Predictive Value of NLR and PLR in Driver-Gene-Negative Advanced Non-Small Cell Lung Cancer Treated with PD-1/PD-L1 Inhibitors: A Single Institutional Cohort Study

Abstract

Objective

To investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for the efficacy and prognosis of programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors in driver-gene-negative advanced non-small-cell lung cancer (NSCLC).

Methods

A retrospective analysis of 107 advanced NSCLC patients without gene mutations who received PD-1/PD-L1 inhibitors in our hospital from January 2020 to June 2022 was performed. NLR and PLR were collected before PD-1/PD-L1 inhibitors, the optimal cut-off values of NLR and PLR were determined according to the receiver operating characteristic (ROC) curve, and the effects of NLR and PLR on the efficacy of PD-1/PD-L1 inhibitors in advanced NSCLC patients were analyzed.

Results

A total of 107 patients were included in this study. Receiver operating characteristic analysis showed that the optimal cut-off values of NLR and PLR were 3.825, 179, respectively. Kaplan–Meier curve showed that low baseline levels NLR and PLR were associated with an improvement in both progression-free survival (PFS) (P < .001, < .001, respectively) and overall survival (OS) (P = .009, .006, respectively). In first-line treatment and non-first-line treatment, low baseline levels NLR and PLR were associated with an improvement in PFS. In multivariate analysis, low baseline NLR and PLR showed a strong association with both better PFS (P = .011, .027, respectively) and longer OS (P = .042, .039, respectively).

Conclusion

Low baseline NLR and PLR levels are significantly associated with better response in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors, which may be indicators to predict the efficacy of immunotherapy in advanced NSCLC with driver-gene-negative.

Keywords

driver-gene-negative non-small-cell lung cancer PD-1/PD-L1 inhibitors predictive NLR PLR

Introduction

Lung cancer is a major global health problem and one of the most common and deadly types of cancer worldwide, accounting for approximately 1.8 million deaths annually.¹ Non-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases and many NSCLC patients are diagnosed with distant metastases, resulting in a 5-year survival rate of less than 19%.² With the advancement of medical technology and the improvement of awareness in physical examination, significant progress has been made in the diagnosis and treatment of lung cancer. For patients with driver-negative-genes advanced NSCLC, chemotherapy has always been the main treatment. In the past few years, with the development of immunology and molecular biology, the approval of immune checkpoint inhibitors (ICIs) provides a critical and effective treatment for these patients,³ specifically targeting the programmed cell death-1 (PD-1) and its ligand programmed cell death-ligand 1 (PD-L1). ICIs monotherapy or combination therapy has become the first-line standard treatment for advanced NSCLC patients with negative driver genes. Numerous studies have shown that compared to chemotherapy, immunotherapy can significantly improve survival.^4,5 Despite the remarkable success of ICIs, there is still a considerable proportion of NSCLC patients who are ineffective, even hyperprogression and severe side effects occurred.⁶ Therefore, there is urgent to identify biomarkers that can accurately predict the efficacy of ICIs. PD-L1, tumor mutational burden (TMB), and microsatellite instability-high (MSI-H) were used to screen patients who would potentially benefit in NSCLC. However, they are not ideal markers because they are expensive, time-consuming, and not easy to obtain for many patients. Therefore, it is crucial to explore biomarkers clinically convenient and practically noninvasive.

Inflammation is an important component of the tumor microenvironment and related to the occurrence and development of malignant tumors, and promoting the proliferation and metastasis of cancer cells.⁷ Inflammation can also affect tumor development and treatment efficacy.⁸ Inflammatory parameters in the circulating blood can reflect the body's immune inflammatory state and promotes its subsequent development.⁹ Multiple studies have shown that tumor-related inflammation plays a critical role in predicting immunotherapy efficacy and can serve as prognostic indicators in various cancers.^10–14 The prognostic value of several peripheral blood parameters, includes the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index and lactate dehydrogenase has been investigated. In recent years, some studies have proven that high NLR and PLR could predict poor survival in advanced NSCLC,^15–19 esophageal squamous cell carcinoma,²⁰ renal cancer,²¹ as well as melanoma²² patients treated with immunotherapy. However, the relationship between prognosis and inflammatory biomarkers in driver-gene-negative advanced NSCLC patients treated with immunotherapy is unclear. This retrospective study comprehensively analyzed associations between inflammation-related peripheral blood markers (NLR and PLR) and outcomes in driver-gene-negative advanced NSCLC patients treated with PD-1/PD-L1 inhibitors.

