Abstract
Objective
Immune checkpoint inhibitors (ICIs), specifically targeting the programmed cell death protein-1 or its ligand (PD-1/PD-L1), have been extensively used in the treatment of a spectrum of malignancies, although the predictive biomarkers remain to be elucidated. This study aims to investigate the association between baseline circulating levels of cytokines and the creatinine/cystatin C ratio (CCR) with the treatment outcomes of ICIs in patients with advanced cancer.
Methods
The pre-treatment circulating levels of 10 cytokines (PD-L1, CTLA4, CXCL10, LAG3, HGF, CCL2, MIG, GRANB, IL-18, and IL-6) were measured via automated capillary-based immunoassay platform in the serum of 65 advanced cancer patients treated with anti-PD-1/PD-L1-based systemic therapy and 10 healthy volunteers. The levels of cytokines and CCR were quantified and categorized into high and low groups based on the median value. The associations of serum cytokines and CCR with response to treatment, survival, and immune-related adverse events were assessed.
Results
Elevated circulating levels of 6 cytokines (PD-L1, CXCL10, HGF, CCL2, MIG, and IL-6) were observed in cancer patients compared with that in healthy volunteers. The correlation coefficients between cytokines, CCR and nutritional risk index were also calculated. In the cancer cohort (N = 65), low circulating HGF (P = 0.023, P = 0.029), low IL-6 (P = 0.002, P < 0.001), and high CCR (P = 0.031, P = 0.008) were associated with significantly improved progression-free survival (PFS) and overall survival (OS). Multi-variable COX analyses adjusted for clinicopathological factors revealed that low HGF, low IL-6, and high CCR were independent favorable prognostic factors for PFS (P = 0.028, P = 0.010, and P = 0.015, respectively) and OS (P = 0.043, P = 0.003, and P = 0.026, respectively). Grade 2 irAEs occurred more frequently in patients with low levels of circulating CCL2 and LAG3.
Conclusions
Pre-treatment circulating levels of serum IL-6, HGF, and CCR may serve as independent predictive and prognostic biomarkers in advanced cancer patients treated with ICIs-based systemic therapy. These findings might help to identify potential patients who would benefit from these therapies.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00520-024-08525-z.
Keywords: Serum cytokines, Creatinine/cystatin C ratio, Prognostic biomarkers, Cancer, Immune checkpoint inhibitors
Introduction
It is estimated that 19.3 million new cancer cases and almost 10.0 million cancer deaths occurred in 2020 worldwide [1]. Immune checkpoint inhibitors (ICIs) specifically targeting PD-1 or PD-L1, have greatly improved the prognosis of cancer patients and emerged as the standard of care for a broad spectrum of malignancies [2]. Despite these advancements, the elevated response rate and prolonged survival is still limited in non-selected patients [3–5]. This may be due to genetic tumor heterogeneity, the non-immunogenic tumor microenvironment, and the impaired nutritional status and immune capacity of individuals [6, 7]. Therefore, identifying predicative and prognostic biomarkers for cancer patients undergoing ICIs-based treatment is crucial and remains to be investigated.
Cytokines are proteins secreted by both immune cells and non-immune cells (such as endothelial cells, epidermal cells, and fibroblasts). They have a wide range of biological activities. Cytokines are associated with the diagnosis and prognosis of various cancerous disease. Among the cytokines, interleukin is usually associated with inflammation [8]. Previous studies have uncovered that some cytokines, such as interleukin 6, 15, and 18(IL-6, IL-15, and IL-18), in peripheral blood are associated with the treatment outcomes of ICIs in NSCLC [9–11]. Hepatocyte growth factor/c-mesenchymal epithelial transition factor (HGF/c-Met) signaling mediates the crosstalk between immune-cell and tumor micro-environment [12]. It is suggested that HGF may be candidate of ICI biomarkers. Circulating cytokines, such as soluble immune checkpoints, can modulate immune response to cancer cells. However, the studies of many circulating cytokines are still unclear in cancer patients receiving ICIs therapy. It is important to find appropriate cytokines to predict the prognosis of patients with cancer treated with ICIs-based systemic therapy.
Serum creatinine (Cr) and cystatin C (Cys C) are commonly utilized to assess renal function in clinical practice. Creatinine is mainly derived from the metabolism of muscles. Cys C is not affected through muscular metabolic processes [13]. Therefore, leveraging the characteristics of these two markers ratio, the serum creatinine/cystatin C ratio (CCR) has been served as an alternative biomarker of sarcopenia and prognostic factor in cancer patients. However, whether CCR is associated with the efficacy of ICIs-based treatment remains unclear.
In this study, we investigated the association of circulating levels of 10 cytokines and CCR with the outcomes of advanced cancer patients treated with ICIs-based systemic therapy.
Materials and methods
Patient characteristics
From June 2021 to December 2021, 65 cancer patients at the General Hospital of Northern Theater Command were enrolled in the study. Additionally, 10 healthy controls, displaying good health without any indications of tumors, were included. The inclusion criteria for the cancer patients were as follows: histopathological confirmation of cancer at advanced stage or cannot be surgically resected, receiving anti-PD-1/PD-L1 inhibitors-based system therapies, the Eastern Cooperative Oncology Group Performances Status (ECOG) ranging from 0 to 2 informed consent written from patients for the collection of blood samples and clinical information. This study was approved by the institutional ethics board of General Hospital of Northern Theater Command and was performed adhering to the principles outlined in the Declaration of Helsinki by World Medical Association.
Response assessment and nutritional index
The clinical response was categorized into four groups: complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). Evaluation of the response to ICIs treatment was conducted according to the response evaluation criteria in solid tumors version 1.1 (RECIST 1.1). Durable clinical benefit (DCB) was defined as achieving a partial response or stable disease with a PFS duration of ≥ 6 months. Non-durable benefit (NDB) encompassed other response categories. PFS and OS were documented based on medical records or follow-up phone calls. Two indexes reflecting the nutritional status of patients were calculated as follows: Nutritional risk index (NRI) = [1.519 × albumin concentration (g/L)] + [41.7 × (current weight/standard weight)]. CCR = creatinine (mg/dL)/ cystatin C (mg/L) × 100.
Sample collection and cytokine detection
Peripheral blood was collected before the initiation of anti-PD-1/PD-L1 therapy and centrifuged at 1000 g at room temperature for 15 min. Subsequently, 1 mL of serum was promptly transferred into a 1.5 mL centrifuge tube and cryopreserved at -80℃ until analysis. We extracted 100 μL of serum from a previously frozen centrifuge tube for each sample. Serum samples were centrifuged at high speed (12000rpm) at room temperature for 3 min. Then, supernate was diluted by the certain and appropriate amount and was shaken on a plate shaker (approximately 400–500 rpm) for 2 h at 25 ℃.Finally, add 1 mL of wash buffer and 50 μL of the diluted sample to each well of the test panel. For cytokine quantification, a customized panel was employed, enabling the simultaneous assessment of multiple cytokines. This analysis was conducted on an automated capillary-based immunoassay platform (ProteinSimple, Ella-21050762) provided by Bio-Techne in China.
