Literature DB >> 30510441

The prognostic impact of neutrophil to lymphocyte ratio in advanced non-small cell lung cancer patients treated with EGFR TKI.

Thang Thanh Phan1, Toan Trong Ho1, Hue Thi Nguyen1, Hang Thuy Nguyen2, Thu Bich Tran3, Son Truong Nguyen1.   

Abstract

PURPOSE: To identify and clarify the roles of inflammatory markers in prognosis for advanced non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitor (TKI). PATIENTS AND METHODS: One hundred and twelve adenocarcinoma, clinical stage IV, NSCLC patients with either EGFR exon 19 deletion (E19del) or EGFR exon 21 L858R substitution mutation (L858R) were selected for this study. The blood cell count at different stages of treatment was used to calculate the inflammatory markers. The Kaplan-Meier statistics and Cox regression model were used to test the differences of progression-free survival (PFS) between groups by the optimal cutoff point of biomarkers.
RESULTS: The median values of white blood cell (WBC), neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) and platelet to lymphocyte ratio (PLR) in NSCLC patients tended to be reduced after 3 months treated with EGFR TKI and increased conversely when the disease develops progression (P<0.001). With an optimal cutoff point of 2.96, NLR is the best prognostic marker in prediction of clinical response among the investigated markers (area under the curve [AUC]=0.873, 95% CI: 0.821-0.926, P<0.001), and it is an independent predictive marker (OR=3.52, 95% CI: 1.42-8.71, P<0.001). With optimal cutoff point of 0.38, MLR is also a predictive marker in response evaluation (AUC=0.762, 95% CI: 0.691-0.832). Univariate analyses have shown that the larger tumor size (>3cm) and the high level of pretreatment NLR were associated with the shortening of PFS (HR=2.24, 95% CI: 1.04-4.83, P=0.039 and HR=2.67, 95% CI: 1.41-5.03, P=0.006, respectively). Multivariate analysis has shown that the elevated NLR is an independent prognostic marker for worse PFS of NSCLC patients treated with EGFR TKI (HR=2.15, 95% CI: 1.15-3.99, P=0.016).
CONCLUSION: NLR and MLR are valuable markers in response evaluation for NSCLC patients treated with EGFR TKI. The elevated NLR is also an independent prognostic factor for worse survival.

Entities:  

Keywords:  EGFR TKI; NLR; NSCLC; neutrophil to lymphocyte ratio

Year:  2018        PMID: 30510441      PMCID: PMC6250106          DOI: 10.2147/IJGM.S174605

Source DB:  PubMed          Journal:  Int J Gen Med        ISSN: 1178-7074


Introduction

Lung cancer is a leading cause of death from cancer worldwide, mostly non-small cell lung cancer (NSCLC).1 This cancer develops silently and has no specific symptoms. Almost all patients were diagnosed in advanced stages (IIIB and IV). Treatment by EGFR tyrosine kinase inhibitor (TKI) helps to enhance the response rate, prolong the progression-free survival (PFS) and overall survival (OS) for NSCLC patients.2 In order to monitor the treatment for patients, several biomarkers in serum such as cyfra 21–1 (cytokeratin 19), CA12-5 (cancer antigen 125) or NSE (neuron specific enolase) are widely used but with limited sensitivity and specificity.3 However, computerized tomography-scanner (CT), positron emission tomography-computed tomography and MRI are also frequently used diagnostic tests, but they are expensive and carry potential risk from radioactive rays. Many studies have shown the roles of systematic inflammation markers which help to monitor the response to chemotherapy, radiotherapy or surgery and the prognosis for NSCLC patients.4–12 The number of granulocytes and platelets (PLTs) are increased in the peripheral blood of patients that make an increase of neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) or platelet to lymphocyte ratio (PLR). These ratios are indirect markers, which indicate tumor cell activity, and can be considered the basis of above evaluations.4–12 A few studies mention the role of these markers on targeted treatment and prognosis for NSCLC patients but with contradictory results.13–18 The results of Lin et al,13 Ding et al14 and Meriggi et al15 have shown that the high level of pretreatment NLR was seen to be an independent prognostic factor for poor outcome (both PFS and OS) in EGFR-mutated NSCLC patients treated with first-line EGFR TKI. Conversely, the results of Minami et al’s study have shown that the elevated NLR is an independent prognostic factor for OS, but not for PFS in NSCLC.16 Other studies with two groups correspond to two treatment methods, Sim et al concluded that the pretreatment NLR was a significant prognostic factor for the chemotherapy group but not for the EGFR TKI therapy group.18 In prognosis of PFS for NSCLC patients treated with EGFR TKI using lymphocyte to monocyte ratio (LMR) marker, Chen et al17 demonstrated that the high LMR is an independent predictive factor for a longer PFS, which is completely different to the results of the Minami et al16 study. On that basis, our study aimed to identify and clarify the roles of inflammatory markers such as white blood cell (WBC), PLT, NLR, MLR and PLR in predicting response to EGFR TKI and prognosis for NSCLC patients.

