Characterization of Pre- and Post-treatment Soluble Immune Mediators and the Tumor Microenvironment in NSCLC Patients Receiving PD-1/L1 Inhibitor Monotherapy

Characterization of Pre- and Post-treatment Soluble Immune Mediators and the Tumor Microenvironment in NSCLC Patients Receiving PD-1/L1 Inhibitor Monotherapy | 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 Characterization of Pre- and Post-treatment Soluble Immune Mediators and the Tumor Microenvironment in NSCLC Patients Receiving PD-1/L1 Inhibitor Monotherapy Daiki Murata, Koichi Azuma, Kenta Murotani, Akihiko Kawahara, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4021078/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Sep, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 11 You are reading this latest preprint version Abstract Background Despite the favorable therapeutic efficacy observed with ICI monotherapy, the majority of non-small cell lung cancer (NSCLC) patients do not respond. Therefore, identifying patients who could optimally benefit from ICI treatment remains a challenge. Methods Among 183 patients with advanced or recurrent NSCLC who received ICI monotherapy, we analyzed 110 patients whose pre- and post-treatment plasma samples were available. Seventy-three soluble immune mediators were measured at ICI initiation and 6 weeks later. To identify useful biomarkers, we analyzed the association of pre-treatment levels and post-treatment changes of soluble immune mediators with survival of patients. The associations of pre-treatment or on-treatment biomarkers with irAE development, PD-L1 expression, CD8 + TIL density, and neutrophil to lymphocyte ratio (NLR) were also analyzed. Results Pre-treatment biomarkers included 6 immune mediators (CCL13, CCL19, CCL21, CXCL5, CXCL10 and TNFSF13B) whereas on-treatment biomarkers included 8 immune mediators (CCL7, CCL19, CCL23, CCL25, IL-10, IL-32, IL-34 and TNFSF12). IrAE development was associated with post-treatment change in CCL23. PD-L1 expression was associated with the pre-treatment levels of TNFSF13B and the post-treatment change in CCL25. CD8 + TIL density was associated with the pre-treatment CXCL10 level, whereas NLR was correlated with pre-treatment levels of CCL13 and CCL17. Conclusion We identified several possible pre-treatment and on-treatment biomarkers in patients with NSCLC who received ICI monotherapy. Some of these biomarkers were associated with other possible predictors, including irAE development, PD-L1 expression, CD8 + TIL density and NLR. Further large-scale studies are needed to establish biomarkers for patients with NSCLC who received ICI monotherapy. NSCLC Immune checkpoint inhibitor Chemokine Cytokine CD8 + TILs Biomarker Figures Figure 1 Figure 2 Figure 3 Introduction Immune checkpoint inhibitors (ICI) have become the new standard of treatment for advanced and recurrent non-small cell lung cancer (NSCLC). Inhibitors of programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) activate tumor-specific T cells and provide therapeutic efficacy. These agents are characterized by long-term survival and sustained therapeutic efficacy even after discontinuation of treatment. Despite the favorable therapeutic efficacy observed, the majority of patients do not respond to ICI monotherapy. Therefore, a current challenge is to identify those patients who could optimally benefit from ICI treatment [1–6]. The cancer-immunity cycle does not function optimally in advanced cancer patients. For an antitumor immune response to result in the effective killing of tumor cells, a series of stepwise events must be initiated and allowed to proceed and expand iteratively. This cycle can be divided into seven major steps, beginning with the release of antigens from tumor cells and ending with the killing of tumor cells. Each step of the cancer-immunity cycle requires the coordination of numerous factors, both stimulatory and inhibitory in nature [1]. Soluble immune mediators, including cytokines and chemokines such as those in the interleukin (IL) family, the tumor necrosis factor superfamily (TNFSF), chemokine ligands (CCL), and C-X-C motif chemokine ligands (CXCL), can stimulate or inhibit each step of the cycle [1, 2, 7–12]. The tumor microenvironment (TME) forms a complex network of cytokines or chemokines that modulate antitumor immunity. While a tremendous amount of research has been conducted on cancer immunology and immunotherapy to implement clinical strategies, the role of various immune cells in the TME remains unclear [1–3, 6–11]. In addition to characterizing soluble immune mediators, each step of the cancer immune cycle can be assessed by immunohistochemical analysis. PD-L1 expression in tumor tissue suggests inhibition of the step of cancer cell killing. The density of CD8 + T cells in tumor tissue indicates the ability of T cells to infiltrate into the tumor [1–3]. The TME has been divided into four different types based on PD-L1 expression and CD8 + tumor-infiltrating lymphocytes (TILs) [3]. This classification provides a framework for predicting the therapeutic outcome of cancer immunotherapy. However, since tissue biopsies are invasive and time-consuming, simpler non-invasive methods are needed. A comprehensive study of biomarkers such as cytokines and chemokines may lead to a better understanding of the relationship between cancer immunity and immunotherapy and may identify novel therapeutic targets [1–3, 7–12]. Therefore, we measured a comprehensive set of soluble immune mediators and performed an exploratory analysis in patients with NSCLC who received PD-1/L1 monotherapy. In the present study, 73 soluble immune mediators were measured at ICI initiation and 6 weeks later. We analyzed the association of patient survival and the levels of soluble immune mediators at ICI initiation as a pre-treatment biomarker. We also analyzed the association between patient survival and the changes in soluble immune mediators 6 weeks after ICI initiation as an on-treatment biomarker, as it may reflect the changes in the TME that are associated with treatment efficacy. The correlation between pre-treatment and on-treatment biomarkers was examined to investigate the association between favorable immune status in the pre-treatment TME and its favorable post-treatment changes. The correlations between pre-treatment or on-treatment biomarkers and other possible predictors, including the development of immune-related adverse events (irAE), PD-L1 expression, CD8 + TIL density, peripheral blood cells and the neutrophil to lymphocyte ratio (NLR) were examined to reveal the underlying mechanisms associated with therapeutic outcome. Materials and Methods Study design We retrospectively screened patients with advanced or recurrent NSCLC who received PD-1/L1 monotherapy at Kurume University Hospital between January 2016 and December 2020. Among 183 patients with NSCLC who received ICI monotherapy, we analyzed 110 patients for whom pre- and post-treatment plasma samples were available. Plasma samples were collected at ICI initiation and 6 weeks later. All patients had pathologically confirmed NSCLC. Progression-free survival (PFS) and overall survival (OS) were calculated for each patient. This study was conducted in accordance with the provisions of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kurume University Hospital (IRB No 20100). Measurement of soluble immune mediators in plasma We collected plasma samples at the time of ICI initiation and 6 weeks later. This was done in order to explore biomarkers associated with prognosis of NSCLC patients (Fig. 1 A). All samples were heparinized and centrifuged at 1600g for 15 min. The plasma supernatants were transferred to new tubes and stored at − 80°C until measurement. At the time of measurement, these samples were allowed to thaw naturally at room temperature. Soluble immune mediators were measured once for each patient and measurements were not repeated. A bead-based multiplex assay was used to measure plasma levels of soluble immune mediators. Post-treatment changes were calculated as the difference in soluble immune mediator levels found at ICI initiation and 6 weeks later. A Bio-Plex 200 system (Bio-Rad Laboratories, Hercules, CA) was used to analyze 100-µL aliquots of two-fold diluted plasma samples in accordance with the manufacturer’s instructions. Kits (Bio-Rad Laboratories) for the various analytes were used to measure the 73 soluble immune mediators, including cytokines, chemokines and growth factors (Supplemental appendix). These soluble immune mediators were selected for their relevance to the TME and the cancer immune cycle [1, 6–11]. Selection of other possible predictors We also analyzed the association and correlation between possible predictors of the therapeutic efficacy of ICI monotherapy and pre- and on-treatment biomarkers. Based on previous reports, irAE development, PD-L1 expression, CD8 + TIL density, peripheral blood cell count and proportion, and NLR were selected for examination [1–3, 13–17]. Definition of irAE, peripheral blood cell, and NLR IrAEs were evaluated according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. Peripheral blood cell counts and proportions were commercially assayed. The data included absolute counts and the proportions of neutrophils, lymphocytes, monocytes, and eosinophils. The NLR was also assessed and calculated using absolute counts of neutrophils and lymphocytes [16]. Post-treatment changes in peripheral blood cells were calculated as the difference between the values at ICI initiation and those 6 weeks later. Immunohistochemical analysis In this study, immunohistochemical analysis of PD-L1 expression and CD8 + TIL density was performed in patients whose tumor biopsy specimens were available. Four-mm-thick sections of formalin-fixed, paraffin-embedded tissues were used. The sections were mounted on glass slides and then incubated with anti-rabbit monoclonal antibody against PD-L1 (clone ELL) (Cell signaling Technology, Denver) for immunohistochemical (IHC) analysis using the BenchMark ULTRA (Ventana Automated Systems, Inc., Tucson, AZ, United States of America). Each slide was heat-treated with Ventana’s CC1 retrieval solution for 30 min and incubated with the PD-L1 antibody for 30 min. This automated system used the ultraVIEW DAB detection kit with 3, 3ʹ diaminobenzidine (DAB) as the chromogen (Ventana Automated Systems). PD-L1 expression was categorized as either 0% or > 1%. Immunostaining for CD8 (Leica Microsystems, Newcastle-upon-Tyne, UK) was performed on the same fully automated Bond-III system (Leica Microsystems) using on-board heat-induced antigen retrieval with epitope retrieval solution 2 for 10 min at 99°C, and incubated with the antibody for 30 min at room temperature. This automated system used a Refine polymer detection kit with horseradish peroxidase-polymer as the secondary antibody and DAB, and incubation with a secondary antibody was performed for 30 min at room temperature. TILs were counted on immuno-stained CD8 preparations and scored using a four-tier scale. Statistical analysis This investigation was an observational study. Thus, the target sample size for this study was set at 100 participants. Comparisons of categorical variables were evaluated using chi-squared or Fisher’s exact tests. PFS and OS were compared between groups using a log-rank test. To explore factors associated with the dependent variables (PFS and OS), univariate Cox proportional hazards model analysis was performed using soluble immune mediators as independent variables. Due to the limited number of events for the dependent variables in this study, adjusted analysis was not performed. Spearman correlation analysis was performed to assess the correlation between pre- and on-treatment biomarkers, pre-treatment biomarkers and pre-treatment peripheral blood cells, and on-treatment biomarkers and post-treatment changes in peripheral blood cells. Wilcoxon rank sum tests were used to analyze the association between pre- or on-treatment biomarkers and irAE development, PD-L1 expression and CD8 + TIL density. All tests utilized a two-sided approach, and differences were considered statistically significant at p < 0.05. Statistical analyses were performed using JMP pro version 16.0 statistical software (SAS Institute Inc.). The cut-off date for the analyses was March 31, 2023. The primary endpoint identified pre-treatment and on-treatment biomarkers that were associated with PFS and OS in patients with NSCLC receiving PD-1/L1 monotherapy. Pre-treatment biomarkers were defined as soluble immune mediators whose levels at ICI initiation were associated with PFS and OS. On-treatment biomarkers were defined as soluble immune mediators whose changes after ICI initiation (levels at 6 weeks after ICI initiation – levels at ICI initiation) were associated with PFS and OS. The secondary endpoint was to examine the correlation between pre-treatment and on-treatment biomarkers. Additional secondary endpoints included the associations of pre-treatment and on-treatment biomarkers with PD-L1 expression or CD8 + TIL density. The correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells and between on-treatment biomarkers and post-treatment changes in peripheral blood cells were also analyzed. Results Patient characteristics and survival Here, we focused on patients with NSCLC who had received ICI