Immature Granulocytes and Nivolumab Outcomes in Stage IV Non-Small Cell Lung Cancer: Insights by Tumor Subtype

Abstract

Background:

In non-small cell lung cancer (NSCLC) treatment, immunotherapy has become the standard therapy when platinum-based chemotherapy is ineffective, in the absence of a targetable mutation. However, a significant proportion of patients do not benefit from this treatment, underscoring the critical need for predictive biomarkers. This study aims to investigate the potential predictive role of immature granulocytes in response to nivolumab treatment, which can be used as a second-line therapy independent of programmed death ligand 1 (PDL-1) expression and other markers. Furthermore, the study seeks to determine whether there is a difference in the treatment response of immature granulocytes between the 2 main subtypes of NSCLC: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD).

Methods:

This retrospective study enrolled 50 patients with NSCLC who underwent treatment at the Kütahya Health Sciences University Evliya Çelebi Education and Research Hospital and Kütahya City Hospital between January 2021 and January 2025. The study examined the difference between patients’ baseline immature granulocyte levels and their initial response to treatment, as assessed by positron emission tomography-computed tomography.

Results:

The study found a statistically significant association between higher baseline immature granulocyte levels and poorer treatment response. Subgroup analysis by lung cancer subtype revealed that the difference was more prominent in the LUSCs group.

Conclusion:

Immature granulocytes may predict response to nivolumab treatment in NSCLC patients, particularly in the LUSCs subgroup. Based on the findings of this study, immature granulocytes and other neutrophil-dependent inflammatory markers could serve as potential predictors of immunotherapy response and provide insights into the mechanisms of immunotherapy resistance, warranting further investigation. Our study may also encourage future research to look for separate markers for LUSCs and LUADs, given the continued critical need for predictive markers in this field.

Keywords

Nivolumab immature granulocyte lung cancer squamous cell cancer adenocarcinoma

Introduction

Lung cancer is the leading cause of cancer-related mortality in both males and females.¹ Lung cancers are typically stratified into small-cell-lung-cancer (SCLC) and non-small-cell-lung-cancer (NSCLC) subtypes.^2,3 NSCLCs comprise approximately 85% of all lung cancer cases.^2,4 The 2 major subtypes of NSCLC are lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).⁵ However, recent evidence suggests that LUADs and LUSCs should be considered and managed as distinct cancer types.⁶

The programmed cell death 1 (PD-1) receptor, which is expressed on activated T-cells, interacts with ligands PD-L1 and PD-L2 that are expressed by tumor cells and infiltrating immune cells.⁷ Elevated tumor PD-L1 expression is common in NSCLC, and this PD-1/PD-L1 and PD-L2 interaction inhibits T-cell activation and promotes tumor immune evasion.⁸ Nivolumab is a fully human IgG4 monoclonal antibody against PD-1 that disrupts this PD-1-mediated signaling and restores antitumor immune responses.^7,9

In locally advanced or metastatic NSCLC patients who progressed after initial chemotherapy, nivolumab treatment has been found to be superior to docetaxel in both LUADs and LUSCs.^10,11 Unfortunately, while long-term remission can be achieved in a subset of responding patients in these 2 studies, the response rate remains below 20%.^10,11 Therefore, identifying predictive factors for nivolumab response is of great importance. The Keynote-001 study found that high levels of PD-L1 expression on tumor cells, as measured by immunohistochemistry, were associated with strong responses to the anti-PD-1 drug pembrolizumab.¹² However, PD-L1 expression is not an ideal predictor of response to nivolumab in NSCLC patients who have failed chemotherapy. These patients receive nivolumab regardless of their PD-L1 expression level.^10,11

Nivolumab therapy can be ineffective if there are immune-suppressive factors present in the tumor microenvironment that disrupt an otherwise functional immune response.¹³ In cancer, an elevated presence of infiltrating innate immune cells, such as neutrophils, is associated with increased angiogenesis and/or less favorable clinical outcomes, whereas an abundance of infiltrating lymphocytes correlates with more favorable prognosis.¹⁴ Emerging evidence indicates that neutrophils, as key components of the tumor microenvironment, promote tumor progression and diminish the effectiveness of immunotherapy by fostering an immunosuppressive tumor microenvironment.^15-17 Moreover, neutrophils also undermine the efficacy of immunotherapy by modulating the adaptive immune system.^18,19

