥3), neoadjuvant therapy (no/yes), synchronicity of pulmonary metastases (synchronous/metachronous), size of largest metastasis (continuous), number of pulmonary metastases at diagnosis (1/≥1), former liver metastases (no/yes), adjuvant therapy (no/yes), and 10-year interval (2000–2009/2010–2020). Even after adjusting for CD3 + and CD8 + T-cell density-based immune cell scoring, higher densities of immature (HLA-DR − ) IDO + monocytic cells in the invasive margin remained associated with shorter survival (adjusted HR 26.37, 95%CI 4.95-140.42, p < 0.001, Table S5). Similarly, higher densities of FCGR3 + IDO + monocytic cells, a subtype significantly correlated with HLA-DR − IDO + subtype, also had a suggestive adverse prognostic effect (adjusted HR 3.91, 95% CI 0.97–15.76, p = 0.056, Table S5). In the tumour centre, IDO + monocytic cells remained associated with longer survival (adjusted HR 0.20, 95% CI 0.06–0.69, p = 0.011, Table S5). Overall granulocyte densities, regardless of ARG1 expression, were not significantly associated with 5-year survival (Fig. 4 , Table 1 ). However, a higher density of FCGR3 − ARG1 + granulocyte subset in the tumour centre associated with shorter survival when adjusted for the immune cell score (adjusted HR 2.68, 95% CI 1.05–6.86, p = 0.039; Table S6). This cell subtype exhibited considerable variability in density, ranging from 0 to 525 cells/mm 2 , with a median of 6.21 cells/mm 2 (Figure S8). A similar, though non-significant trend toward poorer survival was observed for for higher densities of ARG1 + granulocytes in the centre of metastasis (adjusted HR 2.30, 95% CI 0.89–5.96, p = 0.087, Table S6). In the sensitivity analysis including only neoadjuvant-naïve patients (n = 41), higher densities of IDO − monocytic cells and immature HLA-DR − IDO + monocytic cells in the invasive margin remained associated with worse survival (adjusted HRs 10.42, 95%CI 2.28–47.57, p = 0.002 and 41.19, 95% CI 2.31-735.46, p = 0.011, respectively; Table S7), whereas in the tumour centre, densities of monocytic cells, irrespective of their IDO expression status, were not significantly associated with survival after adjusting for confounders (Table S7). IDO + FCGR3 + monocytic cells were not associated with survival in the sensitivity analysis, but ARG1 + FCGR3 − granulocytes in the invasive margin and ARG1 + granulocytes in the tumour centre were also associated with worse survival (adjusted HRs 6.63 95% CI 1.10-39.78, p = 0.039, and 10.95, 95%CI 1.69–70.97, p = 0.012, respectively; Table S8). Association with clinical parameters Higher density of IDO + monocytic cells in the tumour centre of metastases was associated with smaller metastasis size and lower number of resected metastases (Table 2 ), whereas higher ARG1 + granulocyte density in the invasive margin of metastases was associated with a history of liver metastasis (Table S9). Table 2 Baseline characteristics of colorectal cancer cases according to IDO + monocytic cell densities in the first resected pulmonary metastasis. Total CD14 + IDO + monocytic cell density (tumour centre) CD14 + IDO + monocytic cell density (invasive margin) Characteristic N = 57 Low N = 34 1 High N = 23 1 p-value 2 Low N = 31 1 High N = 23 1 p-value 2 Sex 0.6 0.6 Female 27 (47%) 15 (44%) 12 (52%) 14 (45%) 12 (52%) Male 30 (53%) 19 (56%) 11 (48%) 17 (55%) 11 (48%) Age 69 (61, 76) 68 (63, 74) 0.8 70 (64, 82) 68 (56, 73) 0.035 RCS Comorbidity Score 3 0.3 0.3 1 34 (60%) 21 (62%) 13 (57%) 19 (61%) 13 (57%) 2 12 (21%) 5 (15%) 7 (30%) 6 (19%) 8 (35%) ≥3 11 (19%) 8 (24%) 3 (13%) 6 (19%) 2 (8.7%) Neoadjuvant chemotherapy > 0.999 0.5 No 35 (61%) 21 (62%) 14 (61%) 19 (61%) 12 (52%) Yes 22 (39%) 13 (38%) 9 (39%) 12 (39%) 11 (48%) Disease stage 0.12 0.15 I-II 22 (39%) 15 (44%) 7 (30%) 11 (35%) 7 (30%) III 23 (40%) 15 (44%) 8 (35%) 15 (48%) 7 (30%) IV 12 (21%) 4 (12%) 8 (35%) 5 (16%) 9 (39%) Primary tumour location 0.14 0.5 Colon 28 (49%) 14 (41%) 14 (61%) 13 (42%) 12 (52%) Rectum 29 (51%) 20 (59%) 9 (39%) 18 (58%) 11 (48%) Disease-free interval (days) 550 (143, 925) 309 (0, 857) 0.3 482 (67, 813) 309 (0, 1,004) 0.4 Size of largest pulmonary metastasis (cm) 2.55 (1.50, 3.60) 1.40 (1.00, 2.50) 0.017 2.30 (1.20, 3.80) 1.62 (1.10, 2.60) 0.3 Former liver metastases 0.6 > 0.999 No 32 (56%) 20 (59%) 12 (52%) 17 (55%) 13 (57%) Yes 25 (44%) 14 (41%) 11 (48%) 14 (45%) 10 (43%) Synchronicity 0.5 0.056 Metachronous 46 (81%) 26 (76%) 20 (87%) 27 (87%) 15 (65%) Synchronous 11 (19%) 8 (24%) 3 (13%) 4 (13%) 8 (35%) No. of pulmonary metastases 0.016 0.2 1 35 (61%) 16 (47%) 19 (83%) 17 (55%) 18 (78%) 2 18 (32%) 14 (41%) 4 (17%) 11 (35%) 4 (17%) ≥3 4 (7%) 4 (12%) 0 (0%) 3 (9.7%) 1 (4.3%) Laterality of metastases 0.2 0.7 Unilateral 46 (81%) 25 (74%) 21 (91%) 25 (81%) 20 (87%) Bilateral 11 (19%) 9 (26%) 2 (8.7%) 6 (19%) 3 (13%) BRAF > 0.999 > 0.999 Mutated 2 (4%) 1 (3.2%) 1 (4.3%) 1 (3%) 1 (5%) Wild-type 52 (96%) 30 (97%) 22 (96%) 29 (97%) 20 (95%) Tumour regression grade > 0.999 > 0.999 Minimal regression 52 (91%) 30 (88%) 22 (96%) 28 (90%) 21 (91%) Moderate regression 4 (7%) 3 (9%) 1 (4%) 2 (7%) 2 (9%) Near complete regression 1 (2%) 1 (3%) 0 (0%) 1 (3%) 0 (0.0%) Complete regression 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 n (%); Median (Q1, Q3) 2 Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test 3 RCS = Royal College of Surgeons DISCUSSION Here, we conducted a comprehensive analysis of myeloid cell densities and IDO and ARG1 expression patterns in resected pulmonary metastases and their corresponding primary tumours. Our findings highlight the differences in myeloid cell profiles between the invasive margin and tumour centre of CRC pulmonary metastases. The invasive margin exhibits higher myeloid cell densities, greater maturity and FCGR3 positivity in monocytic cells, and increased IDO and ARG1 expression compared to the tumour centre. Prognostically, IDO + monocytic cells seem to behave differently in these regions. After adjusting for confounders and T-cell densities, we found that in the invasive margin, higher densities of monocytic cells, including those expressing IDO (HLA-DR − IDO + and FCGR3 + IDO + ) were associated with worse prognosis. Conversely, in the tumour centre, IDO + monocytic cells (especially mature HLA-DR + and FCGR3 + ) were associated with better prognosis. In neoadjuvant naïve patients, however, IDO expression had a negative prognostic impact only in immature monocytic cells in the invasive margin. In the CEACAM8 + granulocytic lineage, higher densities of FCGR3 − ARG1 + cells in the tumour centre (and higher densities of FCGR3 − ARG1 + cells in the invasive margin of neoadjuvant-naïve patients) were independently associated with worse survival, suggesting a potential role for ARG1 in adverse outcomes. Few studies have examined the expression of immunosuppressive enzymes ARG1 and IDO at the single-cell level in specific cell types within the tumour microenvironment. Most evidence on the prognostic value of IDO expression derives from semiquantitative analysis without distinguishing between tumour regions or cell types [ 13 – 15 , 31 – 35 ]. Some studies have associated IDO with favourable outcomes [ 14 , 15 , 31 – 33 , 35 ], while others link it to poor survival [ 13 , 34 ]. In studies that distinguish between the invasive margin and tumour centre, a TMA-based single-marker IHC study on stage I-III CRC demonstrated a negative survival effect of IDO expression in the invasive margin, but not in the tumour centre [ 12 ], which was also demonstrated in melanoma and lung cancer [ 36 , 37 ]. Similarly, a recent multiplexed immunofluorescence study on lung cancer showed that higher IDO expression in peritumoural tertiary lymphoid structures was associated with worse survival [ 38 ], whereas higher IDO expression in tumour cells was associated with better survival. On the other hand, a recent TMA-based study on oesophageal cancer demonstrated higher IDO expression in the invasive margin being associated with better survival, the difference being more significant in lower T-stage tumours [ 39 ]. Concerning CRC, we previously published a comprehensive multiplexed IHC analysis on the myeloid cell densities in a cohort of primary tumours [ 17 ], and found that higher IDO + monocytic cell densities in both the invasive margin and the tumour centre were independently associated with favourable disease-specific survival, the association being stronger in stage I-III primary tumours compared to stage IV tumours. Higher ARG1 + granulocyte density was similarly associated with better survival in both the invasive margin and tumour centre, and the association was limited to stage I-III tumours. Supporting the notion of a weakening favourable survival effect of IDO expression alongside tumour progression, possibly developing towards a more negative survival effect in the invasive margin, a recent study incorporating single-cell sequencing and spatial transcriptomics in stage I-III CRC primary tumours revealed a stronger immunosuppressive milieu in the epithelial-to-mesenchymal transition (EMT) pseudotime-endpoint trajectories [ 40 ], suggesting that the invasive margin—frequent site for EMT—might have a more immunosuppressive milieu compared to the tumour centre. EMT marker expression between metastases and primary tumours is also proposed to differ during metastatic dissemination [ 41 ] and thus EMT might partly explain the prognostic differences of IDO/ARG1 in tumour types noted in our study. Additionally, myeloid derived suppressor cells and monocytes have a role in the immunosuppressive premetastatic niche formation even before the arrival of circulating tumour cells [ 42 ], which could also account for the prognostic differences between the IDO and ARG1 expression in metastases and primary tumours [ 17 ]. The strong correlation between CD8 + T-cell and IDO + monocytic cell densities in the metastases in our study—noted also in the primary tumours—may appear contradictory, given the immunosuppressive nature of IDO. However, it aligns with a recent TCGA-based RNA sequencing study demonstrating a strong positive correlation between IDO1 expression and CD8A expression (a CD8 + T-cell biomarker) in most cancer types [ 43 ]. Functional