monotherapy. We collected plasma samples at baseline and after 6 weeks of therapy to investigate biomarkers associated with prognosis (Fig. 1 A). Among 183 patients with NSCLC who had received ICI monotherapy, we analyzed 110 patients for whom pre- and post-treatment plasma samples were available. Figure 1 B shows a flow chart of the study patients. The characteristics of the enrolled patients are shown in Table 1 . The median age was 72 years. Of the 110 patients, 79 were male and 81 had a history of smoking. Performance status was 0–1 in 87 patients and 2–3 in 23 patients. Non-squamous and squamous cell carcinoma were present in 78 and 32 patients, respectively. A driver mutation was harbored in 23 patients, an epidermal growth factor receptor mutation in 20 and an anaplastic lymphoma kinase fusion gene in 3. ICI was administered as first-line, second-line, and third-line or later treatment in 25, 65, and 20 patients, respectively. The median PFS was 2.8 months (95%CI: 2.2–4.7), and the median OS was 11.9 months (95%CI: 8.2–14.7). Kaplan-Meier survival curves for study patients are shown in Fig. 1 C. Analysis of pre-treatment or on-treatment biomarkers We analyzed the association between the levels of soluble immune mediators at ICI initiation and PFS and OS. Among 73 soluble immune mediators, PFS was significantly associated with CXCL5 (p = 0.006), CXCL10 (p = 0.009), and CCL17 (p = 0.043). OS was significantly associated with CXCL5 (p = 0.006), CCL13 (p = 0.045), CCL19 (p = 0.022), CCL21 (p = 0.050) and TNFSF13B (p = 0.028). Results of logistic regression analysis are shown in Supplemental Table 1A. A heatmap displaying the association between patient survival and pre-treatment biomarkers is shown in Fig. 2 . We also analyzed the association between changes in soluble immune mediators 6 weeks after ICI initiation as well as with PFS and OS. Among 73 soluble immune mediators, PFS was significantly associated with CCL23 (p = 0.042), CCL25 (p = 0.014), IL-10 (p = 0.041), IL-32 (p = 0.004), IL-34 (p = 0.039) and TNFSF12 (p = 0.009). OS was significantly associated with CCL7 (p = 0.023), CCL19 (p = 0.019), IL-10 (p = 0.037) and IL-32 (p = 0.005). Results of logistic regression analysis are shown in Supplemental Table 1B. Post-treatment changes in soluble immune mediators associated with patient survival are shown in Fig. 3 . Correlation between pre-treatment and on-treatment biomarkers To better understand the significance of soluble immune mediators associated with PFS and OS in ICI monotherapy, we analyzed the correlation between levels of pre-treatment biomarker and changes in on-treatment biomarker. Among biomarkers of PFS, pre-treatment CXCL5 levels were significantly correlated with post-treatment changes in IL-34 (p = 0.039) and CCL25 (p = 0.019). Pre-treatment CCL17 levels were also significantly correlated with post-treatment changes in CCL25 (p = 0.026). For biomarkers of OS, pre-treatment CCL19 levels were significantly correlated with post-treatment changes in CCL19 (p = 0.002) and CCL7 (p = 0.009). Pre-treatment TNFSF13B levels were also significantly correlated with post-treatment change in IL-10 (p = 0.015). The correlation between pre-treatment and on-treatment biomarkers is shown in Supplemental Table 2A and 2B. Association of pre-treatment and on-treatment biomarkers with irAE development The associations of pre-treatment and on-treatment biomarkers with other possible predictors were analyzed (Table 2 A and 2 B). We analyzed the association of pre-treatment and on-treatment biomarkers with the development of irAE. Of the 110 patients in this study, 43 developed one or more irAEs. The irAEs observed in this study are summarized in Supplementary Table 3. None of the pre-treatment biomarkers were significantly associated with irAE development. Among the on-treatment biomarkers, only post-treatment changes in CCL23 were significantly associated with irAE development (p = 0.003). We also analyzed the association of pre-treatment and on-treatment biomarkers with the development of Grade ≥ 3 irAE. In this study, 18 patients developed Grade ≥ 3 irAEs. There were no significant associations of any of the pre-treatment or on-treatment biomarkers with the development of Grade ≥ 3 irAEs (Supplement Table 4A and 4B). Association of pre-treatment and on-treatment biomarkers with PD-L1 expression or CD8 + TIL density We analyzed the association of pre-treatment and on-treatment biomarkers with PD-L1 expression. PD-L1 expression was evaluable in 93 patients; 30 had 0% and 63 had > 1%. Among pre-treatment biomarkers, only TNFSF13B (p = 0.025) was significantly associated with PD-L1 expression. In contrast, among the on-treatment biomarkers, only CCL25 was significantly associated with PD-L1 expression. CD8 + TILs density was assessed in 90 patients who were divided into high and low groups based on median values. Among pre-treatment biomarkers, CXCL10 (p = 0.034) was significantly associated with CD8 + TIL density. However, there was no association between on-treatment biomarkers and CD8 + TIL density. Correlation between pre-treatment biomarkers and pre-treatment peripheral blood cells We analyzed the correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells (Table 3 A). CCL13 was significantly correlated with the proportion of neutrophils (p = 0.030), the eosinophil count (p = 0.007), the eosinophil proportion (p = 0.006), and NLR (p = 0.040). CCL17 was significantly correlated with the neutrophil proportion (p = 0.016), the eosinophil count (p = 0.001), the eosinophil proportion (p < 0.001) and NLR (p = 0.044). CCL19 was significantly correlated with the proportion of neutrophils (p = 0.038) and the lymphocyte count (p = 0.021). CXCL5 was significantly correlated with the neutrophil proportion (p = 0.014). CXCL10 was significantly correlated with the monocyte count (p = 0.027) and the monocyte proportion (p = 0.001). TNFSF13B was significantly correlated with the proportion of eosinophils (p = 0.037). Correlations between on-treatment biomarkers and peripheral blood cells We also analyzed correlations between changes in on-treatment biomarkers and changes in peripheral blood cells (Table 3 B). Post-treatment changes in IL-10 were significantly correlated with post-treatment changes in white blood cell (WBC) count (p < 0.001) and neutrophil count (p < 0.001). Post-treatment changes in IL-32 were significantly correlated with post-treatment changes in WBC count (p = 0.044). However, there were no other significant correlations between on-treatment biomarkers and peripheral blood cells. Discussion In this study, we explored the association between TME and favorable outcomes of PD-1/L1 monotherapy in NSCLC. We comprehensively measured soluble immune mediators in plasma samples at ICI initiation and then 6 weeks later. Our analysis identified several potential soluble immune mediators whose pre-treatment levels or post-treatment changes were significantly associated with the prognosis in NSCLC patients treated with ICI monotherapy. Pre-treatment biomarkers were mostly chemokines such as members of the CXCL family and the CCL family, whereas on-treatment biomarkers included cytokines such as those in the IL family. Some of the levels of pre-treatment biomarkers and post-treatment changes in on-treatment biomarkers were correlated, suggesting that they may reflect changes between the pre-treatment and post-treatment TME that were associated with favorable therapeutic efficacy of ICI monotherapy. To understand the complex network formation of cytokines and chemokines in the TME relevant to cancer immunotherapy, comprehensive measurement of soluble immune mediators in the same patient population as in this study was useful [1–3, 7–9]. In addition, analysis of the relationship with other potential biomarkers such as irAEs, PD-L1 expression, CD8 + TIL density and NLR is expected to provide a more detailed understanding of the TME [1–3, 13–17]. Among the pre-treatment biomarkers, CXCL5 was associated with both PFS and OS, and the heatmap showed a survival-related trend. This suggests that CXCL5 may be an important biomarker for the therapeutic efficacy of ICI monotherapy. CXCL5, also known as neutrophil activating peptide 78, is secreted by cancer cells or other host cells in the TME, including macrophages, fibroblasts and dendritic cells [7, 18, 19]. CXCL5 recruits neutrophils into tumor tissue and promotes tumor cell proliferation and metastasis. CXCL5 was also correlated with neutrophils in peripheral blood in this study and may have promoted tumor development. CXCL5 has been reported to promote PD-L1 expression and decrease CD4 + and CD8 + TILs in tumors, but no significant association with either was observed in this study [18]. The association between CXCL5 and cancer immunotherapy is currently under investigation and has not been established [18, 19]. We found a correlation between pre-treatment CXCL5 level and post-treatment changes in IL-34 and CCL25. IL-34 modulates tumor-associated macrophage function, enhances local immune suppression, and promotes survival of cancer cells resistant to ICI treatment [9, 20, 21]. CCL25 attracts mature CD8 + T cells from the thymus into the peripheral blood [7, 12, 22, 23]. The studies on the effects of IL-34 and CCL25 on the TME may provide a better understanding of the clinical significance of CXCL5 in cancer immunotherapy. Among the on-treatment biomarkers, post-treatment changes in IL-10 and IL-32 are associated with both PFS and OS and they may reflect favorable changes in the TME that are associated with the therapeutic efficacy of ICI monotherapy. The major cellular sources of IL-10 are CD4 + T cells, CD8 + T cells, a subset of Tregs and tumor cells [8]. IL-10 is a potent suppressor of anti-tumor immunity that inhibits tumor antigen presentation [1, 8, 9]. IL-10 acts primarily on dendric cells and macrophages, and it inhibits the differentiation and antigen-presenting properties of dendric cells [1, 8]. Furthermore, in this study, post-treatment changes in IL-10 correlated with pre-treatment TNFSF13B levels, which can promote B cell activation, and post-treatment neutrophil changes in peripheral blood cells. This suggests that pre-treatment B cells and post-treatment changes of neutrophil counts may be important factors in the favorable changes of the TME after ICI administration [10, 24, 25]. IL-32 is derived from NK cells and T cells and has nine different isoforms [9, 26]. IL-32 exhibits both pro- and anti-tumor effects, but the majority of the effects promote tumor growth. IL-32 can modulate the activity of tumor-associated macrophages and induce tumor inflammation [26]. There are few reports exploring the relationship between IL-32 and cancer immunotherapy, and it is necessary to identify the function of each isoform in the cancer immune cycle [9, 26]. Cancer immunotherapy may improve patient survival by altering IL family members in the TME [1, 8, 9, 25, 26]. Since irAEs reflect immune activation by ICI administration and are associated with therapeutic efficacy, we analyzed their association with the pre- and on-treatment biomarkers identified in the present study [13, 14]. Our analysis revealed that pre-treatment biomarkers were not associated with development of irAE, whereas post-treatment changes in CCL23 were associated with the development of irAE. CCL23 is also known as CKbeta8, macrophage inflammatory protein 3 and myeloid progenitor inhibitory factor-1 [7, 11]. CCL23 is produced by eosinophils, monocytes, and monocyte-derived cells, and it acts as a chemoattractant for monocytes and dendritic cells [7, 11]. Our results support the possibility that post-treatment changes in monocytes or dendritic cells induced by CCL23 may be involved in the development of irAE associated with the therapeutic outcome of ICI monotherapy. Considering that post-treatment changes in CCL23 may play an important role in irAE development, it is expected that more detailed investigation will improve the management of irAE. It is well established that PD-L1 expression is associated with ICI treatment outcome [1–6]. In this study, pre-treatment levels of TNFSF13B and post-treatment changes in CCL25 were associated with PD-L1 expression. TNFSF13B, also known as B-cell activating factor, is produced by myeloid cells, activated T cells, and bone marrow stromal cells to promote B-cell development and survival [10, 24, 25]. In solid tumors, TNFSF13B expression varies among different cancer types, and its prognostic and functional roles are not well understood [24]. An association between the presence of intra-tumoral B cells and the therapeutic efficacy of anti-PD-L1 antibodies in NSCLC has been reported, but there are no reports yet on TNFSF13B and PD-L1 expression in NSCLC specimens [25]. Since the functions of B cells in cancer immunity are not as well understood as those of T cells, further investigation is needed to determine whether TNFSF13B regulates PD-L1 expression in NSCLC. PD-L1 expression was also associated with post-treatment changes in CCL25. CCL25, also known as thymus-expressed chemokine, is expressed in the thymus, intestinal tract and tumor cells [7, 12, 22, 23]. CCL25 binds to its receptor on mature CD8 + T cells in the thymus and enhances their migration to secondary lymphoid organs such as lymph nodes. This chemoattraction of CD8 + T cells to secondary lymphoid