Patients and methods

Patients and parameters

The study was conducted on 112 NSCLC patients with either EGFR E19del or L858R mutation who were treated with EGFR TKI (standard dose of erlotinib or gefitinib) from December 2015 to March 2018 at Cho Ray hospital. The NSCLC patients were under a treatment evaluation based on the RECIST v1.1 criteria.19 The number of WBC, neutrophil (NEU), lymphocyte (LYM), monocyte (MONO) and PLT of NSCLC patients at different stages, before treatment, after 3 months of treatment with EGFR TKI, and at progressive disease (PD) stage were recalled from the laboratory database and the markers of NLR, MLR and PLR calculated. The blood cell analysis was performed on UniCel DxH 800 (Beckman Coulter, CA, USA). The marker values of 112 healthy adults (with age and gender similar to the patients) were also used for comparison with the marker values of NSCLC patients post-treatment to assess the reduction level of inflammatory markers in NSCLC patients after a course of treatment. Healthy disease-free adults, who have annual medical examination (AME), were chosen who have the class-I health certificate according to the AME criteria of Cho Ray hospital. The other parameters were retrieved retrospectively from medical records. This study was considered and approved by the Ethics Committees of Cho Ray hospital (approval number: 602/CN-HDDD). The authors were permitted to access the clinical records and laboratory database to select data for the study with the commitment of information confidentiality, therefore, patient consent was not required.

Statistical analysis

The Fisher’s exact test was used to compare the relative frequencies, whereas the Kruskal–Wallis rank test was used to compare the median value of each marker between groups. The logistic regression model was used to construct the receiver operating characteristic (ROC) curve and define the optimal cutoff point of each marker, together with sensitivity, specificity, and the value under the ROC curve (area under the curve [AUC]) in predicting the response to therapy. The median PFS based on subgroups of the inflammatory markers and clinical characteristics were estimated by the Kaplan– Meier statistics with 95% CI. The association of clinical outcome with levels of markers and clinical characteristics were tested by the Cox regression model and calculated the HR with 95% CI. All data analyses were performed on STATA 14.0 statistical software (StataCorp LP, College Station, TX, USA). P<0.05 was considered statistically significant.

Ethics statement

This study was considered and approved by the ethics committees of Cho Ray Hospital (approval number: 602/CN-HDDD). Patient consent was not required.

Results

Patient characteristics

A total of 112 adenocarcinoma, clinical stage IV, NSCLC patients with either EGFR E19del (77 cases) or L858R (35 cases) mutation, and 112 healthy adults with similar age and gender were selected for this study. Patients were categorized into four groups based on the level (below vs above cutoff value) of NLR (2.96) and MLR (0.38) at baseline: 29 patients with NLR<2.96 and MLR<0.38; 7 patients with NLR<2.96 and MLR≥0.38; 22 patients with NLR≥2.96 and MLR <0.38 and 54 patients with NLR ≥2.96 and MLR ≥0.38. The characteristics of patients were presented in Table 1.
Table 1

Patient characteristics

CharacteristicsTotalNLR<2.96
NLR≥2.96
P-value
MLR<0.38MLR≥0.38MLR<0.38MLR≥0.38

Number of patients1122972254
Age, years0.096a
 <5950144527
 ≥59621531727
Gender0.872a
 Female611441330
 Male51153924
ECOG PS0.183a
 0–1952771645
 2–3172069
Number of distant metastasis0.734a
 0–12371411
 ≥2892261843
Brain metastasis0.448a
 No812271537
 Yes3170717
Pleural effusion0.371a
 No802451437
 Yes3252817
Tumor size0.005a
 ≤3cm2513246
 >3cm871651848
<b>EGFR mutation type0.460a
 E19del772351336
 L858R3562918
EGFR TKI treatment0.489a
 First line892262041
 Second/third line2371213
EGFR TKI type0.140a
 Erlotinib972371750
 Gefitinib156054
Clinical response0.268a
 PR+SD591631525
 PD53134729
Laboratory data
 NEU, 109/L (95% CI)5.99 (5.45–7.10)4.80 (3.75–5.45)2.53 (1.65–4.77)7.81 (6.21–9.20)7.55 (5.97–9.31)<0.001b
 LYM, 109/L (95% CI)1.69 (1.45–1.95)2.19 (1.97–2.37)1.29 (0.86–2.28)1.96 (1.69–2.28)1.29 (1.08–1.53)<0.001b
 MONO, 109/L (95% CI)0.62 (0.56–0.69)0.57 (0.45–0.68)0.64 (0.38–0.89)0.57 (0.46–0.66)0.71 (0.60–0.76)0.025b

Notes:

Fisher’s exact test.

Kruskal–Wallis rank test.

Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PR, partial response; SD, stable disease; PD, progressive disease; NEU, neutrophil; LYM, lymphocyte; MONO, monocyte, TKI, tyrosine kinase inhibitor; ECOG PS, Eastern Cooperative Oncology Group performance status.

The median age of all patients was 59 (from 33 to 88 years old). Of 112 patients, 61 (54.5%) were female, and 51 male (45.5%). In overall assessment, 17 patients (15.2%) with severe symptoms were scored with the Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2 who are unable to carry out any work. Most of the patients have ≥2 distant metastasis sites (89/112, 79.5%), and tumor size >3 cm (87/112, 77.7%). The groups with NLR≥2.96 have the higher absolute count of NEU (P<0.001) and MONO (P=0.025), but have the lower absolute count of LYM (P<0.001) compared with the values of groups with NLR <2.96 (Table 1). Eighty-nine cases (79.5%) were treated with EGFR TKI as first-line treatment, whereas 23 cases (20.5%) were treated with EGFR TKI after surgery with or without radiotherapy, and/or chemotherapy. The EGFR TKI type used in treatment was erlotinib (Hoffman-La Roche Ltd, Basel, Switzerland) for 97 cases, with standard dose of 150 mg/day or gefitinib (AstraZeneca plc, London, UK) for 15 cases, with standard dose of 250 mg/day. Good response was achieved after 3 months of EGFR TKI treatment in all cases. After that, 53 cases (47.3%) developed PD, along with various PFS, ranging from 3.9 to 28.3 months (median was 9.0 months).

Median values of inflammatory index in NSCLC and control group

The median values of inflammatory indexes in NSCLC group at baseline, after 3 months of treatment and at the PD stage are shown in Table 2. The results demonstrated that after 3 months treatment with EGFR TKI, the WBC, NLR, MLR and PLR values of NSCLC patients were reduced considerably compared to the pretreatment values (P<0.001). To answer the question of the effective of EGFR TKI in recovering the inflammatory indexes, we compared these values in NSCLC at good response stage (after 3 months of therapy) with the values of the healthy control group. The analysis has shown that the median value of WBC in NSCLC patients did not differ in statistical significance from the value of control group (P=0.588). Meanwhile, the PLT, NLR, MLR and PLR values in NSCLC patients at good response stage were just slightly higher than the values of the control group (P=0.006, 0.001, 0.003 and 0.002, respectively) (Table 2).
Table 2

Inflammatory index values in NSCLC at different stages and in control group

ParameterNSCLC
Control (n=112)P-value
Baseline (n=112)After 3 months of treatment (n=112)Progressive disease (n=53)

WBC, 109/L9.47.19.87.1<0.001a
(95% CI)(8.6–10.3)(6.6–7.5)(8.4–11.2)(6.6–7.6)0.588b
PLT, 109/L289.9273.4271.7240.70.458a
(95% CI)(266.3–313.5)(255.7–291.2)(248.8–294.5)(230.6–250.8)0.006b
NLR4.902.025.511.72<0.001a
(95% CI)(4.11–5.68)(1.84–2.20)(4.16–6.85)(1.55–1.90)0.001b
MLR0.440.280.510.20<0.001a
(95% CI)(0.39–0.49)(0.26–0.31)(0.45–0.57)(0.18–0.22)0.003b
PLR213.9143.2210.3108.8<0.001a
(95% CI)(181.9–245.9)(131.3–155.1)(171.6–248.9)(101.9–115.7)0.002b

Notes:

At baseline vs posttreatment and progressive disease stage.

In NSCLC after treatment vs in control.

Abbreviations: NSCLC, non-small cell lung cancer; WBC, white blood cell; PLT, platelet; NLR, neutrophil to lymphocyte ratio; MLR; monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio.

In the other trend, we noted that the median values of inflammatory markers in NSCLC patients increased again when the disease became progressive (P<0.001), and even higher than the pretreatment values (Table 2). The number of PLT in NSCLC patients had not changed and remained statistically significant during the treatment process (P=0.458).

Inflammatory index in treatment monitoring for NSCLC patients

The logistic regression model was used to construct the ROC curve of each parameter to predict the clinical response. The sensitivity, specificity and the value under the ROC curve equivalent to the optimal cutoff point of each marker were extracted and shown in Table 3.
Table 3

Optimal cutoff point of each study marker, sensitivity, specificity and AUC values in predicting response to EGFR TKI

ParameterCutoffSensitivity, % (95% CI)Specificity,% (95% CI)AUC, % (95% CI)

WBC, 109/L9.048.278.40.699
(37.3–59.3)(69.2–86.0)(0.623–0.775)
PLT, 109/L324.032.977.50.548
(23.1–44.0)(68.1–85.1)(0.464–0.632)
NLR2.9671.890.20.873
(60.5–81.4)(82.6–95.5)(0.821–0.926)
MLR0.3854.186.30.762
(44.7–67.6)(77.5–92.4)(0.691–0.832)
PLR189.849.478.40.689
(38.4–60.5)(69.2–86.0)(0.613–0.766)

Abbreviations: AUC, area under the curve; WBC, white blood cell; PLT, platelet; NLR, neutrophil to lymphocyte ratio, MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; TKI, tyrosine kinase inhibitor.