assays also show the immunosuppressive mechanisms of IDO being mediated through TCR ζ-chain depletion and T-cell exhaustion, along with a reduction T-cells densities [ 2 ]. Similarly, the expression of other immune checkpoint molecules is generally positively correlated with CD8 + T-cells densities [ 44 , 45 ]. However, the prognostic differences of IDO between the primary tumours and metastases still remain unclear. Several studies report significant differences in immune composition not only between primary tumours and matched metastases [ 46 ], but also between metastases in different tissue sites [ 47 , 48 ]. For instance, a study incorporating imaging mass cytometry and RNA sequencing found significantly higher macrophage and monocyte densities in brain metastases compared to primary brain gliomas, with differing interaction-avoidance scores between the tumour types [ 46 ]. Likewise, a recent meta-analysis comparing immunotherapy response − predictive biomarkers—such as PD-L1/PD-1 expression, T-cell infiltration and MSI-status—between primary tumours and corresponding metastases concluded significant discordance between the two [ 49 ], which also could be seen to align with our findings. In our study, the IDO expression in the invasive margin was significantly higher in the metastases compared to the corresponding primary tumours. Still, the surgical patient selection in metastasectomy cohorts might also significantly contribute to the discordance in the comparative analyses between the metastases and primary tumours, and more rigorous comparison would require samples from more disseminated diseases. FCGR3 (CD16), a surface receptor expressed on various myeloid cells, plays a critical role in antibody-dependent cytotoxicity [ 50 ] and mediates antigen uptake, processing, and presentation [ 51 ]. Circulating CD14 + monocytes can transition from classical CD14 + FCGR3 − monocytes into FCGR3 + intermediate and non-classical monocytes [ 52 ]. An increase in circulating intermediate CD14 + FCGR + monocytes has been linked to inflammatory diseases such as atherosclerosis, rheumatoid arthritis and tuberculosis [ 53 ]. In tumour-infiltrating monocytic cells, the immunobiological role of FCGR3 expression is not completely understood. FCGR3 is shown to be highly expressed in immunosuppressive monocytes and TAM populations [ 54 ]. However, an ovarian cancer study found spatially co-localized enrichment of FCGR3 expression in CD68 + myeloid cells, T-cells and NK-cells, associated with improved survival [ 55 ]. In CRC, higher densities of tumour infiltrating FCGR3 + myeloid cells have been linked to improved survival [ 56 ]. In our study, the prognostic value of IDO + monocytic cells in pulmonary metastases was depended on FCGR3 expression, and CD14 + FCGR3 + IDO + cells showed opposing prognostic effects in the invasive margin and tumour centre when including all patients in the analysis. However, this pattern was not observed in neoadjuvant naïve primary CRC tumours [ 17 ], suggesting that the favourable prognostic effect of FCGR3 + IDO + monocytic cells in the tumour centre might reflect responses to neoadjuvant treatment. Monocytic cell maturity can be evaluated based on HLA-DR expression and circulating immature HLA-DR −/low CD14 + monocytes have been linked to systemic immunosuppression and immunotherapy resistance [ 57 ]. A prior CRC study similarly reported that higher densities of CD14 + HLA-DR − monocytic cells were associated with worse survival, whereas mature HLA-DR + monocytic cells were associated with better survival [ 58 ]. In our study, higher density of IDO + HLA-DR − immature monocytic cells in the invasive margin was strongly associated with worse survival, with this finding being even more pronounced in neoadjuvant naïve patients. This might suggest a role for IDO in immunosuppression associated with immature monocytic cells (with a monocytic myeloid derived suppressor cell-like phenotype), though interpretation requires caution due to low cell densities and lack of functional assays demonstrating immunosuppressive capacity. While HLA-DR − IDO + monocytic cells were present at very low densities, their strong correlation with the more abundant FCGR3 + IDO + monocytic cells may help explain their unexpectedly pronounced association with poor survival, though this causality remains speculative. This study has several limitations. First, the selection bias inherent in metastasectomy cohorts restricts generalizability to all CRC patients with metastatic disease. It likely results in differing myeloid cell distribution compared to patients excluded from pulmonary metastasectomy. For example, myeloid cell densities in primary tumours of this study were lower compared to some earlier studies [ 17 , 58 ], and primary tumours were of higher stages. Additionally, all patients had microsatellite stable tumours, and the results cannot be generalized to metastases from microsatellite unstable CRC. Second, TMAs only provide a snapshot of the tumour microenvironment, potentially limiting cell density estimates. However, numerous studies have shown reproducible results using TMAs. Third, data on RAS mutation status was not available. It was not used in clinical decision making at the beginning of the study period, and therefore post hoc determination of RAS status was not performed. Last, the study encompasses a 20-year period during which diagnostics and treatment might have evolved, however, this was addressed in the multivariable analysis. The strengths of this study include a reasonably sized, dual-institutional cohort. The machine learning based digital image analysis along with the multiplexed immunohistochemistry assay enabled detailed cell subtyping, as well as more precise estimates of cell densities compared to semi-quantitative visual estimates. The mortality data from Statistics Finland had full coverage, ensuring the reliability of the primary study outcome. In conclusion, our results suggest distinct prognostic roles for IDO and ARG1 expression in CRC metastases compared to primary tumours. While higher overall IDO + monocytic cell densities generally predict favourable survival, the adverse effect of IDO expression in FCGR3 + HLA-DR − immature monocytic cells in metastases underscores the complexity of immune interactions in metastatic dissemination. Similarly, higher density of ARG1 + FCGR3 − granulocytes is an independent marker of poor survival. These findings provide novel insights into the tumour-immune dynamics in CRC metastasis, offering potential avenues for therapeutic targeting. Declarations Ethics approval and consent to participate: The Oulu University Hospital Ethics Committee (EETMK 81/2008) approved the study. The Finnish National Authority of Medicolegal Affairs (VALVIRA) waived the need for informed consent due to the retrospective nature of the study. The study was performed in accordance with the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests : T.T.S. declares consultation fee from Amgen Finland. T.T.S is CEO and co-owner of Healthfund Finland and Clinical Advisory board member of LS Cancer Diag. The other authors declare no conflict of interest. Funding: This study was funded by Instrumentarium Science Foundation (O.H), Mary and Georg C. Ehrnrooth Foundation (O.H), Finnish State Research Funding (O.H, J-P.M, M.A, J.P.V, T.T.S), Cancer Foundation Finland (J.P.V, T.T.S), Sigrid Jusélius Foundation (J.P.V, T.T.S), J&A Erkko Foundation (J-P.M, T.T.S), Emil Aaltonen Foundation (J.P.V), Academy of Finland (T.T.S), iCAN Precision Medicine Flagship of the Academy of Finland (TTS), Relander Foundation (T.T.S). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contribution Conceptualization: T.Ka, H.E, S.A.V., O.H, F.Y, J.P.V. Methodology: T.Ka., H.E, S.A.V., J.P.V. Formal Analysis: T.Ka, H.E, J.P.V. Investigation: All AuthorsData Curation: All Authors. Writing – Original Draft Preparation: All AuthorsWriting – Review and Editing: All Authors. Supervision: O.H. F.Y. J.P.V. Funding Acquisition: O.H. J.P.V. J-P.M. Acknowledgement The study benefited from samples/data from Northern Finland Biobank Borealis (Oulu, Finland) (https://oys.fi/biopankki/) and from Central Finland Biobank (Jyväskylä, Finland) (https:// https://www.hyvaks.fi/sairaala-nova/biopankki). Data Availability Data generated and/or analysed during this study are not publicly available. The sharing of data will require approval from relevant ethics committees and/or biobanks. Further information including the procedures to obtain and access data from Finnish Biobanks are described at https://finbb.fi/en/fingenious-service References Uyttenhove, C. et al. Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. Nat. Med. 9 (10), 1269–1274 (2003). Fallarino, F. et al. The Combined Effects of Tryptophan Starvation and Tryptophan Catabolites Down-Regulate T Cell Receptor ζ-Chain and Induce a Regulatory Phenotype in Naive T Cells. J. Immunol. 176 (11), 6752–6761 (2006). 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10:45:07","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173504,"visible":true,"origin":"","legend":"","description":"","filename":"1381892bef88452c90c4f84e85a538b31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/c995a41e28828f6ab1c5d5bf.xml"},{"id":97689389,"identity":"dfe2336f-d384-4c16-8236-a2804ae9ebb0","added_by":"auto","created_at":"2025-12-08 10:45:07","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187134,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/c8234f453ce50f2a85c1e4d3.html"},{"id":97893954,"identity":"44c763f3-f2f3-412e-8d5b-7a2db7e35d43","added_by":"auto","created_at":"2025-12-10 15:31:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":417459,"visible":true,"origin":"","legend":"\u003cp\u003eMultiplex immunohistochemistry assay and machine learning-based image analysis. (A) Examples of each marker included in the assay separately and merged into pseudo-coloured multiplexed immunohistochemistry image. Haematoxylin (blue background staining) was used to align the cell nuclei. (B) Machine learning based image analysis for detecting and classifying tissue compartments and cells in QuPath. Tissue compartments were classified into tumour epithelium and stromal areas. Cells were classified into monocytic cells, granulocytes, mast cells, tumour cells, and other cells.