Yang Wang1,2,3,4,5, Ning Zhao2,3,4,5,6, Zhanbo Wu2,3,4,5,6, Na Pan2,3,4,5,6, Xuejie Shen2,3,4,5,6, Ting Liu2,3,4,5,6, Feng Wei2,3,4,5,6, Jian You7,8,9, Wengui Xu10,11,12, Xiubao Ren13,14,15,16,17. 1. Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. 2. National Clinical Research Center of Cancer, Tianjin, 300060, China. 3. Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. 4. Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China. 5. Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, 300060, China. 6. Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. 7. National Clinical Research Center of Cancer, Tianjin, 300060, China. youjiancn@gmail.com. 8. Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. youjiancn@gmail.com. 9. Department of Thoracic surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. youjiancn@gmail.com. 10. National Clinical Research Center of Cancer, Tianjin, 300060, China. wxu06@tmu.edu.cn. 11. Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. wxu06@tmu.edu.cn. 12. Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. wxu06@tmu.edu.cn. 13. Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. renxiubao@tjmuch.com. 14. National Clinical Research Center of Cancer, Tianjin, 300060, China. renxiubao@tjmuch.com. 15. Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. renxiubao@tjmuch.com. 16. Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China. renxiubao@tjmuch.com. 17. Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, 300060, China. renxiubao@tjmuch.com.
Abstract
BACKGROUND: Metabolic information obtained through 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used to evaluate malignancy by calculating the glucose uptake rate, and these parameters play important roles in determining the prognosis of non-small cell lung cancer (NSCLC). The expression of immune-related markers in tumor tissue reflects the immune status in the tumor microenvironment. However, there is lack of reports on the association between metabolic variables and intra-tumor immune markers. Herein, we investigate the correlation between metabolic status on 18F-FDG PET/CT and intra-tumor immunomarkers' expression in NSCLC patients. METHODS: From April 2008 to August 2014, 763 patients were enrolled in the analysis to investigate the role of maximum standardized uptake value (SUVmax) in lung cancer. One hundred twenty-two tumor specimens were analyzed by immunohistochemistry (IHC) to intra-tumor immune cells and programmed death protein ligand 1(PD-L1) expression on tumor cells. The correlation between metabolic variables and the expression of tissue immune markers were analyzed. RESULTS: SUVmax values have significant variations in different epidermal growth factor receptor (EGFR) statuses (wild type vs mutant type), high/low neutrophil-to-lymphocyte ratio (NLR) groups, and high/low platelets-to-lymphocyte ratio (PLR) groups (p < 0.001, p < 0.001, p = 0.003, respectively). SUVmax was an independent prognostic factor in lung cancer patients (p = 0.013). IHC demonstrated a statistically significant correlation between SUVmax and the expression of CD8 tumor-infiltrating lymphocytes (p = 0.015), CD163 tumor-associated macrophages (TAMs) (p = 0.003), and Foxp3-regulatory T cells (Tregs) (p = 0.004), as well as PD-1 and PD-L1 (p = 0.003 and p = 0.012, respectively). With respect to patient outcomes, disease stage, BMI, SUVmax, metabolic tumor volume (MTV), TLG (tumor lesion glycolysis), CD163-TAMs, CD11c-dendritic cells (DCs), PD-L1, and Tregs showed a statistically significant correlation with progression-free survival (PFS) (p < 0.001, 0.023, < 0.001, 0.007, 0.005, 0.004, 0.008, 0.048, and 0.014, respectively), and disease stage, SUVmax, MTV, TLG, CD163-TAMs, CD11c-DCs, and PD-L1 showed a statistically significant correlation with overall survival (OS) (p < 0.001, < 0.001, 0.014, 0.012, < 0.001, 0.001, and < 0.001, respectively). CONCLUSION: This study revealed an association between metabolic variable and immune cell expression in the tumor microenvironment and suggests that SUVmax on 18F-FDG PET/CT could be a potential predictor for selecting candidates for immunotherapy.
BACKGROUND: Metabolic information obtained through 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used to evaluate malignancy by calculating the glucose uptake rate, and these parameters play important roles in determining the prognosis of non-small cell lung cancer (NSCLC). The expression of immune-related markers in tumor tissue reflects the immune status in the tumor microenvironment. However, there is lack of reports on the association between metabolic variables and intra-tumor immune markers. Herein, we investigate the correlation between metabolic status on 18F-FDG PET/CT and intra-tumor immunomarkers' expression in NSCLCpatients. METHODS: From April 2008 to August 2014, 763 patients were enrolled in the analysis to investigate the role of maximum standardized uptake value (SUVmax) in lung cancer. One hundred twenty-two tumor specimens were analyzed by immunohistochemistry (IHC) to intra-tumor immune cells and programmed death protein ligand 1(PD-L1) expression on tumor cells. The correlation between metabolic variables and the expression of tissue immune markers were analyzed. RESULTS: SUVmax values have significant variations in different epidermal growth factor receptor (EGFR) statuses (wild type vs mutant type), high/low neutrophil-to-lymphocyte ratio (NLR) groups, and high/low platelets-to-lymphocyte ratio (PLR) groups (p < 0.001, p < 0.001, p = 0.003, respectively). SUVmax was an independent prognostic factor in lung cancerpatients (p = 0.013). IHC demonstrated a statistically significant correlation between SUVmax and the expression of CD8tumor-infiltrating lymphocytes (p = 0.015), CD163tumor-associated macrophages (TAMs) (p = 0.003), and Foxp3-regulatory T cells (Tregs) (p = 0.004), as well as PD-1 and PD-L1 (p = 0.003 and p = 0.012, respectively). With respect to patient outcomes, disease stage, BMI, SUVmax, metabolic tumor volume (MTV), TLG (tumor lesion glycolysis), CD163-TAMs, CD11c-dendritic cells (DCs), PD-L1, and Tregs showed a statistically significant correlation with progression-free survival (PFS) (p < 0.001, 0.023, < 0.001, 0.007, 0.005, 0.004, 0.008, 0.048, and 0.014, respectively), and disease stage, SUVmax, MTV, TLG, CD163-TAMs, CD11c-DCs, and PD-L1 showed a statistically significant correlation with overall survival (OS) (p < 0.001, < 0.001, 0.014, 0.012, < 0.001, 0.001, and < 0.001, respectively). CONCLUSION: This study revealed an association between metabolic variable and immune cell expression in the tumor microenvironment and suggests that SUVmax on 18F-FDG PET/CT could be a potential predictor for selecting candidates for immunotherapy.
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