Statistical analyses
Independent t-tests were utilized to analyze differences of cytokines and CCR in patients grouped by clinicopathological variables. Mann–Whitney or independent t-tests are used to compare the difference of cytokine between patients and healthy volunteers. Spearman correlation analysis was employed to determine correlations between serum factors. Kaplan–Meier curves for OS and PFS were presented according to the median values. The impact of different blood biomarker levels on OS and PFS was estimated using a Cox regression analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate the utility of the cytokines for predicting 6, 12, and 18 months of OS. The data of irAEs was presented as categorical variables and analyzed using the chi-squared test and Fisher’s exact test. All P values were two-sided, with a significance threshold set at P < 0.05. SPSS (version 26) and R (version 4.1.2) were used for statistical analyses.
Results
Patient characteristics and comparison of cytokine levels between cancer and control groups
The clinicalpathological characteristics of the 65 patients at baseline were summarized in Table 1. The male displayed higher circulating levels of IL-6, mitogen-inducible gene (MIG) and CCR than female. No significant differences in cytokine levels were observed in terms of smoking or drinking history. The predominant cancer types were lung cancer [22(33.8%)] and gastroesophageal cancer [16(24.6%)]. 34 (52.3%) patients were administered PD-1 plus chemotherapy, while 14 patients (21.5%) received PD-L1 plus chemotherapy. Of note, 20 (54.1%) patients displayed positive expression of PD-L1 and exhibited elevated levels of CCL2. Tumor response to ICIs therapy, assessed using RECIST 1.1 criteria, showed an objective response rate (ORR) of 30.7% and a disease control rate (DCR) of 87.1%. No significant differences in terms of cytokines were observed between different treatment response groups. There was no difference in age (P = 0.177) and sex (P = 0.062) between the cancer and control groups. The levels of circulating PD-L1, CXCL10, MIG, HGF, CCL-2, and IL-6 were significantly higher in the cancer group compared with those of the control group. (P = 0.012, P = 0.022, P < 0.001, P < 0.001, P < 0.001, and P = 0.030, respectively) (Fig. 1).
Table 1.
Clinicalpathological characteristics of the patients (mean ± sd)
| Variables | PD-L1 (pg/ml) |
CTLA-4 (pg/ml) |
CXCL-10 (pg/ml) |
MIG (pg/ml) |
HGF (pg/ml) |
IL-18 (pg/ml) |
LAG-3 (pg/ml) |
CCL2 (pg/ml) |
GRANB (pg/ml) |
IL-6 (pg/ml) |
CCR | NRI | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sex | n | ||||||||||||
| Male | 50 | 95.9 ± 33.1 | 12.1 ± 20.2 | 322.1 ± 230.6 | 1314.6 ± 1145.2 | 2375.0 ± 1342.3 | 267.9 ± 85.4 | 1011.2 ± 632.8 | 447.4 ± 221.2 | 14.8 ± 26.3 | 19.7 ± 23.3 | 70.4 ± 13.7 | 100.0 ± 9.5 |
| Female | 15 | 85.2 ± 31.2 | 7.7 ± 11.4 | 296.5 ± 195.4 | 934.5 ± 379.5 | 2275.9 ± 1165.4 | 255.1 ± 84.8 | 1084.7 ± 470.4 | 381.2 ± 195.1 | 8.2 ± 4.5 | 9.4 ± 8.2 | 61.1 ± 16.1 | 103.5 ± 9.1 |
| P value | 0.274 | 0.427 | 0.699 | 0.049 | 0.797 | 0.610 | 0.679 | 0.301 | 0.337 | 0.010 | 0.029 | 0.212 | |
| Age | |||||||||||||
| ≤ 65 | 31 | 97.6 ± 38.5 | 9.4 ± 8.6 | 354.6 ± 264.4 | 1444.7 ± 1304.8 | 2442.1 ± 1559.9 | 268.6 ± 78.8 | 1131.2 ± 767.9 | 444.2 ± 223.8 | 17.3 ± 32.4 | 21.2 ± 24.3 | 68.8 ± 15.8 | 101.0 ± 9.7 |
| > 65 | 34 | 89.6 ± 26.5 | 12.6 ± 24.5 | 281.1 ± 170.8 | 1028.3 ± 652.7 | 2270.1 ± 1014.2 | 261.7 ± 90.9 | 934.2 ± 367.8 | 421.1 ± 211.3 | 9.6 ± 8.2 | 13.9 ± 17.5 | 67.7 ± 13.8 | 100.7 ± 9.3 |
| P value | 0.328 | 0.493 | 0.184 | 0.116 | 0.597 | 0.746 | 0.201 | 0.670 | 0.203 | 0.167 | 0.766 | 0.875 | |
| Smoking | |||||||||||||
| Yes | 36 | 90.9 ± 26.8 | 7.9 ± 8.1 | 285.9 ± 206.3 | 1138.1 ± 885.2 | 2355.0 ± 1044.7 | 270.0 ± 83.2 | 1016.0 ± 708.0 | 415.6 ± 197.2 | 15.4 ± 30.5 | 20.0 ± 22.8 | 70.1 ± 14.2 | 101.3 ± 9.8 |
| No | 29 | 96.5 ± 39.3 | 15..0 ± 26.1 | 353.8 ± 237.9 | 1337.1 ± 1193.5 | 2348.6 ± 1572.3 | 258.7 ± 87.7 | 1043.2 ± 432.0 | 452.7 ± 239.1 | 10.6 ± 8.3 | 14.0 ± 18.9 | 65.9 ± 15.2 | 100.3 ± 9.1 |
| P value | 0.500 | 0.169 | 0.223 | 0.443 | 0.984 | 0.598 | 0.857 | 0.495 | 0.411 | 0.259 | 0.249 | 0.663 | |
| Drinking | |||||||||||||
