Modulating Tumor-Associated Macrophages through CSF1R Inhibition: A Potential Therapeutic Strategy for HNSCC

Modulating Tumor-Associated Macrophages through CSF1R Inhibition: A Potential Therapeutic Strategy for HNSCC | 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 Research Article Modulating Tumor-Associated Macrophages through CSF1R Inhibition: A Potential Therapeutic Strategy for HNSCC Kaiting Chen, Xiaochen Li, Shuyi Dong, Yu Guo, Ziyin Luo, Shi-Min Zhuang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5231239/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: Tumor-associated macrophages (TAMs) are pivotal immune cells within the tumor microenvironment (TME), exhibiting dual roles across various cancer types. Depending on the context, TAMs can either suppress tumor progression and weaken drug sensitivity or facilitate tumor growth and drive therapeutic resistance. This study explores whether targeting TAMs can suppress the progression of head and neck squamous cell carcinoma (HNSCC) and improve the efficacy of chemotherapy. Methods: Bioinformatics analyses were performed to evaluate TAMs infiltration levels in HNSCC tumor tissues and examine their associations with patients’ clinicopathological characteristics and prognosis. Flow cytometry was utilized to measure the expression of key macrophage markers and assess apoptosis following treatment with colony stimulating factor 1 receptor (CSF1R) inhibitors (BLZ945, PLX3397). Additionally, immunohistochemistry was employed to detect CD68 and CD8 expression. In vivo, the antitumor efficacy of CSF1R inhibitors was tested in mouse HNSCC tumor model, both as monotherapy and in combination with cisplatin, to evaluate potential synergistic effects. Results: Bioinformatic analysis identified TAMs as the predominant infiltrating immune cells in the TME of HNSCC, with significantly higher infiltration levels in tumor tissues compared to adjacent non-tumor tissues. High TAMs infiltration was associated with poorer overall survival (OS), disease-free survival (DFS), human papillomavirus (HPV) infection status, and advanced disease staging. The TAMs-related genes prediction model demonstrated high prognostic accuracy. CSF1R is primarily expressed in TAMs, where high CSF1R expression may suppress antigen binding and activation. In vitro experiments showed that CSF1R inhibitors induce TAMs apoptosis, enhance their phagocytic activity, and reduce CD206 expression and IL-10 secretion, thereby diminishing their immunosuppressive function. In vivo experiments revealed that while CSF1R inhibitors alone had limited efficacy in suppressing tumor growth, their combination with cisplatin significantly enhanced therapeutic efficacy, as evidenced by increased CD8 + T cell infiltration within the TME. Conclusion: Regulating TAMs by targeting CSF1R to diminish immunosuppressive functions and enhance anti-tumor immunity represents a promising therapeutic strategy for HNSCC. HNSCC TAMs CSF1R inhibitors cisplatin combination therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Head and neck squamous cell carcinoma (HNSCC) ranks as the seventh most common malignancy worldwide, with approximately 900,000 new cases and 450,000 deaths reported globally in 2022[ 1 ]. The incidence of HNSCC continues to rise, and it is projected to increase by 30% by 2030[ 2 ]. The etiology of HNSCC varies geographically: in Southeast Asia and Australia, smoking, betel nut chewing, and alcohol consumption are the primary risk factors, while in the United States and Western Europe, the growing incidence of oropharyngeal HNSCC is attributed to an increase in HPV infections[ 3 – 6 ]. Other risk factors include radiation exposure, wood dust, asbestos, salted foods, poor oral hygiene, and Epstein-Barr virus (EBV) infection[ 7 , 8 ]. HNSCC is a highly heterogeneous malignancy, encompassing multiple anatomical sites and driven by diverse carcinogenic factors, each necessitating distinct therapeutic strategies. Treatment typically involves a multimodal strategy, incorporating surgery, radiation therapy, chemotherapy, immunotherapy, and targeted therapy. The choice of treatment depends on tumor location, stage, the patient's overall health, and molecular characteristics. Around 30–40% of HNSCC cases are detected at an early stage, where surgery or radiation alone can result in high cure rates[ 9 ]. However, early diagnostic tools remain inadequate, and more than 60% of patients are diagnosed at advanced or metastatic stages, often without evident premalignant lesions[ 10 , 11 ]. According to the 2022 National Comprehensive Cancer Network (NCCN) guidelines, radiation therapy combined with cisplatin is the standard treatment for patients with locally advanced, unresectable HNSCC. For patients with recurrent or metastatic HNSCC, the EXTREME regimen (cetuximab + cisplatin or carboplatin + 5-FU) remains the first-line treatment[ 12 ]. Although initial responses are often favorable, most patients eventually develop resistance[ 13 , 14 ]. Furthermore, conventional chemotherapy is non-selective and associated with significant side effects[ 15 ]. The introduction of epidermal growth factor receptor (EGFR) inhibitors marked a major advance in targeted therapy, yet many patients are either inherently resistant to these drugs or develop resistance during treatment[ 16 ]. In recent years, immune checkpoint inhibitors (PD-1 inhibitors), such as nivolumab and pembrolizumab, have been approved for the treatment of recurrent or metastatic HNSCC that progresses during or after platinum-based chemotherapy. While some patients experience durable responses to PD-1 inhibitors, the overall efficacy remains limited, with only 17% of patients responding to monotherapy and a four-year survival rate below 30%[ 17 ]. Consequently, there is an urgent need for novel therapeutic approaches in HNSCC. As research into the tumor microenvironment (TME) has advanced, the intricate interactions among cells and molecules within the TME have been increasingly recognized for their role in influencing tumor progression and treatment efficacy. TAMs, key regulatory factors within the TME, have emerged as significant players in cancer growth, invasion, metastasis, and treatment resistance[ 18 ]. Blood monocytes migrate to tumor sites in response to chemokines secreted by cancer cells and differentiate into either pro-inflammatory or anti-inflammatory phenotypes. In most solid tumors, TAMs tend to adopt an M2 phenotype, promoting tumor progression, while M1 TAMs exhibit anti-tumor functions[ 19 ]. TAMs can enhance anti-tumor immunity by phagocytosing cancer cells, but they can also facilitate immune evasion and tumor growth. TAMs heterogeneity is evident across different cancer types and stages of tumor progression[ 20 ]. High levels of TAM infiltration are linked to poor prognosis in breast cancer[ 21 ], lung cancers[ 22 ], pancreatic cancers[ 23 ], melanoma[ 24 ] and Hodgkin's lymphoma[ 25 ]. While in colorectal cancer, TAMs infiltration is associated with better outcomes[ 26 ], although this relationship is reversed in colorectal liver metastases[ 27 ]. TAMs are not only linked to prognosis but also play a role in chemotherapy sensitivity[ 28 ]. Given their high plasticity and heterogeneity, targeting TAMs through therapeutic strategies shows potential for improving both patient survival and overall outcomes. Current strategies for targeting TAMs primarily focus on three approaches: depleting TAMs, reprogramming their polarization, and inhibiting their recruitment[ 19 ]. Among these, targeting CSF1R has garnered particular attention. By inhibiting CSF1R, not only can TAMs be depleted, but their recruitment and polarization can also be modulated[ 29 ]. CSF1R is a transmembrane tyrosine kinase receptor found on macrophages. Upon binding with its ligand CSF1, it induces receptor dimerization and tyrosine kinase-mediated phosphorylation, initiating intracellular signaling cascades that regulate macrophage survival, proliferation, differentiation, and migration[ 30 – 32 ]. Furthermore, targeting CSF1R in breast cancer has demonstrated potential synergistic effects when combined with chemotherapy or immunotherapy, and ongoing clinical trials are currently assessing this promising approach[ 33 – 37 ]. Although CSF1R inhibition has demonstrated the ability to regulate TAMs through multiple pathways in cancers such as pancreatic cancer[ 38 ], recent studies highlight that its efficacy may depend on the specific organ and tumor subtype[ 39 ]. While CSF1R inhibitors have shown promise in TAMs regulation, their role in HNSCC remains unclear. Therefore, this study aims to comprehensively investigate the relationship between TAMs infiltration, prognosis, and clinicopathological characteristics in HNSCC through bioinformatics analysis. Additionally, through in vivo and in vitro experiments, we elucidate the mechanisms by which CSF1R-targeted therapies regulate TAMs in HNSCC and evaluate the potential of CSF1R inhibitors as a therapeutic strategy for HNSCC. Methods Data Source and Preprocessing We obtained mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, specifically utilizing datasets GSE113282, GSE270220, and GSE15906. Immune cell infiltration in HNSCC was assessed using CIBERSORT, with TAMs defined as the sum of M0, M1, and M2 macrophages. Patients were categorized into high and low TAMs infiltration groups, as well as CSF1R/CD68 expression groups, using the minimum p -value method determined via X-Tile software[ 40 ]. Kaplan-Meier survival analysis was performed to evaluate overall survival (OS) and disease-free survival (DFS), with statistical significance assessed using the log-rank test. Further analysis of TAMs infiltration levels involved dividing patients into high and low infiltration groups based on the median TAMs infiltration level. The association between TAMs infiltration and clinical characteristics was evaluated using t-tests to determine statistical significance. For single-cell analysis, we employed the Seurat v4.0 package to normalize, pool, and cluster the GSE188737 single-cell dataset. Classic markers were used to annotate broad cell populations, including epithelial cells (KRT7, KRT17), salivary cells (STATH), fibroblasts (COL1A2), endothelial cells (PECAM), and immune cells (PTPRC). Fibroblasts were further categorized into cancer-associated fibroblasts (CAFs; MMP2) and myofibroblasts (ACTA2), while immune cells were divided into T-cells (CD3E, NKG7), NK-cells (NKG7, XCL2), B-cells (CD79A), plasma cells (IGHG1), mast cells (TPSAB1), conventional dendritic cells (LAMP3), plasmacytoid dendritic cells (LILR4), and macrophages/monocytes (CD163)[ 41 ]. Following annotation, clustering was conducted to visualize CSF1R expression across various cell subclusters. TAMs subclusters were extracted, and based on CSF1R expression levels within TAMs, they were divided into CSF1R_High and CSF1R_Low groups using the median expression level as the cutoff. Differential gene expression analysis was performed between these groups, followed by functional enrichment analysis of differentially expressed genes using the clusterProfiler package in R. Gene symbols were converted to Entrez Gene IDs, and Gene Ontology (GO) enrichment analysis (Biological Process, BP) was conducted using Gene Set Enrichment Analysis (GSEA). The significance level for enrichment was set at p < 0.05, with results visualized through dot plots and GSEA enrichment curves. Establishment of a Prognostic Model To assess the prognostic value of TAMs-related genes, we selected 208 macrophage-related marker genes from the CellMarker database. We conducted univariate Cox regression analysis to evaluate the association between these genes and survival in the TCGA cohort, selecting those with p -value < 0.05 for further analysis. From this screening, 42 genes were identified as being significantly associated with survival and were used to develop a prognostic model using the LASSO Cox regression method. Risk scores were then calculated based on this model. 