organs may promote the therapeutic efficacy of ICI treatment [12, 22, 23]. Further studies are needed as CCL25 may be a more direct therapeutic target as a surrogate for pathological PD-L1 expression. CD8 + TIL density is also a pathologic predictor of ICI treatment [1–3]. In this study, pre-treatment CXCL10 was associated with CD8 + TILs, but on-treatment biomarkers were not. CXCL10 showed a trend toward higher levels in the low CD8 + TIL group. CXCL10 is a chemokine that is mainly produced by intra-tumoral myeloid immune cells and it correlated with monocytes in this study. In the cancer immune cycle, CXCL10 promotes T-cell trafficking to tumors, whereas it does not promote T-cell infiltration associated with CD8 + TIL density [1, 27]. This suggests that CXCL10 in this study may have been secondarily upregulated to recruit CD8 + T cells by negative feedback, reflecting the low density of CD8 TIL + cells. Considering that CD8 + TIL density is an important pathological predictor for cancer immunotherapy, its association with biomarkers may identify novel therapeutic targets [3]. The NLR is a predictor of ICI monotherapy that can be routinely measured in daily practice [15–17]. In this study, pre-treatment CCL17 and CCL13 were correlated with the NLR. Although not significantly correlated with the NLR, pre-treatment CXCL5 and CCL19 were correlated with neutrophil and lymphocyte counts. These chemokines may play a role as pre-treatment biomarkers by regulating the chemoattraction of lymphocytes or neutrophils in the peripheral blood [7, 11, 12, 18, 19]. Elucidation of the role of these biomarkers in cancer immunotherapy may allow prediction of the immune status of the TME from the numbers of peripheral blood cells. In summary, we conducted a comprehensive measurement of soluble immune mediators and identified several potential pre-treatment and on-treatment biomarkers in patients with NSCLC who received ICI monotherapy. It is expected that pre-treatment biomarkers might help to identify those patients who could benefit from ICI monotherapy. Moreover, on-treatment biomarkers might help our understanding of the favorable changes in TME associated with therapeutic efficacy. In addition, we found several associations and correlations between these pre- and on-treatment biomarkers and irAE, PD-L1 expression, CD8 + TIL density, and the NLR. Combining these biomarkers identified in the exploratory analysis with other predictors could provide a more detailed understanding of cancer immunotherapy and potentially identify new therapeutic targets. Nevertheless, our study had several limitations. First, this study was a retrospective single-center study. Second, the analysis in this study was exploratory and needs to be verified prospectively. Further large-scale studies are needed to provide a larger number of patients to better characterize the benefits of ICI monotherapy. Abbreviations CCL chemokine ligand CTLA-4 cytotoxic T lymphocyte antigen 4 CXCL C-X-C motif chemokine ligand ICI immune checkpoint inhibitor IL interleukin irAE immune-related adverse event NLR neutrophil to lymphocyte ratio NSCLC non-small cell lung cancer OS overall survival PD-1 programmed cell death-1 PD-L1 programmed cell death ligand-1 PFS progression-free survival TIL tumor-infiltrating lymphocytes TME tumor microenvironment TNFSF tumor necrosis factor superfamily WBC white blood cell. Declarations Conflicts of interest: KA reports receiving personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical. TT reports receiving personal fees from AstraZeneca, Bristol Myers Squibb, MSD, Novartis, and Chugai Pharmaceutical. TS reports receiving personal fees from Chugai Pharmaceutical and Bristol Myers Squibb, and research funds from Taiho, and BrightPath Biotherapeutics. The remaining authors have no conflicts of interest to disclose. CRediT Statement: Daiki Murata: Writing - Data curation, Investigation, Original draft, Visualization. Koichi Azuma: Conceptualization, Resources, Data Curation, Investigation, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition, Methodology, Software. Kenta Murotani: Formal analysis, Data Curation. Akihiko Kawahara: Investigation. Yuuya Nishii: Investigation. Takaaki Tokito: Investigation, Resources. Tetsuro Sasada: Writing - Review & Editing. Tomoaki Hoshino: Supervision, Project administration. Ethics approval: This study was conducted in accordance with the provisions of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kurume University Hospital (IRB No 20100). Consent to participate : Informed consent was obtained from all participants. Consent to publish : Not applicable. Funding: This study was supported by AMED (Grant Number JP19ae0101076). Author Contribution DM: Writing - Data curation, Investigation, Original draft, Visualization. KA: Conceptualization, Resources, Data Curation, Investigation, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition, Methodology, Software. KM: Formal analysis, Data Curation. AK: Investigation. YN: Investigation. TT: Investigation, Resources. TS: Writing - Review & Editing. TH: Supervision, Project administration. Acknowledgements: We would like to thank the participating patients for their contributions to this study. 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Patient characteristics Variables N=110 Age (range) 72 (53-89) Gender Male 79 Female 31 Smoking history + 81 - 29 Performance status 87 / 23 0 – 1 87 2 – 3 23 Histology Squamous 32 Non-Squamous 78 Driver mutation EGFR 20 ALK 3 Wild type 87 Treatment line 1 st 25 2 nd 65 > 3 rd 20 Immune checkpoint inhibitor Atezolizumab 10 Nivolumab 61 Pembrolizumab 39 ALK: anaplastic lymphoma kinase fusion, EGFR: epidermal growth factor receptor. Table 2A. Association of the levels of pre-treatment biomarkers with irAE development, PD-L1 expression and CD8+ TIL density Pre- treatment biomarker irAE development PD-L1 expression CD8+ TIL density Positive (N=43) Negative (N=67) p-value Positive (N=63) Negative (N=30) p-value High (N=44) Low (N=45) p-value CCL13 17.6 [14.4 - 22.2] 19.1 [14.6 - 26.5] 0.323 19.7 [15.9 - 26.0] 17.0 [14.5 - 22.6] 0.207 18.9 [15.3 - 22.4] 18.5 [14.17 - 26.37] 0.617 CCL17 10.1 [5.3 -18.2] 9.0 [1.1 - 16.3] 0.273 9.0 [2.7 - 14.0] 10.1 [5.3 - 24.3] 0.164 8.5 [1.3 - 15.6] 9.4 [4.22 - 17.56] 0.277 CCL19 39.2 [20.6 - 52.0] 36.3 [18.1 - 54.4] 0.762 39.2 [19.9 - 55.4] 35.3 [19.5 - 49.9] 0.895 33.7 [20.1 - 48.6] 40.2 [19.4 - 54.6] 0.458 CCL21 6366.3 [1765.7 - 8497.4] 6706.2 [3464.6 - 8730.9] 0.544 6804.1 [5286.5 - 8811.5] 6366.3 [1054.5 - 8561.3] 0.073 6762.7 [4166.9 - 8535.7] 6543.4 [1537.6 - 8732.8] 0.574 CXLC5 5.7 [5.7 - 124.4] 5.7 [5.7 - 52.1] 0.686 5.7 [5.7 - 51.7] 5.7 [5.7 - 125.0] 0.259 5.7 [5.7 - 73.9] 5.7 [5.7 - 69.5] 0.904 CXCL10 33.0 [22.5 - 51.6] 29.8 [17.8 - 47.7] 0.215 30.9 [18.1 - 50.5] 31.9 [20.3 - 47.3] 0.818 28.1 [17.2 - 39.6] 36.9 [20.37 - 58.86] 0.034 TNFSF13B 18320.7 [7920.0 -26287.8] 19822.3 [8863.2 - 38425.9] 0.330 22888.5 [14996.3 - 38425.9] 12681.6 [4363.1 - 30709.6] 0.025 22680.1 [12847.4 - 38441.8] 15814.9 [8383.3 - 32132.5] 0.128 CCL: chemokine ligand, CXCL: C-X-C motif chemokine ligand, irAE: immune related adverse event, PD-L1: programmed cell death ligand-1, TIL: tumor-infiltrating lymphocyte, TNFSF: tumor necrosis factor superfamily. Table 2B. Association of post-treatment changes in on-treatment biomarkers with irAE development, PD-L1 expression, and CD8+ TIL density On-treatment biomarker irAE development PD-L1 expression CD8+ TIL density Positive (N=43) Negative (N=67) p-value Positive (N=63) Negative (N=30) p-value High (N=44) Low (N=45) p-value CCL7 0.79 [-8.8 - 11.5] 0.0 [-3.5 - 14.5] 0.995 0.8 [-2.2 - 16.9] 0.0 [-8.8 - 3.1] 0.091 1.9 [0.0 - 13.8] 0.0 [-14.5 - 12.0] 0.179 CCL19 6.5 [-8.8 - 16.9] 3.5 [-7.9 - 23.5] 0.973 6.4 [-4.2 - 20.1] 3.9 [-11.4 - 19.4] 0.626 9.1 [0.0 - 23.6] 3.0 [-11.4 - 20.1] 0.146 CCL23 -31.5 [-134.4 - 35.1] 13.0 [-14.8 - 88.8] 0.003 0.0 [-95.7 - 48.7] 36.8 [-45.4 - 120.3] 0.123 -5.7 [-87.5 - 47.4] 5.3 [-105.2 - 64.2] 0.709 CCL25 3.0 [-66.3 - 59.3] 0.0 [-47.5 - 63.9] 0.910 16.2 [-47.5 - 101.0] -27.7 [-66.3 - 10.1] 0.007 10.3 [-42.7 - 48.7] 2.9 [-66.3 - 63.9] 0.841 IL-10 0.8 [-11.3 - 12.7] -1.0 [-13.8 - 14.5] 0.611 -1.7 [-19.8 - 20.5] 0.0 [-4.2 - 1.7] 0.789 -0.6 [-18.3 - 13.2] -1.0 [-13.3 - 7.3] 0.974 IL-32 0.0 [-49.7 - 32.4] -6.6 [-71.6 - 14.7] 0.359 0.0 [-79.8 - 113.7] -24.1 [-77.1 - 0.7] 0.117 -13.5 [-93.4 - 21.3] -6.6 [-51.5 - 7.0] 0.879 IL-34 0.0 [-238.7 - 466.2] 0.0 [-389.9 - 11.7] 0.479 0.0 [-483.1 - 318.7] 0.0 [0.0 - 0.0] 0.809 0.0 [-606.4 - 0.0] 0.0 [0.0 - 516.8] 0.063 TNFSF12 -3.5 [-93.5 - 24.7] -5.4 [-91.8 - 98.8] 0.932 -8.3 [-96.3 - 117.7] -3.6 [-62.8 - 13.7] 0.815 -2.4 [-92.7 - 96.3] -10.6 [-81.2 - 21.8] 0.673 CCL: chemokine ligand, IL: interleukin, irAE: immune related adverse event, PD-L1: programmed cell death ligand-1, TIL: tumor-infiltrating lymphocyte, TNFSF: tumor necrosis factor superfamily. Table 3A. Correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells CCL13 CCL17 CCL19 CCL21 Peripheral blood cells r * p ** r p r p r p WBC count 0.006 0.952 -0.033 0.734 0.060 0.534 0.096 0.315 Neutrophil count -0.066 0.489 -0.132 0.168 -0.026 0.789 0.042 0.666 Neutrophil (%) -0.206 0.030 -0.229 0.016 -0.197 0.038 -0.071 0.463 Lymphocyte count 0.169 0.076 0.150 0.115 0.218 0.021 0.106 0.267 Lymphocyte (%) 0.182 0.056 0.174 0.067 0.163 0.088 0.028 0.772 Monocyte count -0.044 0.647 0.111 0.249 0.106 0.271 0.059 0.543 Monocyte (%) -0.023 0.809 0.179 0.060 0.016 0.865 -0.115 0.231 Eosinophil count 0.254 0.007 0.333 0.001 0.101 0.293 0.046 0.636 Eosinophil (%) 0.260 0.006 0.323 < 0.001 0.071 0.463 0.006 0.951 NLR -0.195 0.040 -0.192 0.044 -0.181 0.057 -0.040 0.679 CXCL5 CXCL10 TNFSF13B Peripheral blood cells r p r p r p WBC count -0.036 0.704 0.003 0.974 0.064 0.502 Neutrophil count -0.103 0.281 -0.034 0.726 0.094 0.326 Neutrophil (%) -0.233 0.014 -0.058 0.545 0.120 0.209 Lymphocyte count 0.104 0.279 -0.040 0.680 -0.104 0.277 Lymphocyte (%) 0.155 0.105 -0.005 0.960 -0.173 0.070 Monocyte count 0.044 0.651 0.209 0.027 0.141 0.141 Monocyte (%) 0.075 0.437 0.308 0.001 -0.014 0.880 Eosinophil count 0.114 0.236 0.018 0.852 -0.181 0.057 Eosinophil (%) 0.123 0.198 0.027 0.781 -0.198 0.037 NLR -0.185 0.052 -0.013 0.891 0.162 0.089 CCL: chemokine ligand, CXCL: C-X-C motif chemokine ligand, NLR: neutrophil to lymphocyte ratio, TNFSF: tumor necrosis factor superfamily, WBC: white blood cell. * r means Spearman correlation coefficient. ** p means p-value. Table 3B. Correlation between post-treatment changes in on-treatment biomarkers and post-treatment change in peripheral blood cells IL-10 IL-32 IL-34 CCL7 Peripheral blood cells r * p ** r p r p r p WBC count -0.414 < 0.001 -0.192 0.044 -0.041 0.673 0.043 0.654 Neutrophil count -0.376 < 0.001 -0.153 0.109 -0.018 0.850 0.012 0.897 Neutrophil (%) -0.178 0.062 -0.064 0.505 -0.002 0.987 -0.062 0.519 Lymphocyte count -0.115 0.232 -0.156 0.103 0.009 0.928 -0.024 0.807 Lymphocyte (%) 0.167 0.080 0.007 0.940 0.013 0.895 -0.014 0.883 Monocyte count 0.182 0.055 0.160 0.940 -0.080 0.409 0.105 0.273 Monocyte (%) 0.159 0.097 0.149 0.118 -0.084 0.385 0.179 0.060 Eosinophil count 0.010 0.913 0.028 0.769 -0.048 0.620 0.071 0.462 Eosinophil (%) 0.080 0.403 0.070 0.469 -0.017 0.858 0.096 0.318 NLR -0.171 0.073 -0.038 0.696 0.000 0.997 -0.053 0.579 CCL19 CCL23 CCL25 TNFSF12 Peripheral blood cells r p r p r p r p WBC count 0.099 0.305 0.087 0.365 0.123 0.198 -0.057 0.555 Neutrophil count 0.080 0.406 0.082 0.391 0.100 0.297 -0.116 0.224 Neutrophil (%) -0.004 0.965 0.028 0.772 0.062 0.518 -0.154 0.106 Lymphocyte count -0.043 0.658 0.059 0.538 -0.031 0.748 0.124 0.196 Lymphocyte (%) 0.556 0.556 0.012 0.897 -0.068 0.482 0.143 0.134 Monocyte count -0.008 0.937 -0.127 0.185 -0.012 0.897 0.106 0.270 Monocyte (%) 0.067 0.487 -0.100 0.297 0.046 0.633 0.084 0.379 Eosinophil count 0.097 0.315 -0.039 0.683 -0.056 0.558 0.136 0.155 Eosinophil (%) 0.091 0.347 -0.052 0.589 -0.087 0.366 0.137 0.151 NLR -0.014 0.887 0.029 0.766 0.073 0.448 -0.136 0.156 CCL: chemokine ligand, IL: IL: interleukin, NLR: neutrophil to lymphocyte ratio, TNFSF: tumor necrosis factor superfamily, WBC: white blood cell. * r means Spearman correlation coefficient. ** p means p-value. Additional Declarations Competing interest reported. KA reports receiving personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical. TT reports receiving personal fees from AstraZeneca, Bristol Myers Squibb, MSD, Novartis, and Chugai Pharmaceutical. TS reports receiving personal fees from Chugai Pharmaceutical and Bristol Myers Squibb, and research funds from Taiho, and BrightPath Biotherapeutics. The remaining authors have no conflicts of interest to disclose. Supplementary Files MurataDICIbiomarkerSupplementFigureforCancerImmunolImmunother.pptx Supplemental Figure 1. The associations of post-treatment biomarker changes with irAE development, PD-L1 expression and CD8+ TIL density. irAE: immune-related adverse event, PD-L1: programmed cell death ligand-1, TILs: tumor-infiltrating lymphocytes MurataDICIbiomarkerSupplementtablesforCancerImmunolImmunother.docx Supplementalappendix.docx Cite Share Download PDF Status: Published Journal Publication published 05 Sep, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 21 Apr, 2024 Reviews received at journal 18 Apr, 2024 Reviews received at journal 14 Apr, 2024 Reviews received at journal 28 Mar, 2024 Reviewers agreed at journal 26 Mar, 2024 Reviewers agreed at journal 26 Mar, 2024 Reviewers agreed at journal 24 Mar, 2024 Reviewers invited by journal 24 Mar, 2024 Editor assigned by journal 08 Mar, 2024 Submission checks completed at journal 08 Mar, 2024 First submitted to journal 06 Mar, 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. 