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For peer review onlyCorrelation of the neutrophil to lymphocyte ratio and clinical outcomes in lung cancer patients receiving immunotherapy:

a meta-analysis

Journal: BMJ Open

Manuscript ID bmjopen-2019-035031

Article Type: Original research

Date Submitted by the Author: 17-Oct-2019

Complete List of Authors: Jin, Jing; Sichuan University West China HospitalYang, Lan; Sichuan University West China HospitalLiu, Dan; Sichuan University West China HospitalLi, Weimin; Sichuan University West China Hospital,

Keywords: Immunology < BASIC SCIENCES, Epidemiology < ONCOLOGY, Respiratory tract tumours < ONCOLOGY

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For peer review only

1

Correlation of the neutrophil to lymphocyte ratio and clinical

outcomes in lung cancer patients receiving immunotherapy: a

meta-analysis

Jing Jin1, Lan Yang1, Dan Liu1, Wei-Min Li1*

1. Department of Pulmonary & Critical Care, West China Hospital, Sichuan

University, Chengdu 610041, China.

Running title: Neutrophil to lymphocyte ratio and lung cancer

Address correspondence to: Dr. Wei-Min Li, Department of Pulmonary & Critical

Care, West China Hospital, Sichuan University, Chengdu 610041, China. Tel: +86 (028)

85423998; E-mail: [email protected]

Article summary

Strengths and limitations of this study

1. Verifying the prognostic value of NLR in a large of lung cancer patients with

immunotherapy

2. Different clinical characteristics could affect the prognostic value

3. High heterogeneity was present in this analysis

Patient and Public Involvement: No patient involved

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Abstract：

Objectives: We conducted a meta-analysis to explore the relationship between

pretreatment or posttreatment neutrophil to lymphocyte ratio (NLR) and overall

survival (OS)/progress free survival (PFS) in lung cancer patients receiving

immunotherapy.

Setting: Medical Center in Southwestern of China

Participants: Studies reporting the prognostic value of NLR in lung cancer patients

receiving immunotherapy were enrolled.

Primary and secondary outcome measures: We searched PubMed, Cochrane

Library, Embase and Web of Science to collect relevant studies conducted until July

2019. We carefully reviewed the full text of included publications and combined the

hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the correlation

between the NLR and survival time in lung cancer patients receiving immunotherapy.

Results: Twenty-three studies with 2068 patients were enrolled. Among all patients,

1305 (64.0%) were males, and 643 (31.38%) were diagnosed with squamous

carcinoma. In a pooled analysis of OS and PFS from all studies, an elevated NLR

predicted poorer OS ((HR=1.62; 95% CI: 1.41-1.87; P< 0.001) and PFS (HR=1.47;

95% CI: 1.25-1.72; P< 0.0001). Subgroup analyses stratified showed that the

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posttreatment NLR was not significantly related to OS and that patients in Asia were

significantly associated with higher HRs than those in Europe and America.

Furthermore, the proportion of squamous cell carcinoma and baseline level of NLR

affect the prognosis value of NLR.

Conclusions: Our study found that an elevated NLR was associated with poorer OS

and PFS in lung cancer patients receiving immunotherapy and that several clinical

factors might have an impact on the predictive value of NLR in the survival of lung

cancer patients. However, further studies are warranted to draw firm conclusions.

Trial registration: The registration number of PROSPERO: CRD42018104856.

Keywords: NLR; systemic inflammation; prognostic markers; lung cancer

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Introduction:

Lung cancer is the most prevalent cancer and life-threatening malignancy

worldwide.(1) The pathogenesis of lung cancer is complicated, and the primary

treatments for lung cancer patients are surgery and chemotherapy. Unfortunately,

most patients with lung cancer are diagnosed at advanced stages, and the benefits

achieved from chemotherapy in advanced lung cancer patients are relatively small.