514 patients in the TCGA-HNSCC cohort were stratified into low-risk and high-risk subgroups according to their risk scores, using the minimum P-value method. LASSO regression analysis, along with univariate and multivariate Cox regression analyses, was employed to validate the model. Kaplan-Meier analysis was performed to compare OS between the two subgroups. The model's sensitivity and specificity were further assessed using time-dependent receiver operating characteristic (ROC) analysis. Cell Culture and Drug We obtained the murine oral squamous cell carcinoma (OSCC) cell line MTCQ1, which is derived from HNSCC, from the Bioresource Collection and Research Center (BCRC). Additionally, we acquired the murine fibroblast cell line L929 from the American Type Culture Collection (ATCC). All cell lines were tested for mycoplasma contamination using a single-step polymerase chain reaction (PCR) method. The cells were cultured in high-glucose DMEM (Procell) medium supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin (Beyotime), and 100 µg/mL streptomycin (Beyotime) in a humidified incubator at 37°C with 5% CO 2 . We purchased anti-mouse CSF1R (clone: AFS98) from BioXcell and CSF1R small-molecule inhibitors Pexidarinib (PLX-3397, HY-16749A) and Sotuletinib (BLZ945, HY-12768A) from MCE for use in mouse tumor treatments and in vitro cell experiments. BMDMs generation and in vitro stimulation Bone marrow cells were isolated from mice and cultured as a single-cell suspension in 10 cm dishes with DMEM supplemented with 10% FBS and 30% L929-conditioned medium. Fresh medium was added on days 3 and 5. On day 7, the bone marrow-derived macrophages (BMDMs) were harvested and subsequently stimulated with MTCQ1 tumor cell supernatant to induce their differentiation into TAMs. Phagocytosis To assess the impact of CSF1R small molecule inhibitors on the phagocytic capacity of TAMs, MTCQ1 tumor cells were first pre-incubated with 5 µM carboxyfluorescein succinimidyl ester (CFSE) at 37°C for 10 minutes. The reaction was halted by adding DMEM containing 10% FBS. After thorough washing with PBS twice, the cells were incubated with 100 µg/mL mitomycin C at 37°C for 10 minutes. The cells were then washed twice with DMEM containing 10% FBS, harvested, and co-cultured with TAMs at a 1:1 ratio (1x10 6 cells/well) in a 6-well plate for 2 hours. Afterward, the supernatant was discarded, and the wells were washed twice with PBS to remove non-phagocytosed tumor cells. Phagocytic activity was evaluated using a fluorescence microscope (Olympus) by calculating the percentage of macrophages containing CFSE-labeled green fluorescence. Flow Cytometry TAMs treated with DMSO, PLX3397, or BLZ945 for 48 hours were stained to detect the expression of relevant mouse proteins using the following antibodies: anti-mouse CD206 (Biolegend, Cat: 41707), anti-mouse CD86 (Biolegend, Cat: 105023), and Anti-Mouse MHC Class II V5-Tag-Alexa Fluor 647 (ONBO Biosciences, Cat: GTX80040). Additionally, apoptosis was assessed using the Annexin V-FITC/PI apoptosis kit(liankebio). The stained cells were analyzed using a CytoFLEX S flow cytometer (Beckman Coulter), and data analysis was performed with FlowJo V10.8 software (TreeStar). Measurement of IL-10 Secretion by ELISA Supernatants from TAMs treated with DMSO, PLX3397, or BLZ945 for 48 hours were collected and stored at − 80°C. A protease inhibitor cocktail (Invitrogen) was added in accordance with the manufacturer’s instructions. IL-10 levels were quantified using the Mouse IL-10 Uncoated ELISA Kit (Invitrogen), following the provided protocol. Mice and Tumor Model We obtained SPF-grade 6–8 week-old male C57BL/6 mice from Guangdong Medical Laboratory Animal Center. All animal experiments were conducted in strict accordance with the guidelines of the Animal Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (IACUC-2023101306). In brief, 3×10 6 MTCQ1 tumor cells were subcutaneously (s.c.) implanted into the right flanks of the mice[ 42 ]. Tumor volume was measured along three orthogonal axes (a, b, and c) and calculated using the formula: Tumor Volume = abc/2. Treatment with the anti-CSF1R antibody (10 mg/kg, i.p.), PLX3397 (60 mg/kg, p.o.), and BLZ945 (60 mg/kg, p.o.) began on the day of implantation and was administered every 3 days until the end of the experiment. When the tumor volume reached approximately 100 cubic millimeters, cisplatin was administered intraperitoneally (i.p.) at a dose of 10 mg/kg once a week. Immunohistochemistry and image analysis Tumor samples and paired adjacent non-tumor tissues were collected from 12 HNSCC patients at the Sixth Affiliated Hospital of Sun Yat-sen University between January 2020 and December 2023. The study received approval from the Ethics Committee (approval numbers 2020ZSLYEC-303 and 2021ZSLYEC-270), and informed consent was obtained from all patients or their legal guardians. Immunohistochemistry (IHC) was performed on these samples using an anti-CD68 antibody. Additionally, subcutaneous tumor samples from MTCQ1 mice were collected for CD8 IHC analysis. Tissue sections of 4 µm were placed in an oven at 60 ℃ for 20 minutes. Deparaffinization was performed using two xylene treatments (10 minutes each), followed by rehydration through a graded ethanol series (100%, 95%, 85%, and 75%, 3 minutes each) and washing in ddH 2 O. Antigen retrieval was conducted by boiling the sections in Tris-HCl buffer (pH 9.2) in a pressure cooker for 10 minutes. After cooling, sections were incubated with endogenous peroxidase blocking solution (Beyotime, Cat: P0100B) at room temperature for 10 minutes. Blocking was then done with 5% bovine serum albumin (BSA) for 30 minutes at room temperature. Sections were incubated overnight at 4°C with anti-CD68 antibody (Sino Biological, Cat: 11192-T56, 1:500) or anti-CD8α antibody (Abcam, Cat: ab217344, 1:500), followed by a 30-minute incubation at room temperature with horseradish peroxidase-conjugated anti-rabbit antibody (Beyotime, Cat: A0208, 1:50). Staining was visualized using a DAB detection kit (ZSGB-BIO, Cat: ZLI-9017). Slides were scanned at ×40 magnification using the SQS-1000 slide scanner (Shenzhen Shengqiang Technology Co., Ltd., China). Digital image analysis was performed using ImageJ software (version 1.54d). Two independent researchers evaluated the tumor samples, examining five distinct areas per section. The CD68 histological score (H-score) was determined by multiplying the proportion score by the intensity score. Additionally, the average optical density (AOD) was used to quantify the proportion of CD8-positive cells, with final results reported as the mean of these measurements. Statistical Analyses Data were analyzed using Prism 8.0.2 software (GraphPad) and are presented as mean ± SEM. The significance of differences between two groups was assessed using the Wilcoxon rank-sum test, paired Student’s t-test, or unpaired Student’s t-test, as appropriate. Differences among multiple groups were evaluated using ANOVA. Overall survival (OS) was defined as the time from surgery to death from any cause. Disease-free survival (DFS) was defined as the time following treatment during which patients remained free from cancer recurrence or progression. Survival rates were estimated using the Kaplan-Meier method, with comparisons made using the log-rank test. All p-values were two-sided, with p < 0.05 considered statistically significant. Results TAMs infiltration and prognostic significance in HNSCC In the TCGA and GSE113282 datasets, TAMs were identified as the predominant component of immune infiltration within HNSCC tumors (Fig. 1 a-b). Kaplan-Meier survival analysis revealed that high TAMs infiltration was significantly associated with poorer overall survival (OS) in the TCGA cohort ( p = 0.012, HR = 1.385). Similarly, in the GSE270220 dataset, elevated TAMs infiltration correlated with lower disease-free survival (DFS) ( p = 0.006, HR = 5.056) (Fig. 1 c-d). Paired analysis of tumor versus non-tumor tissues in the TCGA and GSE107591 datasets demonstrated a significant increase in TAMs infiltration within tumor tissues (Fig. 1 e-f). Immunohistochemical analysis further confirmed this finding, showing markedly higher CD68 expression in HNSCC tissues compared to adjacent non-tumor tissues (Fig. 1 g). Clinical feature analysis of the TCGA-HNSCC cohort indicated that high TAMs infiltration was significantly associated with HPV status ( p < 0.001), T stage ( p < 0.001), N stage ( p = 0.009), overall TNM stage ( p < 0.001), and clinical grade ( p = 0.022) (Table 1 ). These results supported the role of TAMs as a significant biomarker for HNSCC, suggesting that TAMs may serve as a novel therapeutic target for HNSCC. Table 1 TCGA-HNSCC cohort clinical characteristics by high/low TAM infiltration using median cutoff. High TAMs Low TAMs p n 257 257 Age (years) = > 60 (%) 129 (50.2) 130 (50.6) 1 Sex = Male (%) 186 (72.4) 193 (75.1) 0.548 Race (%) 0.966 American Indian or Alaska Native 1 (0.4) 1 (0.4) Asian 6 (2.3) 5 (1.9) Black or African American 25 (9.7) 22 (8.6) White 219 (85.2) 221 (86.0) NA 6 (2.3) 8 (3.1) HPV_state (%) < 0.001 HNSC_HPV- 226 (87.9) 189 (73.5) HNSC_HPV+ 17 (6.6) 55 (21.4) NA 14 (5.4) 13 (5.1) T_stage (%) < 0.001 Low T_stage 84 (32.7) 98 (38.1) High T_stage 155 (60.3) 115 (44.7) NA 18 (7.0) 44 (17.1) N_stage (%) 0.009 Node negative 83 (32.3) 90 (35.0) Node Positive 136 (52.9) 106 (41.2) NA 38 (14.8) 61 (23.7) stage (%) < 0.001 Ⅰ-Ⅱ 46 (17.9) 54 (21.0) Ⅲ-Ⅳ 191 (74.3) 153 (59.5) NA 20 (7.8) 50 (19.5) Grade (%) 0.022 Low Grade 193 (75.1) 169 (65.8) High Grade 58 (22.6) 72 (28.0) NA 6 (2.3) 16 (6.2) Radiation_Therapy (%) 0.753 No 78 (30.4) 76 (29.6) Yes 143 (55.6) 150 (58.4) NA 36 (14.0) 31 (12.1) Construction of the risk signature Univariate Cox analysis identified 42 genes significantly associated with OS, which were further refined using LASSO regression to construct a risk signature (Fig. 2 a-c). This risk model effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly poorer OS ( p < 0.001) (Fig. 2 d). The ROC curve analysis demonstrated that the predictive model had good accuracy (AUC = 0.70 for 1-year, 0.674 for 2-year, and 0.69 for 3-year survival) (Fig. 2 e). Both univariate and multivariate Cox regression analyses confirmed that TAMs infiltration was an independent prognostic factor for OS in HNSCC (Fig. 2 f). CSF1R expression and its impact on immune function and prognosis UMAP analysis of scRNAseq data from seven HNSCC samples in the GSE188737 dataset showed that CSF1R expression was predominantly localized to TAMs, with a strong correlation to macrophage markers (Fig. 3 a). In the TCGA dataset, CSF1R gene expression had the highest Pearson correlation with macrophages (Fig. 3 b), and was significantly higher in tumor tissues compared to non-tumor tissues ( p < 0.05) (Fig. 3 c). Kaplan-Meier analysis in the GSE270220 dataset indicated that high CSF1R / CD68 expression was significantly associated with poorer DFS ( p = 0.025, HR = 2.580) (Fig. 3 d). GO enrichment analysis of TAMs at the single-cell level suggested that TAMs with low CSF1R expression were involved in antigen binding and activation functions, implying that CSF1R inhibition may enhance anti-tumor immunity (Fig. 3 e). These results suggest that high CSF1R expression in HNSCC is closely associated with the functional regulation of tumor-associated macrophages, and that CSF1R inhibition may improve patient prognosis by enhancing anti-tumor immune responses. Effects of CSF1R inhibitors on TAMs function and survival Pexidartinib (PLX3397) and Sotuletinib (BLZ945) are two promising CSF1R inhibitors. PLX3397 primarily targets CSF1R and also inhibits KIT and FLT3[ 43 ]. It has gained considerable attention for its ability to suppress the proliferation of TAMs, which are key players in tumor progression and immune evasion[ 44 ]. Recently, PLX3397 was approved by the U.S. Food and Drug Administration (FDA) for the treatment of tenosynovial giant cell tumors (TGCT)[ 45 , 46 ]. Similarly, Sotuletinib (BLZ945) specifically targets CSF1R, though it differs structurally from PLX3397. Despite