It is well-established that neutrophils comprise a dominant component of the immune landscape in NSCLC.²⁰ Neutrophils exhibit substantial functional diversity due to their inherent plasticity and/or alterations in granulopoiesis.²¹ Among them, immature granulocytes (IG) exhibit altered functional capabilities compared with their mature counterparts, which may contribute to cancer progression.²² IG are developing granulocytes that are prematurely released from the bone marrow in response to infection and inflammation.²³ These IG can be substantially elevated in the peripheral blood of cancer patients, originating from their premature release from the bone marrow hematopoietic niche. This is due to increased systemic chemokines produced by the tumor, or even induced by cancer therapies.²⁴ In addition, the expansion of these immature granulocytes can induce the suppression of T-cell-mediated immune responses, thereby promoting an immunosuppressive tumor microenvironment.²⁵ Therefore, an elevated level of these circulating IG, which can also be characterized as myeloid-derived suppressor cells (MDSCs), may help elucidate the immunological basis for the early lack of response to immunotherapy.^26,27

The aim of our study was to investigate whether IG could predict the response to nivolumab therapy in NSCLC, and to explore any differences in IG levels and the ability of IG to predict nivolumab treatment response between the LUSC and LUAD subtypes.

Methods

Study design and participants

The study included 50 patients diagnosed with stage IV NSCLC between January 2021 and October 2024, who received treatment at Kütahya Health Sciences University Evliya Çelebi Education and Research Hospital and Kütahya City Hospital. These patients were followed until the final data cutoff on January 15, 2025. All data were retrospectively retrieved from hospital records after obtaining ethical approval. No prospective data collection or patient contact was conducted beyond the scope defined in the approved protocol. The required sample size was calculated using GPower* version 3.1.9.4. Assuming a 2-tailed independent samples t test with a 5% type I error rate (α = .05), a medium-to-large Cohen’s effect size (d = 0.50), and a power of 95% (1 − β = 0.95), the minimum total sample size was estimated to be 45 participants. All patients were in stage IV disease and had progressed after initial platinum-based doublet chemotherapy. Nivolumab was administered at a dose of 3 mg/kg every 14 days.

This study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting.²⁸

The inclusion criteria were (1) being at least 18 years old; (2) having received regular medical care and follow-up visits; (3) having undergone positron emission tomography-computed tomography (PET-CT) imaging studies that were performed and reported at the hospitals where the study was conducted; (4) having a baseline PET-CT scan prior to receiving nivolumab; (5) having continued treatment until a PET-CT scan was performed for response evaluation; and (6) having a pathologically confirmed diagnosis of NSCLC, which could be further classified as either squamous or adenocarcinoma.

The exclusion criteria were (1) presence of symptomatic brain metastases; (2) having EGFR, ALK, or ROS1 mutations; (3) presence of uncontrolled autoimmune disease; (4) having received more than 1 prior line of chemotherapy; (5) patients with pathological diagnoses that could not be classified or were determined to be large cell lung cancer; (6) presence of any known infection (7) presence of any chronic inflammatory conditions.

Laboratory assessment

At our hospital, immature granulocyte count (IG#) and immature granulocyte percentage (IG%) were routinely measured as part of the complete blood count panel. IG# and IG% were analyzed using a Mindray BC-6000 analyzer with a Focusing flow-direct current detection methodology. This method involves the 3-dimensional analysis of target cells through their interaction with reagents, generating dual-angle laser scatter signals and fluorescence, a process known as the “SF Cube.” The 3-dimensional scatter diagrams created using information on cell size, complexity, and DNA/RNA content allow clinical experts to identify and differentiate cell populations.

Pathological assessment

All patients’ pathology samples were evaluated by the pathology department of our hospital. Cases that could be differentiated immunohistochemically as squamous cell carcinoma and adenocarcinoma were included. There was no requirement for PDL-1 testing. If EGFR, ALK, or ROS1 testing was performed, a negative status was required.