| Yes | 28 | 90.4 ± 29.5 | 8.1 ± 8.6 | 281.1 ± 197.3 | 1157.4 ± 1014.6 | 2275.0 ± 1006.5 | 266.2 ± 80.9 | 891.8 ± 349.9 | 398.5 ± 189.2 | 9.6 ± 7.3 | 20.6 ± 24.4 | 69.9 ± 15.9 | 101.3 ± 11.2 |
| No | 37 | 95.6 ± 35.3 | 13.4 ± 23.4 | 342.7 ± 237.8 | 1279.4 ± 1053.0 | 2410.5 ± 1488.6 | 264.1 ± 88.7 | 1131.3 ± 718.1 | 457.6 ± 233.4 | 16.1 ± 30.1 | 14.9 ± 18.3 | 67.0 ± 13.7 | 100.5 ± 7.9 |
| P value | 0.528 | 0.265 | 0.271 | 0.640 | 0.680 | 0.921 | 0.082 | 0.278 | 0.272 | 0.281 | 0.434 | 0.728 | |
| ECOG | |||||||||||||
| < 2 | 58 | 91.9 ± 32.3 | 11.6 ± 19.6 | 322.8 ± 228.0 | 1262.7 ± 1072.3 | 2264.3 ± 1268.1 | 267.0 ± 84.8 | 1047.9 ± 613.8 | 443.3 ± 224.8 | 13.2 ± 23.9 | 15.7 ± 20.1 | 69.4 ± 14.6 | 102.0 ± 8.5 |
| = 2 | 7 | 105.9 ± 36.7 | 6.6 ± 6.3 | 261.2 ± 163.7 | 930.0 ± 535.2 | 3080.0 ± 1390.4 | 248.4 ± 89.0 | 864.0 ± 428.0 | 339.7 ± 77.8 | 14.3 ± 18.7 | 30.6 ± 26.7 | 58.8 ± 12.4 | 91.5 ± 11.9 |
| P value | 0.288 | 0.506 | 0.492 | 0.424 | 0.116 | 0.589 | 0.445 | 0.233 | 0.904 | 0.079 | 0.072 | 0.005 | |
| Diagnose | |||||||||||||
| Non-small cell lung cancer | 22 | 86.5 ± 30.2 | 5.6 ± 4.0 | 226.4 ± 124.2 | 874.5 ± 489.3 | 2135.9 ± 952.2 | 262.4 ± 76.4 | 831.4 ± 344.7 | 351.2 ± 218.8 | 9.2 ± 11.1 | 19.4 ± 25.8 | 68.1 ± 14.7 | 104.0 ± 11.3 |
| Gastroesophageal cancer | 16 | 87.7 ± 22.7 | 9.9 ± 11.7 | 321.4 ± 137.1 | 1548.6 ± 1229.4 | 2097.5 ± 766.3 | 233.1 ± 52.1 | 867.8 ± 230.0 | 472.1 ± 207.0 | 8.1 ± 3.6 | 16.5 ± 16.5 | 69.9 ± 15.4 | 98.5 ± 6.5 |
| Other | 27 | 102.4 ± 38.3 | 16.2 ± 26.6 | 386.2 ± 292.9 | 1323.3 ± 1170.3 | 2679.3 ± 1694.0 | 285.9 ± 101.6 | 1283.4 ± 796.0 | 474.4 ± 207.1 | 19.7 ± 34.0 | 16.2 ± 20.2 | 67.4 ± 14.7 | 99.6 ± 8.9 |
| P value | 0.178 | 0.070 | 0.018 | 0.112 | 0.296 | 0.079 | 0.035 | 0.095 | 0.220 | 0.856 | 0.859 | 0.135 | |
| Therapy | |||||||||||||
| PD-1 plus targeted therapy | 9 | 112.3 ± 37.3 | 26.0 ± 41.1 | 528.6 ± 371.8 | 1704.4 ± 1679.9 | 2452.2 ± 1467.1 | 237.7 ± 95.1 | 1455.3 ± 1125.2 | 417.4 ± 148.2 | 15.8 ± 17.4 | 12.1 ± 11.0 | 68.5 ± 20.0 | 97.0 ± 5.2 |
| PD-1 plus chemotherapy | 34 | 89.7 ± 33.4 | 9.3 ± 11.5 | 268.9 ± 153.6 | 1199.1 ± 992.8 | 2283.7 ± 1481.0 | 271.0 ± 76.8 | 849.9 ± 251.5 | 404.5 ± 225.5 | 12.9 ± 29.9 | 21.4 ± 27.9 | 69.4 ± 14.9 | 102.4 ± 10.2 |
| PD-L1 plus chemotherapy | 14 | 88.2 ± 24.6 | 8.1 ± 12.0 | 254.4 ± 192.8 | 908.1 ± 469.4 | 2258.5 ± 751.1 | 262.6 ± 107.4 | 1136.1 ± 711.1 | 459.07 ± 173.4 | 11.9 ± 11.3 | 12.5 ± 5.0 | 67.6 ± 12.6 | 101.0 ± 11.3 |
| PD-1 | 8 | 96.8 ± 35.3 | 7.2 ± 4.2 | 386.4 ± 170.7 | 1365.6 ± 950.2 | 2694.2 ± 1145.1 | 274.1 ± 69.6 | 1116.2 ± 386.5 | 518.9 ± 302.5 | 14.4 ± 12.6 | 14.2 ± 9.3 | 64.5 ± 12.3 | 98.2 ± 4.2 |
| P value | 0.281 | 0.516 | 0.114 | 0.334 | 0.861 | 0.759 | 0.114 | 0.560 | 0.981 | 0.382 | 0.865 | 0.165 | |
| PD-L1 status | |||||||||||||
| Negative | 17 | 91.6 ± 17.9 | 12.5 ± 14.2 | 306.8 ± 201.2 | 1000.6 ± 577.7 | 2775.4 ± 1225.6 | 251.4 ± 77.4 | 1103.0 ± 835.5 | 482.1 ± 179.8 | 12.4 ± 11.4 | 21.3 ± 24.3 | 70.5 ± 14.7 | 101.8 ± 8.6 |
| Positive | 20 | 83.8 ± 29.8 | 7.1 ± 5.4 | 267.7 ± 180.8 | 1142.3 ± 748.5 | 2138.2 ± 943.5 | 259.0 ± 88.9 | 886.8 ± 329.3 | 355.0 ± 93.8 | 21.0 ± 40.0 | 10.4 ± 9.4 | 69.0 ± 13.6 | 101.8 ± 9.8 |
| unknown | 28 | ||||||||||||
| P value | 0.351 | 0.156 | 0.538 | 0.529 | 0.083 | 0.785 | 0.294 | 0.009 | 0.396 | 0.096 | 0.751 | 0.995 | |
| Response to treatment | n | ||||||||||||
| PD | 8 | 114.5 ± 52.7 | 8.7 ± 6.7 | 398.0 ± 387.6 | 1541.8 ± 1819.9 | 3574.1 ± 2362.4 | 278.6 ± 111.1 | 1164.0 ± 585.9 | 451.9 ± 109.1 | 16.2 ± 16.5 | 31.8 ± 28.2 | 61.6 ± 13.6 | 95.7 ± 11.7 |
| SD | 35 | 89.5 ± 29.3 | 12.0 ± 23.3 | 319.1 ± 190.5 | 1265.0 ± 953.6 | 2161.3 ± 1115.9 | 258.4 ± 80.4 | 1127.0 ± 720.4 | 433.4 ± 260.2 | 12.9 ± 29.7 | 12.0 ± 13.9 | 67.3 ± 15.5 | 100.6 ± 8.0 |
| PR | 19 | 95.7 ± 27.2 | 11.2 ± 13.3 | 290.1 ± 206.6 | 1132.3 ± 795.0 | 2275.8 ± 791.2 | 274.5 ± 89.7 | 838.16 ± 241.9 | 414.5 ± 167.2 | 11.6 ± 9.7 | 22.4 ± 27.1 | 70.4 ± 12.3 | 102.5 ± 11.0 |
| unknown | 3 | ||||||||||||
| P value | 0.151 | 0.906 | 0.534 | 0.655 | 0.292 | 0.741 | 0.073 | 0.914 | 0.901 | 0.096 | 0.352 | 0.252 | |
sd Standard deviation; PD Progressive Disease; SD Stable Disease; PR Partial Response; CPS Combined Positive Score; ECOG Eastern Cooperative Oncology Group Performance Status; CCR Creatinine/cystatin C ratio; NRI Nutritional risk index. P < 0.05 was considered statistically significant and shown in bold type
Fig. 1.