these differences, both inhibitors act by blocking CSF1R signaling, which reduces TAMs populations and inhibits their pro-tumor functions[ 47 ]. Both compounds are being investigated for their potential to modulate the tumor microenvironment and serve as therapeutic agents across various cancers. Currently, PLX3397 and BLZ945 are in Phase I and Phase II clinical trials for the treatment of advanced solid tumors (NCT02734433, NCT02829723). Based on their promising mechanisms, we selected these two CSF1R small-molecule inhibitors for investigation in our study. In vitro studies demonstrated that treatment of TAMs with CSF1R inhibitors (PLX3397, BLZ945) led to significant morphological changes and enhanced their phagocytic activity (Figs. 4 a-b). Initial observations suggested that CSF1R inhibitors might inhibit TAMs polarization. To gain deeper insights into the expression of M2 markers and other proteins on TAMs, flow cytometry analysis revealed that both inhibitors reduced the expression of the M2 typical marker CD206. PLX3397 notably increased CD86 expression in TAMs, while BLZ945 only slightly upregulated CD86. However, PLX3397 decreased MHC II expression, whereas BLZ945 slightly increased MHC II expression (Fig. 4 c). Furthermore, CSF1R inhibitors significantly reduced IL-10 secretion and increased TAMs apoptosis (Figs. 4 d-e). Collectively, these findings suggested that CSF1R inhibitors shift TAMs towards the M1 macrophage phenotype, suppressed M2 macrophage differentiation, enhanced tumor cell phagocytosis, and reduced TAMs survival and immunosuppressive functions. Efficacy of CSF1R inhibitors alone and in combination with cisplatin in vivo To further assess the in vivo efficacy of CSF1R inhibition, a subcutaneous HNSCC model was established by injecting cancer cells directly into mice. The results showed that treatment with PLX3397 significantly inhibited tumor growth; however, neither α-CSF1R antibody nor BLZ945 alone effectively controlled tumor progression (Figs. 5 a-c). When combined with cisplatin, all three CSF1R-targeted therapies led to a marked reduction in tumor growth and weight compared to the control or monotherapy groups (Figs. 5 d-e). Immunohistochemical analysis indicated an increase in CD8 + T cell infiltration in the tumors of the combination treatment group, suggesting an enhanced antitumor immune response. We hypothesize that the increased infiltration of CD8 + T cells may contribute to the enhanced efficacy of cisplatin when combined with CSF1R inhibitors (Figs. 5 f-h). Discussion Traditional treatments for HNSCC, including surgery, radiotherapy, and chemotherapy, have notable limitations. Surgery is invasive and often impairs functionality and quality of life; radiotherapy can damage healthy surrounding tissues, causing significant side effects; and chemotherapy is frequently hindered by drug resistance and systemic toxicity. Additionally, high rates of recurrence and metastasis make it challenging for any single therapy to achieve comprehensive disease control. Although immunotherapy, particularly PD-1 inhibitors, has shown promise, it has a relatively low response rate and is associated with immune-related adverse events. As a result, the development of novel targeted therapies and more effective immunotherapies remains a critical focus of research. Reactivating the body's anti-tumor immune response through immunotherapy is a widely accepted strategy in cancer treatment. While T cell-targeted immunotherapies have made significant breakthroughs, many patients still require more effective treatment options. TAMs, the most abundant immune cells infiltrating solid tumors, act as a bridge between innate and adaptive immunity. Targeting TAMs to enhance anti-tumor activity has become a promising new approach in immunotherapy. This study reveals that inhibiting CSF1R to modulate TAMs presents a potential therapeutic strategy for HNSCC. By regulating TAMs, the most infiltrated immune cells in solid tumors, this strategy enhances anti-tumor immune responses, expands the scope of immunotherapy, particularly for patients who do not respond adequately to current T cell-targeted therapies, and offers the potential for more effective treatment options for HNSCC patients. In HNSCC, semi-quantitative immunohistochemistry studies have shown that a high infiltration of CD68-positive macrophages and elevated expression of CD163 in macrophages are linked to poorer OS in oral OSCC patients[ 48 ]. Immunohistochemical analyses further demonstrated that high CD163 expression in primary HNSCC tumors is associated with poorer OS, progression-free survival (PFS), locoregional failure-free survival (LFFS), and distant metastasis-free survival (DMFS), particularly in human papillomavirus-negative (HPV-) patients[ 49 ]. Additionally, another research using tissue microarrays found that high densities of CD163 + M2-type TAMs are correlated with poor prognosis in HNSCC, with macrophage content being closely associated with lymph node metastasis and clinical staging[ 50 ]. Using bioinformatics analysis across multiple datasets, we identified that high infiltration of TAMs was linked to poorer overall survival (OS), disease-free survival (DFS), HPV infection status, and advanced clinical staging and grading. Additionally, our TAMs-related gene prediction model showed strong prognostic accuracy. This study further highlights the prognostic significance of TAMs in HNSCC and provides a strong theoretical foundation for developing TAMs-targeted therapeutic strategies. The study of CSF1R-targeted therapies to inhibit TAMs function is actively underway. However, the effectiveness of these treatments varies significantly across different tumor types and clinical trials. Some studies have shown that targeting macrophages can significantly inhibit tumor progression, while others have not demonstrated substantial efficacy, leading to ongoing debates about the validity and applicability of macrophage-targeted therapies. For example, in a pancreatic ductal adenocarcinoma (PDAC) mouse model, blocking CSF1R signaling effectively reprogrammed macrophages to enhance antigen presentation and promote robust anti-tumor T cell responses. However, this also led to the upregulation of immune checkpoint molecules like PD-L1 and CTLA4, diminishing the therapeutic benefits[ 38 ]. Similar effects were observed in our study, CSF1R inhibitors reduced CD206 expression in TAMs while increasing CD86 and MHC II levels, driving their polarization towards a pro-inflammatory M1 phenotype, thereby enhancing antigen presentation and anti-tumor immune responses. Furthermore, we observed an upregulation of PD-L1 following CSF1R blockade (data not shown). In a murine model of pre-neoplastic glioblastoma, targeting TAMs with CSF1R inhibitors significantly improved survival and suppressed tumor growth. Moreover, CSF1R inhibition markedly slowed the intracranial growth of patient-derived glioblastoma xenografts[ 51 ]. We also discovered that CSF1R inhibitors were able to retard tumor growth in subcutaneous HNSCC mouse models, though their efficacy was limited when used as a monotherapy. Moreover, some studies have shown that combining CSF1R inhibitors with other therapies enhances tumor sensitivity to chemotherapy and strengthens anti-tumor immune responses. For example, combining CSF1R signaling antagonists with paclitaxel blocks macrophage recruitment, thereby improving survival in tumor-bearing mice by slowing primary tumor progression and reducing lung metastasis[ 34 ]. Jonathan B. Mitchem's study demonstrated that targeting TAMs through CSF1R inhibition can reduce the number of pancreatic tumor-initiating cells (TICs), which in turn increases the infiltration of tumor-infiltrating lymphocytes (TILs) and enhances the efficacy of gemcitabine chemotherapy in vivo[ 23 ]. Strachan, D.C. observed that inhibiting CSF1R not only decreased TAMs turnover but also significantly increased the infiltration of CD8 + T cells in cervical and breast cancers[ 52 ]. Likewise, our in vivo experiments demonstrated that the combination of Anti-CSF1R antibodies or small-molecule CSF1R inhibitors with cisplatin significantly enhanced its antitumor efficacy. Additionally, we observed a substantial increase in CD8 + T cell infiltration in the groups receiving the combination therapy. While CSF1R inhibitors alone showed limited efficacy, their combination with cisplatin produced synergistic effects, not only effectively inhibiting tumor growth but also potentially enhancing the tumor's sensitivity to chemotherapy. These results suggest that targeting TAMs can directly modulate the TME and enhance the effects of traditional chemotherapy, providing an experimental basis for developing new combination therapies. Extensive preclinical research has laid a strong foundation for advancing to clinical trials. CSF1R inhibitors, such as PLX3397, have been utilized in the treatment of advanced solid tumors with high TAMs infiltration, including glioblastoma (NCT01349036), pancreatic cancer (NCT02777710), and breast cancer (NCT01525602). These trials demonstrated a reduction in tumor burden and effective disease control in treated patients. However, in various subtypes of breast cancer, although TAMs can be reduced by CSF1R inhibitors, the overall effectiveness in controlling certain breast cancer subtypes remains limited[ 53 ]. Furthermore, some studies have reported that while PLX3397 can reduce circulating CD14 dim /CD16 + monocytes in recurrent glioblastoma[ 54 ], it did not significantly extend overall survival in a Phase II clinical trial for recurrent glioblastoma (NCT01349036), raising concerns about its clinical efficacy. The therapeutic efficacy of CSF1R inhibition in cancer patients can vary significantly depending on the organ-specific and subtype-specific characteristics of the TME. We aspire for our research to offer profound insights for future clinical trials and facilitate the refinement of treatment strategies. PLX3397 is a competitive inhibitor of CSF-1R that blocks it’s signaling, inhibiting macrophage proliferation and promoting the polarization of tumor-associated macrophages (TAMs) towards a pro-inflammatory M1 phenotype, effectively delaying tumor growth in a mouse model of hepatocellular carcinoma[ 55 ]. However, a study by Shi et al. indicated that PLX3397 improves the immunosuppressive tumor microenvironment (TME) primarily by reducing the number of TAMs rather than altering their polarization[ 56 ]. In our in vitro experiments, PLX3397 downregulated CD206 expression in TAMs, upregulated CD86, driving TAMs toward an M1 phenotype, and enhanced apoptosis and phagocytosis in TAMs. However, PLX3397 also reduced MHC II expression, potentially impairing antigen presentation. BLZ945, another highly selective CSF1R inhibitor, suppresses TAM activity and proliferation while reprogramming their immunosuppressive properties. According to a study by Magkouta et al., BLZ945 effectively inhibited tumor progression, reduced TAM infiltration, and promoted M1 polarization in a mouse model of mesothelioma[ 57 ]. Similarly, in our studies, BLZ945 downregulated CD206, enhanced phagocytosis and apoptosis in TAMs, but showed a weaker effect on CD86 upregulation and only mildly increased MHC II expression. Unfortunately, BLZ945 alone did not significantly suppress tumor growth in vivo . Additionally, a murine IgG2a monoclonal antibody targeting CSF-1R effectively depleted macrophages in vivo but, like BLZ945, failed to show significant tumor-suppressive effects. In contrast, PLX3397 demonstrated the most pronounced anti-tumor efficacy in our HNSCC mouse model, possibly due to its ability to inhibit other kinases, such as KIT and FLT3[ 58 ], which may enhance its therapeutic effect. Overall, the differing efficacies of these CSF1R inhibitors may be attributed to variations in their modulation of CSF1R signaling pathways and other molecular targets, suggesting that the selection of CSF1R inhibitors in clinical applications should be tailored to specific pathological conditions and