Materials and Methods

Subjects

Clinical data of advanced NSCLC patients who received PD-1/PD-L1 inhibitors in Nanjing Chest Hospital from January 2020 to June 2022 were collected. Inclusion criteria included age over 18 years; pathological confirmation of NSCLC; clinical stage IIIB or IV; no history of other malignant tumors; completion of at least 4 cycles of PD-1/PD-L1 inhibitors. Exclusion criteria: with gene mutations (include EGFR, ALK, ROS1, RET, NTRK, MET, BRAF etc); hematological diseases; acute or chronic inflammation; autoimmune disease; long-term use of hormone medications; recent blood transfusion; pulmonary interstitial disease; systemic immunosuppression.

Data extraction was collected by two investigators, including name, gender, age, smoking, performance status (PS) score, pathological type, TNM staging, type of PD-1/PD-L1 inhibitors, treatment line, immunotherapy and immune-related adverse events (irAEs). Baseline blood counts were collected from patients 1 week before the first use of immunotherapy, including absolute neutrophil count (NE), absolute lymphocyte count (LY), and platelet count (PLT). The definitions of NLR = NE/LY, PLR = PLT/LY.

The study was approved by the Ethics Committee of Affiliated Nanjing Chest Hospital, Nanjing Medical University (NJXK202037), Nanjing, China. In accordance with national legislation and institutional requirements, written informed consent from participants was not required for this study.

Efficacy Evaluation

Efficacy was performed every 4–8 weeks through CT or MRI scans. According to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1), short-term outcomes were categorized as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Objective response rate (ORR) was defined as the sum of CR and PR, while disease control rate (DCR) included CR, PR and SD. Progression-free survival (PFS) was defined as the time from the treatment start to disease progression or death (event). Overall survival (OS) was defined as the beginning of immunotherapy to death from any cause. The last follow-up was conducted in August 2022.

Statistical Analysis

SPSS 26.0 software was used for the statistical analysis. The counting data were represented by the number of cases (percentage) [n (%)], and the measurement indicators between the two groups were compared by independent sample t test. Receiver operating characteristic (ROC) curve was plotted, the diagnostic value of NLR and PLR was evaluated by calculating the area under the curve (AUC). The optimal cutoff value of NLR and PLR was determined. COX risk regression analysis was performed to find independent indicators associated with PFS and OS. Factors with statistically significant differences in univariate analysis were included in multivariate analysis. The hazard risk (HR) ratio and its corresponding 95% confidence interval (CI) are calculated. Kaplan–Meier method was used to analyze PFS and OS survival curves and Log rank tests were used to assess differences. P < .05 was considered statistically significant.

Result

Clinical Characteristics of Patients

A total of 107 patients who received PD-1/PD-L1 inhibitors were included in this study. Baseline characteristics of the patients are summarized in Table 1. The median age was 66 years old (range 46-85 years). The majority of patients were male (84/107, 78.5%). The proportion of current/former smokers 70.1%. Patients had mostly a PS score of 0 to 1 (95.3%), only 5 patients had a PS score of 2 or more. In total, 17 patients (15.9%) had stage III disease, while 90 patients (84.1%) had stage IV disease. Pathological subtypes included squamous cell carcinoma in 46 cases (43.0%), and other histological subtypes including adenocarcinoma, mixed and unspecified types (57.0%). 50 patients accepted PD-1/PD-L1 inhibitors as first-line treatment, other patients were treated with PD-1/PD-L1 inhibitors in second or later-line. Most patients were administered combination therapy (91.6%), only 9 patients received monotherapy. In total, 90 patients received PD-1 inhibitors, including sintilimab, tislelizumab, camrelizumab, pembrolizumab, and nivolumab, while 17 patients received PD-L1 inhibitors, including durvalumab, atezolizumab, and avelumab. A total of 22 patients (20.6%) experienced irAEs during immunotherapy, mainly including immune pneumonitis, skin rash, bone marrow suppression, hypothyroidism, and hyperthyroidism. Baseline characteristics between NLR and PLR were not significantly different (Table 2).