The results shown that with the optimal cutoff point of 2.96, NLR is the best prognostic marker in predicting response to EGFR TKI among the investigated markers (AUC=0.873, P<0.001) (Figure 1). In addition, at this cutoff point, we can predict the clinical response with a sensitivity reached at 71.8% and a specificity reached up to 90.2% (Table 3). The results of multivariate analysis also shown that NLR is an independent predictive marker, with an OR of 3.52 (95% CI: 1.42–8.71) (P<0.001).
Figure 1

Comparison of area under the ROC curve of inflammatory markers in response evaluation.

Abbreviations: WBC, white blood cell; PLT, platelet; NLR, neutrophil to lymphocyte ratio, MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; ROC, receiver operating characteristic.

We also recorded that MLR marker is useful in prediction of clinical response with the accuracy of ~76.2% (AUC=0.762), equivalent to the optimal cutoff point of 0.38. Despites the high specificity (86.3%), the sensitivity of MLR in response evaluation was low (54.1%). Treatment monitoring by the WBC, PLR or PLT markers, the AUC values of these markers were low or even very low (0.699, 0.689 and 0.548, respectively).

Inflammatory index in prognosis for NSCLC patients

In 112 cases, 53 cases developed progression after a median time of 9.0 (95% CI: 7.6–10.0) months. The median PFS and HR based on subgroups of pretreatment inflammatory markers and clinical characteristics were estimated by the Kaplan–Meier and Cox regression methods (Table 4).
Table 4

Estimation of PFS and prognosis markers for NSCLC patients

ParametersPFS (95% CI)Univariate analysis
Multivariate analysis
HR (95% CI)P-valueHR (95% CI)P-value

Age, years0.96 (0.55–1.68)0.897
 <599.0 (7.5–11.3)
 ≥598.8 (7.7–13.0)
Gender1.13 (0.65–1.98)0.658
 Female8.8 (7.5–12.0)
 Male9.0 (7.9–11.3)
ECOG PS1.91 (0.84–4.33)0.121
 0–19.0 (8.0–11.3)
 2–37.9 (3.2–11.1)
Number of distant metastasis2.01 (0.79–5.10)0.142
 0–110.4 (5.8–12.1)
 ≥28.8 (7.9–11.0)
Brain metastasis1.56 (0.84–2.91)0.160
 No9.2 (7.9–12.0)
 Yes8.0 (6.2–11.0)
Pleural effusion1.55 (0.81–2.98)0.191
 No9.2 (8.0–11.3)
 Yes7.7 (3.1–12.2)
Tumor size2.24 (1.04–4.83)0.039
 ≤3 cm13.6 (5.8–23.3)
 >3 cm8.2 (7.5–10.4)
EGFR mutation type0.84 (0.49–1.46)0.546
 E19del9.0 (7.7–12.2)
 L858R8.0 (6.2–11.1)
EGFR TKI treatment0.96 (0.61–1.79)0.802
 First line8.8 (7.5–11.0)
 Second/third line10.4 (7.5–13.8)
EGFR TKI type0.43 (0.28–1.02)0.506
 Erlotinib8.2 (7.7–10.4)
 Gefitinib11.0 (5.5–23.3)
WBC, 109/L1.65 (0.92–2.96)0.325
 <9.09.3 (7.9–11.2)
 ≥9.08.0 (7.1–9.9)
PLT, 109/L0.92 (0.50–1.72)0.612
 <324.09.0 (7.9–10.0)
 ≥324.08.0 (6.9–14.0)
NLR2.67 (1.41–5.03)0.0062.15 (1.15–3.99)0.016
 <2.9611.1 (9.5–13.9)
 ≥2.967.7 (6.0–8.1)
MLR1.91 (1.07–3.41)0.034
 <0.3810.1 (9.0–13.0)
 ≥0.387.5 (5.9–8.4)
PLR1.09 (0.62–1.92)0.764
 <189.89.1 (8.0–10.4)
 ≥189.87.9 (5.4–10.7)

Abbreviations: PFS, progression-free survival; NSCLC, non-small cell lung cancer; WBC, white blood cell; PLT, platelet; NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; TKI, tyrosine kinase inhibitor; ECOG PS, Eastern Cooperative Oncology Group performance status.