therapeutic goals. In summary, the impact of CSF1R-targeted TAMs modulation is likely influenced by tumor type, subtype, progression, and individual patient differences. Moreover, different CSF1R inhibitors may regulate TAMs functions through distinct mechanisms, affecting antitumor immune responses. Therefore, clinical applications should be tailored to individual patients to devise the most effective treatment strategies. Although CSF1R inhibitors alone did not significantly inhibit tumor growth in vivo , their combination with cisplatin significantly enhanced antitumor efficacy. This suggests that CSF1R inhibitors may enhance the effectiveness of chemotherapy through synergistic mechanisms, overcoming the limitations of monotherapy. These findings not only expand our understanding of TAMs in HNSCC but also offer new insights for future clinical strategies targeting TAMs. Further studies should explore the potential of combining CSF1R inhibitors with other therapies to optimize treatment regimens and maximize antitumor effects, ultimately improving outcomes for HNSCC patients. Abbreviations Tumor-Associated Macrophages TAMs Tumor Microenvironment TME Head and Neck Squamous Cell HNSCC Colony Stimulating Factor 1 Receptor. CSF1R Overall Survival OS Disease-Free Survival DFS Human Papillomavirus HPV Epstein-Barr virus EBV National Comprehensive Cancer Network NCCN Epidermal Growth Factor Receptor EGFR The Cancer Genome Atlas TCGA Gene Expression Omnibus GEO Gene Ontology GO Gene Set Enrichment Analysis GSEA Oral Squamous Cell Carcinoma OSCC American Type Culture Collection ATCC Fetal Bovine Serum FBS Bone Marrow-Derived Macrophages BMDMs Declarations Ethics approval and consent to participate The collection of patient samples for this study was approved by the Ethics Committee under approval numbers 2020ZSLYEC-303 and 2021ZSLYEC-270. All animal experiments were conducted in strict accordance with the guidelines of the Animal Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (IACUC-2023101306). Consent for publication Not applicable. Availability of data and materials The authors confirmed that the data supporting the findings of this study are available within the article and/or supplementary materials. Competing interests The authors declare that they have no competing interests. Author’s Contributions Conceptualization, J.Liao. and K.C.; Investigation, K.C. and X.L., S.D. and Y.G.; Writing - Original Draft, K.C.; Writing - Review & Editing, J.Liao., S.-M.Z., J.Liu. T.L and W.-P.W.; Visualization, K.C. and S.D. , X.L and Z.L.; Supervision, J.Liao., and W.-P.W.; Funding Acquisition, J.Liao. and T.L. 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Supplementary Files Modediagramillustrating.docx Modediagram.pdf Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Major revision 19 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers invited by journal 29 Oct, 2024 Editor assigned by journal 26 Oct, 2024 First submitted to journal 12 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5231239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371578299,"identity":"ef4ae814-9022-41a9-b8df-ae73ca3e254b","order_by":0,"name":"Kaiting Chen","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kaiting","middleName":"","lastName":"Chen","suffix":""},{"id":371578300,"identity":"a0f35d05-b86a-40c4-8e7a-dfe8b89f256d","order_by":1,"name":"Xiaochen Li","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Li","suffix":""},{"id":371578301,"identity":"edb2f69b-8d04-491f-b9b9-17e8f2026a38","order_by":2,"name":"Shuyi Dong","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuyi","middleName":"","lastName":"Dong","suffix":""},{"id":371578302,"identity":"26494ea8-a847-493b-ac7b-cf8660c5fe67","order_by":3,"name":"Yu Guo","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Guo","suffix":""},{"id":371578303,"identity":"c6fe8a24-cff5-463e-bd94-eb28e6ad76e8","order_by":4,"name":"Ziyin Luo","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziyin","middleName":"","lastName":"Luo","suffix":""},{"id":371578304,"identity":"638aaa15-56c4-4757-a860-491ad27f3ed7","order_by":5,"name":"Shi-Min Zhuang","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shi-Min","middleName":"","lastName":"Zhuang","suffix":""},{"id":371578305,"identity":"37ad08c4-31ff-4a36-ac8a-d07991e9899d","order_by":6,"name":"Jie Liu","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liu","suffix":""},{"id":371578306,"identity":"45c55607-ca3a-4666-a26f-1c1b0590e308","order_by":7,"name":"Tianrun Liu","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianrun","middleName":"","lastName":"Liu","suffix":""},{"id":371578307,"identity":"cb1be284-abf0-4c30-84ff-b8339fe71256","order_by":8,"name":"Jing Liao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACPmYgIWHAwMDPwMAGETpAQAsbTItkA9FaYAyDA0RrYWd++MCi4I7d5vPLnz262cYgx3cjgfFzAV6HsRkbSBg8S95240G6cW4bg7HkjQRm6Rn4/WImIWFwONnsxoFj0kAtiRtuJLAx8+DVwv4NrMV4xsE2kJZ6IrTwgG2xM+BvZgNpSTAgQksx0C+HEyRusLFJ55yTMJx55mGzND4t/PzHNz6W+HPYnr//+DPpnDIbeb7jyQc/49MCAswSDAyJDRIJIDaQycDYQEADUMkHBgZ7Bv4DBBWOglEwCkbBCAUARBtErWoRCGYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6613-2696","institution":"Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liao","suffix":""},{"id":371578308,"identity":"6854d029-4849-42a2-88c6-3245ed0474a5","order_by":9,"name":"WeiPing Wen","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"WeiPing","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-10-09 09:30:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5231239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5231239/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-024-06036-3","type":"published","date":"2025-01-08T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69360349,"identity":"5cba8403-8b0d-42bc-85c5-c79a800a0949","added_by":"auto","created_at":"2024-11-19 14:17:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe impact of TAMs infiltration on HNSCC incidence and prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-b) Immune infiltration in HNSCC was assessed using CIBERSORT analysis of the TCGA (a) and GSE113282 (b) datasets. TAMs are defined as the sum of M0, M1, and M2 macrophages.\u003c/p\u003e\n\u003cp\u003e(c) Patients in the TCGA-HNSCC cohort were divided into high and low TAMs infiltration groups based on the minimum p-value approach. Kaplan-Meier survival curves were calculated and analyzed using the log-rank test (\u003cem\u003ep\u003c/em\u003e= 0.012).\u003c/p\u003e\n\u003cp\u003e(d) Patients in the GSE270220 dataset were similarly categorized, and disease-free survival was analyzed with Kaplan-Meier curves and the log-rank test (\u003cem\u003ep\u003c/em\u003e= 0.006).\u003c/p\u003e\n\u003cp\u003e(e-f) TAMs infiltration in paired adjacent non-tumor and tumor tissues from 43 HNSCC patients in TCGA (e) and 23 patients in GSE107591 (f) was assessed using paired \u003cem\u003et\u003c/em\u003e-tests.\u003c/p\u003e\n\u003cp\u003e(g) Differential expression of CD68 in HNSCC and paraneoplastic tissues from twelve patients at The Sixth Affiliated Hospital of Sun Yat-sen University was analyzed by IHC and paired \u003cem\u003et\u003c/em\u003e-tests. Scale bars = 25 μm / 10 μm. **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/7854900cf2b29ff70bc16821.png"},{"id":69360351,"identity":"49b102b2-7aba-49e5-a270-f5c80fd6ae56","added_by":"auto","created_at":"2024-11-19 14:17:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a risk signature in the TCGA cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Univariate Cox analysis identified 42 genes associated with survival (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(b) LASSO regression was performed on these 42 OS-related genes.\u003c/p\u003e\n\u003cp\u003e(c) Variation of each coefficient under different lambda values is shown, with the x-axis representing the logarithm of lambda and the y-axis representing the coefficient.\u003c/p\u003e\n\u003cp\u003e(d) Based on associated risk factors, patients were categorized into high and low TAMs infiltration groups. Kaplan-Meier survival curves were analyzed (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e(e) ROC curve AUCs for 1-, 3-, and 5-year OS prediction in the TCGA-HNSCC cohort.\u003c/p\u003e\n\u003cp\u003e(f) Univariate (left) and multivariate (right) Cox regression analyses for overall survival.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/64a953ac587664e7e8e4f4c9.png"},{"id":69360346,"identity":"f53999f8-808e-4805-80c4-3233db13fa27","added_by":"auto","created_at":"2024-11-19 14:17:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of CSF1R expression on survival prognosis and immune function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) UMAP plot of single cells from twelve patient-derived primary HNSCC samples.\u003c/p\u003e\n\u003cp\u003e(b) UMAP of CSF1R expression in tumor-specific clusters.\u003c/p\u003e\n\u003cp\u003e(c) Pearson correlation between CSF1R and various cell types in TCGA-HNSCC.\u003c/p\u003e\n\u003cp\u003e(d) Expression levels of CSF1R in tumor and adjacent non-tumor tissues from the TCGA-HNSCC database.\u003c/p\u003e\n\u003cp\u003e(e) Patients in the GSE220270 dataset were divided into low and high CSF1R/CD68 groups based on the minimum \u003cem\u003ep\u003c/em\u003e-value statistics. Disease-free survival was analyzed using Kaplan-Meier curves and the log-rank test (\u003cem\u003ep\u003c/em\u003e = 0.012).\u003c/p\u003e\n\u003cp\u003e(f) GO enrichment analysis of low CSF1R TAMs involved in antigen binding and activation functions in the GSE188737 single-cell dataset.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/cf27e942e1b0506b16dd7c68.png"},{"id":69360345,"identity":"815dab77-d60d-47de-8b8d-daa025f6e02b","added_by":"auto","created_at":"2024-11-19 14:17:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":602434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of CSF1R inhibitors on TAMs functions and survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Representative images of TAMs morphology after treatment with PBS, PLX3397, or BLZ945. Scale bar = 50 μm.\u003c/p\u003e\n\u003cp\u003e(b) Phagocytosis was assessed using CFSE-labeled MTCQ1 tumor cells and confocal microscopy, comparing PLX3397, BLZ945, and PBS (control).\u003c/p\u003e\n\u003cp\u003e(c) TAMs treated with PLX3397 and BLZ945 for 48 hours were analyzed by flow cytometry for CD206, CD86, and MHC II expression.\u003c/p\u003e\n\u003cp\u003e(d) IL-10 concentration in supernatants of TAMs treated with PLX3397 and BLZ945 was measured by ELISA.\u003c/p\u003e\n\u003cp\u003e(e) Flow cytometry analysis of TAMs for apoptotic cell percentages after 48 hours of treatment with PLX3397 and BLZ945. *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/af56840690aa47c439821ebb.png"},{"id":69360350,"identity":"dc154ba6-2f4e-400b-97f6-192b6c938329","added_by":"auto","created_at":"2024-11-19 14:17:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1063779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEfficacy of CSF1R inhibitors alone and in combination with cisplatin in vivo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-c) WT C57B/6J mice were injected with MTCQ1 cells and treated with 60 mg/kg PLX3397 (a), 60 mg/kg BLZ945 (b), or PBS (control) orally every 3 days. Additionally, 5 mg/kg anti-CSF1R or PBS (control) was administered intraperitoneally (c).\u003c/p\u003e\n\u003cp\u003e(d-h) Mice were treated similarly, with the addition of 10 mg/kg cisplatin or PBS administered intraperitoneally every 7 days.\u003c/p\u003e\n\u003cp\u003e(d) Tumor growth curves for each treatment group. Line graphs show mean values ± standard errors.\u003c/p\u003e\n\u003cp\u003e(e) Tumor weights for each group.\u003c/p\u003e\n\u003cp\u003e(f) Representative images of tumor morphology on day 33 for each group.\u003c/p\u003e\n\u003cp\u003e(g) IHC staining for CD8 in MTCQ1 mouse HNSCC tissue. Scale bars = 25 μm / 10 μm.