Neutrophil-to-lymphocyte ratio is a prognostic factor reflecting immune condition of tumor microenvironment in squamous cell lung cancer

Inflammatory factors in the peripheral blood, such as the C-reactive protein level and neutrophil-to-lymphocyte ratio (NLR), are prognostic markers in multiple types of cancer, including non-small cell lung cancer (NSCLC). However, the association between inflammatory factors and prognosis based on histological types has not been adequately reported. In addition, the relationship between these factors and the immune condition of the tumor microenvironment (TME) is unclear. In this single center, retrospective study, we first investigated the relationship between preoperative inflammatory markers and clinical outcomes in 176 patients with NSCLC who underwent surgery. Lung adenocarcinoma (LUAD) showed no significant prognostic marker, whereas for lung squamous cell carcinoma (LUSC), a multivariate analysis showed that a high NLR was significantly associated with postoperative recurrence. In LUSC patients, the median time of postoperative recurrence-free survival in patients with a low NLR was longer than that in patients with a high NLR. We then compared the tumor-infiltrating lymphocyte (TIL) profile with inflammatory markers in peripheral blood and found that the NLR was negatively correlated with the frequencies of T cells and B cells in LUSC tissues. Thus, the NLR is a useful predictive biomarker for postoperative recurrence and may reflect the immune condition of the TME in LUSC.


Analysis of prognostic value for patient outcome
Univariate analysis of clinicopathological factors, inflammatory markers, and peripheral blood cell types showed that pathological stage, PLR, WBC and platelet counts, and neutrophil (%), lymphocyte (%), and monocyte (%) levels were correlated with RFS in NSCLC patients (Table 3).In patients with LUAD, only the pathological stage was correlated with RFS.In patients with LUSC, pathological stage, NLR, WBC count, neutrophil count, and neutrophil (%), lymphocyte (%), monocyte (%), and basophil levels (%) were correlated with RFS.Smoking status in patients with LUSC was indicated as not available (NA) because the number of nonsmokers was too small to be suitable for statistical analysis.Similarly, in Tables 4 and 5, the values for which the number of events was small and could not be analyzed are also indicated as NA.
In univariate analysis of the prognostic value for OS, we found that there was no factor correlated with OS in NSCLC and LUAD, and only the basophil (%) were correlated with OS in patients with LUSC.
In LUAD patients, multivariate analysis for OS was not performed because of the small number of patients who died, and no factor was correlated with OS in patients with LUSC (Table 5B).
We then examined the cutoff NLR value to assess its clinical performance.The ROC curve showed that the best NLR cut-off value was 4.787 (Fig. 1).For this cutoff value, the sensitivity was 38.9%, specificity was 92.9%, and the area under the curve (AUC) was 0.634.The cutoff value for NLR was set at 4.8 as an approximation, and 51 (85%) LUSC patients were categorized into the high NLR group (NLR ≥ 4.8), while the remaining 9 (15%)   www.nature.com/scientificreports/LUSC patients were stratified into the low NLR group (NLR < 4.8).LUSC patients with a low NLR had a longer RFS than those with a high NLR (NA vs. 8 months, p < 0.001) (Fig. 2A).Similarly, patients with LUSC with a low NLR had better OS than those with a high NLR (NA vs. 23 months, p = 0.015) (Fig. 2B).www.nature.com/scientificreports/

Association between NLR and the TIL profile in lung cancer
To examine whether NLR reflects the immune condition of the TME, we evaluated the correlations between NLR and the TIL profile.To analyze the TIL profile, we employed a flow cytometry (FCM) panel of 26 markers to identify 13 unique immune cell types and functional subpopulations from 140 NSCLC samples (82 LUAD samples, 49 LUSC samples, and 9 other histological subtype samples).Flow cytometry showed that NLR was negatively correlated with the frequencies of T cells/CD45 + cells (r = − 0.374, p = 0.008) and B cells/CD45 + cells (r = − 0.287, p = 0.046) in the tumor tissues of LUSC patients (Fig. 3).NLR showed a negative correlation with the frequency of CD8 + T cells/CD45 + cells (r = − 0.265, p = 0.066) and a positive correlation propensity with macrophages/CD45 + cells (r = 0.259, p = 0.072) (Table 6).No obvious correlation was found between other TIL immune cells and NLR in patients with LUSC (Table 6).NLR showed a positive correlation with non-Tregs (Fr.III)/CD45 + cells (r = 0.364, p < 0.001) in patients with LUAD 10 .www.nature.com/scientificreports/ reported to predict the efficacy of immune checkpoint therapy in NSCLC patients [42][43][44] .Since TIL compositions influence the efficacy of ICBs, this study may contribute to understanding the reason why NLR is a prognosis predictor for ICBs.This study had several limitations: the sample size was not large, the observation period was not long, and the relationship with the efficacy of ICBs was not examined.More research is needed to confirm the usefulness of the NLR as a prognostic factor and ICB-effect predictor in a large cohort.To understand why prognostic factors in the peripheral blood differ between LUAD and LUSC, the differences in the TME between LUAD and LUSC and the relationship between peripheral blood factors and the TME require elucidation.Furthermore, there are also possible limitations regarding the statistical analysis.For the NLR cutoff, the ROC curve does not account for time factors.It may have been more desirable to perform the analysis using survival-ROC analysis 45 .
In conclusion, a high NLR was significantly associated with poor prognosis in LUSC but not in LUAD patients, reflecting the frequencies of T and B cells in the TME.