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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-4021078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278409105,"identity":"0e0cd4f4-cf1c-420b-923a-53cc984c2780","order_by":0,"name":"Daiki Murata","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daiki","middleName":"","lastName":"Murata","suffix":""},{"id":278409106,"identity":"bd5bcacd-a65e-4e61-9d95-f85e85bd89fa","order_by":1,"name":"Koichi Azuma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYNCCCiA+wIYixIZdJRycIVkLYxumFtyAv/+M6Yaf87bJ8R1gS5OubNsmxyCRwPjhBwNfHi4tEgfOmN3s3XbbWPIA2zHJs223jYFamCV7GNiKcVpzsMfsBu+224kbDrC3STa23U7cfyOBQRrol8QGHDrkD/OY3fw753Y9TEt9A9CW3/i0GBzjMbvN23A7wQDkMKCWBKDD2PDaYniGrey2zLHbhjMPsyVbNpy7bdjA87DNsscAt1/kzh/edvNNzW15vuNthjcbym7LM7AnH77xo+IYzhBDAGY4i7EB5OAEwlrQQA3pWkbBKBgFo2C4AgBHMFhXWdfMEwAAAABJRU5ErkJggg==","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Azuma","suffix":""},{"id":278409107,"identity":"93372b60-df00-4a2f-bee5-42d6c951bc7e","order_by":2,"name":"Kenta Murotani","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kenta","middleName":"","lastName":"Murotani","suffix":""},{"id":278409108,"identity":"955d6afc-e084-42f0-9967-9048913c1b74","order_by":3,"name":"Akihiko Kawahara","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akihiko","middleName":"","lastName":"Kawahara","suffix":""},{"id":278409109,"identity":"27713a5f-c192-45c9-b2d0-620592ec8116","order_by":4,"name":"Yuuya Nishii","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuuya","middleName":"","lastName":"Nishii","suffix":""},{"id":278409110,"identity":"5835816e-6806-4fe6-9497-c8289208b0fb","order_by":5,"name":"Takaaki Tokito","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Takaaki","middleName":"","lastName":"Tokito","suffix":""},{"id":278409111,"identity":"fcd03bcd-8a91-450c-ba52-c561629d4dff","order_by":6,"name":"Tetsuro Sasada","email":"","orcid":"","institution":"Kanagawa Cancer Center Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tetsuro","middleName":"","lastName":"Sasada","suffix":""},{"id":278409112,"identity":"974128f0-991a-4628-b68f-44d32bd0215a","order_by":7,"name":"Tomoaki Hoshino","email":"","orcid":"","institution":"Kurume University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tomoaki","middleName":"","lastName":"Hoshino","suffix":""}],"badges":[],"createdAt":"2024-03-06 13:15:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4021078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4021078/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-024-03781-8","type":"published","date":"2024-09-05T16:08:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52524410,"identity":"6894f057-7fb1-4c5e-a0cb-a28ce154d61f","added_by":"auto","created_at":"2024-03-12 15:36:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Overview of the study. We analyzed 110 patients for whom pre- and post-treatment plasma samples were available. Plasma samples were collected at ICI initiation and 6 weeks later. A multiplex assay was used to measure plasma levels of 73 soluble immune mediators. The associations between the 73 immune mediators and patient prognosis were analyzed. \u003cstrong\u003eB.\u003c/strong\u003e Flow diagram of the study population. Among the 183 patients with advanced or recurrent NSCLC who received ICI monotherapy, we analyzed 110 patients whose pre- and post-treatment plasma samples were available. \u003cstrong\u003eC.\u003c/strong\u003e Kaplan-Meier survival curves for PFS and OS. The median PFS was 2.8 months (95%CI: 2.2 - 4.7), and the median OS was 11.9 months (95%CI: 8.2 - 14.7). \u003cem\u003e95%CI: 95% confidence interval, ICI: immune checkpoint inhibitor, PFS:\u003c/em\u003e \u003cem\u003eprogression-free survival, OS: overall survival.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/14a6f2f4df58638cbfef2726.png"},{"id":52524406,"identity":"036fcdeb-d113-4084-bfef-48d41861ec19","added_by":"auto","created_at":"2024-03-12 15:36:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79444,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the associations of patient survival with pre-treatment biomarkers, irAE development, PD-L1 expression and CD8+ TIL density. The colors of the pre-treatment biomarkers show the levels of each soluble immune mediator in each patient. Patients with any grade irAE or Grade \u0026gt;3 irAE are also colored. PD-L1 expression was categorized as 0% and \u0026gt;1% for evaluable patients. CD8+ TIL density was categorized into high and low groups based on median values. For evaluable patients, PD-L1 expression and CD8+ TIL density are shown in two colors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eirAE: immune-related adverse event, PD-L1: programmed cell death ligand-1, TILs: tumor-infiltrating lymphocytes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/c8aa77342e2333861cbe4c27.png"},{"id":52524427,"identity":"3f4a9cf3-b8da-4530-9ed2-675c4d562844","added_by":"auto","created_at":"2024-03-12 15:36:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34639,"visible":true,"origin":"","legend":"\u003cp\u003ePost-treatment changes in soluble immune mediators associated with patient survival. For each patient, increased post-treatment changes are indicated by orange bars and decreased post-treatment changes are indicated by blue bars.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/26359e32e68c57e86c47acca.png"},{"id":64186256,"identity":"35df4537-b26e-4dbf-9a5d-e6ab64da8f57","added_by":"auto","created_at":"2024-09-09 16:26:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1245666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/1dc19f5e-fd36-4584-9173-5d88c4db1ed1.pdf"},{"id":52524423,"identity":"953c64a0-1dc8-4956-9026-735cfda59fa7","added_by":"auto","created_at":"2024-03-12 15:36:52","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":162074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations of post-treatment biomarker changes with irAE development, PD-L1 expression and CD8+ TIL density.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eirAE: immune-related adverse event, PD-L1: programmed cell death ligand-1, TILs: tumor-infiltrating lymphocytes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"MurataDICIbiomarkerSupplementFigureforCancerImmunolImmunother.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/e1e4287035218bf87e7fd5a6.pptx"},{"id":52524405,"identity":"d5746406-01ef-49e3-a9a2-9fdd54529730","added_by":"auto","created_at":"2024-03-12 15:36:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":66324,"visible":true,"origin":"","legend":"","description":"","filename":"MurataDICIbiomarkerSupplementtablesforCancerImmunolImmunother.docx","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/e5545270cc7429b30a59f1b5.docx"},{"id":52524422,"identity":"81707a31-cbec-437b-80d4-c958fd6af7e8","added_by":"auto","created_at":"2024-03-12 15:36:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12757,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalappendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4021078/v1/606bb5e9c23a2f0c5e70d0ce.docx"}],"financialInterests":"Competing interest reported. KA reports receiving personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical. TT reports receiving personal fees from AstraZeneca, Bristol Myers Squibb, MSD, Novartis, and Chugai Pharmaceutical. TS reports receiving personal fees from Chugai Pharmaceutical and Bristol Myers Squibb, and research funds from Taiho, and BrightPath Biotherapeutics. The remaining authors have no conflicts of interest to disclose.","formattedTitle":"Characterization of Pre- and Post-treatment Soluble Immune Mediators and the Tumor Microenvironment in NSCLC Patients Receiving PD-1/L1 Inhibitor Monotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmune checkpoint inhibitors (ICI) have become the new standard of treatment for advanced and recurrent non-small cell lung cancer (NSCLC). Inhibitors of programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) activate tumor-specific T cells and provide therapeutic efficacy. These agents are characterized by long-term survival and sustained therapeutic efficacy even after discontinuation of treatment. Despite the favorable therapeutic efficacy observed, the majority of patients do not respond to ICI monotherapy. Therefore, a current challenge is to identify those patients who could optimally benefit from ICI treatment [1\u0026ndash;6].\u003c/p\u003e \u003cp\u003eThe cancer-immunity cycle does not function optimally in advanced cancer patients. For an antitumor immune response to result in the effective killing of tumor cells, a series of stepwise events must be initiated and allowed to proceed and expand iteratively. This cycle can be divided into seven major steps, beginning with the release of antigens from tumor cells and ending with the killing of tumor cells. Each step of the cancer-immunity cycle requires the coordination of numerous factors, both stimulatory and inhibitory in nature [1]. Soluble immune mediators, including cytokines and chemokines such as those in the interleukin (IL) family, the tumor necrosis factor superfamily (TNFSF), chemokine ligands (CCL), and C-X-C motif chemokine ligands (CXCL), can stimulate or inhibit each step of the cycle [1, 2, 7\u0026ndash;12]. The tumor microenvironment (TME) forms a complex network of cytokines or chemokines that modulate antitumor immunity. While a tremendous amount of research has been conducted on cancer immunology and immunotherapy to implement clinical strategies, the role of various immune cells in the TME remains unclear [1\u0026ndash;3, 6\u0026ndash;11].\u003c/p\u003e \u003cp\u003eIn addition to characterizing soluble immune mediators, each step of the cancer immune cycle can be assessed by immunohistochemical analysis. PD-L1 expression in tumor tissue suggests inhibition of the step of cancer cell killing. The density of CD8\u0026thinsp;+\u0026thinsp;T cells in tumor tissue indicates the ability of T cells to infiltrate into the tumor [1\u0026ndash;3]. The TME has been divided into four different types based on PD-L1 expression and CD8\u0026thinsp;+\u0026thinsp;tumor-infiltrating lymphocytes (TILs) [3]. This classification provides a framework for predicting the therapeutic outcome of cancer immunotherapy. However, since tissue biopsies are invasive and time-consuming, simpler non-invasive methods are needed.\u003c/p\u003e \u003cp\u003eA comprehensive study of biomarkers such as cytokines and chemokines may lead to a better understanding of the relationship between cancer immunity and immunotherapy and may identify novel therapeutic targets [1\u0026ndash;3, 7\u0026ndash;12]. Therefore, we measured a comprehensive set of soluble immune mediators and performed an exploratory analysis in patients with NSCLC who received PD-1/L1 monotherapy. In the present study, 73 soluble immune mediators were measured at ICI initiation and 6 weeks later. We analyzed the association of patient survival and the levels of soluble immune mediators at ICI initiation as a pre-treatment biomarker. We also analyzed the association between patient survival and the changes in soluble immune mediators 6 weeks after ICI initiation as an on-treatment biomarker, as it may reflect the changes in the TME that are associated with treatment efficacy. The correlation between pre-treatment and on-treatment biomarkers was examined to investigate the association between favorable immune status in the pre-treatment TME and its favorable post-treatment changes. The correlations between pre-treatment or on-treatment biomarkers and other possible predictors, including the development of immune-related adverse events (irAE), PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density, peripheral blood cells and the neutrophil to lymphocyte ratio (NLR) were examined to reveal the underlying mechanisms associated with therapeutic outcome.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe retrospectively screened patients with advanced or recurrent NSCLC who received PD-1/L1 monotherapy at Kurume University Hospital between January 2016 and December 2020. Among 183 patients with NSCLC who received ICI monotherapy, we analyzed 110 patients for whom pre- and post-treatment plasma samples were available. Plasma samples were collected at ICI initiation and 6 weeks later. All patients had pathologically confirmed NSCLC. Progression-free survival (PFS) and overall survival (OS) were calculated for each patient. This study was conducted in accordance with the provisions of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kurume University Hospital (IRB No 20100).