\u003c/p\u003e\n\u003cp\u003e(h) Quantitative analysis of CD8-positive T cells in randomly selected regions. \u003cem\u003ep\u003c/em\u003e-values were calculated by Student’s \u003cem\u003et\u003c/em\u003e-test. *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/beff21939300087e9b59806b.png"},{"id":73693913,"identity":"e93580cd-1b62-4927-9800-57d775e7659b","added_by":"auto","created_at":"2025-01-13 16:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3105985,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/7847342e-cef8-42ea-b618-78605947c9c2.pdf"},{"id":69361686,"identity":"e9b3728c-a724-4be6-8367-5e7bc4bd0a20","added_by":"auto","created_at":"2024-11-19 14:25:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11103,"visible":true,"origin":"","legend":"","description":"","filename":"Modediagramillustrating.docx","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/2572294d0013fc2b95c0afd5.docx"},{"id":69360348,"identity":"e8566e98-aa28-46be-8c41-d871b4a3a74f","added_by":"auto","created_at":"2024-11-19 14:17:03","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":344470,"visible":true,"origin":"","legend":"","description":"","filename":"Modediagram.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5231239/v1/6828b8baa6e771580c154a9c.pdf"}],"financialInterests":"","formattedTitle":"Modulating Tumor-Associated Macrophages through CSF1R Inhibition: A Potential Therapeutic Strategy for HNSCC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) ranks as the seventh most common malignancy worldwide, with approximately 900,000 new cases and 450,000 deaths reported globally in 2022[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of HNSCC continues to rise, and it is projected to increase by 30% by 2030[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The etiology of HNSCC varies geographically: in Southeast Asia and Australia, smoking, betel nut chewing, and alcohol consumption are the primary risk factors, while in the United States and Western Europe, the growing incidence of oropharyngeal HNSCC is attributed to an increase in HPV infections[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Other risk factors include radiation exposure, wood dust, asbestos, salted foods, poor oral hygiene, and Epstein-Barr virus (EBV) infection[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. HNSCC is a highly heterogeneous malignancy, encompassing multiple anatomical sites and driven by diverse carcinogenic factors, each necessitating distinct therapeutic strategies. Treatment typically involves a multimodal strategy, incorporating surgery, radiation therapy, chemotherapy, immunotherapy, and targeted therapy. The choice of treatment depends on tumor location, stage, the patient's overall health, and molecular characteristics. Around 30\u0026ndash;40% of HNSCC cases are detected at an early stage, where surgery or radiation alone can result in high cure rates[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, early diagnostic tools remain inadequate, and more than 60% of patients are diagnosed at advanced or metastatic stages, often without evident premalignant lesions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to the 2022 National Comprehensive Cancer Network (NCCN) guidelines, radiation therapy combined with cisplatin is the standard treatment for patients with locally advanced, unresectable HNSCC. For patients with recurrent or metastatic HNSCC, the EXTREME regimen (cetuximab\u0026thinsp;+\u0026thinsp;cisplatin or carboplatin\u0026thinsp;+\u0026thinsp;5-FU) remains the first-line treatment[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although initial responses are often favorable, most patients eventually develop resistance[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, conventional chemotherapy is non-selective and associated with significant side effects[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The introduction of epidermal growth factor receptor (EGFR) inhibitors marked a major advance in targeted therapy, yet many patients are either inherently resistant to these drugs or develop resistance during treatment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In recent years, immune checkpoint inhibitors (PD-1 inhibitors), such as nivolumab and pembrolizumab, have been approved for the treatment of recurrent or metastatic HNSCC that progresses during or after platinum-based chemotherapy. While some patients experience durable responses to PD-1 inhibitors, the overall efficacy remains limited, with only 17% of patients responding to monotherapy and a four-year survival rate below 30%[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, there is an urgent need for novel therapeutic approaches in HNSCC.\u003c/p\u003e \u003cp\u003eAs research into the tumor microenvironment (TME) has advanced, the intricate interactions among cells and molecules within the TME have been increasingly recognized for their role in influencing tumor progression and treatment efficacy. TAMs, key regulatory factors within the TME, have emerged as significant players in cancer growth, invasion, metastasis, and treatment resistance[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Blood monocytes migrate to tumor sites in response to chemokines secreted by cancer cells and differentiate into either pro-inflammatory or anti-inflammatory phenotypes. In most solid tumors, TAMs tend to adopt an M2 phenotype, promoting tumor progression, while M1 TAMs exhibit anti-tumor functions[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. TAMs can enhance anti-tumor immunity by phagocytosing cancer cells, but they can also facilitate immune evasion and tumor growth. TAMs heterogeneity is evident across different cancer types and stages of tumor progression[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. High levels of TAM infiltration are linked to poor prognosis in breast cancer[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], lung cancers[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], pancreatic cancers[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], melanoma[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Hodgkin's lymphoma[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While in colorectal cancer, TAMs infiltration is associated with better outcomes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], although this relationship is reversed in colorectal liver metastases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. TAMs are not only linked to prognosis but also play a role in chemotherapy sensitivity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Given their high plasticity and heterogeneity, targeting TAMs through therapeutic strategies shows potential for improving both patient survival and overall outcomes.\u003c/p\u003e \u003cp\u003eCurrent strategies for targeting TAMs primarily focus on three approaches: depleting TAMs, reprogramming their polarization, and inhibiting their recruitment[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Among these, targeting CSF1R has garnered particular attention. By inhibiting CSF1R, not only can TAMs be depleted, but their recruitment and polarization can also be modulated[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. CSF1R is a transmembrane tyrosine kinase receptor found on macrophages. Upon binding with its ligand CSF1, it induces receptor dimerization and tyrosine kinase-mediated phosphorylation, initiating intracellular signaling cascades that regulate macrophage survival, proliferation, differentiation, and migration[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, targeting CSF1R in breast cancer has demonstrated potential synergistic effects when combined with chemotherapy or immunotherapy, and ongoing clinical trials are currently assessing this promising approach[\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although CSF1R inhibition has demonstrated the ability to regulate TAMs through multiple pathways in cancers such as pancreatic cancer[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], recent studies highlight that its efficacy may depend on the specific organ and tumor subtype[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While CSF1R inhibitors have shown promise in TAMs regulation, their role in HNSCC remains unclear. Therefore, this study aims to comprehensively investigate the relationship between TAMs infiltration, prognosis, and clinicopathological characteristics in HNSCC through bioinformatics analysis. Additionally, through \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments, we elucidate the mechanisms by which CSF1R-targeted therapies regulate TAMs in HNSCC and evaluate the potential of CSF1R inhibitors as a therapeutic strategy for HNSCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Preprocessing\u003c/h2\u003e \u003cp\u003eWe obtained mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, specifically utilizing datasets GSE113282, GSE270220, and GSE15906. Immune cell infiltration in HNSCC was assessed using CIBERSORT, with TAMs defined as the sum of M0, M1, and M2 macrophages. Patients were categorized into high and low TAMs infiltration groups, as well as CSF1R/CD68 expression groups, using the minimum \u003cem\u003ep\u003c/em\u003e-value method determined via X-Tile software[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Kaplan-Meier survival analysis was performed to evaluate overall survival (OS) and disease-free survival (DFS), with statistical significance assessed using the log-rank test.\u003c/p\u003e \u003cp\u003eFurther analysis of TAMs infiltration levels involved dividing patients into high and low infiltration groups based on the median TAMs infiltration level. The association between TAMs infiltration and clinical characteristics was evaluated using t-tests to determine statistical significance.\u003c/p\u003e \u003cp\u003eFor single-cell analysis, we employed the Seurat v4.0 package to normalize, pool, and cluster the GSE188737 single-cell dataset. Classic markers were used to annotate broad cell populations, including epithelial cells (KRT7, KRT17), salivary cells (STATH), fibroblasts (COL1A2), endothelial cells (PECAM), and immune cells (PTPRC). Fibroblasts were further categorized into cancer-associated fibroblasts (CAFs; MMP2) and myofibroblasts (ACTA2), while immune cells were divided into T-cells (CD3E, NKG7), NK-cells (NKG7, XCL2), B-cells (CD79A), plasma cells (IGHG1), mast cells (TPSAB1), conventional dendritic cells (LAMP3), plasmacytoid dendritic cells (LILR4), and macrophages/monocytes (CD163)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Following annotation, clustering was conducted to visualize CSF1R expression across various cell subclusters. TAMs subclusters were extracted, and based on CSF1R expression levels within TAMs, they were divided into CSF1R_High and CSF1R_Low groups using the median expression level as the cutoff. Differential gene expression analysis was performed between these groups, followed by functional enrichment analysis of differentially expressed genes using the clusterProfiler package in R. Gene symbols were converted to Entrez Gene IDs, and Gene Ontology (GO) enrichment analysis (Biological Process, BP) was conducted using Gene Set Enrichment Analysis (GSEA). The significance level for enrichment was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with results visualized through dot plots and GSEA enrichment curves.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstablishment of a Prognostic Model\u003c/h3\u003e\n\u003cp\u003eTo assess the prognostic value of TAMs-related genes, we selected 208 macrophage-related marker genes from the CellMarker database. We conducted univariate Cox regression analysis to evaluate the association between these genes and survival in the TCGA cohort, selecting those with \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for further analysis. From this screening, 42 genes were identified as being significantly associated with survival and were used to develop a prognostic model using the LASSO Cox regression method. Risk scores were then calculated based on this model. 