Radiological assessment

The patients’ treatment responses were evaluated using PET-CT. Three nuclear medicine specialists at our institution categorized the patients’ treatment responses into 4 groups: progressive disease, stable disease, partial response, and complete response. Treatment response was assessed using The PET Response Criteria in Solid Tumors 1.0 (PERCIST 1.0) criteria. Complete response was defined as the disappearance of dynamic 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG) uptake in all lesions, partial response as a ⩾30% decrease and >0.8 unit decrease in the peak standardized uptake value corrected for lean body mass (SULpeak), stable disease as changes that did not meet the criteria for partial response or progressive disease, and progressive disease as a ⩾30% increase and >0.8 unit increase in SULpeak or the appearance of new FDG-avid lesions.

Data analysis

Statistical analyses were performed using “IBM SPSS Statistics for Windows, Version 25.0.” Descriptive statistics were presented, with categorical variables shown as frequency and percentage, and continuous variables as mean ± standard deviation and median. The study data were assessed for normality, and nonparametric statistical tests were preferred. Kruskal-Wallis test was used to compare independent treatment response groups, and Bonferroni-adjusted Mann-Whitney U test was employed for pairwise comparisons. Categorical variables were compared using the chi-square test. A P value of less than 0.05 was considered statistically significant.

Results

Descriptive statistics, including the mean and median values for patients’ age, height, weight, and IG# and IG%, are presented in Table 1.

Table 1.

Descriptive statistics of sociodemographic and clinical variables for patients (N = 50).

Variables	Mean ± SD	Median (IQR)
Age (years)	61.64 ± 9.76	62.00 (13.75)
Height (cm)	166.86 ± 8.24	168.00 (15.00)
Weight (kg)	67.40 ± 12.17	67.50 (20.00)
IG# (×10³/µL)	0.12 ± 0.15	0.02 (0.19)
IG% (%)	1.32 ± 1.51	0.40 (2.10)

IG# = Immature granulocyte count; IG% = Immature granulocyte percentage; IQR = Interquartile range; M = Mean; SD = Standard deviation. Sample size: N = 50.

The treatment responses of the patients are shown in Figure 1.

Figure 1.

Treatment response outcomes among the study cohort.

The study included 50 patients, who were categorized into 4 groups based on their treatment response: progressive disease, stable disease, partial response, and complete response. The initial IG# and IG% at the start of treatment were assessed in these groups. The progressive disease group had an IG# of 0.17 ± 0.17, the stable disease group had an IG# of 0.15 ± 0.10, the partial response group had an IG# of 0.02 ± 0.03, and the complete response group had an IG# of 0.01 ± 0.00. The analysis revealed a statistically significant decrease in IG# as the treatment response improved (P = 0.003). The progressive disease group had an IG% of 1.73 ± 1.71, the stable disease group had an IG% of 2.15 ± 1.40. the partial response group had an IG% of 0.38 ± 0.30, and the complete response group had an IG% of 0.30 ± 0.06. The analysis revealed a statistically significant decrease in IG% as the treatment response improved (P = 0.020). The results mentioned are presented in Table 2.

Table 2.

Comparison of patient variables by treatment response groups (N = 50).

Variables	Treatment response	Mean ± SD	95% CI of mean	Effect size (r)	P value
Initial IG# at Treatment Start	Progressive disease	0.17 ± 0.17	[0.10-0.24]
	Stable disease	0.15 ± 0.10	[0.11-0.19]	0.2429	0.003
	Partial response	0.02 ± 0.03	[0.01-0.04]
	Complete response	0.01 ± 0.00	[0.01-0.01]
Initial IG% at Treatment Start	Progressive disease	1.73 ± 1.71	[0.91-2.55]
	Stable disease	2.15 ± 1.40	[1.52-2.78]	0.1486	0.020
	Partial response	0.38 ± 0.30	[0.23-0.53]
	Complete response	0.30 ± 0.06	[0.25-0.35]

Values are expressed as mean ± standard deviation (SD) with corresponding 95% confidence intervals (CI). Initial IG# = Immature granulocyte count at treatment start; Initial IG% = Immature granulocyte percentage at treatment start. The overall comparison across treatment response groups (Progressive Disease, Stable Disease, Partial Response, and Complete Response) was conducted using the Kruskal-Wallis test. Pairwise comparisons were performed with Bonferroni-adjusted Mann-Whitney U tests, and effect sizes (r) were calculated from the U statistics. Sample size: N = 50.