Table 1.

Clinical Characteristics of Patients

Characteristics	N	%
Age (years)
≥65	58	54.2%
＜65	49	45.8%
Sex
Male	84	78.5%
Female	23	21.5%
Smoking history
Ever/current	75	71.0%
Never	32	29.9%
ECOG PS
0-1	102	95.3%
≥2	5	4.7%
TNM stage
IIIB	17	15.9%
IV	90	84.1%
Histological type
Squamous cell	46	43.0%
Non-squamous cell	61	57.0%
Type of immunotherapy
Anti-PD-1	90	93.1%
Anti-PD-L1	17	93.1%
Treatment line
First line	50	46.7%
≥Second	57	53.3%
Immunotherapy
Monotherapy	9	8.4%
Combination therapy	98	91.6%
irAEs
Yes	23	21.5%
No	84	78.5%

Abbreviations: PD-L1, programmed cell death-ligand 1; PD-1, programmed cell death-1; irAEs, immune-related adverse events; PS, performance status

Table 2.

Associations Between NLR and PLR and Baseline Characteristics

Characteristics	NLR			PLR
	<3.825, n	≥3.825, n	P value	<179, n	≥179, n	P value
Age (years)
<65	36	13	.21	33	16	.45
≥65	36	22		35	23
Sex
Male	55	29	.44	52	32	.50
Female	17	6		16	7
Smoking history
Ever/current	50	25	.83	48	27	.88
Never	22	10		20	12
ECOG PS
0–1	68	34	.54	64	38	.43
≥2	4	1		4	1
TNM stage
IIIB	13	4	.38	13	4	.23
IV	59	31		55	35
Histological type
Squamous cell	31	15	.98	31	15	.47
Non-squamous cell	41	20		37	24
Type of immunotherapy
Anti-PD-1	61	29	.80	56	34	.51
Anti-PD-L1	11	6		12	5
Treatment line
First line	36	14	.33	35	15	.19
≥Second	36	21		33	24
Immunotherapy
Monotherapy	8	1	.15	6	3	.84
Combination therapy	64	34		62	36
irAEs
Yes	17	6	.45	13	10	.43
No	55	29		55	29

Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PD-L1, programmed cell death-ligand 1; PD-1, programmed cell death-1; irAEs, immune-related adverse events; PS, performance status.

Optimal cut-off Value for ROC Curve

ROC curves were plotted based on the survival status of the patients for NLR and PLR. As shown in Figure 1, the AUC for NLR was 0.740, the AUC of PLR was 0.661, indicating that NLR and PLR were statistically significant in predicting patient efficacy. The optimal cut-off value for NLR was 3.825(sensitivity: 0.667, specificity: 0.771), and for PLR was 179(sensitivity: 0.625, specificity: 0.664).

Figure 1.

The ROC curve for NLR and PLR. Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic curve.