Patients
This retrospective study enrolled 176 patients with pathological stage I-III primary NSCLC who underwent surgery at the National Cancer Center Hospital (Tokyo, Japan) between October 2016 and June 2019.Patients with metastatic or pathological stage IV disease were excluded.This study protocol was approved by the National Cancer Center Ethics Committee (2016-124, dated: August 5th, 2016).All patients provided written informed consent before sampling.The study also abided by the principles of the Declaration of Helsinki.
Adjuvant therapies and neoadjuvant therapies were examined by multidisciplinary discussions for each individual patient based on pathologic findings, patient performance status, age, comorbidity and patient's intension.

Clinical follow-up
Recurrence-free survival (RFS) was defined as the interval between surgery and disease progression or death, whichever occurred first.Patients without any of these events were censored at the final follow-up, without documented progression.Overall survival (OS) was defined as the time from surgery to death from any cause.Patients without any of these events were censored at the final follow-up visit.The median follow-up period for all patients was 24 months (range, 1-56 months).The median follow-up period for the surviving patients was 28 months (range, 1-56 months).

Blood inflammatory markers
Blood samples were collected the day before surgery.The NLR was defined as the number of neutrophils divided by the number of lymphocytes.LMR was defined as the lymphocyte count divided by the monocyte count, and PLR was defined as the platelet count divided by the lymphocyte count.

Flow cytometry
Viable cells from tumor suspensions were counted and incubated with Fixable Viability Dye eFluor™ 506 (Bio-Legend, San Diego, CA, USA) for 30 min at 4 °C for dead cell staining, followed by FcR blocking using the FcR blocking reagent (Miltenyi Biotech) for 10 min at 4 °C.The cells were then stained with the fluorescently labeled antibodies listed in Table S1 at 4 °C for 30 min.Next, intracellular staining was performed with intracellular antibodies and a Foxp3/Transcription Factor Staining Buffer set (Thermo Fisher Scientific) according to the manufacturer's instructions.
After washing, the cells were analyzed using an LSR Symphony instrument (BD Biosciences, Franklin Lakes, NJ, USA).At least 50,000 live events were collected per sample (BD LSR II cytometer).Data were analyzed using the FlowJo software (BD Biosciences).The gating of each sample was based on plots of SSC-Height versus SSC-Width and FSC-Height versus FSC-Width to eliminate aggregates.FVD staining was used to identify and eliminate dead cells as evaluated using contour plots.A propidium iodide overlay was used to validate cell viability in the training set.
The 13 immune cells are defined as shown in Supplementary Table 2.The frequency of each immune cell in the TILs was calculated as the number of transformed cells divided by the number of CD45 + cells.

Statistical analysis
All statistical analyses were conducted using R version 4.0.1 (R Foundation for Statistical Computing, Vienna, Austria).Statistical significance was set at p < 0.05.Cox proportional hazards regression models were used to evaluate the prognostic factors in univariate and multivariate analyses.The p value was adjusted by the False Discovery Rate with 5% significant level.Cutoff values were calculated using receiver operating characteristic (ROC) curves for factors considered significant prognostic markers.RFS and OS curves were analyzed using the Kaplan-Meier method, and statistical differences were determined using the log-rank test.Correlations between immune cells in TILs and inflammatory markers in peripheral blood were calculated using Spearman's rank correlation coefficient test.

Figure 3 .
Figure 3. Correlation between the NLR in preoperative blood tests and the TIL status in the TEM in patients with LUSC.(A) Correlation of the NLR value with T cells/CD45-positive lymphocytes in TILs.(B) Correlation of the NLR value with B cells/CD45-positive lymphocytes in TILs.

Table 1 .
Clinicopathological characteristics of 176 patients with non-small cell lung cancer (NSCLC).

Table 2 .
Comparison of clinicopathological characteristics between 101 patients with lung adenocarcinoma (LUAD) and 60 patients with lung squamous cell carcinoma (LUSC).

Table 3 .
Univariate analysis of clinical characteristics on recurrent free survival (RFS).

Table 4 .
Univariate analysis of clinical characteristics on overall survival (OS).

Table 5 .
Multivariate analysis of clinical characteristics on RFS and OS.