Exploration of prognostic factors and nomogram construction for advanced non-small cell lung cancer treated with immunotherapy based on hematologic indexes

doi:10.3760/cma.j.cn371439-20231109-00023

Abstract

Abstract:

Objective To explore influencing factors affecting the prognosis of patients with advanced non-small cell lung cancer （NSCLC） receiving immunotherapy based on hematologic indexes， thus to construct and evaluate a nomogram prediction model. Methods The clinical data of 80 patients with advanced NSCLC treated with programmed death-1 inhibitor monotherapy or combination regimen from January 2018 to June 2020 at the Affiliated Hospital of North China University of Science and Technology and Tangshan People's Hospital were retrospectively analyzed. Hematologic indexes at the baseline， the optimal remission and the progressive disease （PD） were collected separately， and independent influencing factors for patient prognosis were analyzed using Cox proportional hazards regression model. A nomogram prediction model was constructed based on the results of the multifactorial analysis， and the predictive performance of the model was evaluated by receiver operating characteristic （ROC） curve， concordance index （C-index） and calibration curves. Results As of the follow-up cut-off date， of the 80 patients， 63 had PD， with a median overall survival （OS） of 16.9 months. Univariate analysis showed that， age （HR=2.09， 95%CI： 1.17-3.74， P=0.013）， number of treatment lines （HR=2.23， 95%CI： 1.21-4.12， P=0.010）， lymphocyte to monocyte ratio （LMR） at the baseline （HR=0.75， 95%CI： 0.57-0.97， P=0.028）， D-dimer （HR=1.00， 95%CI： 1.00-1.00， P=0.002） and lactate dehydrogenase （LDH）（HR=1.01， 95%CI： 1.00-1.01， P=0.006） at the optimal remission， haemoglobin （HR=0.97， 95%CI： 0.96-0.99， P<0.001）， D-dimer （HR=1.00， 95%CI： 1.00-1.00， P=0.002）， C-reactive protein （HR=1.01， 95%CI： 1.00-1.01， P=0.011）， albumin （ALB）（HR=0.91， 95%CI： 0.87-0.96， P=0.001）， neutrophil to lymphocyte ratio （NLR）（HR=1.16， 95%CI： 1.05-1.27， P=0.002） and LMR （HR=0.62， 95%CI： 0.42-0.90， P=0.012） at the PD were all influencing factors for the prognosis of advanced NSCLC patients receiving immunotherapy. Least absolute shrinkage and selection operator regression were used to screen the variables for P<0.10 in the univariate analysis， and nine possible influencing factors were obtained， which were age， fibrinogen and LDH at the optimal remission， haemoglobin， D-dimer， C-reactive protein， LDH， ALB and LMR at the PD. Multivariate analysis of the above variables showed that， age （HR=0.91， 95%CI： 0.86-0.97， P=0.004）， LDH （HR=1.01， 95%CI： 1.00-1.01， P=0.013） and ALB （HR=0.82， 95%CI： 0.67-0.99， P=0.041） at the PD were independent influencing factors for the prognosis of patients with advanced NSCLC who received immunotherapy. The area under curve of the nomogram predicting model based on the above indexes， 1- and 2-year OS rates of patients were 0.77 （95%CI： 0.65-0.89） and 0.75 （95%CI： 0.66-0.88）， respectively， and C-index was 0.71 （95%CI： 0.64-0.78）， the calibration curves showed good consistency between predicted and actual probability of occurrence. Patients in the low-risk group （n=40） had a median OS of 29.9 months （95%CI： 22.5 months-NA）， which was significantly better than that of the high-risk group （n=40） [13.4 months （95%CI： 11.4-23.5 months）， χ²=11.30， P<0.001]. Conclusion Age， LDH and ALB at the PD are independent influencing factors affecting the prognosis of patients with advanced NSCLC receiving immunotherapy， and the nomogram model constructed based on the above indexes has good differentiation and calibration for predicting 1- and 2-year OS rates in advanced NSCLC patients receiving immunotherapy.