Recently, many studies have revealed that tumor cells can evade the antitumor

responses of T-cells by controlling the combined responses of programmed cell death

protein 1 (PD-1) and programmed cell death ligand-1 (PD-L1).(2) Nivolumab,

pembrolizumab, atezolizumab, durvalumab, ipilimumab and tremelimumab have

successfully changed clinical experiences in lung cancer treatment.(3) Tumor

mutational burden,(4) neoantigens(5) and classical monocytes in the peripheral

blood(6) are effective predictive biomarkers for immune checkpoint therapy in lung

cancer, especially PD-L1 expression on tumor cells.(7) Systemic inflammation in

patients with cancer is considered to influence the growth and migration of tumors by

some inflammatory factors.(8) Elevated levels of systemic inflammation, including

Glasgow prognostic score (GPS), neutrophil to lymphocyte ratio (NLR), platelet to

lymphocyte ratio (PLR), C-reactive protein to albumin ratio (CAR), has been

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indicated to be associated with worse survival in solid tumors.(9-11) However, the

data of prognosis from pretreatment NLR in lung cancer patients receiving

immunotherapy trials are still scarce and inconsistent. Therefore, we collected the

available publications and conducted a meta-analysis to explore the prognostic value

of pretreatment NLR for OS and PFS in clinical trials of lung cancer patients

receiving immunotherapy.

Materials and Methods

Search strategy

PRISMA guidelines for systematic review and meta-analysis were followed strictly in

this article. An online search was conducted to identify relevant publications in

PubMed, Cochrane Library, Web of Science and Embase databases. The following

words were used: “Pulmonary Neoplasms”, “neutrophil lymphocyte ratio”,

“immunotherapy”, “programmed death receptor-1”, and “immune checkpoint

inhibitor” for studies on the associations between pretreatment NLR and survival time

in patients with lung cancer published before July 2019. A full electronic search

strategy is provided in the supplement materials (Supplement Table 1). Additional

studies were selected for the full text to be reviewed by exploring the references cited

in the selected articles and relevant reviews. The articles were limited to the English

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language but no restrictions were used for the minimum number of patients. Two

authors (J Jin and L Yang) independently reviewed the titles and abstracts of the

retrieved articles to select the potentially relevant articles that to be more carefully

assessed.

Eligibility criteria

The inclusion criteria were as follows: 1) retrospective or prospective studies

published before July 2019; 2) all patients enrolled in studies were diagnosed with

lung cancer by biopsy and received immunotherapy; 3) the value of NLR was

calculated based on the level of neutrophils and lymphocytes; and 4) HRs and 95%

CIs were provided and data necessary to calculate them were reported.

The exclusion criteria: 1) review, meeting abstract, letter, or full text unavailable in

English; 2) nonhuman studies; and 3) research did not present the value of the NLR.

Data extraction

From each study, the name of the study, first author, year of publication, study design,

number of patients, sex distribution, age, median follow-up time, histology, NLR

cutoff value, NLR at baseline, line of therapy, drugs and HRs with 95% CIs for

overall survival (OS) and progress free survival (PFS) were extracted by two authors

(D Liu and L Yang). If univariate and multivariate analysis results were

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simultaneously reported, only multivariate analysis results were extracted. Any

disagreements between the authors were resolved by discussion and consensus. The

most recent study was chosen when duplicate studies occurred.

Quality assessment

The primary studies were assessed by the NOS (Newcastle-Ottawa quality assessment

Scale). The quality assessment was conducted by two independent researchers (J Jin

and D Liu). The studies in which the mark was between 6 and 9 points were regarded

as high-quality studies.