Response

According to the optimal cutoff value of NLR, the patients were divided into the low NLR group (NLR < 3.825) and high NLR group (NLR ≥ 3.825), with 72 and 35 patients, respectively. In the low NLR group, there were no cases of CR, 19 cases of PR, 41 cases of SD, and 12 cases of PD. In the high NLR group, 1 case of CR, 8 cases of PR, 10 cases of SD, and 16 cases of PD. The DCR were 83.3% and 54.3%, respectively, there was significantly different (χ2 = 10.285, P = .001), but the ORR was not significantly different between the two groups (χ2 = 3.541, P = .06). For PLR, 68 patients were classified into the low PLR group (PLR<179), and 39 were classified into the high group (PLR ≥ 179). In both PLR groups, none of the cases occurred CR, 18 and 9 cases of PR, 37 and 14 cases of SD, 12 and 16 cases of PD, respectively. The DCR was 82.4% and 59.0%, the ORR was 27.9% and 23.1%. The DCR in the high PLR group was significantly lower than that in the low PLR group (χ² = 7.011, P = .008), the ORR was not (χ2 = 3.641, P = .06) (Table 3).

Table 3.

Analysis of DCR and ORR of Patients with Different NLR and PLR Levels

	ORR	P	DCR	P
NLR < 3.825	19(26.4%)	.06	60(83.3%)	.001
NLR ≥ 3.825	9(25.7%)		19(54.3%)
PLR < 179	19(27.9%)	.06	56(82.3%)	.008
PLR ≥ 179	9(23.1%)		23(59.0%)

Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ORR, objective response rate; DCR, disease control rate.

Survival Outcomes

For the population overall, median PFS and OS were 7.0 months (95% CI: 5.7-8.3) (Figure 2A) and 12.0 months (95% CI: 9.6-14.4) (Figure 2B), respectively. According to NLR, patients with low NLR were significantly associated with improved PFS and OS compared to subjects with high NLR. Median PFS was 9.0 months (95% CI: 8.19-9.83) versus 4.0 months (95% CI: 2.43-5.57) (P < .001). Median OS was 13.0 months (95% CI: 10.34-15.66) versus 8.0 months (95% CI: 5.05-10.95) (P = .009) (Figure 3A, B). For PLR, the high PLR group had a significantly worse median OS (10.0 months, 95% CI: 4.95-15.05) and median PFS (4.0 months, 95% CI: 2.34-5.66) compared with the low NLR group (median OS: 14.0 months 95% CI: 10.95-17.05 P < .001, median PFS: 9 months 95% CI: 8.03-9.97, P = .006) (Figure 4A, B).

Figure 2.

Kaplan–Meier survival analysis for PFS (A) and OS (B) in the study population. Abbreviations: OS, overall survival; PFS, progression-free survival.

Figure 3.

Kaplan-Meier survival analysis for PFS and OS. (A) Kaplan-Meier survival curves for PFS in patients grouped by NLR. (B) Kaplan-Meier survival curves for OS in patients grouped by NLR. Abbreviations: NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival.

Figure 4.

Kaplan-Meier survival analysis for PFS and OS. (A) Kaplan-Meier survival curves for PFS in patients grouped by PLR. (B) Kaplan-Meier survival curves for OS in patients grouped by PLR. Abbreviations: PLR, platelet-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival.

The subgroup analysis was conducted according to the treatment line. In first-line treatment, low NLR and PLR were significantly associated with improved PFS compared to high NLR (P = .03) and PLR(P = .01) (Figure 5A, B). Low PLR had significantly better OS(P = .011), and there was no better OS for low NLR (P = .053) (Figure 5C, D). In non-first-line treatment, low NLR had significantly better OS(P = .002) and PFS (P = .021) than high NLR patients (Figure 5E, F). PFS(P = .001) is also significantly better in the low PLR than high PLR, low PLR had no significantly favorable OS (P = .095) (Figure 5G, H).

Figure 5.

Kaplan–Meier survival curves of stratified baseline characteristics. In first-line treatment: PFS (A) and OS (B) for patients stratified by NLR; PFS and OS (D) for patients stratified by PLR. In non-first-line treatment: PFS (E) and OS (F) for patients stratified by NLR; PFS (G) and (H) OS for patients stratified by PLR. Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival.