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/b4a2ae60e7b45b4ff663381a.png"},{"id":97893576,"identity":"a5656336-5307-459e-b76a-f25d6d076cf7","added_by":"auto","created_at":"2025-12-10 15:30:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191723,"visible":true,"origin":"","legend":"\u003cp\u003eNearest neighbour distance (NND) analysis for myeloid cells and tumour cells. (A) Example multiplex immunohistochemistry image representing granulocytes (CEACAM8\u003csup\u003e+\u003c/sup\u003e), monocytic cells (CD14\u003csup\u003e+\u003c/sup\u003e), mast cells (TPSAB1\u003csup\u003e+\u003c/sup\u003e) and tumour cells (KRT\u003csup\u003e+\u003c/sup\u003e), as well as IDO and ARG1 expression. (B) Cell phenotype map and NNDs from myeloid cells to the closest tumour cell. (C) Boxplots representing mean NNDs of myeloid cell phenotypes in metastases and primary tumours. (D) Boxplots representing mean NNDs of IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells and ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes between metastases and primary tumours. (E) Boxplot representing mean NNDs between IDO\u003csup\u003e+\u003c/sup\u003e and IDO\u003csup\u003e−\u003c/sup\u003e monocytic cells between ARG1\u003csup\u003e+\u003c/sup\u003e and ARG1\u003csup\u003e−\u003c/sup\u003e granulocytes in metastatic tumours. Wilcoxon rank-sum tests were applied for mean comparison. *** P\u0026lt;0.0001.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/d576f70f89de418278496154.png"},{"id":97689377,"identity":"5643f3fe-68ea-46be-b874-ef6200376ae7","added_by":"auto","created_at":"2025-12-08 10:45:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56196,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of CD14\u003csup\u003e+\u003c/sup\u003e monocytic cells and CEACAM8\u003csup\u003e+\u003c/sup\u003e granulocytic cells in colorectal cancer pulmonary metastases and corresponding primary tumours. Boxplots compare proportions of HLA-DR and FCGR3 expression in the monocytic cells (\u003cstrong\u003eA\u003c/strong\u003e), the densities and proportions of IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells (\u003cstrong\u003eB\u003c/strong\u003e) and ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes (\u003cstrong\u003eC\u003c/strong\u003e) in the invasive margins (IM) and tumour centres (TC) between first resected metastases (MET) and corresponding primary tumours (PRIM). Statistical significance was tested with the Wilcoxon signed rank test.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/074c6e7059b1b57491c52262.png"},{"id":97689378,"identity":"97283bfd-5dc6-4cdd-af90-4b99e811f6b9","added_by":"auto","created_at":"2025-12-08 10:45:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100476,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier 5-year survival curves after pulmonary metastasectomy stratified by overall (intraepithelial + stromal) CD14\u003csup\u003e+\u003c/sup\u003e monocytic cell densities and their IDO expression and CEACAM8\u003csup\u003e+\u003c/sup\u003e granulocyte cell densities and their arginase1 expression (ARG1) in the invasive margin (IM) and centre (TC) of the metastases. Log rank tests were applied.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/70fe671cfb1677339fea2a95.png"},{"id":104252204,"identity":"2c33763c-d42d-4f39-8cc6-52dafaa2437f","added_by":"auto","created_at":"2026-03-09 16:17:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2416352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/235321aa-d255-48c1-be80-0631642da037.pdf"},{"id":97892689,"identity":"d30d57e4-1202-4e43-9860-34887377087d","added_by":"auto","created_at":"2025-12-10 15:18:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4526293,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFilesMyeloidPMCRC.docx","url":"https://assets-eu.researchsquare.com/files/rs-7420974/v1/ac5985ce3f3b8e353ce913ba.docx"}],"financialInterests":"Competing interest reported. T.T.S. declares consultation fee from Amgen Finland. T.T.S is CEO and co-owner of Healthfund Finland and Clinical Advisory board member of LS Cancer Diag. The other authors declare no conflict of interest.","formattedTitle":"Quantitative Analysis of Myeloid Cell Patterns and Immunosuppressive Enzyme (IDO, ARG1) Expression in Colorectal Cancer Pulmonary Metastases and Corresponding Primary Tumours","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eIndoleamine 2,3-dioxygenase (IDO) is a cytosolic enzyme involved in the catabolism of tryptophan in the first and rate-limiting step of kynurenine pathway that is frequently overexpressed in cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The catabolism of tryptophan, an essential amino-acid, leads to the functional and proliferative arrest of T-cells in the tumour microenvironment via kynurenine metabolites and tryptophan deprivation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another enzyme with immunosuppressive features is arginase-1 (ARG1), catalysing the hydrolysis of L-arginine into immunologically active metabolites [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. ARG1 is also overexpressed in several solid tumours [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It takes part in the complex interplay of immunoediting and is associated with poor survival in several solid tumours [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. IDO is expressed in tumour cells, as well as in monocytic cells, endothelial cells, and T-cells [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], whereas ARG1 is usually expressed in granulocytes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given their immunosuppressive nature, ARG1 and IDO have been targets of immunotherapeutic development in recent clinical trials [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eColorectal cancer (CRC) is one of the most common malignancies globally, ranking as the third leading cause of cancer mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The prognostic significance of IDO and ARG1 expression in CRC have been contradictory [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This inconsistency may stem from the frequent use of qualitative, single-plex analysis methods that fail to distinguish between cell types contributing to their expression. Additionally, many studies have not differentiated the invasive margin and tumour centre in the analysis, thus overlooking the intricate complexity of the tumour microenvironment (TME). Applying multiplexed immunohistochemistry, we recently demonstrated a positive survival effect for both enzymes in CRC primary tumours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf all CRC patients, 15\u0026thinsp;\u0026minus;\u0026thinsp;30% have synchronous metastases at the time of diagnosis and around 20% develop metachronous metastases within 5-years following primary tumour resection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While the 5-year overall survival of CRC across all stages exceeds 60%, patients with stage IV disease have a 5-year survival of only 14% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In pulmonary metastases, the myeloid cell distributions and the prognostic impact of the myeloid cell subsets remain largely unexplored.\u003c/p\u003e\u003cp\u003eThe aim of this study was to evaluate the prognostic effect of myeloid cell densities, monocytic cell maturity (HLA-DR), myeloid cell FCGR3 expression, and immunosuppressive potential (IDO and ARG1) in resected CRC pulmonary metastases with a comparison to primary tumours. We hypothesized that higher expression of IDO and ARG1 would be associated with adverse survival outcomes, and the effect would be dependent on HLA-DR and FCGR3 expression. Additionally, we aimed to evaluate the spatial distribution of the myeloid cells in metastases and primary tumours according to their IDO/ARG1 expression.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003eStudy design\u003c/p\u003e\u003cp\u003eThis retrospective population-based study included all patients who underwent pulmonary metastasectomy for histologically confirmed CRC metastases with curative intent at Oulu University Hospital and Central Finland Central Hospital between 2000 and 2020. These hospitals are the sole providers of thoracic surgical services within their respective regions. Patient identification was based on surgical and pathology records, and relevant clinical data were retrospectively gathered from electronic patient records. Tumour staging was updated to conform to the 8th edition of the Union for International Cancer Control (UICC) tumour-node-metastasis (TNM) classification system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Survival data, 100% complete through December 31, 2021, were obtained from Statistics Finland. Haematoxylin and eosin (H\u0026amp;E) stained slides of primary tumours and pulmonary metastases were retrieved from pathology archives of Northern Finland Biobank Borealis and Central-Finland Biobank, and reviewed by a pathologist (V-M.P). For cases involving multiple metastatic nodules resected in a single procedure, a representative nodule was arbitrarily selected for further analysis. Digitalization of the slides was performed using either a NanoZoomer XR (Hamamatsu Photonics, Hamamatsu City, Japan) or Aperio AT2 (Leica Biosystems Imaging Inc., Wetzlar, Germany) scanner equipped with a 20\u0026times; objective.\u003c/p\u003e\u003cp\u003eImmunohistochemical screening for DNA mismatch repair (MMR) deficiency (MLH1, MSH2, MSH6 and PMS2) and \u003cem\u003eBRAF\u003c/em\u003e V600E mutation status (VE1) was conducted as described previously [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All patients were MMR proficient and only two exhibited \u003cem\u003eBRAF\u003c/em\u003e V600E mutations. The densities of CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cells in the invasive margin and tumour centre were analysed from whole section slides using methods outlined in prior studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A three-tiered immune cell score was generated based on CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell densities, following the principles of the consensus Immunoscore [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultiplex immunohistochemistry\u003c/p\u003e\u003cp\u003eFormalin-fixed paraffin-embedded tissue samples were used to construct tissue microarrays (TMAs) with 1-mm core diameter. Core selection was guided by H\u0026amp;E-stained slides, with two cores taken from the invasive margin and two from the tumour centre for both primary tumours and metastases. TMA-blocks were cut into 3.5-\u0026micro;m sections for staining.