The results of univariate analysis shown that the tumor size and NLR were two parameters that help to predict the clinical outcome for NSCLC patients treated with EGFR TKI (Figure 2). Of 53 resistant patients, 44 patients (83.0%) have the tumor size >3 cm. The median PFS of patients with tumor size >3 cm was 8.2 (95% CI: 7.5–10.4) months, whereas the value of patients with tumor size ≤3 cm was 13.6 (95% CI: 5.8–23.3) months. This difference was statistically significant (HR=2.24, 95% CI: 1.04–4.83) (P=0.039). The median PFS of patients with pretreatment NLR≥2.96 (n=36/53) was only around 7.7 (95% CI: 6.0–8.1) months, which was shorter than the value of the NLR<2.96 groups (PFS=11.1, 95% CI: 9.5–13.9 months), HR=2.67 (95% CI: 1.41–5.03) (P=0.006). Whereas the median PFS of patients with high level of pretreatment MLR (≥0.38) (n=32/53) was 7.5 (95% CI: 5.9–8.4) months, the median PFS in patients with low level of MRL was 10.1 (95% CI: 9.0–13.0) months, HR=1.91 (95% CI: 1.07–3.41) (P=0.034). However, the crossover between survival curves at the end of the Kaplan–Meier plot (Figure 2C) indicates that the difference of PFS between groups of the lower and higher MLR level might not be significant. We checked this with the proportional hazards assumption tests and confirmed that MLR is not a significant prognostic factor in this situation (the log–log plot shown the crossed lines while the observed values and predicted values are different; figures not shown). No differences of PFS between groups of age, gender, ECOG PS, number of distant metastasis, status of brain metastasis, pleural effusion, EGFR mutation type, EGFR TKI type, and the level of WBC, PLT and PLR were recorded.
Figure 2

Progression-free survival of NSCLC patients according to the tumor size (A), the pretreatment levels of NLR (B), and MLR (C).

Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; NSCLC, non-small cell lung cancer.

In multivariate analysis, we noted that the elevated NLR is an independent prognostic marker for worse PFS of EGFR-mutant NSCLC patients treated with EGFR TKI, HR=2.15 (95% CI: 1.15–3.99) (P=0.016) (Table 4).

Discussion

In the past 10 years (2009–2018), more than 20 studies have focused on evaluating the roles of inflammatory markers in NSCLC.12 Most of these studies have been performed on the patients treated with surgery, chemotherapy or radiotherapy. Relatively few studies have been conducted on patients treated with targeted therapies, especially with EGFR TKI.13–18 In these studies, the authors also show the different viewpoints about the role of inflammatory markers, especially NLR and lymphocyte to monocyte ratio in prognosis for NSCLC patients treated with EGFR TKI.13–18 Through the study on 112 NSCLC patients treated with EGFR TKI, we took note that the WBC, NLR, MLR and PLR values have been reduced significantly in patients with good response after 3 months compared with pretreatment. The results also show that at the good response stage, these markers in NSCLC patients are nearly equivalent to the same markers of healthy adults. Under the pressure of medicine for targeted therapy, such as erlotinib or gefitinib, hypersensitive tumor cells were annihilated, parallel with reducing the secretion of some cytokines such as granulocyte-macrophage colony stimulating factor, granulocyte colony stimulating factor, IL-1 or IL-6, thus the inflammatory markers tend to return to normal.20 We also found out the decrease of NLR and MLR helped to predict the ability of good response to EGFR TKI, in which NLR is an independent predictive marker. Moreover, together with an accuracy of over 87%, sensitivity reached at good level (71%) and high specificity (90%), we believe that these markers are very useful in response evaluation for NSCLC patients. Compared with other serum markers as cyfra 21–1, CA12-5, NSE or imaging diagnostic tools as PET-CT, CT-scanner or MRI, the use of inflammatory markers as NLR and MLR in treatment monitoring will ensure the safety of patients while maintaining low cost and being easy to implement. However, we assume that the use of these inflammatory indexes in combination with conventional serum biomarkers might help to increase the accuracy in prediction of clinical response. We have not selected enough data of conventional biomarkers for further analysis. Large-scale research should be conducted to clarify this hypothesis. In this study, we also found that the inflammatory markers tended to increase conversely when the disease develops progression, and even more higher compared to pretreatment values. The tumor cells that are resistant to EGFR TKI survived posttreatment; they will keep growing and return to develop strongly. The tumor will increase production of the differentiation markers of WBCs, attract, control and utilize neutrophil or monocyte in a manner benefitting the development.20 Based on this principle, the high level of inflammatory index is an indicator in predicting the risk of early resistance. Some studies show that having various prognostic models for NSCLC, patients such as the combination of NEU with MONO, NLR with prognostic nutritional index, NLR with PLR, and NLR with PL.10 We noted that MLR, PLT and PLR did not have significance in prognosis for NSCLC patients. Meanwhile, the larger tumor size (>3 cm) and the high level of pretreatment NLR have been closely associated with the shortening of PFS. In addition, we confirmed that NLR is an independent prognostic marker for NSCLC patients treated with EGFR TKI which is similar to the results of three previous studies.13–15 However, this is contrary to the results of Minami et al, and Sim et al.16,18 There are some differences between our study and these two studies. The patient age of our study was lower (59 vs 70 and 67 years, respectively), whereas the proportion of male patients was higher (45.5% vs 37.5% and 30.6%, respectively) than in these two studies. The lower proportion of severely ill patients (ECOG PS ≥2) was also observed in our study (15.2% vs 27% and 30.6%, respectively). These factors might affect the pretreatment NLR value, and thus affect the prognostic models that lead to the different results. Besides, the proportion of EGFR TKI as first-line treatment, the cutoff value, or even the blood cell count method might be the bias factors in prognostic models that should be considered in a large-scale study. In conclusion, the results of this study indicated that NLR and MLR are valuable inflammatory markers in response evaluation for NSCLC patients treated with EGFR TKI. Moreover, the elevated NLR is an independent prognostic marker for poor survival.
  19 in total

