Significance of plasma cytokine profile in predicting clinical benefits and immune related adverse events in patients with non-small cell lung cancer

doi:10.3877/cma.j.issn.1674-6902.2025.04.011

Abstract

Abstract:

Objective

To analyze the correlation between plasma cytokine profiles, immune checkpoint inhibitors (ICIs), clinical benefits, and immune-related adverse events (irAEs) in patients with non-small cell lung cancer (NSCLC).

Methods

A total of 56 patients with unresectable stage Ⅲ~Ⅳ advanced NSCLC treated at our hospital from January 2020 to October 2024 were enrolled. Among them, 33 received ICIs monotherapy and 23 received combination ICIs therapy. 33 patients with durable clinical benefit (DCB) were assigned to the observation group, while 23 cases with non-durable clinical benefit (NDB) served as the control group. Clinical characteristics, plasma inflammatory cytokines, and complete blood counts were analyzed to identify biomarkers associated with clinical benefits and irAEs.

Results

The median progression-free survival (PFS) for all 56 patients was 9.0 months (95%CI: 4.41~13.6). Immune partial response (iPR) was observed in 21 patients (37.50%), immune stable disease (iSD) in 14 (25.0%), immune unconfirmed progressive disease (iUPD) in 13 (23.21%), and immune confirmed progressive disease (iCPD) in 8 (14.29%). Pretreatment levels of IL-6 (P=0.024), IL-10 (P=0.035), and neutrophil-to-lymphocyte ratio (NLR, P=0.005) were higher in the control group compared to the observation group. Post-treatment changes in IL-6+ 2.14 pg/ml and IL-10 + 1.88 pg/ml in the control group exceeded those in the observation group (IL-6: 1.95 pg/ml, Z=3.123, P=0.002; IL-10: 0.27 pg/ml, Z=2.773, P=0.006). ROC curve analysis revealed that changes in IL-6 and IL-10 differentiated DCB from NDB with areas under the curve (AUC) of 0.747 (95%CI: 0.613~0.854) and 0.701 (95%CI: 0.583~0.831), respectively. Cox regression indicated that pretreatment NLR (HR=1.103, 95%CI=1.001~1.153, P=0.041) and elevated IL-6 (HR=2.027, 95%CI: 1.001~4.258, P=0.019) and IL-10 (HR=2.053, 95%CI: 1.018~4.089, P=0.002) during ICIs treatment were risk factors for reduced PFS. Among the 56 patients, 33 (58.93%) experienced at least one irAE. Pretreatment IL-6 (HR=4.458, 95%CI: 1.329~14.953, P=0.015) and increased IL-10 during ICIs therapy (HR=5.712, 95%CI: 1.088~29.993, P=0.039) were predictive of irAEs, with AUCs of 0.754 and 0.706, respectively. Patients with immune-related pneumonia exhibited higher plasma IL-6 levels, while IL-10 decreased during ICIs treatment (P<0.05).

Conclusion

Plasma cytokine profiling is a minimally invasive, accessible, and reproducible approach. IL-6 and IL-10 may serve as biomarkers for predicting clinical outcomes and irAEs in advanced NSCLC patients undergoing ICIs therapy.

Key words: Non-small cell lung cancer, Plasma cytokine profile, Immune checkpoint inhibitors, Tumor response, Immune-related adverse events

Yanling Jiang, Jinzhuo Ren, Junjie Chen, Xiuli Tian, Yixiang Shen, Hua Zhang. Significance of plasma cytokine profile in predicting clinical benefits and immune related adverse events in patients with non-small cell lung cancer[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2025, 18(04): 558-563.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhfbjbzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-6902.2025.04.011