\u003c/p\u003e\u003cp\u003eA 10-plex multiplex immunohistochemistry assay was designed to characterize myeloid cell densities and maturity and expression of immunosuppressive enzymes. The panel included markers for myeloid cells (ITGAM [CD11b], CD33), monocytic cells (CD14), granulocytes (CEACAM8 [CD66b]), mast cells (TPSAB1 [mast cell tryptase]), and tumour cells (KRT [keratin]), along with HLA-DR (human leukocyte antigen, DR-isotype), FCGR3 (Fcγ receptor 3A, [CD16]), IDO and ARG1. Haematoxylin was used to align cell nuclei. Examples of IDO and ARG1 expression in TMA cores are presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Standardized nomenclature were used for protein names [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Antibody optimization and assay validation have been described previously [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Staining was performed sequentially on a Bond-III automated stainer using a Bond Refine Detection kit (DS9800, Leica Biosystems), with 3,3'-dia-minobenzidine replaced with 3-amino-9-ethylcarbazole (AEC). All slides were stained in one batch ensuring uniform staining. The procedure included staining each marker individually, followed by slide scanning, heat-mediated antibody removal, and ethanol-based AEC removal to enable sequential staining of multiple markers.\u003c/p\u003e\u003cp\u003eImage analysis\u003c/p\u003e\u003cp\u003eTissue microarray cores were identified and extracted into single-core images using the \u003cem\u003eTMA dearrayer\u003c/em\u003e function in QuPath [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Non-representative cores (e.g., those with minimal tumour cell content, necrosis, or procedural damage) were excluded. Representative cores were combined into pseudo-coloured multiplex immunohistochemistry images using Fiji ImageJ [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Staining patterns for each marker in a representative image are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The \u003cem\u003ecell detection\u003c/em\u003e function in QuPath was used to identify cells, and the \u003cem\u003eobject classifier\u003c/em\u003e function to identify myeloid cells (ITGAM\u003csup\u003e+\u003c/sup\u003e/CD33\u003csup\u003e+\u003c/sup\u003e) and further phenotype them into 3 main categories\u0026mdash;monocytic cells (CD14\u003csup\u003e+\u003c/sup\u003eCEACAM8\u003csup\u003e\u0026minus;\u003c/sup\u003eTBSAB1\u003csup\u003e\u0026minus;\u003c/sup\u003eKRT\u003csup\u003e\u0026minus;\u003c/sup\u003e), granulocytes (CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCEACAM8\u003csup\u003e+\u003c/sup\u003eTBSAB1\u003csup\u003e\u0026minus;\u003c/sup\u003eKRT\u003csup\u003e\u0026minus;\u003c/sup\u003e), and mast cells (CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCEACAM8\u003csup\u003e\u0026minus;\u003c/sup\u003eTBSAB1\u003csup\u003e+\u003c/sup\u003eKRT\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u0026mdash;with the remaining cells categorized into tumour cells CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCEACAM8\u003csup\u003e\u0026minus;\u003c/sup\u003eTBSAB1\u003csup\u003e\u0026minus;\u003c/sup\u003eKRT\u003csup\u003e+\u003c/sup\u003e) and other cells (CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCEACAM8\u003csup\u003e\u0026minus;\u003c/sup\u003eTBSAB1\u003csup\u003e\u0026minus;\u003c/sup\u003eKRT\u003csup\u003e\u0026minus;\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For tissue categorization, the \u003cem\u003epixel classifier\u003c/em\u003e function in QuPath was trained to identify tumour epithelium, stroma, and other regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), with other excluded from further analysis. Further data processing, quantification and spatial analysis was performed using packages \u003cem\u003etidyverse\u003c/em\u003e (2.0.0), \u003cem\u003ecutpointr\u003c/em\u003e (1.1.2), \u003cem\u003epurrr\u003c/em\u003e (1.0.2), \u003cem\u003eggplot2\u003c/em\u003e (3.4.2), \u003cem\u003egtsummary\u003c/em\u003e (2.0.1), \u003cem\u003ecorrplot\u003c/em\u003e (0.92), \u003cem\u003eggpubr\u003c/em\u003e (0.6.0), \u003cem\u003epatchwork\u003c/em\u003e (1.2.0) \u003cem\u003espatstat\u003c/em\u003e (3.0.5), \u003cem\u003esurvival\u003c/em\u003e (3.5-5) and \u003cem\u003esurv-miner\u003c/em\u003e (0.4.9) in RStudio (version 2024.09.1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCells were categorized according to their cytoplasmic staining intensities of FCGR3, HLA-DR, IDO and ARG1 by setting fixed cut-off values (positive/negative staining). Cell densities were calculated for each marker in regions of interest, with mean densities used for statistical evaluation. IDO expression in tumour cells was assessed by calculating the percentage of IDO\u003csup\u003e+\u003c/sup\u003e tumour cells relative to all tumour cells; tumours with less than 1% of IDO\u003csup\u003e+\u003c/sup\u003e tumour cells were categorized as negative. Myeloid cell densities were categorized into two categories (high vs. low) based on receiver-operating characteristics (ROC) curve analysis. The point closest to (0,1) coordinate was used as cut-point as suggested previous literature [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The density cutoffs based on ROC analysis are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTo analyse spatial interactions between myeloid cells and tumour cells, we used the \u003cem\u003espatstat\u003c/em\u003e (3.0.5) package to calculate nearest neighbour distances (NNDs) from myeloid cells to their closest neighbour points of specific categories (e.g., tumour cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). To visualize the results, scaled intensities of myeloid cell markers were plotted against NNDs from tumour cells using the \u003cem\u003eggplot2\u003c/em\u003e (3.3.3) package and generalized additive model smoothing [formula y\u0026thinsp;~\u0026thinsp;s(x)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOutcomes and definitions\u003c/p\u003e\u003cp\u003eComorbidity was classified using the Royal College of Surgeons Comorbidity Score (RCSCC) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Disease-free interval (DFI) was defined as the time between primary tumour resection and pulmonary metastasis detection. None of the patients had a lung-first approach. Pulmonary metastases diagnosed within 6 months post-primary treatment were classified as synchronous, while those detected later were labelled metachronous. The primary endpoint was 5-year overall survival following pulmonary metastasectomy. Given that only one death was unrelated to cancer, cancer-specific survival was not analysed.\u003c/p\u003e\u003cp\u003eTumour regression grade (TRG) following neoadjuvant treatment was assessed by a pathologist (V-M.P) using a modified Dworak grading system [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were compared using Chi-square or Fisher\u0026rsquo;s exact tests, while continuous variables were analysed using Student\u0026rsquo;s T-test or Mann-Whitney U-test. Normality and skewness of data distribution was visually inspected and confirmed with z-scores of skewness values for adequate use of statistical tests. Spearman coefficients were calculated for correlation analyses. Kaplan-Meier curves were used to visualize survival rates, and hazard ratios (HRs) with 95% confidence intervals (CIs) were computed using Cox proportional hazards regression models. The multivariable models were adjusted for sex (male/female), age (continuous variable), comorbidity (RCSCC 1/\u0026ge;2), neoadjuvant therapy (no/yes), number of pulmonary metastases at diagnosis (1/\u0026ge;2), size of the largest resected metastasis (continuous variable), former liver metastases (no/yes), synchronicity of pulmonary metastases (synchronous/metachronous), laterality of metastases (unilateral/bilateral), adjuvant therapy (no/yes), and time interval of surgery (2000\u0026ndash;2010/2011\u0026ndash;2020). The selection of adjustable variables was based on a recent meta-analysis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Of all adjustable variables, missing values were detected in 7.7% (4 patients) of adjuvant therapy data and were imputed using the mode value (chemotherapy). The Schoenfeld residuals were analysed to ensure the proportionality of hazards assumption; no violations occurred (Figure S2). Patients with an incomplete R1 resection or death within 30 days post-surgery were excluded from survival analysis. All statistical tests were two-tailed and the statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analysis was performed using RStudio (version 2024.09.1) and R statistical programming (version 4.0.3).\u003c/p\u003e\u003cp\u003eEthical aspects\u003c/p\u003e\u003cp\u003e The Oulu University Hospital Ethics Committee (EETMK 152/2008) approved the study. The Finnish National Authority of Medicolegal Affairs (VALVIRA) waived the need for informed consent due to the retrospective nature of the study (D.no 3916/06.01.03.01/2016). The study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003cp\u003eThis study examined 106 pulmonary metastasectomies performed on 74 patients, including 36 repeat procedures on 21 individuals. The median DFI following primary CRC surgery was 337 days (IQR 0-783). Bilateral pulmonary metastases were observed in 16% of patients (n\u0026thinsp;=\u0026thinsp;12), and 34% (n\u0026thinsp;=\u0026thinsp;25) presented with more than one pulmonary metastasis. One patient had microscopic tumour residuals in the surgical margin, and three underwent metastasis enucleation, all classified as R1 resections and excluded from survival analysis. A history of CRC liver metastasis treatment was reported in 43% of patients (n\u0026thinsp;=\u0026thinsp;33). The median follow-up period was 26.5 months (IQR 18.6-48.48, range 1-209 months). An overall 5-year survival rate was 32%.