The difference between cancer (n = 65) and healthy group (n = 10) in terms of cytokines. The line in the middle of the box shows the mean value. Error bars show the interquartile range. (MIG: P < 0.001, HGF: P < 0.001, CCL2: P < 0.001, IL-6: P = 0.03, PD-L1: P = 0.012, CXCL10: P = 0.022, LAG-3: P = 0.761, GRANB: P = 0.068, IL-18: P = 0.462, CTLA4: P = 0.9441)
Correlation analysis of cytokines and nutritional indexes
A heat-map of the correlation between the soluble cytokines and nutritional indexes were presented in Fig. 2. It’s essential to highlight that the correlation coefficient of CCR with NRI is 0.27 (P = 0.030). The circulating level of CCR was negatively correlated with that of LAG-3 (R = -0.303, P = 0.014) and HGF (R = -0.311, P = 0.012). Likewise, NRI was negatively correlated with PD-L1 (R = -0.423, P < 0.001), IL-6 (R = -0.288, P = 0.02), and CXCL10 (R = -0.306, P = 0.013).LAG-3 was positively correlated with PD-L1 (R = 0.407, P < 0.001), CTLA4 (R = 0.371, P = 0.001), MIG (R = 0.402, P < 0.001), IL-18 (R = 0.321, P = 0.009), CXCL10 (R = 0.349, P = 0.004), CCL2 (R = 0.272, P = 0.028), IL-6 (R = 0.371, P = 0.003) and HGF (R = 0.294, P = 0.018). CCL2 was positively correlated with PD-L1 (R = 0.251, P = 0.044), CTLA4 (R = 0.274, P = 0.027), MIG (R = 0.264, P = 0.034), HGF (R = 0.337, P = 0.006), CXCL10 (R = 0.354, P = 0.004), IL-6 (R = 0.444, P < 0.001) and GRANB (R = 0.435, P < 0.001). The correlation coefficient of HGF and IL-6 is 0.687.The detail of correlation analysis of cytokines and CCR was shown in Supplementary Table S1 and Figure S1.
Fig. 2.
The heatmap showing the correlation between cytokines, CCR, and NRI. The color shows the degree of correlation
Associations between cytokines and clinical benefits
We compared serum cytokines and CCR concentrations in DCB and NCB groups. The NCB group had elevated serum concentrations of HGF (2365 pg/mL vs. 1769 pg/mL, P = 0.006) and IL-6(16.6 pg/mL vs. 7.195 pg/mL, P = 0.001) compared with the DCB group (Table 2). Univariate analysis showed that low HGF and low IL-6 were significant prognostic factors for DCB. Multivariate logistics regression analysis revealed that the low level of IL-6 tends to independently predict DCB (P = 0.062, Table 3). The highest AUC (0.743) for DCB was observed in patients with IL-6 alone.The AUCs for DCB in patients of either IL-6low and/or HGFlow levels (n = 39), and IL-6low and HGFlow (n = 25) were 0.729 and 0.629, respectively, which were lower than the AUC of IL-6 alone. (Supplementary Figure S2).
Table 2.
Differences of cytokines in patients between NCB and DCB groups
| NCB | DCB | P value | |
|---|---|---|---|
| n | 23 | 42 | |
| PD-L1, median (IQR) | 89.8 (71.5, 109.0) | 84.3 (72.5, 102.1) | 0.676 |
| CTLA-4, median (IQR) | 5.1 (3.4, 9.1) | 5.6 (3.7, 9.2) | 0.597 |
| CXCL-10, median (IQR) | 250.0 (184.0, 443.5) | 262 (173.5, 367.3) | 0.773 |
| MIG, median (IQR) | 778 (523.5, 1184.5) | 932.0 (680.8, 1669.5) | 0.219 |
| HGF, median (IQR) | 2365.0 (1876.5, 3278.0) | 1769.0 (1557.8, 2309.2) | 0.006 |
| IL-18, mean ± sd | 265.7 ± 88.0 | 264.4 ± 85.0 | 0.954 |
| LAG-3, median (IQR) | 874 (741.5, 1180.5) | 804.5 (718.5, 1125.5) | 0.403 |
| CCL2, median (IQR) | 454 (311.5, 555.5) | 372.5 (303.8, 447.8) | 0.227 |
| GRANB, median (IQR) | 8.4 (6.3, 14.0) | 6.52 (4.8, 11.2) | 0.110 |
| IL-6, median (IQR) | 16.6 (9.1, 34.6) | 7.195 (3.7, 10.8) | 0.001 |
| CCR, mean ± sd | 65.6 ± 15.8 | 69.7 ± 14.0 | 0.280 |
| NRI, mean ± sd | 99.6 ± 11.2 | 101.5 ± 8.4 | 0.465 |
NCB Non-durable clinical benefit; DCB Durable clinical benefit; IQR Inter-quartile range; CCR Creatinine/cystatin C ratio; NRI Nutritional risk index
Table 3.
Univariate and multivariate logistic regression analysis for NBC/DBC
| Characteristics | Total(N) | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|---|
| Odds Ratio (95% CI) | P value | Odds Ratio (95% CI) | P value | ||
| Sex | 65 | ||||
| Male | 50 | Reference | Reference | ||
| Female | 15 | 1.125 (0.332—3.807) | 0.850 | 1.769 (0.319—9.808) | 0.514 |
| Age | 65 | ||||
| ≤ 65 | 31 | Reference | Reference | ||
| > 65 | 34 | 0.769 (0.277—2.139) | 0.615 | 0.676 (0.205—2.232) | 0.520 |
| Smoking history | 65 | ||||
| Yes | 36 | Reference | Reference | ||
| No | 29 | 1.074 (0.386—2.990) | 0.891 | 1.261 (0.270—5.879) | 0.768 |
| Alcohol history | 65 | ||||
| Yes | 28 | Reference | Reference | ||
| No | 37 | 0.438 (0.149—1.281) | 0.132 | 0.336 (0.076—1.493) | 0.152 |
| Diagnose | 65 | ||||
| Other | 27 | Reference | Reference | ||
| Gastroesophageal cancer | 16 | 2.062 (0.525—8.096) | 0.299 | 1.420 (0.285—7.072) | 0.668 |
| Non-small cell lung cancer | 22 | 1.203 (0.377—3.835) | 0.755 | 1.292 (0.318—5.251) | 0.720 |
| HGF | 65 | ||||
| High | 33 | Reference | Reference | ||
| Low | 32 | 3.361 (1.140—9.908) | 0.028 | 1.628 (0.402—6.588) | 0.494 |
| IL-6 | 65 | ||||
| High | 33 | Reference | Reference | ||
| Low | 32 | 6.480 (2.002—20.978) | 0.002 | 3.993 (0.933—17.100) | 0.062 |
ECOG Eastern Cooperative Oncology Group Performance Status; CCR Creatinine/cystatin C ratio; NRI Nutritional risk index; CI Confidence interval
Prognostic value of cytokines and nutritional indexes
We next tested whether survival curves for PFS and OS were stratified by the levels of the 10 cytokines and CCR using the median values as cut-offs. Higher levels of circulating IL-6 and HGF were significantly associated with shorter PFS and OS (P = 0.002 and P < 0.001 for IL-6, P = 0.023 and P = 0.029 for HGF). Patients with CCR levels above the median showed longer PFS and OS (P = 0.031 and P = 0.008 respectively, Fig. 3). Furthermore, we performed ROC analysis to determine the utility of HGF and IL-6 in predicting survival. HGF was determined at cut-off values of 6-month, 12-month, and 18-month OS, and yielded AUCs of 0.728, 0.630, and 0.590 respectively, indicating that the 6- month OS cutoff possessed greater predictive value. These cutoffs were compared with IL-6, which generated AUCs of 0.739, 0.680, and 0.641 at 6-month, 12-month, and 18-month OS cutoffs, respectively. These results demonstrated that IL-6 was superior to HGF in predicting survival (Fig. 4A, B). Notably, to further explore the predictive abilities of these biomarkers, we combined the patients into the following three groups: both IL-6 and HGF low group; either IL-6 or HGF high group; and both IL-6 and HGF high group. Survival curves for PFS and OS were clearly stratified into the two distinct groups, with the worst PFS and OS in the IL-6 and HGF high group (P = 0.002 and P = 0.005, Fig. 4C, D). The AUCs for 6-month, 12-month, and 18-month OS in patients with IL-6low and HGFlow levels were 0.654, 0.640 and 0.595, respectively. The AUCs for 6-month, 12-month, and 18-month OS in patients with IL-6low and/or HGFlow levels were 0.791, 0.655 and 0.636, respectively (Fig. 4E, F). Finally, we analyzed the data using the Cox proportional hazards model with known risk factors for OS and PFS. As shown in Table 4, HGF IL-6 and CCR were shown to be possibly related to OS and PFS in the univariate analysis. When adjusting for HGF, IL-6 or CCR alone, all were independent prognostic factors (PFS: P = 0.028, P = 0.010, and P = 0.016, respectively, OS: P = 0.043, P = 0.003, and P = 0.026, respectively, Tables 5 and 6). However, when adjusting for 3 factors simultaneously including HGF, IL6, and CCR, only IL-6 and CCR were independent factors for prognosis, indicating that the prognostic effect of HGF could be obscured by IL-6 and CCR (Supplement Table S2).