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of soluble immune mediators in plasma\u003c/h2\u003e \u003cp\u003eWe collected plasma samples at the time of ICI initiation and 6 weeks later. This was done in order to explore biomarkers associated with prognosis of NSCLC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All samples were heparinized and centrifuged at 1600g for 15 min. The plasma supernatants were transferred to new tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until measurement. At the time of measurement, these samples were allowed to thaw naturally at room temperature. Soluble immune mediators were measured once for each patient and measurements were not repeated. A bead-based multiplex assay was used to measure plasma levels of soluble immune mediators. Post-treatment changes were calculated as the difference in soluble immune mediator levels found at ICI initiation and 6 weeks later.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e A Bio-Plex 200 system (Bio-Rad Laboratories, Hercules, CA) was used to analyze 100-\u0026micro;L aliquots of two-fold diluted plasma samples in accordance with the manufacturer\u0026rsquo;s instructions. Kits (Bio-Rad Laboratories) for the various analytes were used to measure the 73 soluble immune mediators, including cytokines, chemokines and growth factors (Supplemental appendix). These soluble immune mediators were selected for their relevance to the TME and the cancer immune cycle [1, 6\u0026ndash;11].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of other possible predictors\u003c/h2\u003e \u003cp\u003eWe also analyzed the association and correlation between possible predictors of the therapeutic efficacy of ICI monotherapy and pre- and on-treatment biomarkers. Based on previous reports, irAE development, PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density, peripheral blood cell count and proportion, and NLR were selected for examination [1\u0026ndash;3, 13\u0026ndash;17].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of irAE, peripheral blood cell, and NLR\u003c/h2\u003e \u003cp\u003eIrAEs were evaluated according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. Peripheral blood cell counts and proportions were commercially assayed. The data included absolute counts and the proportions of neutrophils, lymphocytes, monocytes, and eosinophils. The NLR was also assessed and calculated using absolute counts of neutrophils and lymphocytes [16]. Post-treatment changes in peripheral blood cells were calculated as the difference between the values at ICI initiation and those 6 weeks later.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical analysis\u003c/h2\u003e \u003cp\u003eIn this study, immunohistochemical analysis of PD-L1 expression and CD8\u0026thinsp;+\u0026thinsp;TIL density was performed in patients whose tumor biopsy specimens were available. Four-mm-thick sections of formalin-fixed, paraffin-embedded tissues were used. The sections were mounted on glass slides and then incubated with anti-rabbit monoclonal antibody against PD-L1 (clone ELL) (Cell signaling Technology, Denver) for immunohistochemical (IHC) analysis using the BenchMark ULTRA (Ventana Automated Systems, Inc., Tucson, AZ, United States of America). Each slide was heat-treated with Ventana\u0026rsquo;s CC1 retrieval solution for 30 min and incubated with the PD-L1 antibody for 30 min. This automated system used the ultraVIEW DAB detection kit with 3, 3ʹ diaminobenzidine (DAB) as the chromogen (Ventana Automated Systems). PD-L1 expression was categorized as either 0% or \u0026gt;\u0026thinsp;1%.\u003c/p\u003e \u003cp\u003eImmunostaining for CD8 (Leica Microsystems, Newcastle-upon-Tyne, UK) was performed on the same fully automated Bond-III system (Leica Microsystems) using on-board heat-induced antigen retrieval with epitope retrieval solution 2 for 10 min at 99\u0026deg;C, and incubated with the antibody for 30 min at room temperature. This automated system used a Refine polymer detection kit with horseradish peroxidase-polymer as the secondary antibody and DAB, and incubation with a secondary antibody was performed for 30 min at room temperature. TILs were counted on immuno-stained CD8 preparations and scored using a four-tier scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis investigation was an observational study. Thus, the target sample size for this study was set at 100 participants. Comparisons of categorical variables were evaluated using chi-squared or Fisher\u0026rsquo;s exact tests. PFS and OS were compared between groups using a log-rank test. To explore factors associated with the dependent variables (PFS and OS), univariate Cox proportional hazards model analysis was performed using soluble immune mediators as independent variables. Due to the limited number of events for the dependent variables in this study, adjusted analysis was not performed. Spearman correlation analysis was performed to assess the correlation between pre- and on-treatment biomarkers, pre-treatment biomarkers and pre-treatment peripheral blood cells, and on-treatment biomarkers and post-treatment changes in peripheral blood cells. Wilcoxon rank sum tests were used to analyze the association between pre- or on-treatment biomarkers and irAE development, PD-L1 expression and CD8\u0026thinsp;+\u0026thinsp;TIL density. All tests utilized a two-sided approach, and differences were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were performed using JMP pro version 16.0 statistical software (SAS Institute Inc.). The cut-off date for the analyses was March 31, 2023.\u003c/p\u003e \u003cp\u003eThe primary endpoint identified pre-treatment and on-treatment biomarkers that were associated with PFS and OS in patients with NSCLC receiving PD-1/L1 monotherapy. Pre-treatment biomarkers were defined as soluble immune mediators whose levels at ICI initiation were associated with PFS and OS. On-treatment biomarkers were defined as soluble immune mediators whose changes after ICI initiation (levels at 6 weeks after ICI initiation \u0026ndash; levels at ICI initiation) were associated with PFS and OS. The secondary endpoint was to examine the correlation between pre-treatment and on-treatment biomarkers. Additional secondary endpoints included the associations of pre-treatment and on-treatment biomarkers with PD-L1 expression or CD8\u0026thinsp;+\u0026thinsp;TIL density. The correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells and between on-treatment biomarkers and post-treatment changes in peripheral blood cells were also analyzed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics and survival\u003c/h2\u003e \u003cp\u003eHere, we focused on patients with NSCLC who had received ICI monotherapy. We collected plasma samples at baseline and after 6 weeks of therapy to investigate biomarkers associated with prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Among 183 patients with NSCLC who had received ICI monotherapy, we analyzed 110 patients for whom pre- and post-treatment plasma samples were available. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows a flow chart of the study patients. The characteristics of the enrolled patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 72 years. Of the 110 patients, 79 were male and 81 had a history of smoking. Performance status was 0\u0026ndash;1 in 87 patients and 2\u0026ndash;3 in 23 patients. Non-squamous and squamous cell carcinoma were present in 78 and 32 patients, respectively. A driver mutation was harbored in 23 patients, an epidermal growth factor receptor mutation in 20 and an anaplastic lymphoma kinase fusion gene in 3. ICI was administered as first-line, second-line, and third-line or later treatment in 25, 65, and 20 patients, respectively. The median PFS was 2.8 months (95%CI: 2.2\u0026ndash;4.7), and the median OS was 11.9 months (95%CI: 8.2\u0026ndash;14.7). Kaplan-Meier survival curves for study patients are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of pre-treatment or on-treatment biomarkers\u003c/h2\u003e \u003cp\u003eWe analyzed the association between the levels of soluble immune mediators at ICI initiation and PFS and OS. Among 73 soluble immune mediators, PFS was significantly associated with CXCL5 (p\u0026thinsp;=\u0026thinsp;0.006), CXCL10 (p\u0026thinsp;=\u0026thinsp;0.009), and CCL17 (p\u0026thinsp;=\u0026thinsp;0.043). OS was significantly associated with CXCL5 (p\u0026thinsp;=\u0026thinsp;0.006), CCL13 (p\u0026thinsp;=\u0026thinsp;0.045), CCL19 (p\u0026thinsp;=\u0026thinsp;0.022), CCL21 (p\u0026thinsp;=\u0026thinsp;0.050) and TNFSF13B (p\u0026thinsp;=\u0026thinsp;0.028). Results of logistic regression analysis are shown in Supplemental Table\u0026nbsp;1A. A heatmap displaying the association between patient survival and pre-treatment biomarkers is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also analyzed the association between changes in soluble immune mediators 6 weeks after ICI initiation as well as with PFS and OS. Among 73 soluble immune mediators, PFS was significantly associated with CCL23 (p\u0026thinsp;=\u0026thinsp;0.042), CCL25 (p\u0026thinsp;=\u0026thinsp;0.014), IL-10 (p\u0026thinsp;=\u0026thinsp;0.041), IL-32 (p\u0026thinsp;=\u0026thinsp;0.004), IL-34 (p\u0026thinsp;=\u0026thinsp;0.039) and TNFSF12 (p\u0026thinsp;=\u0026thinsp;0.009). OS was significantly associated with CCL7 (p\u0026thinsp;=\u0026thinsp;0.023), CCL19 (p\u0026thinsp;=\u0026thinsp;0.019), IL-10 (p\u0026thinsp;=\u0026thinsp;0.037) and IL-32 (p\u0026thinsp;=\u0026thinsp;0.005). Results of logistic regression analysis are shown in Supplemental Table\u0026nbsp;1B. Post-treatment changes in soluble immune mediators associated with patient survival are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between pre-treatment and on-treatment biomarkers\u003c/h2\u003e \u003cp\u003eTo better understand the significance of soluble immune mediators associated with PFS and OS in ICI monotherapy, we analyzed the correlation between levels of pre-treatment biomarker and changes in on-treatment biomarker. Among biomarkers of PFS, pre-treatment CXCL5 levels were significantly correlated with post-treatment changes in IL-34 (p\u0026thinsp;=\u0026thinsp;0.039) and CCL25 (p\u0026thinsp;=\u0026thinsp;0.019). Pre-treatment CCL17 levels were also significantly correlated with post-treatment changes in CCL25 (p\u0026thinsp;=\u0026thinsp;0.026). For biomarkers of OS, pre-treatment CCL19 levels were significantly correlated with post-treatment changes in CCL19 (p\u0026thinsp;=\u0026thinsp;0.002) and CCL7 (p\u0026thinsp;=\u0026thinsp;0.009). Pre-treatment TNFSF13B levels were also significantly correlated with post-treatment change in IL-10 (p\u0026thinsp;=\u0026thinsp;0.015). The correlation between pre-treatment and on-treatment biomarkers is shown in Supplemental Table\u0026nbsp;2A and 2B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of pre-treatment and on-treatment biomarkers with irAE development\u003c/h2\u003e \u003cp\u003eThe associations of pre-treatment and on-treatment biomarkers with other possible predictors were analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We analyzed the association of pre-treatment and on-treatment biomarkers with the development of irAE. Of the 110 patients in this study, 43 developed one or more irAEs. The irAEs observed in this study are summarized in Supplementary Table\u0026nbsp;3. None of the pre-treatment biomarkers were significantly associated with irAE development. Among the on-treatment biomarkers, only post-treatment changes in CCL23 were significantly associated with irAE development (p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eWe also analyzed the association of pre-treatment and on-treatment biomarkers with the development of Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 irAE. In this study, 18 patients developed Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 irAEs. There were no significant associations of any of the pre-treatment or on-treatment biomarkers with the development of Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 irAEs (Supplement Table\u0026nbsp;4A and 4B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of pre-treatment and on-treatment biomarkers with PD-L1 expression or CD8\u0026thinsp;+\u0026thinsp;TIL density\u003c/h2\u003e \u003cp\u003eWe analyzed the association of pre-treatment and on-treatment biomarkers with PD-L1 expression. PD-L1 expression was evaluable in 93 patients; 30 had 0% and 63 had\u0026thinsp;\u0026gt;\u0026thinsp;1%. Among pre-treatment biomarkers, only TNFSF13B (p\u0026thinsp;=\u0026thinsp;0.025) was significantly associated with PD-L1 expression. In contrast, among the on-treatment biomarkers, only CCL25 was significantly associated with PD-L1 expression. CD8\u0026thinsp;+\u0026thinsp;TILs density was assessed in 90 patients who were divided into high and low groups based on median values. Among pre-treatment biomarkers, CXCL10 (p\u0026thinsp;=\u0026thinsp;0.034) was significantly associated with CD8\u0026thinsp;+\u0026thinsp;TIL density. However, there was no association between on-treatment biomarkers and CD8\u0026thinsp;+\u0026thinsp;TIL density.