514 patients in the TCGA-HNSCC cohort were stratified into low-risk and high-risk subgroups according to their risk scores, using the minimum P-value method. LASSO regression analysis, along with univariate and multivariate Cox regression analyses, was employed to validate the model. Kaplan-Meier analysis was performed to compare OS between the two subgroups. The model's sensitivity and specificity were further assessed using time-dependent receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003ch3\u003eCell Culture and Drug\u003c/h3\u003e\n\u003cp\u003eWe obtained the murine oral squamous cell carcinoma (OSCC) cell line MTCQ1, which is derived from HNSCC, from the Bioresource Collection and Research Center (BCRC). Additionally, we acquired the murine fibroblast cell line L929 from the American Type Culture Collection (ATCC). All cell lines were tested for mycoplasma contamination using a single-step polymerase chain reaction (PCR) method. The cells were cultured in high-glucose DMEM (Procell) medium supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin (Beyotime), and 100 \u0026micro;g/mL streptomycin (Beyotime) in a humidified incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWe purchased anti-mouse CSF1R (clone: AFS98) from BioXcell and CSF1R small-molecule inhibitors Pexidarinib (PLX-3397, HY-16749A) and Sotuletinib (BLZ945, HY-12768A) from MCE for use in mouse tumor treatments and in vitro cell experiments.\u003c/p\u003e\n\u003ch3\u003eBMDMs generation and in vitro stimulation\u003c/h3\u003e\n\u003cp\u003eBone marrow cells were isolated from mice and cultured as a single-cell suspension in 10 cm dishes with DMEM supplemented with 10% FBS and 30% L929-conditioned medium. Fresh medium was added on days 3 and 5. On day 7, the bone marrow-derived macrophages (BMDMs) were harvested and subsequently stimulated with MTCQ1 tumor cell supernatant to induce their differentiation into TAMs.\u003c/p\u003e\n\u003ch3\u003ePhagocytosis\u003c/h3\u003e\n\u003cp\u003eTo assess the impact of CSF1R small molecule inhibitors on the phagocytic capacity of TAMs, MTCQ1 tumor cells were first pre-incubated with 5 \u0026micro;M carboxyfluorescein succinimidyl ester (CFSE) at 37\u0026deg;C for 10 minutes. The reaction was halted by adding DMEM containing 10% FBS. After thorough washing with PBS twice, the cells were incubated with 100 \u0026micro;g/mL mitomycin C at 37\u0026deg;C for 10 minutes. The cells were then washed twice with DMEM containing 10% FBS, harvested, and co-cultured with TAMs at a 1:1 ratio (1x10\u003csup\u003e6\u003c/sup\u003e cells/well) in a 6-well plate for 2 hours. Afterward, the supernatant was discarded, and the wells were washed twice with PBS to remove non-phagocytosed tumor cells. Phagocytic activity was evaluated using a fluorescence microscope (Olympus) by calculating the percentage of macrophages containing CFSE-labeled green fluorescence.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFlow Cytometry\u003c/h2\u003e \u003cp\u003eTAMs treated with DMSO, PLX3397, or BLZ945 for 48 hours were stained to detect the expression of relevant mouse proteins using the following antibodies: anti-mouse CD206 (Biolegend, Cat: 41707), anti-mouse CD86 (Biolegend, Cat: 105023), and Anti-Mouse MHC Class II V5-Tag-Alexa Fluor 647 (ONBO Biosciences, Cat: GTX80040). Additionally, apoptosis was assessed using the Annexin V-FITC/PI apoptosis kit(liankebio). The stained cells were analyzed using a CytoFLEX S flow cytometer (Beckman Coulter), and data analysis was performed with FlowJo V10.8 software (TreeStar).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of IL-10 Secretion by ELISA\u003c/h3\u003e\n\u003cp\u003eSupernatants from TAMs treated with DMSO, PLX3397, or BLZ945 for 48 hours were collected and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. A protease inhibitor cocktail (Invitrogen) was added in accordance with the manufacturer\u0026rsquo;s instructions. IL-10 levels were quantified using the Mouse IL-10 Uncoated ELISA Kit (Invitrogen), following the provided protocol.\u003c/p\u003e\n\u003ch3\u003eMice and Tumor Model\u003c/h3\u003e\n\u003cp\u003eWe obtained SPF-grade 6\u0026ndash;8 week-old male C57BL/6 mice from Guangdong Medical Laboratory Animal Center. All animal experiments were conducted in strict accordance with the guidelines of the Animal Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (IACUC-2023101306).\u003c/p\u003e \u003cp\u003eIn brief, 3\u0026times;10\u003csup\u003e6\u003c/sup\u003e MTCQ1 tumor cells were subcutaneously (s.c.) implanted into the right flanks of the mice[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Tumor volume was measured along three orthogonal axes (a, b, and c) and calculated using the formula: Tumor Volume\u0026thinsp;=\u0026thinsp;abc/2.\u003c/p\u003e \u003cp\u003eTreatment with the anti-CSF1R antibody (10 mg/kg, i.p.), PLX3397 (60 mg/kg, p.o.), and BLZ945 (60 mg/kg, p.o.) began on the day of implantation and was administered every 3 days until the end of the experiment. When the tumor volume reached approximately 100 cubic millimeters, cisplatin was administered intraperitoneally (i.p.) at a dose of 10 mg/kg once a week.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry and image analysis\u003c/h2\u003e \u003cp\u003eTumor samples and paired adjacent non-tumor tissues were collected from 12 HNSCC patients at the Sixth Affiliated Hospital of Sun Yat-sen University between January 2020 and December 2023. The study received approval from the Ethics Committee (approval numbers 2020ZSLYEC-303 and 2021ZSLYEC-270), and informed consent was obtained from all patients or their legal guardians. Immunohistochemistry (IHC) was performed on these samples using an anti-CD68 antibody. Additionally, subcutaneous tumor samples from MTCQ1 mice were collected for CD8 IHC analysis.\u003c/p\u003e \u003cp\u003eTissue sections of 4 \u0026micro;m were placed in an oven at 60 ℃ for 20 minutes. Deparaffinization was performed using two xylene treatments (10 minutes each), followed by rehydration through a graded ethanol series (100%, 95%, 85%, and 75%, 3 minutes each) and washing in ddH\u003csub\u003e2\u003c/sub\u003eO. Antigen retrieval was conducted by boiling the sections in Tris-HCl buffer (pH 9.2) in a pressure cooker for 10 minutes. After cooling, sections were incubated with endogenous peroxidase blocking solution (Beyotime, Cat: P0100B) at room temperature for 10 minutes. Blocking was then done with 5% bovine serum albumin (BSA) for 30 minutes at room temperature. Sections were incubated overnight at 4\u0026deg;C with anti-CD68 antibody (Sino Biological, Cat: 11192-T56, 1:500) or anti-CD8α antibody (Abcam, Cat: ab217344, 1:500), followed by a 30-minute incubation at room temperature with horseradish peroxidase-conjugated anti-rabbit antibody (Beyotime, Cat: A0208, 1:50). Staining was visualized using a DAB detection kit (ZSGB-BIO, Cat: ZLI-9017).\u003c/p\u003e \u003cp\u003eSlides were scanned at \u0026times;40 magnification using the SQS-1000 slide scanner (Shenzhen Shengqiang Technology Co., Ltd., China). Digital image analysis was performed using ImageJ software (version 1.54d). Two independent researchers evaluated the tumor samples, examining five distinct areas per section. The CD68 histological score (H-score) was determined by multiplying the proportion score by the intensity score. Additionally, the average optical density (AOD) was used to quantify the proportion of CD8-positive cells, with final results reported as the mean of these measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eData were analyzed using Prism 8.0.2 software (GraphPad) and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. The significance of differences between two groups was assessed using the Wilcoxon rank-sum test, paired Student\u0026rsquo;s t-test, or unpaired Student\u0026rsquo;s t-test, as appropriate. Differences among multiple groups were evaluated using ANOVA. Overall survival (OS) was defined as the time from surgery to death from any cause. Disease-free survival (DFS) was defined as the time following treatment during which patients remained free from cancer recurrence or progression. Survival rates were estimated using the Kaplan-Meier method, with comparisons made using the log-rank test. All p-values were two-sided, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTAMs infiltration and prognostic significance in HNSCC\u003c/h2\u003e \u003cp\u003eIn the TCGA and GSE113282 datasets, TAMs were identified as the predominant component of immune infiltration within HNSCC tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). Kaplan-Meier survival analysis revealed that high TAMs infiltration was significantly associated with poorer overall survival (OS) in the TCGA cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, HR\u0026thinsp;=\u0026thinsp;1.385). Similarly, in the GSE270220 dataset, elevated TAMs infiltration correlated with lower disease-free survival (DFS) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, HR\u0026thinsp;=\u0026thinsp;5.056) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-d). Paired analysis of tumor versus non-tumor tissues in the TCGA and GSE107591 datasets demonstrated a significant increase in TAMs infiltration within tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-f). Immunohistochemical analysis further confirmed this finding, showing markedly higher CD68 expression in HNSCC tissues compared to adjacent non-tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Clinical feature analysis of the TCGA-HNSCC cohort indicated that high TAMs infiltration was significantly associated with HPV status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), N stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), overall TNM stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and clinical grade (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results supported the role of TAMs as a significant biomarker for HNSCC, suggesting that TAMs may serve as a novel therapeutic target for HNSCC.\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\u003eTCGA-HNSCC cohort clinical characteristics by high/low TAM infiltration using median cutoff.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh TAMs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow TAMs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;60 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u0026thinsp;=\u0026thinsp;Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\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 \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV_state (%)\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHNSC_HPV-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHNSC_HPV+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_stage (%)\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow T_stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh T_stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_stage (%)\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 \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage (%)\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e46 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ-Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade (%)\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 \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation_Therapy (%)\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 \u003cp\u003e0.753\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\u003e78 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e143 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the risk signature\u003c/h2\u003e \u003cp\u003eUnivariate Cox analysis identified 42 genes significantly associated with OS, which were further refined using LASSO regression to construct a risk signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). This risk model effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly poorer OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The ROC curve analysis demonstrated that the predictive model had good accuracy (AUC\u0026thinsp;=\u0026thinsp;0.70 for 1-year, 