Fig. 3.
Kaplan–Meier curve for PFS and OS between patients with high HGF A, D, IL6 B, E, or CCR C, F and those with low HGF, IL6, or CCR
Fig. 4.
ROC curves of HGF A and IL6 B predicting 6, 12, and 18 months survival. Kaplan–Meier curves for OS C and PFS D on the basis of HGF and IL-6 levels classified as both low, either high, or both high. E ROC curves of IL-6low and HGFlow predicting 6, 12, and 18 months survival. F ROC curves of IL-6low and HGFlow predicting 6, 12, and 18 months survival
Table 4.
Univariate COX regression analysis for PFS and OS
| Characteristics | Total(N) | PFS | OS | ||
|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | ||
| Sex | 65 | ||||
| Male | 50 | Reference | Reference | ||
| Female | 15 | 1.277 (0.695—2.346) | 0.432 | 1.647 (0.843—3.220) | 0.144 |
| Age | 65 | 1.008 (0.979—1.038) | 0.580 | 0.998 (0.964—1.033) | 0.912 |
| Smoking history | 65 | ||||
| Yes | 29 | Reference | Reference | ||
| No | 36 | 1.034 (0.608—1.757) | 0.901 | 1.063 (0.583—1.936) | 0.842 |
| Alcohol history | 65 | ||||
| Yes | 37 | Reference | Reference | ||
| No | 28 | 0.980 (0.578—1.663) | 0.941 | 1.264 (0.692—2.309) | 0.446 |
| Diagnose | 65 | ||||
| Other | 27 | Reference | Reference | ||
| Gastroesophageal cancer | 16 | 0.747 (0.376—1.486) | 0.406 | 0.648 (0.302—1.389) | 0.264 |
| Non-small cell lung cancer | 22 | 1.145 (0.628—2.089) | 0.658 | 0.753 (0.380—1.492) | 0.416 |
| ECOG | 65 | ||||
| < 2 | 58 | Reference | Reference | ||
| = 2 | 7 | 2.975 (1.326—6.678) | 0.008 | 2.544 (1.069—6.052) | 0.035 |
| HGF | 65 | ||||
| Low | 33 | Reference | Reference | ||
| High | 32 | 1.831 (1.078—3.111) | 0.025 | 1.934 (1.059—3.533) | 0.032 |
| IL-6 | 65 | ||||
| Low | 33 | Reference | Reference | ||
| High | 32 | 2.286 (1.337—3.908) | 0.003 | 2.746 (1.477—5.107) | 0.001 |
| CCR | 65 | ||||
| Low | 33 | Reference | Reference | ||
| High | 32 | 0.559 (0.328—0.954) | 0.033 | 0.451 (0.245—0.828) | 0.010 |
ECOG Eastern Cooperative Oncology Group Performance Status; CCR Creatinine/cystatin C ratio; NRI nutritional risk index; CI Confidence interval
Table 5.
Multivariate COX regression analysis for PFS
| Characteristics | Total(N) | HGF model | IL-6 model | CCR model | |||
|---|---|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | ||
| Sex | 65 | ||||||
| Male | 50 | Reference | Reference | Reference | |||
| Female | 15 | 0.985 (0.457—2.121) | 0.969 | 1.156 (0.530—2.519) | 0.716 | 0.685 (0.301—1.558) | 0.367 |
| Age | 65 | 1.001 (0.966—1.037) | 0.955 | 1.006 (0.970—1.043) | 0.745 | 0.996 (0.962—1.032) | 0.841 |
| Smoking history | 65 | ||||||
| Yes | 36 | Reference | Reference | Reference | |||
| No | 29 | 1.083 (0.501—2.340) | 0.839 | 1.128 (0.513—2.481) | 0.765 | 0.784 (0.341—1.806) | 0.568 |
| Alcohol history | 65 | ||||||
| Yes | 28 | Reference | Reference | Reference | |||
| No | 37 | 0.970 (0.469—2.006) | 0.934 | 0.878 (0.418—1.845) | 0.732 | 1.116 (0.522—2.385) | 0.778 |
| Diagnose | 65 | ||||||
| Non-small cell lung carcinoma | 22 | Reference | Reference | Reference | |||
| Gastrointestinal cancer | 17 | 0.614 (0.278—1.356) | 0.227 | 0.629 (0.285—1.391) | 0.252 | 0.585 (0.258—1.327) | 0.200 |
| Others | 26 | 0.887 (0.440—1.786) | 0.737 | 0.941 (0.487—1.819) | 0.856 | 0.935 (0.468—1.869) | 0.849 |
| ECOG | 65 | ||||||
| < 2 | 58 | Reference | Reference | Reference | |||
| = 2 | 7 | 3.184 (1.316—7.702) | 0.010 | 2.093 (0.829—5.283) | 0.118 | 3.500 (1.448—8.458) | 0.005 |
| HGF | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 1.866 (1.071—3.257) | 0.028 | ||||
| IL-6 | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 2.179 (1.209 -3.922) | 0.010 | ||||
| CCR | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 0.430 (0.218—0.849) | 0.015 | ||||
HGF model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and HGF
IL-6 model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and IL-6
CCR model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and CCR
ECOG Eastern Cooperative Oncology Group Performance Status; CCR Creatinine/cystatin C ratio; NRI Nutritional risk index; CI Confidence interval
Table 6.