Review 1.  Molecular targeted therapy in the treatment of advanced stage non-small cell lung cancer (NSCLC).

Authors:  Nesaretnam Barr Kumarakulasinghe; Nico van Zanwijk; Ross A Soo
Journal:  Respirology       Date:  2015-02-17       Impact factor: 6.424

2.  Elevated neutrophil-to-lymphocyte ratio predicts poor outcome in patients with advanced non-small-cell lung cancer receiving first-line gefitinib or erlotinib treatment.

Authors:  Gui-Nan Lin; Jie-Wen Peng; Pan-Pan Liu; Dong-Ying Liu; Jian-Jun Xiao; Xiao-Qin Chen
Journal:  Asia Pac J Clin Oncol       Date:  2014-10-31       Impact factor: 2.601

3.  Assessment of Prognostic Value of "Neutrophil to Lymphocyte Ratio" and "Prognostic Nutritional Index" as a Sytemic Inflammatory Marker in Non-small Cell Lung Cancer.

Authors:  Fahriye Tugba Kos; Cemil Hocazade; Mehmet Kos; Dogan Uncu; Esra Karakas; Mutlu Dogan; Hikmet Gulsen Uncu; Nuriye Ozdemir; Nurullah Zengin
Journal:  Asian Pac J Cancer Prev       Date:  2015

4.  Pretreatment neutrophil to lymphocyte ratio is associated with response to therapy and prognosis of advanced non-small cell lung cancer patients treated with first-line platinum-based chemotherapy.

Authors:  Yanwen Yao; Dongmei Yuan; Hongbing Liu; Xiaoling Gu; Yong Song
Journal:  Cancer Immunol Immunother       Date:  2012-09-18       Impact factor: 6.968

5.  Systemic inflammatory status at baseline predicts bevacizumab benefit in advanced non-small cell lung cancer patients.

Authors:  Cirino Botta; Vito Barbieri; Domenico Ciliberto; Antonio Rossi; Danilo Rocco; Raffaele Addeo; Nicoletta Staropoli; Pierpaolo Pastina; Giulia Marvaso; Ignazio Martellucci; Annamaria Guglielmo; Luigi Pirtoli; Pasquale Sperlongano; Cesare Gridelli; Michele Caraglia; Pierfrancesco Tassone; Pierosandro Tagliaferri; Pierpaolo Correale
Journal:  Cancer Biol Ther       Date:  2013-06       Impact factor: 4.742

6.  Baseline and Trend of Lymphocyte-to-Monocyte Ratio as Prognostic Factors in Epidermal Growth Factor Receptor Mutant Non-Small Cell Lung Cancer Patients Treated with First-Line Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors.

Authors:  Yu-Mu Chen; Chien-Hao Lai; Huang-Chih Chang; Tung-Ying Chao; Chia-Cheng Tseng; Wen-Feng Fang; Chin-Chou Wang; Yu-Hsiu Chung; Yi-Hsi Wang; Mao-Chang Su; Kuo-Tung Huang; Hung-Chen Chen; Ya-Chun Chang; Meng-Chih Lin
Journal:  PLoS One       Date:  2015-08-27       Impact factor: 3.240

7.  Neutrophil-to-Lymphocyte Ratio Predicts Overall Survival of Advanced Non-Small Cell Lung Cancer Harboring Mutant Epidermal Growth Factor Receptor.

Authors:  Seigo Minami; Yoshitaka Ogata; Shouichi Ihara; Suguru Yamamoto; Kiyoshi Komuta
Journal:  World J Oncol       Date:  2017-12-28

8.  Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study.