(http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp)

Statistical analysis

The primary endpoints were OS and PFS of lung cancer patients receiving

immunotherapy. PFS was defined as the time from the initial date of immunotherapy

to the date of progression or death. OS was calculated from the date of inclusion to

the time of death from any cause. HRs with 95% CIs were directly obtained from the

articles or estimated from the Kaplan-Meier curves according to the methods reported

by Tierney et al.(12) We calculated the pooled HRs of OS and PFS using random

effects or fixed effects model. We performed the Q-test to assess between-study

heterogeneity and calculated the I² statistic, which expresses the percentage of the

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total observed variability due to study heterogeneity. The heterogeneity between

studies was considered small if the I2 statistic was less than 50% and the P value for

Q-test was less than 0.05. We performed a subgroup analysis to detect the source of

heterogeneity. In addition, we only considered subgroups that included more than two

studies. Publication bias was assessed by Egger’s and Begg’s test, and a significant

publication bias was defined as a P<0.10.(13) Trim and fill method was applied when

significant publication bias was found to confirm the pooled results. Sensitivity

analyses were carried out by excluding each study individually from the meta-

analysis.(14) All statistical analyses were performed with R (Version: 3.5.2).

Result

The characteristics of the included studies

A total of 1102 studies were retrieved in this meta-analysis, and 26 of these studies

were selected for full-text review. In total, 23 studies with 2068 patients fulfilled the

inclusion criteria, with publication dates ranging from 2017 to 2018.(15-37) The flow

diagram of this study is shown in Fig 1. The sample size was between 19 and 201

patients. Of these studies, 9 were conducted in Europe, (16, 17, 21, 24, 27, 30, 35, 36)

5 were conducted in America, (22, 28, 31, 33, 37) and the remaining studies were

conducted in Asia. Among all patients included, 1305 (64.0%) were males, and 643

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(31.38%) were diagnosed with squamous carcinoma. Twenty studies explored the

association between NLR and OS; fifteen studies investigated the relationship

between PFS and NLR. Additionally, 7 of 23 studies provided data about

posttreatment NLR. (21, 23, 25, 28, 29, 32, 33) If the study provided data about

posttreatment NLR and OS, we treated it as an independent research in the subsequent

analysis. Six trials were performed as first-line therapy, (16, 19, 25, 28, 31, 36) and

the other studies were second or additional lines of therapy. Almost all patients

received nivolumab as an immunotherapy for PD-1 inhibitor. The cutoff value of

NLR was not the same in all studies; the value of 5 was mostly used among all

publications, and the median cutoff value for all enrolled publications was also 5. The

NOS scores of the enrolled studies ranged from 6-9. Detailed information about these

studies is presented in Table 1.

Relationship between NLR and OS in lung cancer patients receiving

immunotherapy

A total of 1629 patients treated with immunotherapy from 20 studies provided the

NLR value or the data to calculate the NLR and OS values. Five of these studies

provided data about posttreatment NLR and OS. A total of 23 researches to combine

the HR and 95% CI. In the pooled analysis of NLR and OS, we found that a higher

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level of NLR was associated with a poorer OS with a high heterogeneity (HR=1.62;

95% CI: 1.41-1.87; P< 0.001) (I2=81.7%, P< 0.0001) (Fig 2). To detect the source of

heterogeneity, we conducted a subgroup analysis on some clinical factors that may

influence the final results, such as study design, the time of detecting NLR, ethnicity,

sex ratio, the proportion of squamous cell carcinoma (SCC%), the level of NLR at

baseline, the treatment line, median follow-up time, sample size and the drug for

immunotherapy (Fig 3). Interestingly, the association between the pretreatment NLR

and OS showed a similar trend to the pooled result (HR=1.87; 95% CI: 1.46-2.39; P <

0.0001). However, the posttreatment NLR seemed to not be significantly related to

the OS in lung cancer patients (HR=1.80; 95% CI: 0.81-4.00; P= 0.11). However,

these results were still highly heterogeneous (pretreatment: I2=79.80%, P< 0.0001;

posttreatment: I2=83.5%, P< 0.0001). Furthermore, the NLR was significantly

unrelated to the OS in the studies in which the proportion of patients with squamous

cell carcinoma or the proportion of patients whose NLR baseline levels exceeded the

cutoff value was greater than 50% (Fig 3).The subgroup analysis stratified by

ethnicity found that patients in Asia were significantly associated with a higher HR

(HR=2.76; 95% CI: 1.88- 4.06) and smaller heterogeneity (I²=45.7%, P=0.09) than

those in Europe and America (Pinteraction=0.030).