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between pre-treatment biomarkers and pre-treatment peripheral blood cells\u003c/h2\u003e \u003cp\u003eWe analyzed the correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). CCL13 was significantly correlated with the proportion of neutrophils (p\u0026thinsp;=\u0026thinsp;0.030), the eosinophil count (p\u0026thinsp;=\u0026thinsp;0.007), the eosinophil proportion (p\u0026thinsp;=\u0026thinsp;0.006), and NLR (p\u0026thinsp;=\u0026thinsp;0.040). CCL17 was significantly correlated with the neutrophil proportion (p\u0026thinsp;=\u0026thinsp;0.016), the eosinophil count (p\u0026thinsp;=\u0026thinsp;0.001), the eosinophil proportion (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NLR (p\u0026thinsp;=\u0026thinsp;0.044). CCL19 was significantly correlated with the proportion of neutrophils (p\u0026thinsp;=\u0026thinsp;0.038) and the lymphocyte count (p\u0026thinsp;=\u0026thinsp;0.021). CXCL5 was significantly correlated with the neutrophil proportion (p\u0026thinsp;=\u0026thinsp;0.014). CXCL10 was significantly correlated with the monocyte count (p\u0026thinsp;=\u0026thinsp;0.027) and the monocyte proportion (p\u0026thinsp;=\u0026thinsp;0.001). TNFSF13B was significantly correlated with the proportion of eosinophils (p\u0026thinsp;=\u0026thinsp;0.037).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between on-treatment biomarkers and peripheral blood cells\u003c/h2\u003e \u003cp\u003eWe also analyzed correlations between changes in on-treatment biomarkers and changes in peripheral blood cells (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Post-treatment changes in IL-10 were significantly correlated with post-treatment changes in white blood cell (WBC) count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and neutrophil count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-treatment changes in IL-32 were significantly correlated with post-treatment changes in WBC count (p\u0026thinsp;=\u0026thinsp;0.044). However, there were no other significant correlations between on-treatment biomarkers and peripheral blood cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the association between TME and favorable outcomes of PD-1/L1 monotherapy in NSCLC. We comprehensively measured soluble immune mediators in plasma samples at ICI initiation and then 6 weeks later. Our analysis identified several potential soluble immune mediators whose pre-treatment levels or post-treatment changes were significantly associated with the prognosis in NSCLC patients treated with ICI monotherapy. Pre-treatment biomarkers were mostly chemokines such as members of the CXCL family and the CCL family, whereas on-treatment biomarkers included cytokines such as those in the IL family. Some of the levels of pre-treatment biomarkers and post-treatment changes in on-treatment biomarkers were correlated, suggesting that they may reflect changes between the pre-treatment and post-treatment TME that were associated with favorable therapeutic efficacy of ICI monotherapy. To understand the complex network formation of cytokines and chemokines in the TME relevant to cancer immunotherapy, comprehensive measurement of soluble immune mediators in the same patient population as in this study was useful [1\u0026ndash;3, 7\u0026ndash;9]. In addition, analysis of the relationship with other potential biomarkers such as irAEs, PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density and NLR is expected to provide a more detailed understanding of the TME [1\u0026ndash;3, 13\u0026ndash;17].\u003c/p\u003e \u003cp\u003eAmong the pre-treatment biomarkers, CXCL5 was associated with both PFS and OS, and the heatmap showed a survival-related trend. This suggests that CXCL5 may be an important biomarker for the therapeutic efficacy of ICI monotherapy. CXCL5, also known as neutrophil activating peptide 78, is secreted by cancer cells or other host cells in the TME, including macrophages, fibroblasts and dendritic cells [7, 18, 19]. CXCL5 recruits neutrophils into tumor tissue and promotes tumor cell proliferation and metastasis. CXCL5 was also correlated with neutrophils in peripheral blood in this study and may have promoted tumor development. CXCL5 has been reported to promote PD-L1 expression and decrease CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;TILs in tumors, but no significant association with either was observed in this study [18]. The association between CXCL5 and cancer immunotherapy is currently under investigation and has not been established [18, 19]. We found a correlation between pre-treatment CXCL5 level and post-treatment changes in IL-34 and CCL25. IL-34 modulates tumor-associated macrophage function, enhances local immune suppression, and promotes survival of cancer cells resistant to ICI treatment [9, 20, 21]. CCL25 attracts mature CD8\u0026thinsp;+\u0026thinsp;T cells from the thymus into the peripheral blood [7, 12, 22, 23]. The studies on the effects of IL-34 and CCL25 on the TME may provide a better understanding of the clinical significance of CXCL5 in cancer immunotherapy.\u003c/p\u003e \u003cp\u003eAmong the on-treatment biomarkers, post-treatment changes in IL-10 and IL-32 are associated with both PFS and OS and they may reflect favorable changes in the TME that are associated with the therapeutic efficacy of ICI monotherapy. The major cellular sources of IL-10 are CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, a subset of Tregs and tumor cells [8]. IL-10 is a potent suppressor of anti-tumor immunity that inhibits tumor antigen presentation [1, 8, 9]. IL-10 acts primarily on dendric cells and macrophages, and it inhibits the differentiation and antigen-presenting properties of dendric cells [1, 8]. Furthermore, in this study, post-treatment changes in IL-10 correlated with pre-treatment TNFSF13B levels, which can promote B cell activation, and post-treatment neutrophil changes in peripheral blood cells. This suggests that pre-treatment B cells and post-treatment changes of neutrophil counts may be important factors in the favorable changes of the TME after ICI administration [10, 24, 25]. IL-32 is derived from NK cells and T cells and has nine different isoforms [9, 26]. IL-32 exhibits both pro- and anti-tumor effects, but the majority of the effects promote tumor growth. IL-32 can modulate the activity of tumor-associated macrophages and induce tumor inflammation [26]. There are few reports exploring the relationship between IL-32 and cancer immunotherapy, and it is necessary to identify the function of each isoform in the cancer immune cycle [9, 26]. Cancer immunotherapy may improve patient survival by altering IL family members in the TME [1, 8, 9, 25, 26].\u003c/p\u003e \u003cp\u003eSince irAEs reflect immune activation by ICI administration and are associated with therapeutic efficacy, we analyzed their association with the pre- and on-treatment biomarkers identified in the present study [13, 14]. Our analysis revealed that pre-treatment biomarkers were not associated with development of irAE, whereas post-treatment changes in CCL23 were associated with the development of irAE. CCL23 is also known as CKbeta8, macrophage inflammatory protein 3 and myeloid progenitor inhibitory factor-1 [7, 11]. CCL23 is produced by eosinophils, monocytes, and monocyte-derived cells, and it acts as a chemoattractant for monocytes and dendritic cells [7, 11]. Our results support the possibility that post-treatment changes in monocytes or dendritic cells induced by CCL23 may be involved in the development of irAE associated with the therapeutic outcome of ICI monotherapy. Considering that post-treatment changes in CCL23 may play an important role in irAE development, it is expected that more detailed investigation will improve the management of irAE.\u003c/p\u003e \u003cp\u003eIt is well established that PD-L1 expression is associated with ICI treatment outcome [1\u0026ndash;6]. In this study, pre-treatment levels of TNFSF13B and post-treatment changes in CCL25 were associated with PD-L1 expression. TNFSF13B, also known as B-cell activating factor, is produced by myeloid cells, activated T cells, and bone marrow stromal cells to promote B-cell development and survival [10, 24, 25]. In solid tumors, TNFSF13B expression varies among different cancer types, and its prognostic and functional roles are not well understood [24]. An association between the presence of intra-tumoral B cells and the therapeutic efficacy of anti-PD-L1 antibodies in NSCLC has been reported, but there are no reports yet on TNFSF13B and PD-L1 expression in NSCLC specimens [25]. Since the functions of B cells in cancer immunity are not as well understood as those of T cells, further investigation is needed to determine whether TNFSF13B regulates PD-L1 expression in NSCLC. PD-L1 expression was also associated with post-treatment changes in CCL25. CCL25, also known as thymus-expressed chemokine, is expressed in the thymus, intestinal tract and tumor cells [7, 12, 22, 23]. CCL25 binds to its receptor on mature CD8\u0026thinsp;+\u0026thinsp;T cells in the thymus and enhances their migration to secondary lymphoid organs such as lymph nodes. This chemoattraction of CD8\u0026thinsp;+\u0026thinsp;T cells to secondary lymphoid organs may promote the therapeutic efficacy of ICI treatment [12, 22, 23]. Further studies are needed as CCL25 may be a more direct therapeutic target as a surrogate for pathological PD-L1 expression.\u003c/p\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;TIL density is also a pathologic predictor of ICI treatment [1\u0026ndash;3]. In this study, pre-treatment CXCL10 was associated with CD8\u0026thinsp;+\u0026thinsp;TILs, but on-treatment biomarkers were not. CXCL10 showed a trend toward higher levels in the low CD8\u0026thinsp;+\u0026thinsp;TIL group. CXCL10 is a chemokine that is mainly produced by intra-tumoral myeloid immune cells and it correlated with monocytes in this study. In the cancer immune cycle, CXCL10 promotes T-cell trafficking to tumors, whereas it does not promote T-cell infiltration associated with CD8\u0026thinsp;+\u0026thinsp;TIL density [1, 27]. This suggests that CXCL10 in this study may have been secondarily upregulated to recruit CD8\u0026thinsp;+\u0026thinsp;T cells by negative feedback, reflecting the low density of CD8 TIL\u0026thinsp;+\u0026thinsp;cells. Considering that CD8\u0026thinsp;+\u0026thinsp;TIL density is an important pathological predictor for cancer immunotherapy, its association with biomarkers may identify novel therapeutic targets [3].\u003c/p\u003e \u003cp\u003eThe NLR is a predictor of ICI monotherapy that can be routinely measured in daily practice [15\u0026ndash;17]. In this study, pre-treatment CCL17 and CCL13 were correlated with the NLR. Although not significantly correlated with the NLR, pre-treatment CXCL5 and CCL19 were correlated with neutrophil and lymphocyte counts. These chemokines may play a role as pre-treatment biomarkers by regulating the chemoattraction of lymphocytes or neutrophils in the peripheral blood [7, 11, 12, 18, 19]. Elucidation of the role of these biomarkers in cancer immunotherapy may allow prediction of the immune status of the TME from the numbers of peripheral blood cells.\u003c/p\u003e \u003cp\u003eIn summary, we conducted a comprehensive measurement of soluble immune mediators and identified several potential pre-treatment and on-treatment biomarkers in patients with NSCLC who received ICI monotherapy. It is expected that pre-treatment biomarkers might help to identify those patients who could benefit from ICI monotherapy. Moreover, on-treatment biomarkers might help our understanding of the favorable changes in TME associated with therapeutic efficacy. In addition, we found several associations and correlations between these pre- and on-treatment biomarkers and irAE, PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density, and the NLR. Combining these biomarkers identified in the exploratory analysis with other predictors could provide a more detailed understanding of cancer immunotherapy and potentially identify new therapeutic targets. Nevertheless, our study had several limitations. First, this study was a retrospective single-center study. Second, the analysis in this study was exploratory and needs to be verified prospectively. Further large-scale studies are needed to provide a larger number of patients to better characterize the benefits of ICI monotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echemokine ligand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTLA-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytotoxic T lymphocyte antigen 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCXCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-X-C motif chemokine ligand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eirAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune-related adverse event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil to lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-L1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death ligand-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-infiltrating lymphocytes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNFSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor superfamily\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e KA reports receiving personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical. TT reports receiving personal fees from AstraZeneca, Bristol Myers Squibb, MSD, Novartis, and Chugai Pharmaceutical. TS reports receiving personal fees from Chugai Pharmaceutical and Bristol Myers Squibb, and research funds from Taiho, and BrightPath Biotherapeutics. The remaining authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Statement:\u0026nbsp;\u003c/strong\u003eDaiki Murata: Writing - Data curation, Investigation, Original draft, Visualization. Koichi Azuma: Conceptualization, Resources, Data Curation, Investigation, Writing - Review \u0026amp; Editing, Visualization, Supervision, Project administration, Funding acquisition, Methodology, Software. Kenta Murotani: Formal analysis, Data Curation. Akihiko Kawahara: Investigation. Yuuya Nishii: Investigation. Takaaki Tokito: Investigation, Resources. Tetsuro Sasada: Writing - Review \u0026amp; Editing. Tomoaki Hoshino: Supervision, Project administration.