Authors:  Christina Fitzmaurice; Christine Allen; Ryan M Barber; Lars Barregard; Zulfiqar A Bhutta; Hermann Brenner; Daniel J Dicker; Odgerel Chimed-Orchir; Rakhi Dandona; Lalit Dandona; Tom Fleming; Mohammad H Forouzanfar; Jamie Hancock; Roderick J Hay; Rachel Hunter-Merrill; Chantal Huynh; H Dean Hosgood; Catherine O Johnson; Jost B Jonas; Jagdish Khubchandani; G Anil Kumar; Michael Kutz; Qing Lan; Heidi J Larson; Xiaofeng Liang; Stephen S Lim; Alan D Lopez; Michael F MacIntyre; Laurie Marczak; Neal Marquez; Ali H Mokdad; Christine Pinho; Farshad Pourmalek; Joshua A Salomon; Juan Ramon Sanabria; Logan Sandar; Benn Sartorius; Stephen M Schwartz; Katya A Shackelford; Kenji Shibuya; Jeff Stanaway; Caitlyn Steiner; Jiandong Sun; Ken Takahashi; Stein Emil Vollset; Theo Vos; Joseph A Wagner; Haidong Wang; Ronny Westerman; Hajo Zeeb; Leo Zoeckler; Foad Abd-Allah; Muktar Beshir Ahmed; Samer Alabed; Noore K Alam; Saleh Fahed Aldhahri; Girma Alem; Mulubirhan Assefa Alemayohu; Raghib Ali; Rajaa Al-Raddadi; Azmeraw Amare; Yaw Amoako; Al Artaman; Hamid Asayesh; Niguse Atnafu; Ashish Awasthi; Huda Ba Saleem; Aleksandra Barac; Neeraj Bedi; Isabela Bensenor; Adugnaw Berhane; Eduardo Bernabé; Balem Betsu; Agnes Binagwaho; Dube Boneya; Ismael Campos-Nonato; Carlos Castañeda-Orjuela; Ferrán Catalá-López; Peggy Chiang; Chioma Chibueze; Abdulaal Chitheer; Jee-Young Choi; Benjamin Cowie; Solomon Damtew; José das Neves; Suhojit Dey; Samath Dharmaratne; Preet Dhillon; Eric Ding; Tim Driscoll; Donatus Ekwueme; Aman Yesuf Endries; Maryam Farvid; Farshad Farzadfar; Joao Fernandes; Florian Fischer; Tsegaye Tewelde G/Hiwot; Alemseged Gebru; Sameer Gopalani; Alemayehu Hailu; Masako Horino; Nobuyuki Horita; Abdullatif Husseini; Inge Huybrechts; Manami Inoue; Farhad Islami; Mihajlo Jakovljevic; Spencer James; Mehdi Javanbakht; Sun Ha Jee; Amir Kasaeian; Muktar Sano Kedir; Yousef S Khader; Young-Ho Khang; Daniel Kim; James Leigh; Shai Linn; Raimundas Lunevicius; Hassan Magdy Abd El Razek; Reza Malekzadeh; Deborah Carvalho Malta; Wagner Marcenes; Desalegn Markos; Yohannes A Melaku; Kidanu G Meles; Walter Mendoza; Desalegn Tadese Mengiste; Tuomo J Meretoja; Ted R Miller; Karzan Abdulmuhsin Mohammad; Alireza Mohammadi; Shafiu Mohammed; Maziar Moradi-Lakeh; Gabriele Nagel; Devina Nand; Quyen Le Nguyen; Sandra Nolte; Felix A Ogbo; Kelechi E Oladimeji; Eyal Oren; Mahesh Pa; Eun-Kee Park; David M Pereira; Dietrich Plass; Mostafa Qorbani; Amir Radfar; Anwar Rafay; Mahfuzar Rahman; Saleem M Rana; Kjetil Søreide; Maheswar Satpathy; Monika Sawhney; Sadaf G Sepanlou; Masood Ali Shaikh; Jun She; Ivy Shiue; Hirbo Roba Shore; Mark G Shrime; Samuel So; Samir Soneji; Vasiliki Stathopoulou; Konstantinos Stroumpoulis; Muawiyyah Babale Sufiyan; Bryan L Sykes; Rafael Tabarés-Seisdedos; Fentaw Tadese; Bemnet Amare Tedla; Gizachew Assefa Tessema; J S Thakur; Bach Xuan Tran; Kingsley Nnanna Ukwaja; Benjamin S Chudi Uzochukwu; Vasiliy Victorovich Vlassov; Elisabete Weiderpass; Mamo Wubshet Terefe; Henock Gebremedhin Yebyo; Hassen Hamid Yimam; Naohiro Yonemoto; Mustafa Z Younis; Chuanhua Yu; Zoubida Zaidi; Maysaa El Sayed Zaki; Zerihun Menlkalew Zenebe; Christopher J L Murray; Mohsen Naghavi
Journal:  JAMA Oncol       Date:  2017-04-01       Impact factor: 31.777

Review 9.  Neutrophils in Cancer: Two Sides of the Same Coin.

Authors:  Eileen Uribe-Querol; Carlos Rosales
Journal:  J Immunol Res       Date:  2015-12-24       Impact factor: 4.818

10.  Pretreatment neutrophil-lymphocyte ratio is not a significant prognostic factor in epidermal growth factor receptor-mutant non-small cell lung cancer patients treated with tyrosine kinase inhibitors.