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Relationship between NLR and PFS in lung cancer patients receiving

immunotherapy

The data for NLR and PFS of 1612 patients treated with immunotherapy in 15 studies

(20 researches) were extracted to obtain a pooled HR and 95% CI. Four of these

studies provided the posttreatment NLR and its relationship with PFS. The random

effects model revealed a significant association between an elevated NLR and PFS in

lung cancer patients receiving immunotherapy (HR=1.47; 95% CI: 1.25-1.72; P<

0.0001) with high heterogeneity (I2=72.5%, P< 0.0001) (Fig 4). To detect the

potential source of heterogeneity in studies reporting PFS data, subgroup analysis

stratified by the factors that affect the HR were proposed, as previously mentioned

(Fig 5). Similar to the relationship between the NLR and OS, the NLR was

significantly unrelated to the PFS in studies in which the proportion of patients with

squamous cell carcinoma was greater than 50% (Pinteraction=0.005). However, the

pooled results for subgroups by other factors were not markedly changed with low-

level heterogeneity.

Sensitivity analysis and Publication bias

We found high heterogeneity among studies enrolled in the analysis of the

relationship among pretreatment NLR, OS and PFS. Therefore, we performed a

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sensitivity analysis on all enrolled studies. The effect of each single study set on the

combined HRs was evaluated by excluding each study individually from the meta-

analysis. The results of the sensitivity analysis showed that the pooled HRs for OS

and PFS were robust in our meta-analysis (Fig 6A and 6B). We conducted a subgroup

analysis stratified by various factors to detect the source of heterogeneity. The Begg’s

test presented no evidence of obvious publication bias in both studies reporting the

association between NLR and OS (P=0.673) and those reporting the association

between NLR and PFS (P= 0.074), but the Egger’s test showed significant publication

bias in both (P<0.001 for both). Therefore, we performed a trim and fill analysis on

studies reporting the relationship between NLR and OS/PFS. However, the result was

unchanged after eliminating the influence of publication bias (OS: HR=1.40; 95%

CI:1.22-1.60; P< 0.0001, PFS: HR=1.33; 95% CI:1.14-1.56; P= 0.0004, supplement

fig 1).

Discussion:

The results of our meta-analysis revealed the prognostic effect of both pretreatment

and posttreatment NLR on OS and PFS in lung cancer patients receiving

immunotherapy. Twenty-three studies involving a total of 2049 lung cancer patients

showed that an increased NLR was significantly associated with a poorer OS

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(HR=1.62; 95% CI: 1.41-1.87; P< 0.001) and PFS (HR=1.47; 95% CI: 1.25-1.72; P<

0.0001). Interestingly, the posttreatment NLR did not seem to be significantly related

to OS, and patients in Asia were significantly associated with higher HRs than those

in Europe and America.

The immune checkpoint is a kind of mechanism that plays a protective role in the

human immune system and acts like a brake to prevent inflammatory damage caused

by excessive activation of T cells.(38) Human anti-PD-1 IgG4 mAb is now widely

used and shows higher efficacy than standard therapies in lung cancer. (39) Despite a

wide consensus on testing tumor tissues for PD-L1 expression, it is limited by its

“unperfected dichotomy” across studies and molecules; patients with low levels of

PD-L1 expression have responded at rates of up to 17%, and roughly half of patients

are “not-responders” despite high tumor PD-L1 levels. Several factors could affect the

response and survival of patients receiving immunotherapy.(39) In addition to tumor

mutation loads and the expression of tumor antigens, the status of systemic

inflammation also occupies an important position in lung cancer patients receiving

immunotherapy. The tumor-associated cytokine and relevant signaling pathway could

be reflected by the level of systemic inflammation, which has been proven to be

associated with a worse survival in patients with solid tumors.(8) Biomarkers such as

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NLR, PLR, GPS and mGPS have been used as prognostic factors in lung cancer.(9-

11) In addition, the role of systemic inflammation in patients receiving

immunotherapy is particularly important for their survival. Several studies have

explored the effect of pretreatment NLR on lung cancer patients receiving

immunotherapy.(31, 40-45) There are also two meta-analyses concerning

pretreatment NLR and survival in patients with advanced cancer. (46) (47) In

summary, NLR is a reliable prognostic factor for patients with various cancer types.