\u003c/p\u003e \u003ch2\u003eEthics approval:\u003c/h2\u003e \u003cp\u003eThis study was conducted in accordance with the provisions of the Declaration of Helsinki and was approved by the Institutional Review Board of the Kurume University Hospital (IRB No 20100).\u003c/p\u003e\u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent to participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent to publish\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by AMED (Grant Number JP19ae0101076).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDM: Writing - Data curation, Investigation, Original draft, Visualization. KA: Conceptualization, Resources, Data Curation, Investigation, Writing - Review \u0026amp; Editing, Visualization, Supervision, Project administration, Funding acquisition, Methodology, Software. KM: Formal analysis, Data Curation. AK: Investigation. YN: Investigation. TT: Investigation, Resources. TS: Writing - Review \u0026amp; Editing. TH: Supervision, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eWe would like to thank the participating patients for their contributions to this study.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity 2013; 39: 1\u0026ndash;10. DOI: 10.1016/j.immuni.2013.07.012\u003c/li\u003e\n\u003cli\u003eSharma P, Goswami S, Raychaudhuri D, et al. Immune checkpoint therapy\u0026mdash;Current perspectives and future directions. Cell 2023; 186: 1652\u0026ndash;69. DOI: 10.1016/j.cell.2023.03.006\u003c/li\u003e\n\u003cli\u003eTeng MWL, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res 2015; 75: 2139-45. DOI: 10.1158/0008-5472.CAN-15-0255\u003c/li\u003e\n\u003cli\u003eReck M, Rodriguez-Abreu D, Robinson AG, et al. Five-Year Outcomes with Pembrolizumab Versus Chemotherapy for Metastatic Non-Small-Cell Lung Cancer with Pd-L1 Tumor Proportion Score \u0026ge; 50. J Clin Oncol 2021; 39: 2339\u0026ndash;49. DOI: 10.1200/JCO.21.00174\u003c/li\u003e\n\u003cli\u003ede Castro G, Jr, Kudaba I, Wu YL, et al. Five-Year Outcomes with Pembrolizumab Versus Chemotherapy as First-Line Therapy in Patients with Non-Small-Cell Lung Cancer and Programmed Death Ligand-1 Tumor Proportion Score \u0026ge; 1% in the KEYNOTE-042 Study. J Clin Oncol 2023; 41: 1986-91. DOI: 10.1200/JCO.21.02885\u003c/li\u003e\n\u003cli\u003eHerbst RS, Giaccone G, de Marinis F, et al. Atezolizumab for first-line treatment of PD-L1-selected patients with NSCLC. N Engl J Med 2020; 383: 1328\u0026ndash;39. DOI: 10.1056/NEJMoa1917346\u003c/li\u003e\n\u003cli\u003eNagarsheth N, Wicha MS, Zou W. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol 2017; 17: 559\u0026ndash;72. DOI: 10.1038/nri.2017.49\u003c/li\u003e\n\u003cli\u003eLippitz BE. Cytokine patterns in patients with cancer: a systematic review. Lancet Oncol 2013; 14: e218\u0026ndash;e28. DOI: 10.1016/S1470-2045(12)70582-X\u003c/li\u003e\n\u003cli\u003eBriukhovetska D, D\u0026ouml;rr J, Endres S, et al. Interleukins in cancer: From biology to therapy. Nat Rev Cancer 2021; 21: 481\u0026ndash;499. DOI: 10.1038/s41568-021-00363-z\u003c/li\u003e\n\u003cli\u003eCroft M, Benedict CA, Ware CF. Clinical targeting of the TNF and TNFR superfamilies. Nat Rev Drug Discov 2013; 12: 147\u0026ndash;168. DOI: 10.1038/nrd3930\u003c/li\u003e\n\u003cli\u003eKorbecki J, Kojder K, Simińska D, et al. CC Chemokines in a Tumor: A Review of Pro-Cancer and Anti-Cancer Properties of the Ligands of Receptors CCR1, CCR2, CCR3, and CCR4. Int J Mol Sci 2020; 21: 8412. DOI: 10.3390/ijms21218412\u003c/li\u003e\n\u003cli\u003eKorbecki J, Grochans S, Gutowska I, et al. CC chemokines in a tumor: A review of pro-cancer and anti-cancer properties of receptors CCR5, CCR6, CCR7, CCR8, CCR9, and CCR10 ligands. Int J Mol Sci 2020; 21: 7619.DOI: 10.3390/ijms21207619\u003c/li\u003e\n\u003cli\u003eHaratani K, Hayashi H, Chiba Y, et al. Association of immune-related adverse events with nivolumab efficacy in non\u0026ndash;small-cell lung cancer. JAMA Oncol 2018;4 :374-8. DOI:10.1001/jamaoncol.2017.2925\u003c/li\u003e\n\u003cli\u003eCook S, Samuel V, Meyers DE, et al. Immune-Related Adverse Events and Survival Among Patients with Metastatic NSCLC Treated with Immune Checkpoint Inhibitors. JAMA Netw Open 2024; 7: e2352302. DOI: 10.1001/jamanetworkopen.2023.52302\u003c/li\u003e\n\u003cli\u003eGu XB, Tian T, Tian XJ, Zhang XJ. Prognostic significance of neutrophil-to-lymphocyte ratio in non-small cell lung cancer: A meta-analysis. Sci. Rep 2015; 5: 12493. DOI: 10.1038/srep12493\u003c/li\u003e\n\u003cli\u003eBagley SJ, Kothari S, Aggarwal C, et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung Cancer 2017;106 :1\u0026ndash;7. DOI: 10.1016/j.lungcan.2017.01.013\u003c/li\u003e\n\u003cli\u003eHommes JW, Verheijden RJ, Suijkerbuijk KPM, Hamann D. Biomarkers of checkpoint inhibitor induced immune-related adverse events\u0026mdash;a comprehensive review. Front Oncol 2021;10: 585311. DOI: 10.3389/fonc.2020.585311\u003c/li\u003e\n\u003cli\u003eZhang W, Wang H, Sun M, et al. CXCL5/CXCR2 axis in tumor microenvironment as potential diagnostic biomarker and therapeutic target. Cancer Communications 2020; 40: 69\u0026ndash;80. DOI: 10.1002/cac2.12010\u003c/li\u003e\n\u003cli\u003eKorbecki J, Kupnicka P, Chlubek M, et al. CXCR2 receptor: regulation of expression, signal transduction, and involvement in cancer. Int J Mol Sci 2022; 23: 2168. DOI: 10.3390/ijms23042168\u003c/li\u003e\n\u003cli\u003eBaghdadi M, Wada H, Nakanishi S, et al. Chemotherapy-induced IL34 enhances immunosuppression by tumor-associated macrophages and mediates survival of chemoresistant lung cancer cells. Cancer Res 2016; 76: 6030-42. DOI: 10.1158/0008-5472.CAN-16-1170\u003c/li\u003e\n\u003cli\u003eAlshaebi F, Safi M, Algabri YA, et al. Interleukin-34 and immune checkpoint inhibitors: Unified weapons against cancer. Front Oncol 2023; 13: 1099696. DOI: 10.3389/fonc.2023.1099696\u003c/li\u003e\n\u003cli\u003eChen HM, Cong XX, Wu CX, et al. Intratumoral delivery of CCL25 enhances immunotherapy against triple-negative breast cancer by recruiting CCR9(+) T cells. Sci Adv 2020; 6: eaax4690. DOI: 10.1126/sciadv.aax4690\u003c/li\u003e\n\u003cli\u003eCarramolino L, Zaballos A, Kremer L, et al. Expression of CCR9 beta-chemokine receptor is modulated in thymocyte differentiation and is selectively maintained in CD8(+) T cells from secondary lymphoid organs. Blood 2001; 97:850\u0026ndash;7. DOI: 10.1182/blood.V97.4.850\u003c/li\u003e\n\u003cli\u003eLiu W, Stachura P, Xu HC, et al. BAFF attenuates immunosuppressive monocytes in the melanoma tumor microenvironment. Cancer Res 2022: 82; 264\u0026ndash;77. DOI: 10.1158/0008-5472.CAN-21-1171\u003c/li\u003e\n\u003cli\u003ePatil NS, Nabet BY, Muller S, et al. Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell Lung cancer. Cancer Cell 2022: 40; 289\u0026ndash;300e4. DOI: 10.1016/j.ccell.2022.02.002\u003c/li\u003e\n\u003cli\u003eHough JT, Zhao L, Lequio M, et al. IL-32 and its paradoxical role in neoplasia. Crit Rev Oncol Hematol 2023; 186: 104011. DOI: 10.1016/j.critrevonc.2023.104011\u003c/li\u003e\n\u003cli\u003eChow MT, Ozga AJ, Servis RL, et al. Intratumoral Activity of the CXCR3 Chemokine System Is Required for the Efficacy of Anti-PD-1 Therapy. Immunity 2019; 50: 1498\u0026ndash;1512.e5. DOI: 10.1016/j.immuni.2019.04.010 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Patient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003eN=110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eAge (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e72 (53-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003ePerformance status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e87 / 23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0 \u0026ndash; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2 \u0026ndash; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Squamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Squamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eDriver mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;ALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Wild type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eTreatment line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1 st\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2 nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cu\u003e\u0026gt;\u003c/u\u003e3 rd\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003eImmune checkpoint inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Atezolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Nivolumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.474747474747474%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Pembrolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.525252525252526%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eALK: anaplastic lymphoma kinase fusion, EGFR: epidermal growth factor receptor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2A. Association of the levels of pre-treatment biomarkers with irAE development, PD-L1 expression and CD8+ TIL density\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\" rowspan=\"2\"\u003e\n \u003cp\u003ePre-\u003c/p\u003e\n \u003cp\u003etreatment\u003c/p\u003e\n \u003cp\u003ebiomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.07625649913345%\" colspan=\"3\"\u003e\n \u003cp\u003eirAE development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.07625649913345%\" colspan=\"3\"\u003e\n \u003cp\u003ePD-L1 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.902946273830157%\" colspan=\"3\"\u003e\n \u003cp\u003eCD8+ TIL density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.75257731958763%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.958762886597938%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.690721649484535%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75257731958763%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.958762886597938%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.690721649484535%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75257731958763%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003e(N=44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75257731958763%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003e(N=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.690721649484535%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCCL13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003cp\u003e[14.4 - 22.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003cp\u003e[14.6 - 26.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003cp\u003e[15.9 - 26.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003cp\u003e[14.5 - 22.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003cp\u003e[15.3 - 22.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003cp\u003e[14.17 - 26.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCCL17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e[5.3 -18.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003cp\u003e[1.1 - 16.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003cp\u003e[2.7 - 14.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e[5.3 - 24.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003cp\u003e[1.3 - 15.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003cp\u003e[4.22 - 17.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCCL19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003cp\u003e[20.6 - 52.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003cp\u003e[18.1 - 54.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003cp\u003e[19.9 - 55.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003cp\u003e[19.5 - 49.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003cp\u003e[20.1 - 48.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003cp\u003e[19.4 - 54.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCCL21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e6366.3\u003c/p\u003e\n \u003cp\u003e[1765.7 - 8497.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e6706.2\u003c/p\u003e\n \u003cp\u003e[3464.6 - 8730.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e6804.1\u003c/p\u003e\n \u003cp\u003e[5286.5 - 8811.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e6366.3\u003c/p\u003e\n \u003cp\u003e[1054.5 - 8561.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e6762.7\u003c/p\u003e\n \u003cp\u003e[4166.9 - 8535.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e6543.4\u003c/p\u003e\n \u003cp\u003e[1537.6 - 8732.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCXLC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 124.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 52.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 51.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 125.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 73.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e[5.7 - 69.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eCXCL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e33.0\u003c/p\u003e\n \u003cp\u003e[22.5 - 51.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003cp\u003e[17.8 - 47.