0.674 for 2-year, and 0.69 for 3-year survival) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Both univariate and multivariate Cox regression analyses confirmed that TAMs infiltration was an independent prognostic factor for OS in HNSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCSF1R expression and its impact on immune function and prognosis\u003c/h2\u003e \u003cp\u003eUMAP analysis of scRNAseq data from seven HNSCC samples in the GSE188737 dataset showed that CSF1R expression was predominantly localized to TAMs, with a strong correlation to macrophage markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In the TCGA dataset, \u003cem\u003eCSF1R\u003c/em\u003e gene expression had the highest Pearson correlation with macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), and was significantly higher in tumor tissues compared to non-tumor tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Kaplan-Meier analysis in the GSE270220 dataset indicated that high \u003cem\u003eCSF1R\u003c/em\u003e / \u003cem\u003eCD68\u003c/em\u003e expression was significantly associated with poorer DFS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, HR\u0026thinsp;=\u0026thinsp;2.580) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). GO enrichment analysis of TAMs at the single-cell level suggested that TAMs with low \u003cem\u003eCSF1R\u003c/em\u003e expression were involved in antigen binding and activation functions, implying that CSF1R inhibition may enhance anti-tumor immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). These results suggest that high \u003cem\u003eCSF1R\u003c/em\u003e expression in HNSCC is closely associated with the functional regulation of tumor-associated macrophages, and that CSF1R inhibition may improve patient prognosis by enhancing anti-tumor immune responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEffects of CSF1R inhibitors on TAMs function and survival\u003c/h2\u003e \u003cp\u003ePexidartinib (PLX3397) and Sotuletinib (BLZ945) are two promising CSF1R inhibitors. PLX3397 primarily targets CSF1R and also inhibits KIT and FLT3[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It has gained considerable attention for its ability to suppress the proliferation of TAMs, which are key players in tumor progression and immune evasion[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Recently, PLX3397 was approved by the U.S. Food and Drug Administration (FDA) for the treatment of tenosynovial giant cell tumors (TGCT)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, Sotuletinib (BLZ945) specifically targets CSF1R, though it differs structurally from PLX3397. Despite these differences, both inhibitors act by blocking CSF1R signaling, which reduces TAMs populations and inhibits their pro-tumor functions[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Both compounds are being investigated for their potential to modulate the tumor microenvironment and serve as therapeutic agents across various cancers. Currently, PLX3397 and BLZ945 are in Phase I and Phase II clinical trials for the treatment of advanced solid tumors (NCT02734433, NCT02829723). Based on their promising mechanisms, we selected these two CSF1R small-molecule inhibitors for investigation in our study.\u003c/p\u003e \u003cp\u003eIn vitro studies demonstrated that treatment of TAMs with CSF1R inhibitors (PLX3397, BLZ945) led to significant morphological changes and enhanced their phagocytic activity (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-b). Initial observations suggested that CSF1R inhibitors might inhibit TAMs polarization. To gain deeper insights into the expression of M2 markers and other proteins on TAMs, flow cytometry analysis revealed that both inhibitors reduced the expression of the M2 typical marker CD206. PLX3397 notably increased CD86 expression in TAMs, while BLZ945 only slightly upregulated CD86. However, PLX3397 decreased MHC II expression, whereas BLZ945 slightly increased MHC II expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Furthermore, CSF1R inhibitors significantly reduced IL-10 secretion and increased TAMs apoptosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e). Collectively, these findings suggested that CSF1R inhibitors shift TAMs towards the M1 macrophage phenotype, suppressed M2 macrophage differentiation, enhanced tumor cell phagocytosis, and reduced TAMs survival and immunosuppressive functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEfficacy of CSF1R inhibitors alone and in combination with cisplatin in vivo\u003c/h2\u003e \u003cp\u003eTo further assess the in vivo efficacy of CSF1R inhibition, a subcutaneous HNSCC model was established by injecting cancer cells directly into mice. The results showed that treatment with PLX3397 significantly inhibited tumor growth; however, neither α-CSF1R antibody nor BLZ945 alone effectively controlled tumor progression (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). When combined with cisplatin, all three CSF1R-targeted therapies led to a marked reduction in tumor growth and weight compared to the control or monotherapy groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-e). Immunohistochemical analysis indicated an increase in CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration in the tumors of the combination treatment group, suggesting an enhanced antitumor immune response. We hypothesize that the increased infiltration of CD8\u003csup\u003e+\u003c/sup\u003e T cells may contribute to the enhanced efficacy of cisplatin when combined with CSF1R inhibitors (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef-h).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTraditional treatments for HNSCC, including surgery, radiotherapy, and chemotherapy, have notable limitations. Surgery is invasive and often impairs functionality and quality of life; radiotherapy can damage healthy surrounding tissues, causing significant side effects; and chemotherapy is frequently hindered by drug resistance and systemic toxicity. Additionally, high rates of recurrence and metastasis make it challenging for any single therapy to achieve comprehensive disease control. Although immunotherapy, particularly PD-1 inhibitors, has shown promise, it has a relatively low response rate and is associated with immune-related adverse events. As a result, the development of novel targeted therapies and more effective immunotherapies remains a critical focus of research. Reactivating the body's anti-tumor immune response through immunotherapy is a widely accepted strategy in cancer treatment. While T cell-targeted immunotherapies have made significant breakthroughs, many patients still require more effective treatment options. TAMs, the most abundant immune cells infiltrating solid tumors, act as a bridge between innate and adaptive immunity. Targeting TAMs to enhance anti-tumor activity has become a promising new approach in immunotherapy. This study reveals that inhibiting CSF1R to modulate TAMs presents a potential therapeutic strategy for HNSCC. By regulating TAMs, the most infiltrated immune cells in solid tumors, this strategy enhances anti-tumor immune responses, expands the scope of immunotherapy, particularly for patients who do not respond adequately to current T cell-targeted therapies, and offers the potential for more effective treatment options for HNSCC patients.\u003c/p\u003e \u003cp\u003eIn HNSCC, semi-quantitative immunohistochemistry studies have shown that a high infiltration of CD68-positive macrophages and elevated expression of CD163 in macrophages are linked to poorer OS in oral OSCC patients[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Immunohistochemical analyses further demonstrated that high CD163 expression in primary HNSCC tumors is associated with poorer OS, progression-free survival (PFS), locoregional failure-free survival (LFFS), and distant metastasis-free survival (DMFS), particularly in human papillomavirus-negative (HPV-) patients[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, another research using tissue microarrays found that high densities of CD163\u003csup\u003e+\u003c/sup\u003e M2-type TAMs are correlated with poor prognosis in HNSCC, with macrophage content being closely associated with lymph node metastasis and clinical staging[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Using bioinformatics analysis across multiple datasets, we identified that high infiltration of TAMs was linked to poorer overall survival (OS), disease-free survival (DFS), HPV infection status, and advanced clinical staging and grading. Additionally, our TAMs-related gene prediction model showed strong prognostic accuracy. This study further highlights the prognostic significance of TAMs in HNSCC and provides a strong theoretical foundation for developing TAMs-targeted therapeutic strategies.\u003c/p\u003e \u003cp\u003eThe study of CSF1R-targeted therapies to inhibit TAMs function is actively underway. However, the effectiveness of these treatments varies significantly across different tumor types and clinical trials. Some studies have shown that targeting macrophages can significantly inhibit tumor progression, while others have not demonstrated substantial efficacy, leading to ongoing debates about the validity and applicability of macrophage-targeted therapies. For example, in a pancreatic ductal adenocarcinoma (PDAC) mouse model, blocking CSF1R signaling effectively reprogrammed macrophages to enhance antigen presentation and promote robust anti-tumor T cell responses. However, this also led to the upregulation of immune checkpoint molecules like PD-L1 and CTLA4, diminishing the therapeutic benefits[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similar effects were observed in our study, CSF1R inhibitors reduced CD206 expression in TAMs while increasing CD86 and MHC II levels, driving their polarization towards a pro-inflammatory M1 phenotype, thereby enhancing antigen presentation and anti-tumor immune responses. Furthermore, we observed an upregulation of PD-L1 following CSF1R blockade (data not shown). In a murine model of pre-neoplastic glioblastoma, targeting TAMs with CSF1R inhibitors significantly improved survival and suppressed tumor growth. Moreover, CSF1R inhibition markedly slowed the intracranial growth of patient-derived glioblastoma xenografts[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We also discovered that CSF1R inhibitors were able to retard tumor growth in subcutaneous HNSCC mouse models, though their efficacy was limited when used as a monotherapy. Moreover, some studies have shown that combining CSF1R inhibitors with other therapies enhances tumor sensitivity to chemotherapy and strengthens anti-tumor immune responses. For example, combining CSF1R signaling antagonists with paclitaxel blocks macrophage recruitment, thereby improving survival in tumor-bearing mice by slowing primary tumor progression and reducing lung metastasis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Jonathan B. Mitchem's study demonstrated that targeting TAMs through CSF1R inhibition can reduce the number of pancreatic tumor-initiating cells (TICs), which in turn increases the infiltration of tumor-infiltrating lymphocytes (TILs) and enhances the efficacy of gemcitabine chemotherapy in vivo[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Strachan, D.C. observed that inhibiting CSF1R not only decreased TAMs turnover but also significantly increased the infiltration of CD8\u003csup\u003e+\u003c/sup\u003e T cells in cervical and breast cancers[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Likewise, our \u003cem\u003ein vivo\u003c/em\u003e experiments demonstrated that the combination of Anti-CSF1R antibodies or small-molecule CSF1R inhibitors with cisplatin significantly enhanced its antitumor efficacy. Additionally, we observed a substantial increase in CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration in the groups receiving the combination therapy. While CSF1R inhibitors alone showed limited efficacy, their combination with cisplatin produced synergistic effects, not only effectively inhibiting tumor growth but also potentially enhancing the tumor's sensitivity to chemotherapy. These results suggest that targeting TAMs can directly modulate the TME and enhance the effects of traditional chemotherapy, providing an experimental basis for developing new combination therapies. Extensive preclinical research has laid a strong foundation for advancing to clinical trials. CSF1R inhibitors, such as PLX3397, have been utilized in the treatment of advanced solid tumors with high TAMs infiltration, including glioblastoma (NCT01349036), pancreatic cancer (NCT02777710), and breast cancer (NCT01525602). These trials demonstrated a reduction in tumor burden and effective disease control in treated patients. However, in various subtypes of breast cancer, although TAMs can be reduced by CSF1R inhibitors, the overall effectiveness in controlling certain breast cancer subtypes remains limited[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Furthermore, some studies have reported that while PLX3397 can reduce circulating CD14\u003csup\u003edim\u003c/sup\u003e/CD16\u003csup\u003e+\u003c/sup\u003e monocytes in recurrent glioblastoma[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], it did not significantly extend overall survival in a Phase II clinical trial for recurrent glioblastoma (NCT01349036), raising concerns about its clinical efficacy. The therapeutic efficacy of CSF1R inhibition in cancer patients can vary significantly depending on the organ-specific and subtype-specific characteristics of the TME. We aspire for our research to offer profound insights for future clinical trials and facilitate the refinement of treatment strategies.