Key words: Carcinoma, non-small-cell lung, Immunotherapy, Prognosis, Nomogram

Sun Weiwei, Yao Xuemin, Wang Pengjian, Wang Jing, Jia Jinghao. Exploration of prognostic factors and nomogram construction for advanced non-small cell lung cancer treated with immunotherapy based on hematologic indexes[J]. Journal of International Oncology, 2024, 51(3): 143-150.

Figures/Tables 9

References 25

[1]	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]	de Castro G Jr, Kudaba I, Wu YL, et al. Five-year outcomes with pembrolizumab versus chemotherapy as first-line therapy in patients with non-small-cell lung cancer and programmed death ligand-1 tumor proportion score ≥ 1% in the KEYNOTE-042 study[J]. J Clin Oncol, 2023, 41(11): 1986-1991. DOI: 10.1200/JCO.21.02885.
[3]	Zhou F, Qiao M, Zhou C. The cutting-edge progress of immune-checkpoint blockade in lung cancer[J]. Cell Mol Immunol, 2021, 18(2): 279-293. DOI: 10.1038/s41423-020-00577-5. pmid: 33177696
[4]	Shankar B, Zhang J, Naqash AR, et al. Multisystem immune-related adverse events associated with immune checkpoint inhibitors for treatment of non-small cell lung cancer[J]. JAMA Oncol, 2020, 6(12): 1952-1956. DOI: 10.1001/jamaoncol.2020.5012. pmid: 33119034
[5]	Rebuzzi SE, Prelaj A, Friedlaender A, et al. Prognostic scores including peripheral blood-derived inflammatory indices in patients with advanced non-small-cell lung cancer treated with immune checkpoint inhibitors[J]. Crit Rev Oncol Hematol, 2022, 179: 103806. DOI: 10.1016/j.critrevonc.2022.103806.
[6]	Zhang N, Jiang J, Tang S, et al. Predictive value of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis[J]. Int Immunopharmacol, 2020, 85: 106677. DOI: 10.1016/j.intimp.2020.106677.
[7]	Zheng L, Xiong A, Wang S, et al. Decreased monocyte-to-lymphocyte ratio was associated with satisfied outcomes of first-line PD-1 inhibitors plus chemotherapy in stage ⅢB -Ⅳ non-small cell lung cancer[J]. Front Immunol, 2023, 14: 1094378. DOI: 10.3389/fimmu.2023.1094378.
[8]	Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)[J]. Eur J Cancer, 2009, 45(2): 228-247. DOI: 10.1016/j.ejca.2008.10.026. pmid: 19097774
[9]	Li X, Wenes M, Romero P, et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy[J]. Nat Rev Clin Oncol, 2019, 16(7): 425-441. DOI: 10.1038/s41571-019-0203-7. pmid: 30914826
[10]	Xia L, Oyang L, Lin J, et al. The cancer metabolic reprogramming and immune response[J]. Mol Cancer, 2021, 20(1): 28. DOI: 10.1186/s12943-021-01316-8. pmid: 33546704
[11]	Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer[J]. N Engl J Med, 2016, 375(19): 1823-1833. DOI: 10.1056/NEJMoa1606774.
[12]	Rittmeyer A, Barlesi F, Waterkamp D, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial[J]. Lancet, 2017, 389(10066): 255-265. DOI: 10.1016/S0140-6736(16)32517-X. pmid: 27979383
[13]	Wang X, Ma L, Pei X, et al. Comprehensive assessment of cellular senescence in the tumor microenvironment[J]. Brief Bioinform, 2022, 23(3): bbac118. DOI: 10.1093/bib/bbac118.
[14]	Yang F, Markovic SN, Molina JR, et al. Association of sex, age, and eastern cooperative oncology group performance status with survival benefit of cancer immunotherapy in randomized clinical trials: a systematic review and meta-analysis[J]. JAMA Netw Open, 2020, 3(8): e2012534. DOI: 10.1001/jamanetworkopen.2020.12534.
[15]	Molinier O, Besse B, Barlesi F, et al. IFCT-1502 CLINIVO: real-world evidence of long-term survival with nivolumab in a nationwide cohort of patients with advanced non-small-cell lung cancer[J]. ESMO Open, 2022, 7(1): 100353. DOI: 10.1016/j.esmoop.2021.100353.
[16]	Waterhouse D, Lam J, Betts KA, et al. Real-world outcomes of immunotherapy-based regimens in first-line advanced non-small cell lung cancer[J]. Lung Cancer, 2021, 156: 41-49. DOI: 10.1016/j.lungcan.2021.04.007. pmid: 33894493
[17]	Kumagai S, Koyama S, Itahashi K, et al. Lactic acid promotes PD-1 expression in regulatory T cells in highly glycolytic tumor microenvironments[J]. Cancer Cell, 2022, 40(2): 201-218.e9. DOI: 10.1016/j.ccell.2022.01.001.
[18]	Van Wilpe S, Koornstra R, Den Brok M, et al. Lactate dehydrogenase: a marker of diminished antitumor immunity[J]. Oncoimmunology, 2020, 9(1): 1731942. DOI: 10.1080/2162402X.2020.1731942.
[19]	Callejo A, Frigola J, Iranzo P, et al. Interrelations between patients' clinicopathological characteristics and their association with response to immunotherapy in a real-world cohort of NSCLC patients[J]. Cancers (Basel), 2021, 13(13): 3249. DOI: 10.3390/cancers13133249.
[20]	Tjokrowidjaja A, Lord SJ, John T, et al. Pre- and on-treatment lactate dehydrogenase as a prognostic and predictive biomarker in advanced non-small cell lung cancer[J]. Cancer, 2022, 128(8): 1574-1583. DOI: 10.1002/cncr.34113. pmid: 35090047
[21]	Yoo SK, Chowell D, Valero C, et al. Pre-treatment serum albumin and mutational burden as biomarkers of response to immune checkpoint blockade[J]. NPJ Precis Oncol, 2022, 6(1): 23. DOI: 10.1038/s41698-022-00267-7.
[22]	Takada K, Takamori S, Yoneshima Y, et al. Serum markers associated with treatment response and survival in non-small cell lung cancer patients treated with anti-PD-1 therapy[J]. Lung Cancer, 2020, 145: 18-26. DOI: 10.1016/j.lungcan.2020.04.034. pmid: 32388276
[23]	Gouez M, Delrieu L, Bouleuc C, et al. Association between nutritional status and treatment response and survival in patients treated with immunotherapy for lung cancer: a retrospective french study[J]. Cancers (Basel), 2022, 14(14): 3439. DOI: 10.3390/cancers14143439.
[24]	Ma G, Zhang Z, Li P, et al. Reprogramming of glutamine metabolism and its impact on immune response in the tumor microenvironment[J]. Cell Commun Signal, 2022, 20(1): 114. DOI: 10.1186/s12964-022-00909-0. pmid: 35897036
[25]	de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth[J]. Cancer Cell, 2023, 41(3): 374-403. DOI: 10.1016/j.ccell.2023.02.016. pmid: 36917948