Multivariate COX regression analysis for OS
| Characteristics | Total(N) | HGF model | IL-6 model | CCR model | |||
|---|---|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | Hazard ratio (95% CI) | P value | ||
| Sex | 65 | ||||||
| Male | 50 | Reference | Reference | Reference | |||
| Female | 15 | 1.639 (0.712—3.773) | 0.245 | 1.980 (0.833—4.707) | 0.122 | 1.210 (0.505—2.901) | 0.669 |
| Age | 65 | 0.993 (0.954—1.034) | 0.739 | 0.991 (0.951—1.033) | 0.676 | 0.985 (0.946—1.026) | 0.468 |
| Smoking history | 65 | ||||||
| Yes | 36 | Reference | Reference | Reference | |||
| No | 29 | 0.907 (0.393—2.090) | 0.818 | 1.031 (0.438—2.427) | 0.945 | 0.690 (0.289—1.644) | 0.402 |
| Alcohol history | 65 | ||||||
| Yes | 28 | Reference | Reference | Reference | |||
| No | 37 | 1.160 (0.509—2.644) | 0.723 | 1.071 (0.462—2.485) | 0.873 | 1.320 (0.569—3.063) | 0.518 |
| Diagnose | 65 | ||||||
| Non-small cell lung carcinoma | 22 | Reference | Reference | Reference | |||
| Gastrointestinal cancer | 17 | 0.763 (0.313—1.859) | 0.552 | 0.879 (0.361—2.141) | 0.777 | 0.764 (0.307—1.906) | 0.564 |
| Others | 26 | 0.599 (0.260—1.378) | 0.228 | 0.676 (0.312—1.465) | 0.321 | 0.663 (0.290—1.511) | 0.328 |
| ECOG | 65 | ||||||
| < 2 | 58 | Reference | Reference | Reference | |||
| = 2 | 7 | 2.562 (0.993—6.612) | 0.052 | 1.764 (0.660—4.714) | 0.257 | 2.577 (0.990—6.710) | 0.053 |
| HGF | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 1.946(1.022—3.704) | 0.043 | ||||
| IL-6 | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 2.899 (1.449 -5.814) | 0.003 | ||||
| CCR | 65 | ||||||
| Low | 32 | Reference | |||||
| High | 33 | 0.447 (0.220—0.907) | 0.026 | ||||
HGF model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and HGF
IL-6 model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and IL-6
CCR model: adjusting for sex, age, smoking history, alcohol history, diagnose, ECOG and CCR
ECOG Eastern Cooperative Oncology Group Performance Status; CCR Creatinine/cystatin C ratio; NRI Nutritional risk index; CI Confidence interval
Associations between cytokines and irAEs
We next explored cytokines in relation to clinical appearance of irAEs. At the time of analysis, 11 of these patients were identified as having grade 2 irAEs. Patients in DCB group were more prone to occur irAEs (Fig. 5A, B). Low level of lymphocyte activation gene-3 (LAG3) was correlated with the occurrence of irAEs (Fig. 5C). Patients experiencing grade 2 irAEs also expressed high levels of C–C motif chemokine ligand 2 (CCL2) than patients with grade 1 irAEs (Fig. 5D).
Fig. 5.
Cytokines in relation to irAEs. A The frequency, severity, and type of irAEs. B Proportion of patients who experienced different grade of irAEs on the basis of DCB and NCB. C Proportion of patients with high or low level LAG3 on the basis of grade of irAEs. D Comparisons of serum CCL2 levels according to the grade of irAEs
Discussion
In this study, we demonstrated significantly higher levels of circulating PD-L1, CXCL10, MIG, HGF, CCL-2, and IL-6 in cancer patients compared to normal healthy subjects. In cancer patients, low circulating HGF, low IL-6, and high CCR were associated with improved PFS and OS. Moreover, pre-treatment levels of circulating IL-6, HGF, and CCR may serve as independent predictive and prognostic biomarkers in advanced cancer patients. Finally, low levels of CCL2 and LAG3 were correlated with the occurrence of irAEs.
As a pro-inflammatory cytokine implicated in chronic inflammatory conditions, IL-6 is reported to affect the efficacy of immunotherapy [14, 15]. The potential mechanism involves the IL-6-STAT3 signaling pathway, which attenuates the cytotoxic effects of cluster of differentiation 8 positive (CD8 +) T cells and leads to the down-regulation of the expression of major histocompatibility complex class II on the surface of dendritic cells [16–18]. Some recent studies have also shown an association between high baseline IL-6 levels and reduced benefits from immunotherapy in hepatocellular carcinoma, melanoma, renal cell carcinoma, lung cancer and cutaneous squamous cell carcinoma patients [14, 19–22]. These studies support our findings that high level of IL-6 indicates a poor prognosis for advanced cancer patients treated with ICIs. Targeting IL-6 and ICIs could be a new therapeutic strategy [23].
HGF is secreted by mesenchymal cells and acts as a multi-functional cytokine mainly on epithelial cells. HGF binds to the proto-oncogenic c-Met receptor, activating tyrosine kinase signaling cascades and regulating cell growth, cell motility, and apoptosis [24]. The neutrophils could be recruited into T cell-inflamed environments through HGF/c-MET [25]. Therefore, the activated HGF/c-MET pathway is considered as a negative regulator for cancer immunotherapy [26, 27]. Conversely, inhibition of c-MET was reported to activate the anti-cancer immune response by promoting the cytotoxic effects of CD8 + T cells [28]. A recent study showed that low level of circulating HGF correlated to superior response to anti-PD-1 therapy in patients with metastatic melanoma [29]. And another study demonstrated that lower baseline plasma HGF are potential biomarkers for anti-angiogenesis therapy and immunotherapy in advanced triple-negative breast cancer patients [30]. Our study also found that low level of serum HGF might be a good prognostic factor for cancer patients receiving ICIs-based systemic therapy.
Emerging evidence indicated that CCR is a promising indicator in predicting the sarcopenia [31–33]. A recent study showed that high level of pre-treatment CCR significantly correlated to improved response and survival in NSCLC patients who received PD-1 inhibitor monotherapy [34]. In another study, a lower CCR and a lower SI (sarcopenia index, SI = serum creatinine × cystatin C) are independent predictors of mortality, in metastatic non-small cell lung cancer patients treated with PD-1 inhibitors [35]. However, in our study, neither SI and sarcopenia (according to the criteria commonly referenced in Asian cancer patients [36]) was found to be predictive of prognosis, nor SI was found to be associated with SMA (Supplementary Figure S3). On the other hand, in accordance with the above mentioned two studies, we also demonstrated that high level of serum CCR indicated better outcomes in our cohort of heterogenous cancer patients receiving ICIs-based systemic therapy.
Although high-grade irAEs are rare, they can significantly impact both quality of life and treatment outcomes. A study on esophageal cancer showed that CCL2 inhibits the recruitment of tumor-associated macrophages and the polarization of type II macrophages. It stops immunosuppression against tumor effector T cells through the PD-1 signaling pathway [37]. According to a recent study, CCL2 was reported to be a potential predictor of neurotoxicity in cancer patients treated with ICIs [38]. LAG-3 is a type of immune checkpoint receptor protein, which is mainly expressed on activated T cells and negatively regulates T cell function. Recently, LAG-3 was found to be involved in the differentiation and survival of auto-reactive CD8 + T-cell [39, 40]. PD-1/LAG-3 double-positive CD8 + T cells showed enhanced cytotoxic capacity in patients receiving ICIs with irAEs [41]. Therefore, serum LAG3 might predict both therapeutic and adverse effects of ICIs in cancer patients. In this study, low levels of LAG3 and CCL2 seemed to be associated with high-grade irAEs, although their role in predicting treatment response was not proved in our data analysis, which might be explained by the limited sample size and heterogeneity of disease context.