Sacdalan, D. B. reported that an increased NLR resulted in a worse PFS among several

kinds of cancers, such as melanoma, non-small-cell lung cancer and genitourinary

cancer,(46) which was consistent with our results. However, only three publications

about lung cancer were enrolled in that meta-analysis, and a nonsignificant association

was discovered between pretreatment NLR and OS. In addition, two of the three studies

included in the meta-analysis previously mentioned only provided abstracts, and we

cannot obtain more details about those cohorts or study designs. Another meta-analysis

conducted by Jiang, T also revealed a trend similar to our results, but the results of the

subgroup analysis showed that posttreatment NLR was also significantly related to a

poorer OS and PFS, and this result was different from ours. We enrolled more research

articles in our study. In addition, we performed subgroup analyses stratified by more

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clinical factors. Furthermore, our results showed that the ethnicity, NLR level at

baseline and proportion of squamous cell carcinoma may affect the prognosis value of

the NLR. However, due to the high heterogeneity, the results must be interpreted with

caution. Neutrophils were the most abundant immune cell type identified in NSCLC

patients and accounted for nearly 20% of all CD45+ cells in patients from America.(48)

However, this result was not found in Asia or Europe. The systemic inflammatory

response might be different among different ethnicities. Furthermore, we collected

baseline patient information, including the proportion of squamous cell carcinoma,

from all studies, and our results showed that the histology of lung cancer might have an

impact on the prognosis value of NLR. However, the mechanism needs further

exploration, and many cofounding factors could affect the systemic inflammatory

response.

The current research had several limitations. First, high heterogeneity was present in

this analysis although we conducted sensitivity analyses on all studies. The results

were robust after eliminating every study from a combination of HRs. In addition, we

performed subgroup analyses on some possible impact factors to detect the source of

heterogeneity. Second, the Egger’s test showed that obvious publication bias was

present in this current study. The pooled results should be treated with caution

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although a trim and fill analysis testing indicated credibility for this study.

Additionally, considering the high heterogeneity after subgroup analysis, other factors

might be responsible for the high heterogeneity in this meta-analysis.

Conclusion:

Generally, our meta-analysis focused on the clinical prognostic agreement of NLR for

OS and PFS in lung cancer patients. Importantly, given the limitations mentioned

above, these findings should be treated with caution in clinical practice. More

prospective cohort studies are needed to test our results.

Contributorship statement:

(I) Conception and design: W Li, J Jin, Lan Y; II) Administrative support: J Jin, W Li;

(III) Provision of study materials or patients: D Liu; (IV) Collection and assembly of

data: J Jin, D Liu; (V) Data analysis and interpretation: D Liu, Lan Y; (VI)

Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Competing interests

The authors have no conflicts of interest to declare

Funding

None

Data sharing statement:

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All data in the current study were available in published articles

Acknowledgments:

This work was supported by the Transformation Projects of Sci-Tech Achievements

of Sichuan Province (2016CZYD0001), the Sci-Tech Support Program of Science and

Technology Department of Sichuan Province (2016SZ0073), the National Major Sci-

Tech Project (2017ZX10103004-012) and the National Key Development Plan for

Precision Medicine Research (2017YFC0910004).

Compliance with Ethical Standards

Ethical approval: All procedures performed in the studies involving human

participants were in accordance with the ethical standards of the institutional and/or

national research committee and with the 1964 Helsinki declaration and its later

amendment.

Reference:

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29.2. Callahan MK, Postow MA, Wolchok JD. Targeting T Cell Co-receptors for Cancer Therapy. Immunity. 2016;44(5):1069-78.3. Chen YM. Immune checkpoint inhibitors for nonsmall cell lung cancer treatment. Journal of the Chinese Medical Association Jcma. 2016;80(1):7.4. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. The New England journal of medicine. 2017;377(25):2500-1.5. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer discovery. 2017;7(3):264-76.