Univariate analysis showed that NLR level (P = .007) and PLR level (P = .034) were significantly associated with PFS. Multivariate analysis further indicated that NLR level (P = .011) and PLR (P = .025) level were prognostic factors for PFS (Table 4). Univariate analysis confirmed TNM stage (P = .019), treatment line (P = .008), NLR level (P = .018), and PLR (P = .047) level associated with OS, further multivariate analysis showed that TNM stage (P = .046), NLR level (P = .042) and PLR (P = .039) level were the potential predictive factors on OS (Table 5).

Table 4.

Univariate and Multivariate Analysis for PFS

Factors	Univariate analysis		Multivariate analysis
	HR(95%CI)	P	HR(95%CI)	P
Gender Male versus Female	1.112(0.493–2.509)	0.798
Age <65 versus ≥65	1.345(0.771–2.347)	0.296
Smoking history Ever/Current versus Never	1.865(0.890–3.909)	0.099
ECOG PS 0–1vs ≥ 2	1.471(0.383–5.652)	0.574
TNM stage IIIB versus IV	2.288(0.932–5.618)	0.071
Histological type Squamous versus Nonsquamous	0.984(0.558–1.735)	0.956
Type of immunotherapy Anti-PD-1 versus Anti-PD-L1	1.218(0.570–2.605)	0.611
Treatment line First line versus ≥ Second	0.646(0.376–1.111)	0.114
Immunotherapy protocols Monotherapy versus Combination	0.822(0.250–2.703)	0.747
irAEs Yes versus No	0.786(0.419–1.474)	0.453
NLR NLR＜3.825 versus NLR＜3.825	2.388(1.263–4.517)	0.007	2.131(1.191–3.813)	0.011
PLR PLR＜179 versus PLR ≥ 179	1.995(1.055–3.773)	0.034	1.907(1.074–3.838)	0.027

Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PFS, progression-free survival; PD-L1, programmed cell death-ligand 1; PD-1, programmed cell death-1; irAEs, immune-related adverse events; PS, performance status.

Table 5.

Univariate and Multivariate Analysis for OS

Factors	Univariate analysis		Multivariate analysis
	HR(95%CI)	P	HR(95%CI)	P
Gender Male versus female	1.149(0.383–3.443)	.804
Age <65 versus ≥65	1.149(0.383–3.443)	.804
Smoking history Ever/current versus never	2.021(0.733–5.572)	.174
ECOG PS 0–1vs ≥ 2	1.301(0.229–7.372)	.767
TNM stage IIIB versus IV	3.850(1.250–11.856)	.019	2.681(1.017–7.068)	.046
Histological type Squamous versus Nonsquamous	1.209(0.576–2.541)	.616
Type of immunotherapy Anti-PD-1 versus Anti-PD-L1	0.731(0.247–2.165)	.571
Treatment line First line versus ≥ Second	0.367(0.176–0.766)	.008
Immunotherapy protocols Monotherapy versus combination	1.947(0.446–8.492)	.375
irAEs Yes versus No	0.776(0.351–1.717)	.531
NLR NLR＜3.825 versus NLR＜3.825	2.767(1.187–6.452)	.018	2.293(1.031–5.100)	.042
PLR PLR＜179 versus PLR ≥ 179	2.278(1.012–5.127)	.047	2.270(1.041–4.947)	.039

Abbreviations: NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; OS, overall survival; PD-L1, programmed cell death-ligand 1; PD-1, programmed cell death-1; irAEs, immune-related adverse events; PS, performance status.

Discussion

Immunotherapy has become an important treatment strategy for cancer patients. ICIs have been widely applied in a variety of malignant tumors, and achieve unprecedented results in terms of prognosis.²³ A large number of studies and clinical trials have demonstrated the efficacy of ICIs in the treatment of lung cancer, especially in driver-gene-negative advanced NSCLC.²⁴ However, many patients still have a low response to immunotherapy. In addition, a series of serious adverse reactions triggered by immune response limits the clinical application of ICIs. PD-L1 was often used to screen patients who would potentially benefit from NSCLC. Several studies have shown that PD-L1 expression is closely related to the efficacy of immunotherapy.^25,26 However, there are also some studies that have failed to observe these associations, particularly in trials of immunotherapy combined with chemotherapy.^27–30

Pathological type, TMB can also effectively predict the efficacy of immunotherapy. However, the detection methods of these markers are complex and can only reflect the characteristics of a fixed time period, and cannot be dynamically predicted, so it is particularly important to predict the efficacy of immunotherapy through routine laboratory test indicators. Inflammation plays an important role in the occurrence and development of tumors, and inflammatory markers can be used to judge the prognosis of tumors.³¹ Compared with current biomarkers such as PD-L1, TMB and MSI, peripheral blood biomarkers have the advantages of convenience, inexpensive, and reproducibility.