Several limitations of this study should be acknowledged. Firstly, this study enrolled heterogeneous cancer patients treated with different regiments based on anti-PD-1/PD-L1, thus the conclusions should be interpreted with caution. Secondly, the sample size is too small to design a validation cohort, which might improve the strength of the study. Finally, we did not evaluate the expression of these cytokines or their receptors on cancer cells and immune cells, which might also be involved in regulating the response of immunotherapy. We hope to initiate a largescale study focusing on homogenous disease treated with ICIs in the future.
In conclusion, pre-treatment circulating levels of serum IL-6, HGF, and CCR may serve as independent predictive and prognostic biomarkers in advanced cancer patients treated with ICIs-based systemic therapy. These findings might help to identify potential patients who would benefits from these therapies.
Supplementary Information
Below is the link to the electronic supplementary material.
Authors’ contributions
Conceptualization, Cheng Du; methodology, Shan-xiu Jin and Hong-juan Ji; software, Shan-xiu Jin; validation, Bo-na Liu and Cheng Du; formal analysis, Shan-xiu jin and Cheng Du; investigation, Cheng Du and Shan-xiu Jin; resources, Xiao-li Gao, Na Li and Zhen-dong Zheng; data curation, Bao-lei Li and Jing-ran Wu; writing—original draft preparation, Shan-xiu jin; writing—review and editing, Cheng Du and Na Li; visualization, Shan-xiu jin; supervision, Bo-na Liu and Zhen-dong Zheng; project administration, Cheng Du; funding acquisition, Na Li and Cheng Du. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by Foundation of Education Department of Jilin Province (JJKH20190002KJ), Natural Science Foundation of Jilin Province (20190201089JC) and Natural Science Foundation of Liaoning Province (2023020976-JH2/1017).
Declarations
Ethical approval
This study was approved by the General Hospital of Northern Theater Command Ethics Review Center (approval number: k201851).
Competing interests
The authors declare no conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shan-xiu Jin, Bo-Na Liu and Hong-juan Ji contributed equally to this work.
Contributor Information
Na Li, Email: linawyswcy@163.com.
Zhen-dong Zheng, Email: gcp_zzd@sina.com.
Cheng Du, Email: dc1115010@sina.com.
References
- 1.Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin 71(3):209–249. 10.3322/caac.21660 10.3322/caac.21660 [DOI] [PubMed] [Google Scholar]
- 2.Kraehenbuehl L, Weng C-H, Eghbali S et al (2022) Enhancing immunotherapy in cancer by targeting emerging immunomodulatory pathways[J]. Nat Rev Clin Oncol 19(1):37–50. 10.1038/s41571-021-00552-7 10.1038/s41571-021-00552-7 [DOI] [PubMed] [Google Scholar]
- 3.Larroquette M, Domblides C, Lefort F et al (2021) Combining immune checkpoint inhibitors with chemotherapy in advanced solid tumours: A review[J]. Eur J Cancer 158:47–62. 10.1016/j.ejca.2021.09.013 10.1016/j.ejca.2021.09.013 [DOI] [PubMed] [Google Scholar]
- 4.Rimassa L, Finn RS, Sangro B (2023) Combination immunotherapy for hepatocellular carcinoma[J]. J Hepatol 79(2):506–515. 10.1016/j.jhep.2023.03.003 10.1016/j.jhep.2023.03.003 [DOI] [PubMed] [Google Scholar]
- 5.Quhal F, Mori K, Bruchbacher A et al (2021) First-line immunotherapy-based combinations for metastatic renal cell carcinoma: a systematic review and network meta-analysis[J]. Eur Urol Oncol 4(5):755–765. 10.1016/j.euo.2021.03.001 10.1016/j.euo.2021.03.001 [DOI] [PubMed] [Google Scholar]
- 6.Reck M, Rodríguez-Abreu D, Robinson AG et al (2016) Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer[J]. N Engl J Med 375(19):1823–1833 10.1056/NEJMoa1606774 [DOI] [PubMed] [Google Scholar]
- 7.Le DT, Durham JN, Smith KN et al (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade[J]. Science (New York, N.Y.) 357(6349):409–413. 10.1126/science.aan6733 10.1126/science.aan6733 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Liu C, Chu D, Kalantar-Zadeh K et al (2021) Cytokines: From clinical significance to quantification[J]. Adv Sci (Weinh) 8(15):e2004433. 10.1002/advs.202004433 10.1002/advs.202004433 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wang Y, Chen H, Zhang T et al (2021) Plasma cytokines interleukin-18 and C-X-C motif chemokine ligand 10 are indicative of the anti-programmed cell death protein-1 treatment response in lung cancer patients[J]. Ann Transl Med 9(1):33. 10.21037/atm-20-1513 10.21037/atm-20-1513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Keegan A, Ricciuti B, Garden P et al. (2020) Plasma IL-6 changes correlate to PD-1 inhibitor responses in NSCLC[J]. J Immunother Cancer 8(2) 10.1136/jitc-2020-000678 [DOI] [PMC free article] [PubMed]
- 11.Inoue Y, Inui N, Karayama M et al (2023) Cytokine profiling identifies circulating IL-6 and IL-15 as prognostic stratifiers in patients with non-small cell lung cancer receiving anti-PD-1/PD-L1 blockade therapy[J]. Cancer Immunol Immunother 72(8):2717–2728. 10.1007/s00262-023-03453-z 10.1007/s00262-023-03453-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zambelli A, Biamonti G, Amato A (2021) HGF/c-Met signalling in the tumor microenvironment[J]. Adv Exp Med Biol 1270:31–44. 10.1007/978-3-030-47189-7_2 10.1007/978-3-030-47189-7_2 [DOI] [PubMed] [Google Scholar]
- 13.Rodenhuis S, Mandjes IAM, Wesseling J et al (2010) A simple system for grading the response of breast cancer to neoadjuvant chemotherapy[J]. Ann Oncol 21(3):481–487. 10.1093/annonc/mdp348 10.1093/annonc/mdp348 [DOI] [PubMed] [Google Scholar]
- 14.Laino AS, Woods D, Vassallo M et al. (2020) Serum interleukin-6 and C-reactive protein are associated with survival in melanoma patients receiving immune checkpoint inhibition[J]. J Immunother Cancer 8(1). 10.1136/jitc-2020-000842 [DOI] [PMC free article] [PubMed]
- 15.Grivennikov SI, Karin M (2011) Inflammatory cytokines in cancer: tumour necrosis factor and interleukin 6 take the stage[J]. Ann Rheum Dis 70(Suppl 1):i104–i108. 10.1136/ard.2010.140145 10.1136/ard.2010.140145 [DOI] [PubMed] [Google Scholar]
- 16.Jung H-Y, Fattet L, Yang J (2015) Molecular pathways: linking tumor microenvironment to epithelial-mesenchymal transition in metastasis[J]. Clin Cancer Res 21(5):962–968. 10.1158/1078-0432.CCR-13-3173 10.1158/1078-0432.CCR-13-3173 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Jones SA, Jenkins BJ (2018) Recent insights into targeting the IL-6 cytokine family in inflammatory diseases and cancer[J]. Nat Rev Immunol 18(12):773–789. 10.1038/s41577-018-0066-7 10.1038/s41577-018-0066-7 [DOI] [PubMed] [Google Scholar]