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003cp\u003e[18.1 - 50.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003cp\u003e[20.3 - 47.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003cp\u003e[17.2 - 39.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e36.9\u003c/p\u003e\n \u003cp\u003e[20.37 - 58.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.944540727902947%\"\u003e\n \u003cp\u003eTNFSF13B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e18320.7\u003c/p\u003e\n \u003cp\u003e[7920.0 -26287.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e19822.3\u003c/p\u003e\n \u003cp\u003e[8863.2 - 38425.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e22888.5\u003c/p\u003e\n \u003cp\u003e[14996.3 - 38425.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\"\u003e\n \u003cp\u003e12681.6\u003c/p\u003e\n \u003cp\u003e[4363.1 - 30709.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e22680.1\u003c/p\u003e\n \u003cp\u003e[12847.4 - 38441.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.878682842287695%\"\u003e\n \u003cp\u003e15814.9\u003c/p\u003e\n \u003cp\u003e[8383.3 - 32132.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCCL: chemokine ligand, CXCL:\u0026nbsp;C-X-C motif chemokine ligand, irAE: immune related adverse event, PD-L1: programmed cell death ligand-1, TIL:\u0026nbsp;tumor-infiltrating lymphocyte,\u0026nbsp;TNFSF: tumor necrosis factor superfamily.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2B. Association of post-treatment changes in on-treatment biomarkers with irAE development, PD-L1 expression, and CD8+ TIL density\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.720848056537102%\" rowspan=\"2\"\u003e\n \u003cp\u003eOn-treatment biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.328621908127207%\" colspan=\"3\"\u003e\n \u003cp\u003eirAE development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.328621908127207%\" colspan=\"3\"\u003e\n \u003cp\u003ePD-L1 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.62190812720848%\" colspan=\"3\"\u003e\n \u003cp\u003eCD8+ TIL density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N=43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.717171717171718%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.707070707070708%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.717171717171718%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.707070707070708%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003e(N=44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003e(N=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.707070707070708%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eCCL7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e[-8.8 - 11.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-3.5 - 14.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e[-2.2 - 16.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-8.8 - 3.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e[0.0 - 13.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-14.5 - 12.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eCCL19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e[-8.8 - 16.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e[-7.9 - 23.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003cp\u003e[-4.2 - 20.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003cp\u003e[-11.4 - 19.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003cp\u003e[0.0 - 23.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003cp\u003e[-11.4 - 20.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eCCL23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-31.5\u003c/p\u003e\n \u003cp\u003e[-134.4 - 35.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003cp\u003e[-14.8 - 88.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-95.7 - 48.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003cp\u003e[-45.4 - 120.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003cp\u003e[-87.5 - 47.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003cp\u003e[-105.2 - 64.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eCCL25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003cp\u003e[-66.3 - 59.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-47.5 - 63.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003cp\u003e[-47.5 - 101.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-27.7\u003c/p\u003e\n \u003cp\u003e[-66.3 - 10.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003cp\u003e[-42.7 - 48.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e[-66.3 - 63.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eIL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e[-11.3 - 12.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003cp\u003e[-13.8 - 14.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003cp\u003e[-19.8 - 20.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-4.2 - 1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003cp\u003e[-18.3 - 13.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003cp\u003e[-13.3 - 7.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eIL-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-49.7 - 32.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003cp\u003e[-71.6 - 14.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-79.8 - 113.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-24.1\u003c/p\u003e\n \u003cp\u003e[-77.1 - 0.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-13.5\u003c/p\u003e\n \u003cp\u003e[-93.4 - 21.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003cp\u003e[-51.5 - 7.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eIL-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-238.7 - 466.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-389.9 - 11.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-483.1 - 318.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[0.0 - 0.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[-606.4 - 0.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e[0.0 - 516.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.698412698412698%\"\u003e\n \u003cp\u003eTNFSF12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003cp\u003e[-93.5 - 24.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003cp\u003e[-91.8 - 98.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003cp\u003e[-96.3 - 117.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.229276895943563%\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003cp\u003e[-62.8 - 13.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003cp\u003e[-92.7 - 96.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e-10.6\u003c/p\u003e\n \u003cp\u003e[-81.2 - 21.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.347442680776014%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCCL: chemokine ligand,\u0026nbsp;IL: interleukin, irAE: immune related adverse event, PD-L1: programmed cell death ligand-1,\u0026nbsp;TIL:\u0026nbsp;tumor-infiltrating lymphocyte,\u0026nbsp;TNFSF: tumor necrosis factor superfamily.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3A. Correlations between pre-treatment biomarkers and pre-treatment peripheral blood cells\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"bottom\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003ePeripheral blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003eCXCL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003eCXCL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" colspan=\"2\"\u003e\n \u003cp\u003eTNFSF13B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003ePeripheral blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCCL: chemokine ligand, CXCL:\u0026nbsp;C-X-C motif chemokine ligand, NLR:\u0026nbsp;neutrophil to lymphocyte ratio, TNFSF: tumor necrosis factor superfamily, WBC: white blood cell.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* r means Spearman correlation coefficient. \u0026nbsp; ** p means p-value.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3B. Correlation between post-treatment changes in on-treatment biomarkers and post-treatment change in peripheral blood cells\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eIL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eIL-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eIL-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003ePeripheral blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eCCL25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\" colspan=\"2\"\u003e\n \u003cp\u003eTNFSF12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003ePeripheral blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003er\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNeutrophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eLymphocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eMonocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eEosinophil (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCCL: chemokine ligand, IL: IL: interleukin, NLR: neutrophil to lymphocyte ratio, TNFSF: tumor necrosis factor superfamily, WBC: white blood cell.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* r means Spearman correlation coefficient. \u0026nbsp;** p means p-value.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"NSCLC, Immune checkpoint inhibitor, Chemokine, Cytokine, CD8 + TILs, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-4021078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4021078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the favorable therapeutic efficacy observed with ICI monotherapy, the majority of non-small cell lung cancer (NSCLC) patients do not respond. Therefore, identifying patients who could optimally benefit from ICI treatment remains a challenge.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmong 183 patients with advanced or recurrent NSCLC who received ICI monotherapy, we analyzed 110 patients whose pre- and post-treatment plasma samples were available. Seventy-three soluble immune mediators were measured at ICI initiation and 6 weeks later. To identify useful biomarkers, we analyzed the association of pre-treatment levels and post-treatment changes of soluble immune mediators with survival of patients. The associations of pre-treatment or on-treatment biomarkers with irAE development, PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density, and neutrophil to lymphocyte ratio (NLR) were also analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePre-treatment biomarkers included 6 immune mediators (CCL13, CCL19, CCL21, CXCL5, CXCL10 and TNFSF13B) whereas on-treatment biomarkers included 8 immune mediators (CCL7, CCL19, CCL23, CCL25, IL-10, IL-32, IL-34 and TNFSF12). IrAE development was associated with post-treatment change in CCL23. PD-L1 expression was associated with the pre-treatment levels of TNFSF13B and the post-treatment change in CCL25. CD8\u0026thinsp;+\u0026thinsp;TIL density was associated with the pre-treatment CXCL10 level, whereas NLR was correlated with pre-treatment levels of CCL13 and CCL17.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe identified several possible pre-treatment and on-treatment biomarkers in patients with NSCLC who received ICI monotherapy. Some of these biomarkers were associated with other possible predictors, including irAE development, PD-L1 expression, CD8\u0026thinsp;+\u0026thinsp;TIL density and NLR. Further large-scale studies are needed to establish biomarkers for patients with NSCLC who received ICI monotherapy.\u003c/p\u003e","manuscriptTitle":"Characterization of Pre- and Post-treatment Soluble Immune Mediators and the Tumor Microenvironment in NSCLC Patients Receiving PD-1/L1 Inhibitor Monotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 15:33:26","doi":"10.21203/rs.3.rs-4021078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-21T23:38:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T21:03:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-15T03:01:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-28T20:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0fa88a0f-916c-4268-a16e-4521a3e35437","date":"2024-03-26T18:35:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76f5ca27-54a0-47e9-8dc3-0cabcad5641e","date":"2024-03-26T10:49:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"df773be1-6caf-45c3-a86c-542c08ab52e6","date":"2024-03-24T21:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-24T15:19:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T07:54:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-08T07:54:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2024-03-06T13:12:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"631fba57-4090-40f8-b484-5de81410adf7","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:18:04+00:00","versionOfRecord":{"articleIdentity":"rs-4021078","link":"https://doi.org/10.1007/s00262-024-03781-8","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2024-09-05 16:08:23","publishedOnDateReadable":"September 5th, 2024"},"versionCreatedAt":"2024-03-12 15:33:26","video":"","vorDoi":"10.1007/s00262-024-03781-8","vorDoiUrl":"https://doi.org/10.1007/s00262-024-03781-8","workflowStages":[]},"version":"v1","identity":"rs-4021078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4021078","identity":"rs-4021078","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}