一般临床资料	结果
年龄（岁）
≤63	53（66.2）
>63	27（33.8）
性别
男	64（80.0）
女	16（20.0）
ECOG评分（分）
0~1	72（90.0）
≥2	8（10.0）
吸烟史
无	35（43.8）
有	41（51.2）
未知	4（5.0）
病理类型
鳞状细胞癌	41（51.2）
非鳞状细胞癌	39（48.8）
临床分期
ⅢB~ⅢC期	25（31.2）
Ⅳ期	55（68.8）
放疗史
无	48（60.0）
有	32（40.0）
治疗方案
免疫单药	14（17.5）
免疫联合	66（82.5）
治疗线数
1	37（46.2）
≥2	43（53.8）

血液学指标	基线期	最佳缓解期	疾病进展期
中性粒细胞（×10⁹/L）	4.63（3.20，6.05）	4.16（2.63，5.51）	4.45（3.36，5.28）
血小板（×10⁹/L）	261.70±91.68	223.12±93.57	229.80±78.53
血红蛋白（g/L）	131.27±16.65	124.33±18.21	126.39±22.60
嗜酸性粒细胞（×10⁹/L）	0.11（0.07，0.25）	0.09（0.03，0.16）	0.13（0.07，0.21）
纤维蛋白原（g/L）	4.14±0.86	3.72±0.78	4.02±0.95
D-二聚体（μg/L）	212.00（103.50，395.50）	152.00（86.50，277.80）	181.00（115.00，608.00）
C-反应蛋白（mg/L）	17.30（5.60，33.45）	5.75（2.20，17.58）	14.30（4.83，36.18）
LDH（U/L）	222.00（179.00，284.00）	210.00（166.25，246.25）	207.00（175.25，296.00）
ALB（g/L）	41.23±6.03	41.67±3.93	40.53±5.66
NLR	3.21（2.20，4.39）	2.73（1.63，4.09）	3.27（2.23，4.91）
PLR	170.35（131.32，229.47）	149.16（100.39，225.99）	169.79（122.19，236.74）
LMR	2.43（1.79，3.20）	2.41（1.93，3.60）	2.33（1.63，3.03）