\u003c/p\u003e\u003cp\u003eNeoadjuvant chemotherapy was given to 42% of patients (n\u0026thinsp;=\u0026thinsp;45) for pulmonary metastases. For primary tumours, preoperative radiotherapy was given to 11% (n\u0026thinsp;=\u0026thinsp;7), chemotherapy to 4.6% (n\u0026thinsp;=\u0026thinsp;3), and chemoradiotherapy 23% (n\u0026thinsp;=\u0026thinsp;15). Post-metastasectomy, only two patients did not receive any adjuvant treatment, while 93% (n\u0026thinsp;=\u0026thinsp;54) received chemotherapy and 3% (n\u0026thinsp;=\u0026thinsp;2) underwent radiotherapy. Tumour regression grading revealed minimal regression (TRG\u0026thinsp;=\u0026thinsp;1) in 94% of neoadjuvant treated pulmonary metastases.\u003c/p\u003e\u003cp\u003eMyeloid cell densities\u003c/p\u003e\u003cp\u003eAnalysis was successful for 291 TMA cores from 91 pulmonary metastases and 153 TMA cores from 54 primary tumours. Myeloid cells were predominantly located in the stromal areas of tumours, with higher densities in the invasive margins than in tumour centres (Table S2). Metastases had higher densities of invasive margin granulocytes and mast cells, as well as higher tumour centre granulocyte densities, compared to the primary tumours (Figure S3). In metastases, monocytic cells in the invasive margin were more mature (HLA-DR\u003csup\u003e+\u003c/sup\u003e) compared to those in the tumour centre, a pattern not observed in primary tumours (Table S3). Monocytic cell maturity, based on HLA-DR expression, did not significantly differ between metastatic and primary tumours, though FCGR3 expression in monocytic cells was significantly higher in the invasive margin of metastases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). No significant correlations were identified among different myeloid cell lineages or between their densities in primary tumours and metastases (Figures S4 and S5). Neoadjuvant chemotherapy was also not associated with myeloid cell densities or IDO/ARG1 expression of monocytic cells/granulocytes (Table S4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIDO and ARG1 expression and distribution\u003c/p\u003e\u003cp\u003eOf all CD14\u003csup\u003e+\u003c/sup\u003e monocytic cells, 2.4% expressed IDO in metastases, and 1.8% in primary tumours. 2.9% of tumour cells also expressed IDO in the metastases. The cell density ranges of IDO expressing monocytic cell subtypes in metastases are presented in Figure S6. IDO expression was higher in mature (HLA-DR\u003csup\u003e+\u003c/sup\u003e 2.6% vs. HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e 1.6%) and FCGR3\u003csup\u003e+\u003c/sup\u003e monocytic cells (FCGR3\u003csup\u003e+\u003c/sup\u003e 3.1% vs. FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003e 1.2%) in the metastases, a same pattern also noticed in the primary tumours. In terms of cell densities, HLA-DR\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e and FCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the invasive margin in the metastases had the highest densities up to 184 cells/mm\u003csup\u003e2\u003c/sup\u003e and 182 cells/mm\u003csup\u003e2\u003c/sup\u003e, respectively (Figure S6). Densities of IDO expressing immature HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cells ranged from 0 to 63 cells/mm\u003csup\u003e2\u003c/sup\u003e, with a median cell density of 0 cells/mm\u003csup\u003e2\u003c/sup\u003e, indicating their sparse presence. All IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell subtypes were significantly correlated with each other (invasive margin: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.45\u0026ndash;0.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S7) and with the proportion of IDO\u003csup\u003e+\u003c/sup\u003e tumour cells, as well as with CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e cell densities. Invasive margin monocytic cell IDO expression was significantly higher in metastases compared to the primary tumours (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In spatial analysis, IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells were located closer to the tumour cells compared to the IDO\u003csup\u003e\u0026minus;\u003c/sup\u003e counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eMost of the granulocytes expressed ARG1 (72.3% in the metastases; 71.5% in the primary tumours). The expression was higher in FCGR3\u003csup\u003e+\u003c/sup\u003e granulocytes compared to FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocytes (Figure S8), with invasive margin median densities of 46 cells/mm\u003csup\u003e2\u003c/sup\u003e and 8 cells/mm\u003csup\u003e2\u003c/sup\u003e, respectively (Table S3, Figure S8). FCGR3\u003csup\u003e+\u003c/sup\u003eARG1\u003csup\u003e+\u003c/sup\u003e and FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003eARG1\u003csup\u003e+\u003c/sup\u003e granulocyte subtypes were significantly correlated (invasive margin: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Figure S7). The granulocyte ARG1 expression and monocytic cell IDO expression, however, were not intercorrelated, suggesting varying mechanisms of immunosuppression in the metastases. ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes, irrespective of FCGR3 expression status, had a weaker correlation with CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cells compared to IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells (Figure S7). The invasive margin ARG1 expression was higher in the metastases compared to the primary tumours (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In the spatial analysis, ARG1\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocytes were located closer to the tumour cells compared to the ARG1\u003csup\u003e+\u003c/sup\u003e counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eThe proportion of IDO\u003csup\u003e+\u003c/sup\u003e tumour cells was significantly correlated with IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the invasive margin and tumour centre, but not with ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes or mast cells (Figure S7). The proportion of IDO\u003csup\u003e+\u003c/sup\u003e tumour cells also positively correlated with the densities of CD8\u003csup\u003e+\u003c/sup\u003e (tumour centre \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; invasive margin \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figure S7) and CD3\u003csup\u003e+\u003c/sup\u003e T-cells (tumour centre \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; invasive margin \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e=0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figure S7).\u003c/p\u003e\u003cp\u003eSurvival analysis\u003c/p\u003e\u003cp\u003eThe prognostic impact of IDO expression differed between the invasive margin and tumour centre. Higher densities of IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the tumour centre were associated with longer 5-year overall survival (low 12% vs. high 63%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; adjusted HR 0.17, 95% CI 0.05\u0026ndash;0.59, p\u0026thinsp;=\u0026thinsp;0.005, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This prognostic association was limited to mature and FCGR3\u003csup\u003e+\u003c/sup\u003e monocytic cells (Figure S9, Table S5). Conversely, in the invasive margin, overall IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell population was not prognostic, but higher densities of immature (HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e) IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells were associated with shorter survival (adjusted HR 5.60, 95% CI 1.70-18.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Table S5, Model 2). Similarly, IDO\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cell densities in the invasive margin were associated with poorer outcomes (Figure S10; adjusted HR 4.19, 95% CI 1.45\u0026ndash;12.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as did overall monocytic cell densities (Figure S11, adjusted HR 2.87, 95% CI 1.10\u0026ndash;7.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, Table S5). The proportion of IDO expression in the tumour cells was not associated with prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHazard ratios with 95% confidence intervals for 5-year all-cause mortality according to myeloid cell densities and tumour cell IDO expression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultivariable\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumour centre\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cell density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23 (0.09\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;0.58; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17 (0.05\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;0.59; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cell density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.49\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.09; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21 (0.51\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.666)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARG1\u003csup\u003e+\u003c/sup\u003e granulocyte density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58 (0.75\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;3.35; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.31 (0.89\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;5.