https://zhfbjbzz.cma-cmc.com.cn/EN/Y2025/V18/I04/558

Figures/Tables 5

References 30

1	刘斌，郭述良. 晚期非小细胞肺癌免疫检查点抑制剂治疗的研究进展[J]. 中国癌症防治杂志，2024, 16(2): 243-248.
2	Liang X, Xiao H, Li H, et al. Adverse events associated with immune checkpoint inhibitors in non-small cell lung cancer: a safety analysis of clinical trials and FDA pharmacovigilance system[J]. Front Immunol, 2024, 15: 1396752.
3	郑清月，闫春良，赵秋红，等. 血清离子对免疫检查点抑制剂治疗非小细胞肺癌疗效影响分析[J]. 中华肿瘤防治杂志，2024, 31(17): 1067-1072.
4	陈旭，牛凯，孙建国. 放疗联合免疫治疗对驱动基因阴性NSCLC的困惑分析及应对策略[J/CD]．中华肺部疾病杂志(电子版), 2024, 17(3): 341-348.
5	Chu Y, Dai E, Li Y, et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance[J]. Nat Med, 2023, 29(6): 1550-1562.
6	Nuñez NG, Berner F, Friebel E, et al. Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors[J]. Med, 2023, 4(2): 113-129.
7	Litchfield K, Reading JL, Puttick C, et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition[J]. Cell, 2021, 184(3): 596-614.
8	Müller-Jensen L, Schulz AR, Mei HE, et al. Immune signatures of checkpoint inhibitor-induced autoimmunity-A focus on neurotoxicity[J]. Neuro Oncol, 2024, 26(2): 279-294.
9	Kim H, Park S, Han KY, et al. Clonal expansion of resident memory T cells in peripheral blood of patients with non-small cell lung cancer during immune checkpoint inhibitor treatment[J]. J Immunother Cancer, 2023, 11(2): e005509.
10	Rami-Porta R, Nishimura KK, Giroux DJ, et al. The international association for the study of lung cancer lung cancer staging project: Proposals for revision of the TNM stage groups in the forthcoming (Ninth) edition of the TNM classification for lung cancer[J]. J Thorac Oncol, 2024, 19(7): 1007-1027.
11	Song P, Zhang J, Shang C, et al. Curative effect assessment of immunotherapy for non-small cell lung cancer: The "blind area" of Immune Response Evaluation Criteria in Solid Tumors (iRECIST)[J]. Thorac Cancer, 2019, 10(4): 587-592.
12	Freites-Martinez A, Santana N, Arias-Santiago S, et al. Using the common terminology criteria for adverse events (CTCAE- Version 5.0) to evaluate the severity of adverse events of anticancer therapies[J]. Actas Dermosifiliogr (Engl Ed), 2021, 112(1): 90-92.
13	李慧婷，莫冉冉，宋鹏，等. 非小细胞肺癌免疫检查点抑制剂治疗进展[J/CD]. 中华肺部疾病杂志(电子版), 2019, 12(3): 382-386.
14	Ikeda H, Kawase K, Nishi T, et al. Immune evasion through mitochondrial transfer in the tumour microenvironment[J]. Nature, 2025, 638(8049): 225-236.
15	宋春莲，杨立伟，马维艳. 血清CXCL12水平预测非小细胞肺癌免疫检查点抑制剂治疗反应的临床意义[J]. 临床肿瘤学杂志，2024, 29(10): 968-972.
16	Jin SX, Liu BN, Ji HJ, et al. Serum cytokines and creatinine/cystatin C ratio as prognostic biomarkers in advanced cancer patients treated with anti-PD-1/PD-L1？therapy[J]. Support Care Cancer, 2024, 32(6)：370.
17	Mitsuhashi A, Kondoh K, Horikawa K, et al. Programmed death (PD)-1/PD-ligand 1 blockade mediates antiangiogenic effects by tumor-derived？CXCL10/11 as a potential predictive biomarker[J]. Cancer Sci, 2021, 112(12): 4853-4866.
18	Yi L, Wang X, Fu S, et al. Association between response to anti-PD-1 treatment and blood soluble PD-L1 and IL-8 changes in patients with NSCLC[J]. Discov Oncol, 2023, 14(1): 35.
19	苏星星，金铮，贾罄竹，等. S100A8作为非小细胞肺癌免疫检查点抑制剂治疗疗效预测指标的研究[J]. 免疫学杂志，2021, 37(3): 256-262.
20	朱玲玲，张亚妮，史婷婷，等. 白细胞介素-6在肿瘤免疫检查点抑制剂治疗中的作用研究进展[J]. 中国肿瘤临床，2024, 51(11): 585-589.
21	Li XN, Peng YH, Yue W, et al. A cohort study using IL-6/Stat3 activity and PD-1/PD-L1 expression to predict five-year survival for patients after gastric cancer resection[J]. PLoS One, 2022, 17(12): e0277908.
22	邓悦，周晓阳. 肺癌患者免疫检查点抑制剂治疗后发生心脏毒性的危险因素研究[J]. 实用心脑肺血管病杂志，2024, 32(8): 23-27.
23	吴玉佩，尹岳松，孙杰威，等. 免疫检查点抑制剂停用后迟发性免疫相关不良事件的回顾性分析[J]. 实用药物与临床，2025, 28(3): 216-220.
24	Les I, Martínez M, Pérez-Francisco I, et al. Predictive biomarkers for checkpoint inhibitor immune-related adverse events[J]. Cancers (Basel), 2023, 15(5): 1629.
25	Hailemichael Y, Johnson DH, Abdel-Wahab N, et al. Interleukin-6 blockade abrogates immunotherapy toxicity and promotes tumor immunity[J]. Cancer Cell, 2022, 40(5): 509-523.
26	Ponvilawan B, Khan AW, Subramanian J, et al. Non-invasive predictive biomarkers for immune-related adverse events due to immune checkpoint inhibitors[J]. Cancers (Basel), 2024, 16(6): 1225.
27	Betzler AC, Theodoraki MN, Schuler PJ, et al. NF-kappaB and its role in checkpoint control[J]. Int J Mol Sci, 2020, 21(11): 3949.
28	Yamazaki N, Kiyohara Y, Uhara H, et al. Cytokine Biomarkers to Predict Antitumor Responses to Nivolumab Suggested in a Phase 2 Study for Advanced Melanoma[J]. Cancer Sci, 2017, 108(5): 1022-1031.
29	Shiri AM, Zhang T, Bedke T, et al. IL-10 dampens antitumor immunity and promotes liver metastasis via PD-L1 inductio[J]. J Hepatol, 2024, 80(4): 634-644.
30	Hanna BS, Llaó-Cid L, Iskar M, et al. Interleukin-10？receptor signaling promotes the maintenance of a PD-1(int) TCF-1(＋) CD8(＋) T cell population that sustains anti-tumor immunity[J]. Immunity, 2021, 54(12): 2825-2841.