The enumeration of neutrophils, lymphocytes, and platelets in the peripheral blood can serve as indicators for evaluating the immune and inflammatory status of the body.⁹ Neutrophils are the main participants in inflammation and cancer, and the high ratio of neutrophils to lymphocytes has become a prognostic indicator for poor OS in cancer patients.³² Cancer is considered an incurable chronic wound. In the process of tumor occurrence, vascular leakage of inflammation provides an opportunity for platelet invasion into the tumor. Platelet mitosis stimulates tumor growth, which leads to angiogenesis and tumor cells entering the bloodstream. The circulating tumor cells rebind with additional platelets, promoting tumor cell metastasis.³³ Immune status is a key factor in cancer development and progression. The difference between tumor cells and healthy cells lies in the presence of tumor antigens. Tumor antigens provide immune stimulation, and lymphocytes play an important role in inducing cell apoptosis, inhibiting tumor cell proliferation and migration, and playing a crucial role in immune therapy for malignant tumors.^7,34,35 Malignant tumor patients with low levels of lymphocyte counts have a poor clinical prognosis.³⁶ Numerous studies have elucidated that inflammatory markers have similar prognostic value in patients with NSCLC,¹⁹ renal cancer,²¹ and melanoma patients²² received immunotherapy. Previous researches suggested that NLR and PLR level are potential prognostic predictor in NSCLC patients received ICIs.^18,37–39 However, the efficacy of inflammatory markers on patients with driver-gene-negative advanced NSCLC treated with PD-1/PD-L1 inhibitors is not comprehensively assess.

This study retrospectively analyzed the assessment of the baseline NLR and PLR predicted survival of patients treated with PD-1/PD-L1 inhibitors in driver-gene-negative advanced NSCLC independently. According to short-term outcomes, our results indicated that the high NLR and PLR levels had a significantly lower DCR than the low NLR and PLR group which suggests that inflammatory markers may be associated with the short-term efficacy of immunotherapy. Patients with low NLR and PLR had significantly favorable OS and PFS compared with patients with high NLR and PLR. The result is consistent with previous studies showing that high NLR and PLR associated with a poor prognosis in patients lung cancer.^40–43 Liu et al⁴⁴ performed a meta-analysis of 34 studies involving 3124 patients, they reported that high NLR and PLR in non-small-cell lung carcinoma patients treated with ICIs are associated with low survival rates, low lymphocyte to monocyte ratio before and after treatment was also associated with poor prognosis. Zhang et al⁴⁵ conducted a meta-analysis of 1845 NSCLC patients receiving ICIs treatment to evaluate the impact of NLR and PLR on survival, and found that higher NLR level was associated with poor survival prognosis, and similarly, patients with a higher PLR level had poorer survival. Some studies have explored the prognostic role of NLR and PLR changes at different time points in advanced NSCLC patients treated with ICIs. Xia et al⁴⁶ found that NLR < 3 at week 6 was associated with higher ORR, and NLR < 3 at week 12 was associated with higher ORR and DCR. Patients in the NLR ≥ 3 group had a higher risk of disease progression at 6 weeks or 12 weeks (P = .001, .028, respectively), and a higher risk of death at 0, 6, and 12 weeks. However, this study did not find the value of PLR in predicting immunotherapy, and there was no significant difference between baseline NLR and PFS, which is different from the results of this study. Khunger et al⁴⁷ found that after 2 cycles of nivolumab treatment, the OS of patients with NLR ≥ 5 was worse than that of patients with NLR < 5 (the median OS was 24.2 months and 29.1 months, respectively, P < .001), and compared with those who responded to treatment, NLR in nonresponders increased by 6.6 ± 21.8 (P = .027). These studies demonstrated that patients with higher baseline NLR and PLR tend to have shorter OS and PFS, and the increase of NLR and PLR during treatment may predict poor prognosis.