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARG1\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocyte density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44 (0.70\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59 (0.69\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;3.67; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.281)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e+\u003c/sup\u003e tumour cell percentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43 (0.18\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 (0.15\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.27; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.128)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvasive margin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cell density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59 (0.27\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06 (0.47\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.38; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.881)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cell density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52 (1.19\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;5.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.19 (1.45\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;12.05; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARG1\u003csup\u003e+\u003c/sup\u003e granulocyte density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88 (0.41\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.88; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09 (0.50\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.35; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.831)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARG1\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocyte density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61 (0.28\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.32; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54 (0.21\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.206)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDO\u003csup\u003e+\u003c/sup\u003e tumour cell percentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (referent)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.35\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;1.66; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.501)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01 (0.36\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;2.80; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.987)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003eCox proportional hazards regression model adjusted for sex (female/male), age (continuous), RCSCC (1/2/\u0026ge;3), neoadjuvant therapy (no/yes), synchronicity of pulmonary metastases (synchronous/metachronous), size of largest metastasis (continuous), number of pulmonary metastases at diagnosis (1/\u0026ge;1), former liver metastases (no/yes), adjuvant therapy (no/yes), and 10-year interval (2000\u0026ndash;2009/2010\u0026ndash;2020).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEven after adjusting for CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T-cell density-based immune cell scoring, higher densities of immature (HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e) IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the invasive margin remained associated with shorter survival (adjusted HR 26.37, 95%CI 4.95-140.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table S5). Similarly, higher densities of FCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells, a subtype significantly correlated with HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e subtype, also had a suggestive adverse prognostic effect (adjusted HR 3.91, 95% CI 0.97\u0026ndash;15.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056, Table S5). In the tumour centre, IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells remained associated with longer survival (adjusted HR 0.20, 95% CI 0.06\u0026ndash;0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, Table S5).\u003c/p\u003e\u003cp\u003eOverall granulocyte densities, regardless of ARG1 expression, were not significantly associated with 5-year survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, a higher density of FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003eARG1\u003csup\u003e+\u003c/sup\u003e granulocyte subset in the tumour centre associated with shorter survival when adjusted for the immune cell score (adjusted HR 2.68, 95% CI 1.05\u0026ndash;6.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; Table S6). This cell subtype exhibited considerable variability in density, ranging from 0 to 525 cells/mm\u003csup\u003e2\u003c/sup\u003e, with a median of 6.21 cells/mm\u003csup\u003e2\u003c/sup\u003e (Figure S8). A similar, though non-significant trend toward poorer survival was observed for for higher densities of ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes in the centre of metastasis (adjusted HR 2.30, 95% CI 0.89\u0026ndash;5.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087, Table S6).\u003c/p\u003e\u003cp\u003eIn the sensitivity analysis including only neoadjuvant-na\u0026iuml;ve patients (n\u0026thinsp;=\u0026thinsp;41), higher densities of IDO\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cells and immature HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the invasive margin remained associated with worse survival (adjusted HRs 10.42, 95%CI 2.28\u0026ndash;47.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 and 41.19, 95% CI 2.31-735.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, respectively; Table S7), whereas in the tumour centre, densities of monocytic cells, irrespective of their IDO expression status, were not significantly associated with survival after adjusting for confounders (Table S7). IDO\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e+\u003c/sup\u003e monocytic cells were not associated with survival in the sensitivity analysis, but ARG1\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocytes in the invasive margin and ARG1\u003csup\u003e+\u003c/sup\u003e granulocytes in the tumour centre were also associated with worse survival (adjusted HRs 6.63 95% CI 1.10-39.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, and 10.95, 95%CI 1.69\u0026ndash;70.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, respectively; Table S8).\u003c/p\u003e\u003cp\u003eAssociation with clinical parameters\u003c/p\u003e\u003cp\u003eHigher density of IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the tumour centre of metastases was associated with smaller metastasis size and lower number of resected metastases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas higher ARG1\u003csup\u003e+\u003c/sup\u003e granulocyte density in the invasive margin of metastases was associated with a history of liver metastasis (Table S9).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of colorectal cancer cases according to IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell densities in the first resected pulmonary metastasis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eCD14\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cell density (tumour centre)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eCD14\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cell density (invasive margin)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;34\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;23\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;31\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;23\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (61, 76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (63, 74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70 (64, 82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68 (56, 73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRCS Comorbidity Score\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeoadjuvant chemotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary tumour location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease-free interval (days)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e550 (143, 925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e309 (0, 857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e482 (67, 813)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e309 (0, 1,004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSize of largest pulmonary metastasis (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.55 (1.50, 3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40 (1.00, 2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.30 (1.20, 3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.62 (1.10, 2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFormer liver metastases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSynchronicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetachronous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSynchronous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. of pulmonary metastases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaterality of metastases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20 (87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRAF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMutated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWild-type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20 (95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumour regression grade\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimal regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21 (91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNear complete regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003en (%); Median (Q1, Q3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003ePearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003eRCS = Royal College of Surgeons\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eHere, we conducted a comprehensive analysis of myeloid cell densities and IDO and ARG1 expression patterns in resected pulmonary metastases and their corresponding primary tumours. Our findings highlight the differences in myeloid cell profiles between the invasive margin and tumour centre of CRC pulmonary metastases. The invasive margin exhibits higher myeloid cell densities, greater maturity and FCGR3 positivity in monocytic cells, and increased IDO and ARG1 expression compared to the tumour centre. Prognostically, IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells seem to behave differently in these regions. After adjusting for confounders and T-cell densities, we found that in the invasive margin, higher densities of monocytic cells, including those expressing IDO (HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e and FCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e) were associated with worse prognosis. Conversely, in the tumour centre, IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells (especially mature HLA-DR\u003csup\u003e+\u003c/sup\u003e and FCGR3\u003csup\u003e+\u003c/sup\u003e) were associated with better prognosis. In neoadjuvant na\u0026iuml;ve patients, however, IDO expression had a negative prognostic impact only in immature monocytic cells in the invasive margin. In the CEACAM8\u003csup\u003e+\u003c/sup\u003e granulocytic lineage, higher densities of FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003eARG1\u003csup\u003e+\u003c/sup\u003e cells in the tumour centre (and higher densities of FCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003eARG1\u003csup\u003e+\u003c/sup\u003e cells in the invasive margin of neoadjuvant-na\u0026iuml;ve patients) were independently associated with worse survival, suggesting a potential role for ARG1 in adverse outcomes.