检测指标	观察组(n＝33)	对照组(n＝23)	Z值	P值
IL-1β(pg/ml)	0.12(0.06，0.36)	0.13(0.07，0.27)	－1.117	0.264
IL-2(pg/ml)	0.31(0.21，0.63)	0.34(0.25，0.54)	－1.166	0.244
IL-4(pg/ml)	0.02(0.02，0.03)	0.03(0.02，0.06)	－0.225	0.822
IL-6(pg/ml)	7.83(3.38，12.81)	9.21(5.40，26.98)	－2.257	0.024
IL-8(pg/ml)	6.01(4.46，12.04)	7.05(6.53，13.56)	－1.557	0.119
IL-10(pg/ml)	5.53(0.91，12.10)	11.61(1.83，17.24)	－2.122	0.035
IL-12p70(pg/ml)	0.26(0.16，0.42)	0.18(0.16，0.43)	－0.816	0.414
IL-13(pg/ml)	1.44(1.07，2.01)	1.53(1.41，1.86)	－0.064	0.949
IFN-γ(pg/ml)	11.56(6.98，21.80)	12.28(9.33，19.75)	－0.529	0.597
TNF-α(pg/ml)	5.81(4.57，7.68)	6.14(4.66，7.23)	－0.724	0.469
白细胞计数(×10⁹个/ml)	7.20(6.12，9.29)	6.99(6.44，7.65)	－1.499	0.134
中性粒细胞计数(×10⁹个/ml)	4.49(3.99，5.92)	4.52(3.65，6.21)	－1.807	0.071
淋巴细胞计数(×10⁹个/ml)	1.24(1.09，1.86)	1.24(0.81，1.81)	－0.317	0.807
NLR	3.53(2.76，5.21)	5.37(3.98，14.07)	－2.806	0.005
C反应蛋白(pg/ml)	0.15(0.01，1.12)	0.21(0.01，1.24)	－0.302	0.583