- 18.Kitamura H, Ohno Y, Toyoshima Y et al (2017) Interleukin-6/STAT3 signaling as a promising target to improve the efficacy of cancer immunotherapy[J]. Cancer Sci 108(10):1947–1952. 10.1111/cas.13332 10.1111/cas.13332 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kang DH, Park C-K, Chung C et al (2020) Baseline serum interleukin-6 levels predict the response of patients with advanced non-small cell lung cancer to PD-1/PD-L1 inhibitors[J]. Immune Netw 20(3):e27. 10.4110/in.2020.20.e27 10.4110/in.2020.20.e27 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Yang H, Kang B, Ha Y et al (2023) High serum IL-6 correlates with reduced clinical benefit of atezolizumab and bevacizumab in unresectable hepatocellular carcinoma[J]. JHEP Rep 5(4):100672. 10.1016/j.jhepr.2023.100672 10.1016/j.jhepr.2023.100672 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Sang YB, Yang H, Lee WS et al. (2022) High serum levels of IL-6 predict poor responses in patients treated with pembrolizumab plus Axitinib for advanced renal cell carcinoma[J]. Cancers (Basel) 14(23). 10.3390/cancers14235985 [DOI] [PMC free article] [PubMed]
- 22.Mallardo D, Simeone E, Festino L et al (2023) IL-6 as new prognostic factor in patients with advanced cutaneous squamous cell carcinoma treated with cemiplimab[J]. J Transl Med 21(1):140. 10.1186/s12967-023-03971-5 10.1186/s12967-023-03971-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Wang M, Zhai X, Li J et al (2021) The role of cytokines in predicting the response and adverse events related to immune checkpoint inhibitors[J]. Front Immunol 12:670391. 10.3389/fimmu.2021.670391 10.3389/fimmu.2021.670391 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Fu J, Su X, Li Z et al (2021) HGF/c-MET pathway in cancer: from molecular characterization to clinical evidence[J]. Oncogene 40(28):4625–4651. 10.1038/s41388-021-01863-w 10.1038/s41388-021-01863-w [DOI] [PubMed] [Google Scholar]
- 25.Glodde N, Bald T, van den Boorn-Konijnenberg D. et al. 2017 Reactive neutrophil responses dependent on the receptor tyrosine kinase c-MET limit cancer immunotherapy[J]. Immunity 47(4).10.1016/j.immuni.2017.09.012 [DOI] [PubMed]
- 26.Lam BQ, Dai L, Qin Z (2016) The role of HGF/c-MET signaling pathway in lymphoma[J]. J Hematol Oncol 9(1):135 10.1186/s13045-016-0366-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Papaccio F, Della Corte CM, Viscardi G et al. (2018) HGF/MET and the immune system: Relevance for cancer immunotherapy[J]. Int J Mol Sci 19(11). 10.3390/ijms19113595 [DOI] [PMC free article] [PubMed]
- 28.Zhang Y, Xia M, Jin K et al (2018) Function of the c-Met receptor tyrosine kinase in carcinogenesis and associated therapeutic opportunities[J]. Mol Cancer 17(1):45. 10.1186/s12943-018-0796-y 10.1186/s12943-018-0796-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kubo Y, Fukushima S, Inamori Y et al (2019) Serum concentrations of HGF are correlated with response to anti-PD-1 antibody therapy in patients with metastatic melanoma[J]. J Dermatol Sci 93(1):33–40. 10.1016/j.jdermsci.2018.10.001 10.1016/j.jdermsci.2018.10.001 [DOI] [PubMed] [Google Scholar]
- 30.Liu J, Li Y, Li Q et al (2021) Biomarkers of response to camrelizumab combined with apatinib: an analysis from a phase II trial in advanced triple-negative breast cancer patients[J]. Breast Cancer Res Treat 186(3):687–697. 10.1007/s10549-021-06128-4 10.1007/s10549-021-06128-4 [DOI] [PubMed] [Google Scholar]
- 31.Fu X, Tian Z, Wen S et al (2021) A new index based on serum creatinine and cystatin C is useful for assessing sarcopenia in patients with advanced cancer[J]. Nutrition 82:111032. 10.1016/j.nut.2020.111032 10.1016/j.nut.2020.111032 [DOI] [PubMed] [Google Scholar]
- 32.Sun J, Yang H, Cai W et al (2022) Serum creatinine/cystatin C ratio as a surrogate marker for sarcopenia in patients with gastric cancer[J]. BMC Gastroenterol 22(1):26. 10.1186/s12876-022-02093-4 10.1186/s12876-022-02093-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Suzuki K, Furuse H, Tsuda T et al (2015) Utility of creatinine/cystatin C ratio as a predictive marker for adverse effects of chemotherapy in lung cancer: A retrospective study[J]. J Int Med Res 43(4):573–582. 10.1177/0300060515579116 10.1177/0300060515579116 [DOI] [PubMed] [Google Scholar]
- 34.Tang T, Xie L, Hu S et al (2022) Serum creatinine and cystatin C-based diagnostic indices for sarcopenia in advanced non-small cell lung cancer[J]. J Cachexia Sarcopenia Muscle 13(3):1800–1810. 10.1002/jcsm.12977 10.1002/jcsm.12977 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ashton E, Arrondeau J, Jouinot A et al (2023) Impact of sarcopenia indexes on survival and severe immune acute toxicity in metastatic non-small cell lung cancer patients treated with PD-1 immune checkpoint inhibitors[J]. Clin Nutr 42(6):944–953. 10.1016/j.clnu.2023.03.023 10.1016/j.clnu.2023.03.023 [DOI] [PubMed] [Google Scholar]
- 36.Zhuang C-L, Huang D-D, Pang W-Y et al (2016) Sarcopenia is an independent predictor of severe postoperative complications and long-term survival after radical gastrectomy for gastric cancer: Analysis from a large-scale cohort[J]. Medicine (Baltimore) 95(13):e3164. 10.1097/MD.0000000000003164 10.1097/MD.0000000000003164 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Yang H, Zhang Q, Xu M et al (2020) CCL2-CCR2 axis recruits tumor associated macrophages to induce immune evasion through PD-1 signaling in esophageal carcinogenesis[J]. Mol Cancer 19(1):41. 10.1186/s12943-020-01165-x 10.1186/s12943-020-01165-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Möhn N, Mahjoub S, Duzzi L et al (2023) Monocyte chemoattractant protein 1 as a potential biomarker for immune checkpoint inhibitor-associated neurotoxicity[J]. Cancer Med 12(8):9373–9383. 10.1002/cam4.5695 10.1002/cam4.5695 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Guo M, Qi F, Rao Q et al (2021) Serum LAG-3 Predicts outcome and treatment response in hepatocellular carcinoma patients with transarterial chemoembolization[J]. Front Immunol 12:754961. 10.3389/fimmu.2021.754961 10.3389/fimmu.2021.754961 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Burke KP, Patterson DG, Liang D et al (2023) Immune checkpoint receptors in autoimmunity[J]. Curr Opin Immunol 80:102283. 10.1016/j.coi.2023.102283 10.1016/j.coi.2023.102283 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.van Eijs MJM, Verheijden RJ, van der Wees SA et al (2023) Toxicity-specific peripheral blood T and B cell dynamics in anti-PD-1 and combined immune checkpoint inhibition[J]. Cancer Immunol Immunother 72(12):4049–4064. 10.1007/s00262-023-03541-0 10.1007/s00262-023-03541-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
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