\u003c/p\u003e\u003cp\u003eFew studies have examined the expression of immunosuppressive enzymes ARG1 and IDO at the single-cell level in specific cell types within the tumour microenvironment. Most evidence on the prognostic value of IDO expression derives from semiquantitative analysis without distinguishing between tumour regions or cell types [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Some studies have associated IDO with favourable outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], while others link it to poor survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In studies that distinguish between the invasive margin and tumour centre, a TMA-based single-marker IHC study on stage I-III CRC demonstrated a negative survival effect of IDO expression in the invasive margin, but not in the tumour centre [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which was also demonstrated in melanoma and lung cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, a recent multiplexed immunofluorescence study on lung cancer showed that higher IDO expression in peritumoural tertiary lymphoid structures was associated with worse survival [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], whereas higher IDO expression in tumour cells was associated with better survival. On the other hand, a recent TMA-based study on oesophageal cancer demonstrated higher IDO expression in the invasive margin being associated with better survival, the difference being more significant in lower T-stage tumours [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConcerning CRC, we previously published a comprehensive multiplexed IHC analysis on the myeloid cell densities in a cohort of primary tumours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and found that higher IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell densities in both the invasive margin and the tumour centre were independently associated with favourable disease-specific survival, the association being stronger in stage I-III primary tumours compared to stage IV tumours. Higher ARG1\u003csup\u003e+\u003c/sup\u003e granulocyte density was similarly associated with better survival in both the invasive margin and tumour centre, and the association was limited to stage I-III tumours. Supporting the notion of a weakening favourable survival effect of IDO expression alongside tumour progression, possibly developing towards a more negative survival effect in the invasive margin, a recent study incorporating single-cell sequencing and spatial transcriptomics in stage I-III CRC primary tumours revealed a stronger immunosuppressive milieu in the epithelial-to-mesenchymal transition (EMT) pseudotime-endpoint trajectories [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], suggesting that the invasive margin\u0026mdash;frequent site for EMT\u0026mdash;might have a more immunosuppressive milieu compared to the tumour centre. EMT marker expression between metastases and primary tumours is also proposed to differ during metastatic dissemination [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and thus EMT might partly explain the prognostic differences of IDO/ARG1 in tumour types noted in our study. Additionally, myeloid derived suppressor cells and monocytes have a role in the immunosuppressive premetastatic niche formation even before the arrival of circulating tumour cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which could also account for the prognostic differences between the IDO and ARG1 expression in metastases and primary tumours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strong correlation between CD8\u003csup\u003e+\u003c/sup\u003e T-cell and IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell densities in the metastases in our study\u0026mdash;noted also in the primary tumours\u0026mdash;may appear contradictory, given the immunosuppressive nature of IDO. However, it aligns with a recent TCGA-based RNA sequencing study demonstrating a strong positive correlation between IDO1 expression and CD8A expression (a CD8\u003csup\u003e+\u003c/sup\u003e T-cell biomarker) in most cancer types [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Functional assays also show the immunosuppressive mechanisms of IDO being mediated through TCR ζ-chain depletion and T-cell exhaustion, along with a reduction T-cells densities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Similarly, the expression of other immune checkpoint molecules is generally positively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T-cells densities [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, the prognostic differences of IDO between the primary tumours and metastases still remain unclear. Several studies report significant differences in immune composition not only between primary tumours and matched metastases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], but also between metastases in different tissue sites [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For instance, a study incorporating imaging mass cytometry and RNA sequencing found significantly higher macrophage and monocyte densities in brain metastases compared to primary brain gliomas, with differing interaction-avoidance scores between the tumour types [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Likewise, a recent meta-analysis comparing immunotherapy response\u0026thinsp;\u0026minus;\u0026thinsp;predictive biomarkers\u0026mdash;such as PD-L1/PD-1 expression, T-cell infiltration and MSI-status\u0026mdash;between primary tumours and corresponding metastases concluded significant discordance between the two [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which also could be seen to align with our findings. In our study, the IDO expression in the invasive margin was significantly higher in the metastases compared to the corresponding primary tumours. Still, the surgical patient selection in metastasectomy cohorts might also significantly contribute to the discordance in the comparative analyses between the metastases and primary tumours, and more rigorous comparison would require samples from more disseminated diseases.\u003c/p\u003e\u003cp\u003eFCGR3 (CD16), a surface receptor expressed on various myeloid cells, plays a critical role in antibody-dependent cytotoxicity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and mediates antigen uptake, processing, and presentation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Circulating CD14\u003csup\u003e+\u003c/sup\u003e monocytes can transition from classical CD14\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytes into FCGR3\u003csup\u003e+\u003c/sup\u003e intermediate and non-classical monocytes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. An increase in circulating intermediate CD14\u003csup\u003e+\u003c/sup\u003eFCGR\u003csup\u003e+\u003c/sup\u003e monocytes has been linked to inflammatory diseases such as atherosclerosis, rheumatoid arthritis and tuberculosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In tumour-infiltrating monocytic cells, the immunobiological role of FCGR3 expression is not completely understood. FCGR3 is shown to be highly expressed in immunosuppressive monocytes and TAM populations [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, an ovarian cancer study found spatially co-localized enrichment of FCGR3 expression in CD68\u003csup\u003e+\u003c/sup\u003e myeloid cells, T-cells and NK-cells, associated with improved survival [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In CRC, higher densities of tumour infiltrating FCGR3\u003csup\u003e+\u003c/sup\u003e myeloid cells have been linked to improved survival [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In our study, the prognostic value of IDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in pulmonary metastases was depended on FCGR3 expression, and CD14\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e cells showed opposing prognostic effects in the invasive margin and tumour centre when including all patients in the analysis. However, this pattern was not observed in neoadjuvant na\u0026iuml;ve primary CRC tumours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], suggesting that the favourable prognostic effect of FCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells in the tumour centre might reflect responses to neoadjuvant treatment.