Statistical significance was set at P < 0.05.

Of the 50 patients, 22 had a pathological subtype of LUAD and 28 had LUSC. In the LUAD group, the IG# was 0.88 ± 0.15 in the progressive disease group, 0.15 ± 0.12 in the stable disease group, 0.02 ± 0.04 in the partial response group, and 0.01 ± 0.01 in the complete response group. No statistically significant differences were found in the IG# values across these subgroups (P = 0.284). The IG% was 0.67 ± 0.84 in the progressive disease group, 2.32 ± 1.50 in the stable disease group, 0.38 ± 0.40 in the partial response group, and 0.33 ± 0.05 in the complete response group. No statistically significant differences were observed in the IG% values between the groups (P = 0.074). The results are presented in Table 3.

Table 3.

Comparing variables in LUADs by treatment response (N = 22).

Variables	Treatment response	Mean ± SD	95% CI of mean	Effect size (r)	P value
Initial IG# at Treatment Start	Progressive disease	0.88 ± 0.15	[0.81-0.95]
	Stable disease	0.15 ± 0.12	[0.10-0.20]	0.0446	0.284
	Partial response	0.02 ± 0.04	[0.00-0.04]
	Complete response	0.01 ± 0.01	[0.01-0.01]
Initial IG% at Treatment Start	Progressive disease	0.67 ± 0.84	[0.30-1.04]
	Stable disease	2.32 ± 1.50	[1.65-2.99]	0.2186	0.074
	Partial response	0.38 ± 0.40	[0.20-0.56]
	Complete response	0.33 ± 0.05	[0.31-0.35]

Data are presented as mean ± standard deviation (SD) with 95% confidence intervals (CI). Initial IG# = Immature granulocyte count at treatment start; Initial IG% = Immature granulocyte percentage at treatment start. Treatment response groups include Progressive Disease, Stable Disease, Partial Response, and Complete Response. Group comparisons were performed using the Kruskal-Wallis test, followed by Bonferroni-adjusted Mann-Whitney U tests for pairwise comparisons. Effect sizes (r) were derived from U statistics. CI values are indicative and should be calculated from raw data unless otherwise specified. Sample size: N = 22.

Statistical significance was defined as P < 0 .05.

In the LUSC group, the IG% was 0.23 ± 0.17 in the progressive disease group, 0.14 ± 0.11 in the stable disease group, 0.02 ± 0.03 in the partial response group, and 0.01 ± 0.00 in the complete response group. Statistically significant differences were found in the IG# values among the treatment response groups (P = 0.004). The IG% was 2.40 ± 1.80 in the progressive disease group, 1.93 ± 1.55 in the stable disease group, 0.39 ± 0.22 in the partial response group, and 0.26 ± 0.05 in the complete response group. Statistically significant differences were found in the IG% values among the treatment response groups (P = 0.009). The results are presented in Table 4.

Table 4.

Comparing variables in LUSCs by treatment response (N = 28).

Variables	Treatment response	Mean ± SD	95% CI of mean	Effect size (r)	P value
Initial IG# at Treatment Start	Progressive disease	0.23 ± 0.17	[0.17-0.29]
	Stable disease	0.14 ± 0.11	[0.10-0.18]	0.4195	0.004
	Partial response	0.02 ± 0.03	[0.01-0.03]
	Complete response	0.01 ± 0.00	[0.01-0.01]
Initial IG% at Treatment Start	Progressive disease	2.40 ± 1.80	[1.81-2.99]
	Stable disease	1.93 ± 1.55	[1.42-2.44]	0.36	0.009
	Partial response	0.39 ± 0.22	[0.32-0.46]
	Complete response	0.26 ± 0.05	[0.24-0.28]

Data are presented as mean ± standard deviation (SD) with 95% confidence intervals (CI). Initial IG# = Immature granulocyte count at treatment start; Initial IG% = Immature granulocyte percentage at treatment start. Treatment response groups include Progressive Disease, Stable Disease, Partial Response, and Complete Response. Group comparisons were conducted using the Kruskal-Wallis test, and where applicable, Bonferroni-adjusted Mann-Whitney U tests were used for pairwise comparisons. Effect sizes (r) were calculated from Mann-Whitney U statistics. CI values are approximate unless computed directly from raw data. Sample size: N = 28.