Univariate cox analysis showed that baseline NLR and PLR were associated with PFS. Treatment line, TNM stage, baseline NLR and PLR were associated with OS. Multivariate cox analysis indicated that baseline NLR and PLR were independent prognostic indicators for both PFS and OS. In clinical practice, immunotherapy combined with chemotherapy has become the standard treatment in driver-gene-negative advanced NSCLC. First-line immunotherapy is more effective than non-first-line treatment, and immunotherapy should be used as soon as possible.⁴⁸ After stratification according to the number of treatment lines, we further found that patients with NLR ≤ 3.825 had significantly favorable PFS compared with patients with NLR > 3.825, the PFS and OS of patients with PLR ≤ 179 were significantly better than those of PLR > 179 in the group of first-line treatment. Among the non-first-line treatment patients, the patients with NLR ≤ 3.825 had favorable OS and PFS compared with patients with NLR > 3.825, and the OS of patients with PLR ≤ 179 was significantly better than that of PLR > 179. This may be related to myelosuppression after second and multiple lines of therapy.

The emergence of ICIs has changed the treatment strategy of advanced lung cancer, creating a new milestone in antitumor therapy. However, the overall effect of immunotherapy is slow, the response rate is low, and there are many immune adverse reactions during the treatment. Biomarkers for predicting efficacy may help to screen suitable patients for treated with ICIs. At present, tumor-related biomarkers, such as PD-L1, TMB, MSI-H/MMR, are commonly used to predict the efficacy of immunotherapy, but these indicators are difficult to obtain and expensive, and it is difficult to large-scale test in clinical practice. In addition, the prediction of immunotherapy response by a single marker has certain risks, and the prediction and evaluation of efficacy are not accurate. Assessment of baseline NLR and PLR may provide meaningful information for the selection of suitable patients for treatment with PD-1/PD-L1 inhibitors. On the one hand, patients with higher baseline NLR and PLR tend to have shorter OS and PFS, DCR showed a similar correlation. On the other hand, the increase of NLR and PLR during treatment also predicts poor prognosis. NLR and PLR may be used as new clinical indicators for evaluating the efficacy of immunotherapy.

There were several limitations should be noted in this study. Firstly, the patients were enrolled at a single center with relatively small sample size, so selection biases may have been present, the prognostic value of NLR needs to be further verified by expanding the number of cases. Secondly, the levels of inflammatory markers may be affected by other factors, especially changes in hematologic parameters caused by chemotherapy and require patients receiving chemotherapy alone as a control group. However, we were unable to recruit a sufficient number of patients because chemotherapy only are rare in clinical practice. Thirdly, all the patients did not receive a uniform treatment when they received PD-1/PD-L1 inhibitors, the baseline level of NLR and PLR may be affected by different treatments before immunotherapy. Finally, comprehensive multivariable and subgroup analysis were not performed due small cohort size. Therefore, the results need to be further verified by multicenter and large sample trials to better guide clinical practice.

Conclusions

In conclusion, the results of our study suggest that the baseline levels of peripheral blood NLR and PLR are potential biomarkers in driver-gene-negative advanced NSCLC treated with PD-1/PD-L1 inhibitors, which are easy to obtain, cost-effective, and minimally invasive in clinical practice.

Footnotes

Abbreviations

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

The study was approved by the Ethics Committee of Affiliated Nanjing Chest Hospital, Nanjing Medical University (NJXK202037), Nanjing, China.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Natural Science Foundation Youth Science Foundation Project (grant number No. 81700068).

ORCID iD

Qian Zhang

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