\u003c/p\u003e\u003cp\u003eMonocytic cell maturity can be evaluated based on HLA-DR expression and circulating immature HLA-DR\u003csup\u003e\u0026minus;/low\u003c/sup\u003e CD14\u003csup\u003e+\u003c/sup\u003e monocytes have been linked to systemic immunosuppression and immunotherapy resistance [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. A prior CRC study similarly reported that higher densities of CD14\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytic cells were associated with worse survival, whereas mature HLA-DR\u003csup\u003e+\u003c/sup\u003e monocytic cells were associated with better survival [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In our study, higher density of IDO\u003csup\u003e+\u003c/sup\u003e HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e immature monocytic cells in the invasive margin was strongly associated with worse survival, with this finding being even more pronounced in neoadjuvant na\u0026iuml;ve patients. This might suggest a role for IDO in immunosuppression associated with immature monocytic cells (with a monocytic myeloid derived suppressor cell-like phenotype), though interpretation requires caution due to low cell densities and lack of functional assays demonstrating immunosuppressive capacity. While HLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells were present at very low densities, their strong correlation with the more abundant FCGR3\u003csup\u003e+\u003c/sup\u003eIDO\u003csup\u003e+\u003c/sup\u003e monocytic cells may help explain their unexpectedly pronounced association with poor survival, though this causality remains speculative.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the selection bias inherent in metastasectomy cohorts restricts generalizability to all CRC patients with metastatic disease. It likely results in differing myeloid cell distribution compared to patients excluded from pulmonary metastasectomy. For example, myeloid cell densities in primary tumours of this study were lower compared to some earlier studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and primary tumours were of higher stages. Additionally, all patients had microsatellite stable tumours, and the results cannot be generalized to metastases from microsatellite unstable CRC. Second, TMAs only provide a snapshot of the tumour microenvironment, potentially limiting cell density estimates. However, numerous studies have shown reproducible results using TMAs. Third, data on \u003cem\u003eRAS\u003c/em\u003e mutation status was not available. It was not used in clinical decision making at the beginning of the study period, and therefore post hoc determination of \u003cem\u003eRAS\u003c/em\u003e status was not performed. Last, the study encompasses a 20-year period during which diagnostics and treatment might have evolved, however, this was addressed in the multivariable analysis. The strengths of this study include a reasonably sized, dual-institutional cohort. The machine learning based digital image analysis along with the multiplexed immunohistochemistry assay enabled detailed cell subtyping, as well as more precise estimates of cell densities compared to semi-quantitative visual estimates. The mortality data from Statistics Finland had full coverage, ensuring the reliability of the primary study outcome.\u003c/p\u003e\u003cp\u003eIn conclusion, our results suggest distinct prognostic roles for IDO and ARG1 expression in CRC metastases compared to primary tumours. While higher overall IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell densities generally predict favourable survival, the adverse effect of IDO expression in FCGR3\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e immature monocytic cells in metastases underscores the complexity of immune interactions in metastatic dissemination. Similarly, higher density of ARG1\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e\u0026minus;\u003c/sup\u003e granulocytes is an independent marker of poor survival. These findings provide novel insights into the tumour-immune dynamics in CRC metastasis, offering potential avenues for therapeutic targeting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003e The Oulu University Hospital Ethics Committee (EETMK 81/2008) approved the study. The Finnish National Authority of Medicolegal Affairs (VALVIRA) waived the need for informed consent due to the retrospective nature of the study. The study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003e\u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eT.T.S. declares consultation fee from Amgen Finland. T.T.S is CEO and co-owner of Healthfund Finland and Clinical Advisory board member of LS Cancer Diag. The other authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study was funded by Instrumentarium Science Foundation (O.H), Mary and Georg C. Ehrnrooth Foundation (O.H), Finnish State Research Funding (O.H, J-P.M, M.A, J.P.V, T.T.S), Cancer Foundation Finland (J.P.V, T.T.S), Sigrid Jus\u0026eacute;lius Foundation (J.P.V, T.T.S), J\u0026amp;A Erkko Foundation (J-P.M, T.T.S), Emil Aaltonen Foundation (J.P.V), Academy of Finland (T.T.S), iCAN Precision Medicine Flagship of the Academy of Finland (TTS), Relander Foundation (T.T.S). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: T.Ka, H.E, S.A.V., O.H, F.Y, J.P.V. Methodology: T.Ka., H.E, S.A.V., J.P.V. Formal Analysis: T.Ka, H.E, J.P.V. Investigation: All AuthorsData Curation: All Authors. Writing \u0026ndash; Original Draft Preparation: All AuthorsWriting \u0026ndash; Review and Editing: All Authors. Supervision: O.H. F.Y. J.P.V. Funding Acquisition: O.H. J.P.V. J-P.M.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe study benefited from samples/data from Northern Finland Biobank Borealis (Oulu, Finland) (https://oys.fi/biopankki/) and from Central Finland Biobank (Jyv\u0026auml;skyl\u0026auml;, Finland) (https:// https://www.hyvaks.fi/sairaala-nova/biopankki).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData generated and/or analysed during this study are not publicly available. The sharing of data will require approval from relevant ethics committees and/or biobanks. Further information including the procedures to obtain and access data from Finnish Biobanks are described at https://finbb.fi/en/fingenious-service\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUyttenhove, C. et al. Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. \u003cem\u003eNat. 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(2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pulmonary metastases, colorectal cancer, cancer immunology, myeloid cells, IDO, ARG1","lastPublishedDoi":"10.21203/rs.3.rs-7420974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7420974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndoleamine-2,3-dioxygeanse (IDO) and arginase1 (ARG1) are immunosuppressive enzymes frequently overexpressed in cancer, with higher expression in colorectal cancer primary tumours paradoxically linked to favourable survival. Their significance in metastases remain unexplored. Employing multiplexed immunohistochemistry and supervised machine learning-based digital image analysis, we analysed the IDO and ARG1 expression in monocytic cells, granulocytes, mast-cells and tumour cells in 91 resected pulmonary metastases from 53 corresponding primary tumours. We also evaluated the spatial distribution of IDO\u003csup\u003e+\u003c/sup\u003e and ARG1\u003csup\u003e+\u003c/sup\u003e cells relative to tumour cells and compared these patterns between metastases and primary tumours. In metastases, higher IDO\u003csup\u003e+\u003c/sup\u003e monocytic cell densities in the tumour centre were associated with favourable survival (adjusted HR 0.17, 95%CI 0.05\u0026ndash;0.59, p\u0026thinsp;=\u0026thinsp;0.005), while higher IDO\u003csup\u003e\u0026ndash;\u003c/sup\u003e monocytic cell densities in the invasive margins were associated with worse survival (adjusted HR 4.19, 95%CI 1.45\u0026ndash;2.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Moreover, higher densities of IDO\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e\u0026minus;\u003c/sup\u003e immature monocytic cells and IDO\u003csup\u003e+\u003c/sup\u003eFCGR3\u003csup\u003e+\u003c/sup\u003e monocytic cells in the invasive margin were also associated with poor survival, after adjusting for T-cell densities (adjusted HRs 26.4, 95%CI 4.95\u0026thinsp;\u0026minus;\u0026thinsp;140.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 3.91 95%CI 0.97\u0026thinsp;\u0026minus;\u0026thinsp;15.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056, respectively). These findings provide detailed insights into the immunosuppressive myeloid cell landscape in colorectal cancer pulmonary metastases, highlighting key differences from primary tumours and potential implications for therapy development.\u003c/p\u003e","manuscriptTitle":"Quantitative Analysis of Myeloid Cell Patterns and Immunosuppressive Enzyme (IDO, ARG1) Expression in Colorectal Cancer Pulmonary Metastases and Corresponding Primary Tumours","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:45:02","doi":"10.21203/rs.3.rs-7420974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T07:57:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T03:31:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T03:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187117189883996313467546066314590729111","date":"2025-12-31T02:33:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85099091677723901568187304024593844607","date":"2025-12-26T13:51:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T19:25:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T19:23:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-28T06:55:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-23T16:48:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-23T16:44:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e2ee1c7-4100-47b9-baf8-1210795b090d","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59052667,"name":"Biological sciences/Cancer"},{"id":59052668,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-09T16:16:03+00:00","versionOfRecord":{"articleIdentity":"rs-7420974","link":"https://doi.org/10.1038/s41598-026-42097-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-02 15:58:33","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-12-08 10:45:02","video":"","vorDoi":"10.1038/s41598-026-42097-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42097-8","workflowStages":[]},"version":"v1","identity":"rs-7420974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7420974","identity":"rs-7420974","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}