Significance threshold was set at P < 0.05.

Figure 2 illustrates the comparison of IG# and IG% levels across treatment response groups in the overall patient cohort, as well as in the LUAD and LUSC subgroups.

Figure 2.

Comparison of immature granulocyte count (IG#) and percentage (IG%) levels across treatment response groups in all patients, lung adenocarcinoma (LUAD) subgroup, and lung squamous cell carcinoma (LUSC) subgroup.

Discussion

In our study, 11 patients (22.0%) achieved a partial response, and 6 patients (12.0%) achieved a complete response. The literature review reveals that in one study, second-line nivolumab treatment resulted in a partial response rate of 5.4% and a complete response rate of 1.4%.²⁹ Another study reported a partial response rate of 19% and a complete response rate of 1%.¹⁰ In addition, a third study found a partial response rate of 18% and a complete response rate of 1% with nivolumab.¹¹ Our study found relatively higher partial and complete response rates compared with other reported findings. Combining CT with FDG PET is expected to enhance accuracy and enable earlier identification of progressive disease, as changes in cellular metabolism precede alterations in tumor size.³⁰ Furthermore, post-treatment response assessment with FDG PET/CT was more effective at predicting complete response compared with CT alone. In our study, all patients underwent response evaluation using PET-CT, whereas previous studies primarily utilized CT for this purpose.³¹ The relatively higher complete response rate observed in our cohort may be associated with the routine use of FDG PET-CT for response assessment.

Our study identified a significant relationship between the treatment response of the enrolled patients and their IG levels, as assessed by both the IG# and IG%. To our knowledge, this is the first study to suggest that IG may have predictive value in relation to nivolumab treatment for second-line NSCLC, in an area with unmet need for predictive biomarkers. Neutrophils demonstrate a dichotomous function within the tumor environment, exhibiting both tumor-suppressive and tumor-promoting properties. While neutrophils act as defenders of the immune system and exert inhibitory effects on tumors, the tumor microenvironment can stimulate the release of immature neutrophil subtypes that paradoxically enhance tumor progression.³² Neutrophils can play diverse roles in different cancer types. While tumor-associated neutrophils are generally known to have a detrimental impact in cancer, research has demonstrated that they may potentially exert a beneficial role in colorectal cancer.^33-36 Furthermore, the role of neutrophils in cancer can vary depending on the tumor stage. According to one study, in the early N0 stage, neutrophil infiltration is associated with improved patient survival. Conversely, in the later N1-3 stages, a high presence of neutrophils is indicative of a decrease in patient survival rate.³⁷ In addition, neutrophils are known to have negative effects on cancer progression as well as the direct effectiveness of immunotherapy.³⁸ Given the known ability of neutrophil phenotypes to exert diverse effects across different cancer types and stages, the findings of our study suggest that an increase in IG may have a detrimental impact in the context of second-line treatment for metastatic NSCLC treated with nivolumab.

The neutrophil compartment is a diverse population, comprising a key subgroup of IG that may confer a potentially deleterious impact on cancer progression.^20,24 Moreover, these IG could also undermine the efficacy of immunotherapy.³⁹ It is known that the expansion of IG can induce the suppression of T-cell-mediated immune responses, thereby promoting an immunosuppressive environment.²⁵ This circumstance can have an impact on cancer progression, and it becomes especially crucial when immunotherapy is the preferred treatment option for these patients. A study has observed that an elevated neutrophil-to-lymphocyte ratio negatively impacts the response to immunotherapy, while the same effect was not observed in chemotherapy patients.³⁹ This finding suggests that the detrimental influence of IG on the treatment response of immunotherapy patients may be more prominent than their general poor prognostic effect, highlighting the specific negative impact of IG on the efficacy of immunotherapy.