\u003c/p\u003e \u003cp\u003ePLX3397 is a competitive inhibitor of CSF-1R that blocks it\u0026rsquo;s signaling, inhibiting macrophage proliferation and promoting the polarization of tumor-associated macrophages (TAMs) towards a pro-inflammatory M1 phenotype, effectively delaying tumor growth in a mouse model of hepatocellular carcinoma[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, a study by Shi et al. indicated that PLX3397 improves the immunosuppressive tumor microenvironment (TME) primarily by reducing the number of TAMs rather than altering their polarization[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In our \u003cem\u003ein vitro\u003c/em\u003e experiments, PLX3397 downregulated CD206 expression in TAMs, upregulated CD86, driving TAMs toward an M1 phenotype, and enhanced apoptosis and phagocytosis in TAMs. However, PLX3397 also reduced MHC II expression, potentially impairing antigen presentation. BLZ945, another highly selective CSF1R inhibitor, suppresses TAM activity and proliferation while reprogramming their immunosuppressive properties. According to a study by Magkouta et al., BLZ945 effectively inhibited tumor progression, reduced TAM infiltration, and promoted M1 polarization in a mouse model of mesothelioma[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Similarly, in our studies, BLZ945 downregulated CD206, enhanced phagocytosis and apoptosis in TAMs, but showed a weaker effect on CD86 upregulation and only mildly increased MHC II expression. Unfortunately, BLZ945 alone did not significantly suppress tumor growth \u003cem\u003ein vivo\u003c/em\u003e. Additionally, a murine IgG2a monoclonal antibody targeting CSF-1R effectively depleted macrophages \u003cem\u003ein vivo\u003c/em\u003e but, like BLZ945, failed to show significant tumor-suppressive effects. In contrast, PLX3397 demonstrated the most pronounced anti-tumor efficacy in our HNSCC mouse model, possibly due to its ability to inhibit other kinases, such as KIT and FLT3[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which may enhance its therapeutic effect. Overall, the differing efficacies of these CSF1R inhibitors may be attributed to variations in their modulation of CSF1R signaling pathways and other molecular targets, suggesting that the selection of CSF1R inhibitors in clinical applications should be tailored to specific pathological conditions and therapeutic goals.\u003c/p\u003e \u003cp\u003eIn summary, the impact of CSF1R-targeted TAMs modulation is likely influenced by tumor type, subtype, progression, and individual patient differences. Moreover, different CSF1R inhibitors may regulate TAMs functions through distinct mechanisms, affecting antitumor immune responses. Therefore, clinical applications should be tailored to individual patients to devise the most effective treatment strategies. Although CSF1R inhibitors alone did not significantly inhibit tumor growth \u003cem\u003ein vivo\u003c/em\u003e, their combination with cisplatin significantly enhanced antitumor efficacy. This suggests that CSF1R inhibitors may enhance the effectiveness of chemotherapy through synergistic mechanisms, overcoming the limitations of monotherapy. These findings not only expand our understanding of TAMs in HNSCC but also offer new insights for future clinical strategies targeting TAMs. Further studies should explore the potential of combining CSF1R inhibitors with other therapies to optimize treatment regimens and maximize antitumor effects, ultimately improving outcomes for HNSCC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor-Associated Macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor Microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHead and Neck Squamous Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHNSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eColony Stimulating Factor 1 Receptor.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCSF1R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHuman Papillomavirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEpstein-Barr virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEBV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Comprehensive Cancer Network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNCCN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEpidermal Growth Factor Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOral Squamous Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmerican Type Culture Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFetal Bovine Serum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFBS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBone Marrow-Derived Macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMDMs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collection of patient samples for this study was approved by the Ethics Committee under approval numbers 2020ZSLYEC-303 and 2021ZSLYEC-270. All animal experiments were conducted in strict accordance with the guidelines of the Animal Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (IACUC-2023101306).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirmed that the data supporting the findings of this study are available within the article and/or supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.Liao. and K.C.; Investigation, K.C. and X.L., S.D. and Y.G.; Writing - Original Draft, K.C.; Writing - Review \u0026amp; Editing, J.Liao., S.-M.Z., J.Liu. T.L and W.-P.W.; Visualization, K.C. and S.D. , X.L and Z.L.; Supervision, J.Liao., and W.-P.W.; Funding Acquisition, J.Liao. and T.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by project grants from the National Science Foundation of China (81972896 to T.L.), the Guangdong Province Natural Science Foundation (2019A1515010288 to T.L.), the Guangdong Basic and Applied Basic Research Foundation (2021A1515012620 to J.Liao.), the MOE Key Laboratory of Gene Function and Regulation, the National Key Clinical Discipline, and the State Key Laboratory of Oncology in South China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Agency for Research on Cancer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Clin Cancer Res. 2014;20(12):3146\u0026ndash;58.\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HNSCC, TAMs, CSF1R inhibitors, cisplatin, combination therapy","lastPublishedDoi":"10.21203/rs.3.rs-5231239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5231239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTumor-associated macrophages (TAMs) are pivotal immune cells within the tumor microenvironment (TME), exhibiting dual roles across various cancer types. Depending on the context, TAMs can either suppress tumor progression and weaken drug sensitivity or facilitate tumor growth and drive therapeutic resistance. This study explores whether targeting TAMs can suppress the progression of head and neck squamous cell carcinoma (HNSCC) and improve the efficacy of chemotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eBioinformatics analyses were performed to evaluate TAMs infiltration levels in HNSCC tumor tissues and examine their associations with patients’ clinicopathological characteristics and prognosis. Flow cytometry was utilized to measure the expression of key macrophage markers and assess apoptosis following treatment with colony stimulating factor 1 receptor (CSF1R) inhibitors (BLZ945, PLX3397). Additionally, immunohistochemistry was employed to detect CD68 and CD8 expression. In vivo, the antitumor efficacy of CSF1R inhibitors was tested in mouse HNSCC tumor model, both as monotherapy and in combination with cisplatin, to evaluate potential synergistic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Bioinformatic analysis identified TAMs as the predominant infiltrating immune cells in the TME of HNSCC, with significantly higher infiltration levels in tumor tissues compared to adjacent non-tumor tissues. High TAMs infiltration was associated with poorer overall survival (OS), disease-free survival (DFS), human papillomavirus (HPV) infection status, and advanced disease staging. The TAMs-related genes prediction model demonstrated high prognostic accuracy. CSF1R is primarily expressed in TAMs, where high CSF1R expression may suppress antigen binding and activation. \u003cem\u003eIn vitro\u003c/em\u003e experiments showed that CSF1R inhibitors induce TAMs apoptosis, enhance their phagocytic activity, and reduce CD206 expression and IL-10 secretion, thereby diminishing their immunosuppressive function. \u003cem\u003eIn vivo\u003c/em\u003e experiments revealed that while CSF1R inhibitors alone had limited efficacy in suppressing tumor growth, their combination with cisplatin significantly enhanced therapeutic efficacy, as evidenced by increased CD8\u003csup\u003e+ \u003c/sup\u003eT cell infiltration within the TME.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Regulating TAMs by targeting CSF1R to diminish immunosuppressive functions and enhance anti-tumor immunity represents a promising therapeutic strategy for HNSCC.\u003c/p\u003e","manuscriptTitle":"Modulating Tumor-Associated Macrophages through CSF1R Inhibition: A Potential Therapeutic Strategy for HNSCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 14:16:58","doi":"10.21203/rs.3.rs-5231239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2024-11-20T02:31:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-10-30T14:02:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-29T06:34:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-26T18:15:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-10-12T04:22:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8fb7c7a-2fb0-48fd-8e3b-6983810e7645","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:02:38+00:00","versionOfRecord":{"articleIdentity":"rs-5231239","link":"https://doi.org/10.1186/s12967-024-06036-3","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-01-08 15:57:41","publishedOnDateReadable":"January 8th, 2025"},"versionCreatedAt":"2024-11-19 14:16:58","video":"","vorDoi":"10.1186/s12967-024-06036-3","vorDoiUrl":"https://doi.org/10.1186/s12967-024-06036-3","workflowStages":[]},"version":"v1","identity":"rs-5231239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5231239","identity":"rs-5231239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}