Authors:  Sung Hoon Sim; Seung-Hoon Beom; Yong-Oon Ahn; Bhumsuk Keam; Tae Min Kim; Se-Hoon Lee; Dong-Wan Kim; Dae Seog Heo
Journal:  Thorac Cancer       Date:  2015-08-28       Impact factor: 3.500

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  12 in total

1.  Elevated peripheral inflammatory markers are related with the recurrence and canceration of vocal fold leukoplakia.

Authors:  Yi Fang; Yue Yang; Min Chen; Peijie He; Lei Cheng; Jian Chen; Haitao Wu
Journal:  Eur Arch Otorhinolaryngol       Date:  2019-07-31       Impact factor: 2.503

2.  Prognostic role of pretreatment neutrophil-to-lymphocyte ratio in non-small cell lung cancer patients treated with systemic therapy: a meta-analysis.

Authors:  Zimu Wang; Ping Zhan; Yanling Lv; Kaikai Shen; Yuqing Wei; Hongbing Liu; Yong Song
Journal:  Transl Lung Cancer Res       Date:  2019-06

3.  Platelet-to-lymphocyte ratio as a potential prognostic factor in nasopharyngeal carcinoma: A meta-analysis.

Authors:  Rui-Xiang Cen; Yu-Gang Li
Journal:  Medicine (Baltimore)       Date:  2019-09       Impact factor: 1.817

4.  Neutrophil-Lymphocyte Ratio as a Prognostic Parameter in NSCLC Patients Receiving EGFR-TKIs: A Systematic Review and Meta-Analysis.

Authors:  Mingbo Tang; Xinliang Gao; He Sun; Suyan Tian; Junxue Dong; Zhao Liu; Wei Liu
Journal:  J Oncol       Date:  2021-01-20       Impact factor: 4.375

5.  Pretreatment neutrophil-to-lymphocyte ratio predicts treatment efficacy and prognosis of cytotoxic anticancer drugs, molecular targeted drugs, and immune checkpoint inhibitors in patients with advanced non-small cell lung cancer.

Authors:  Masashi Ishihara; Ryosuke Ochiai; Terunobu Haruyama; Takahiko Sakamoto; Shigeru Tanzawa; Takeshi Honda; Shuji Ota; Yasuko Ichikawa; Tsuyoshi Ishida; Kiyotaka Watanabe; Nobuhiko Seki
Journal:  Transl Lung Cancer Res       Date:  2021-01

6.  Association Between PDL1 Genetic Variation and Efficacy of Apatinib Monotherapy in Patients with Previously Treated Advanced NSCLC: A Real-World Retrospective Study.

Authors:  Wenxia Hu; Bin Li; Nan Geng; Xin He; Hui Ge; Ping Wang; Cuimin Ding
Journal:  Int J Gen Med       Date:  2021-06-21

7.  Lymphocyte percentage and platelet count correlate with the treatment outcome to tyrosine kinase inhibitors in epidermal growth factor receptor-mutated lung adenocarcinoma.

Authors:  Chi-Cheng Li; Chih-Bin Lin; Sung-Chao Chu; Wei-Han Huang; Jen-Jyh Lee; Gee-Gwo Yang; Tso-Fu Wang; Yi-Feng Wu
Journal:  Medicine (Baltimore)       Date:  2020-07-17       Impact factor: 1.817

8.  Association of PD-1 polymorphisms with the risk and prognosis of lung adenocarcinoma in the northeastern Chinese Han population.

Authors:  Kun Huang; Erqiang Hu; Wan Li; Junjie Lv; Yuehan He; Gui Deng; Jinling Xiao; Chengcheng Yang; Xinyu Zhao; Lina Chen; Xinyan Wang
Journal:  BMC Med Genet       Date:  2019-11-12       Impact factor: 2.103

9.  A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development.

Authors:  Eda G Ramirez-Valles; Alicia Rodríguez-Pulido; Marcelo Barraza-Salas; Isaac Martínez-Velis; Iván Meneses-Morales; Víctor M Ayala-García; Carlos A Alba-Fierro
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec

10.  Neutrophil-to-Lymphocyte Ratio Is a Predictive Biomarker in Patients with Epidermal Growth Factor Receptor (EGFR) Mutated Advanced Non-Small Cell Lung Cancer (NSCLC) Treated with Tyrosine Kinase Inhibitor (TKI) Therapy.

Authors:  Nicole K Yun; Sherin J Rouhani; Christine M Bestvina; Ethan M Ritz; Brendan A Gilmore; Imad Tarhoni; Jeffrey A Borgia; Marta Batus; Philip D Bonomi; Mary Jo Fidler
Journal:  Cancers (Basel)       Date:  2021-03-20       Impact factor: 6.639

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