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10.1007/s00520-024-08525-z
Research
Serum cytokines and creatinine/cystatin C ratio as prognostic biomarkers in advanced cancer patients treated with anti-PD-1/PD-L1 therapy
Jin Shan-xiu 1
Liu Bo-Na 1
Ji Hong-juan 1
Wu Jing-ran 1
Li Bao-lei 2
Gao Xiao-li 1
Li Na linawyswcy@163.com

3
Zheng Zhen-dong gcp_zzd@sina.com

1
Du Cheng dc1115010@sina.com

1
1 Department of Oncology, General Hospital of Northern Theater Command, Shenyang, China
2 Department of Oncology, Anshan Tumor Hospital, Anshan, China
3 https://ror.org/034haf133 grid.430605.4 0000 0004 1758 4110 Department of Gynaecology and Obstetrics, The First Hospital of Jilin University, Jilin, China
22 5 2024
22 5 2024
2024
32 6 37011 2 2024
25 4 2024
© The Author(s) 2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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
http://dx.doi.org/10.13039/501100010211 Education Department of Jilin Province JJKH20190002KJ Li Na http://dx.doi.org/10.13039/100007847 Natural Science Foundation of Jilin Province 20190201089JC Li Na http://dx.doi.org/10.13039/501100005047 Natural Science Foundation of Liaoning Province 2023020976-JH2/1017 Du Cheng issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

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.Supplementary file1 (DOCX 895 KB)

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.

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.
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