检测指标	观察组(n＝33)	对照组(n＝23)	Z值	P值
IL-1β(pg/ml)	0.12(0.06，0.36)	0.13(0.07，0.27)	－1.117	0.264
IL-2(pg/ml)	0.31(0.21，0.63)	0.34(0.25，0.54)	－1.166	0.244
IL-4(pg/ml)	0.02(0.02，0.03)	0.03(0.02，0.06)	－0.225	0.822
IL-6(pg/ml)	7.83(3.38，12.81)	9.21(5.40，26.98)	－2.257	0.024
IL-8(pg/ml)	6.01(4.46，12.04)	7.05(6.53，13.56)	－1.557	0.119
IL-10(pg/ml)	5.53(0.91，12.10)	11.61(1.83，17.24)	－2.122	0.035
IL-12p70(pg/ml)	0.26(0.16，0.42)	0.18(0.16，0.43)	－0.816	0.414
IL-13(pg/ml)	1.44(1.07，2.01)	1.53(1.41，1.86)	－0.064	0.949
IFN-γ(pg/ml)	11.56(6.98，21.80)	12.28(9.33，19.75)	－0.529	0.597
TNF-α(pg/ml)	5.81(4.57，7.68)	6.14(4.66，7.23)	－0.724	0.469
白细胞计数(×10⁹个/ml)	7.20(6.12，9.29)	6.99(6.44，7.65)	－1.499	0.134
中性粒细胞计数(×10⁹个/ml)	4.49(3.99，5.92)	4.52(3.65，6.21)	－1.807	0.071
淋巴细胞计数(×10⁹个/ml)	1.24(1.09，1.86)	1.24(0.81，1.81)	－0.317	0.807
NLR	3.53(2.76，5.21)	5.37(3.98，14.07)	－2.806	0.005
C反应蛋白(pg/ml)	0.15(0.01，1.12)	0.21(0.01，1.24)	－0.302	0.583

自变量	单变量分析		多变量分析
自变量	HR(95%CI)	P值	HR(95%CI)	P值
DCB(DCB vs. NDB)	0.054(0.020~0.147)	0.000	0.071(0.097~0.377)	0.021
PD-L1检测(阳性率≥25% vs.阳性率<25%)	0.307(0.141~0.668)	0.003	0.374(0.081~1.038)	0.083
NLR	1.073(1.036~1.112)	0.000	1.103(1.001~1.153)	0.041
IL-6	2.328(1.236~4.382)	0.001	2.027(1.001~4.258)	0.019
IL-10	2.326(1.166~4.637)	0.000	2.053(1.018~4.089)	0.002

自变量	单变量分析		多变量分析
自变量	HR(95%CI)	P值	HR(95%CI)	P值
DCB(DCB vs. NDB)	0.054(0.020~0.147)	0.000	0.071(0.097~0.377)	0.021
PD-L1检测(阳性率≥25% vs.阳性率<25%)	0.307(0.141~0.668)	0.003	0.374(0.081~1.038)	0.083
NLR	1.073(1.036~1.112)	0.000	1.103(1.001~1.153)	0.041
IL-6	2.328(1.236~4.382)	0.001	2.027(1.001~4.258)	0.019
IL-10	2.326(1.166~4.637)	0.000	2.053(1.018~4.089)	0.002

自变量	单变量分析		多变量分析
自变量	HR(95%CI)	P值	HR(95%CI)	P值
现在/既往吸烟史(是vs.否)	2.136(1.135~4.018)	0.019	1.807(0.943~3.463)	0.074
ECOG PS评分(1分vs. 0分)	0.295(0.019~0.998)	0.029	0.441(0.095~2.055)	0.297
治疗前
IL-6	5.537(1.689~18.152)	0.005	4.458(1.329~14.953)	0.015
治疗后较治疗前变化值	2.739(0.958~7.832)	0.060	2.279(0.739~7.027)	0.152
IL-6	4.375(1.027~18.629)	0.046	2.359(0.464~12.006)	0.301
IL-10	7.857(1.651~37.403)	0.010	5.712(1.088~29.993)	0.039

Please choose a citation manager

Content to export