In our study, we examined the impact of IG# and IG% values, which are routinely measured as part of the hemogram parameters in our hospital’s laboratory, on treatment outcomes. Significant associations were found with both parameters. The statistical significance level for IG# was higher than that of IG%, suggesting that using the IG# value may be more valuable in guiding treatment decisions.

The study cohort comprised 22 patients with LUAD and 28 with LUSC; patients outside these 2 histological subgroups were excluded from the analysis. When examining the association between IG and treatment response by histological subtype, a statistically significant difference was observed in the LUSC subgroup, while no such difference was detected in the LUAD subtype. It is well-established that lung LUAD and LUSC, which were previously considered a single entity of NSCLC and shared similar treatment approaches, are now recognized as distinct disease entities. The tumor immune microenvironment suggests that LUAD and LUSC exhibit significant differences in their immune landscape.⁴⁰ This understanding of the disparities in the immune microenvironment may indicate a heterogeneous response to immunotherapy. Various microenvironmental factors differentially induce distinct immune subtypes in LUAD and LUSC, as well as influencing immune checkpoint expression.⁴¹ For instance, tumor-associated macrophages play a key role in the immune landscape of LUSC, while regulatory B cells exert an immunosuppressive function in LUAD. Furthermore, the complexity of the NSCLC immune landscape arises from factors such as molecular subtype, oncogenic drivers, nonsynonymous mutational load, tumor aneuploidy, clonal heterogeneity, and tumor evolution.⁴² In light of the results of our study, it can be surmised that the neutrophil pathway, particularly the involvement of IG, may play a significant role in the response to immunotherapy in LUSC.

Limitations

Although our study’s power analysis indicated that 45 patients would be sufficient, a sample size of 50 may appear small for such a common disease. Furthermore, subgroup analyses based on histology would reduce the statistical power due to the smaller number of patients in each subgroup. Conducting larger prospective investigations involving more patients could enable us to reach more definitive conclusions regarding treatment decisions. Given the potential for IG to exert both poor prognostic effects and specific negative impacts on immunotherapy response, designing a comparative study with a control chemotherapy arm could yield more definitive conclusions. Additional biomarkers, such as PD-L1 expression, inflammatory indicators like C-reactive protein, and infection markers like procalcitonin, could have been measured, and clinical parameters such as performance score, smoking history, and comorbidities could also have been evaluated by performing multivariate analysis during data collection for our study to provide further insights. The retrospective nature and single-center setting of our study inevitably introduce potential biases stemming from retrospective data collection and hospital-specific PET-CT assessment protocols. Although our study demonstrated that elevated levels of IG could predict an unfavorable response to nivolumab treatment, we were unable to determine a clear cut-off value from our data that could reliably guide clinical decision-making. Therefore, future prospective studies specifically designed for this purpose will be necessary to establish the appropriate thresholds that may influence clinical management.

Conclusion

Although nivolumab treatment has demonstrated long-term survival benefits for responsive patients, the overall response rates remain low. Therefore, identifying predictive biomarkers to guide patient selection for this therapy remains a crucial challenge. Our study suggests that a simple laboratory test, namely the measurement of IG, may serve as a potential predictor of treatment response. Notably, the significant association between IG and treatment outcomes was observed specifically in the LUSC subtype, which highlights the importance of understanding the distinct differences between LUAD and LUSC. However, at present, there is no clear mechanism to explain this difference between IG in LUAD versus LUSC. Future prospective studies with a larger number of patients will clarify this clinical issue and enhance our understanding of potential mechanisms, helping us to analyze the LUAD-LUSC difference and guide future clinical practices.

Footnotes

Acknowledgements

Not applicable.

ORCID iD

Mustafa Ersoy

Ethics committee approval

This study received approval from the Non-Interventional Research Ethics Committee of Kütahya Health Sciences University, numbered E-41997688-050.99-120141 and dated December 19, 2023. All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki.

Informed consent

Given the retrospective design of this study, written informed consent was not obtained from patients, as the Kütahya Health Sciences University Ethics Committee indicated that it was not required in such cases.

Author contributions

ME: Study conception, data collection, designed data analysis, wrote first draft of the manuscript, approved final version to be published.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Availability of data and material

All data from the study have been stored in a database and can be obtained from Dr. Mustafa Ersoy upon request.

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