因素	HR值	95%CI	P值	因素	HR值	95%CI	P值
年龄	2.09	1.17~3.74	0.013	血小板	1.00	1.00~1.00	0.640
性别	1.01	0.49~2.11	0.974	血红蛋白	1.00	0.97~1.02	0.701
ECOG评分	1.67	0.49~5.73	0.414	嗜酸性粒细胞	0.63	0.19~2.08	0.446
吸烟史	1.16	0.64~2.09	0.629	纤维蛋白原	1.55	0.97~2.46	0.064
病理类型	0.90	0.51~1.60	0.715	D-二聚体	1.00	1.00~1.00	0.002
临床分期	1.02	0.56~1.87	0.951	C-反应蛋白	1.01	0.99~1.02	0.485
放疗史	0.64	0.36~1.13	0.123	LDH	1.01	1.00~1.01	0.006
治疗方案	1.51	0.78~2.90	0.222	ALB	0.93	0.85~1.03	0.151
治疗线数	2.23	1.21~4.12	0.010	NLR	1.07	0.95~1.21	0.281
基线期				PLR	1.00	1.00~1.01	0.354
中性粒细胞	0.98	0.92~1.05	0.576	LMR	0.73	0.50~1.06	0.096
血小板	1.00	1.00~1.00	0.289	疾病进展期
血红蛋白	0.99	0.97~1.00	0.090	中性粒细胞	1.15	0.97~1.37	0.106
嗜酸性粒细胞	1.06	0.75~1.48	0.754	血小板	1.00	1.00~1.00	0.900
纤维蛋白原	1.31	0.91~1.89	0.150	血红蛋白	0.97	0.96~0.99	<0.001
D-二聚体	1.00	1.00~1.00	0.421	嗜酸性粒细胞	1.52	0.78~2.95	0.221
C-反应蛋白	1.00	1.00~1.01	0.101	纤维蛋白原	1.28	0.96~1.72	0.094
LDH	1.00	1.00~1.00	0.138	D-二聚体	1.00	1.00~1.00	0.002
ALB	0.99	0.92~1.06	0.735	C-反应蛋白	1.01	1.00~1.01	0.011
NLR	1.05	0.97~1.13	0.228	LDH	1.00	1.00~1.01	0.055
PLR	1.00	1.00~1.00	0.699	ALB	0.91	0.87~0.96	0.001
LMR	0.75	0.57~0.97	0.028	NLR	1.16	1.05~1.27	0.002
最佳缓解期				PLR	1.00	1.00~1.01	0.052
中性粒细胞	0.98	0.93~1.04	0.555	LMR	0.62	0.42~0.90	0.012

因素	β值	SE值	Wald值	HR值	95%CI	P值
年龄	-0.09	0.03	8.10	0.91	0.86~0.97	0.004
最佳缓解期
纤维蛋白原	-0.03	0.38	0.00	0.98	0.47~2.04	0.947
LDH	0.00	0.01	0.24	1.00	0.99~1.01	0.626
疾病进展期
血红蛋白	0.00	0.01	0.02	1.00	0.97~1.03	0.891
D-二聚体	0.00	0.00	0.09	1.00	1.00~1.00	0.763
C-反应蛋白	-0.01	0.01	1.23	1.00	0.97~1.01	0.268
LDH	0.01	0.00	6.11	1.01	1.00~1.01	0.013
ALB	-0.20	1.00	4.20	0.82	0.67~0.99	0.041
LMR	-0.22	0.35	0.40	0.80	0.40~1.60	0.528