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6. Goswami S, Basu S, Sharma P. A potential biomarker for anti-PD-1 immunotherapy. Nature medicine. 2018;24(2):123-4.7. Marchetti A, Barberis M, Franco R, De Luca G, Pace MV, Staibano S, et al. Multicenter Comparison of 22C3 PharmDx (Agilent) and SP263 (Ventana) Assays to Test PD-L1 Expression for NSCLC Patients to Be Treated with Immune Checkpoint Inhibitors. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer. 2017;12(11):1654-63.8. Hooper CE, Lyburn ID, Searle J, Darby M, Hall T, Hall D, et al. The South West Area Mesothelioma and Pemetrexed trial: a multicentre prospective observational study evaluating novel markers of chemotherapy response and prognostication. British journal of cancer. 2015;112(7):1175-82.9. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor for small-cell lung cancer. OncoTargets and therapy. 2016;9:5761-70.10. Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer development. Lung cancer (Amsterdam, Netherlands). 2016;97:28-34.11. Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Prognostic role of pretreatment platelet/lymphocyte ratio in patients with non-small cell lung cancer. Wien Klin Wochenschr. 2016;128(17-18):635-40.12. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8(1):16.13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088.14. O’Rourke K, Shea B, Wells GA. Meta-Analysis of Clinical Trials: Springer New York; 2001. 397-424 p.15. Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). OncoTargets and therapy. 2019;12:4235-44.16. Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral Blood Markers Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with Immune-Checkpoint Inhibitors. The oncologist. 2019.17. Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo V, Listi A, et al. Monitoring blood biomarkers to predict nivolumab effectiveness in NSCLC patients. Therapeutic advances in medical oncology. 2019;11:1758835919839928.18. Minami S, Ihara S, Ikuta S, Komuta K. Gustave Roussy Immune Score and Royal Marsden Hospital Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer. World journal of oncology. 2019;10(2):90-100.19. Ichiki Y, Taira A, Chikaishi Y, Matsumiya H, Mori M, Kanayama M, et al. Prognostic factors of

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advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point inhibitors. Journal of thoracic disease. 2019;11(4):1117-23.20. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: A Prospective Observational Study. Clinical lung cancer. 2019;20(3):208-14.e2.21. Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T, Melloni B, et al. Neutrophil-to-lymphocyte ratio evolution is an independent predictor of early progression of second-line nivolumab-treated patients with advanced non-small-cell lung cancers. PloS one. 2019;14(7):e0219060.22. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients With Advanced Non-Small-Cell Lung Cancer. Clinical lung cancer. 2018;19(5):426-+.23. Takeda T, Takeuchi M, Saitoh M, Takeda S. Neutrophil-to-lymphocyte ratio after four weeks of nivolumab administration as a predictive marker in patients with pretreated non-small-cell lung cancer. Thoracic cancer. 2018;9(10):1291-9.24. Svaton M, Zemanova M, Skrickova J, Jakubikova L, Kolek V, Kultan J, et al. Chronic Inflammation as a Potential Predictive Factor of Nivolumab Therapy in Non-small Cell Lung Cancer. Anticancer research. 2018;38(12):6771-82.25. Suh KJ, Kim SH, Kim YJ, Kim M, Keam B, Kim TM, et al. Post-treatment neutrophil-to-lymphocyte ratio at week 6 is prognostic in patients with advanced non-small cell lung cancers treated with anti-PD-1 antibody. Cancer Immunology Immunotherapy. 2018;67(3):459-70.26. Shiroyama T, Suzuki H, Tamiya M, Tamiya A, Tanaka A, Okamoto N, et al. Pretreatment advanced lung cancer inflammation index (ALI) for predicting early progression in nivolumab-treated patients with advanced non-small cell lung cancer. Cancer medicine. 2018;7(1):13-20.27. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. Journal of cellular physiology. 2018;233(10):6337-43.28. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical lung cancer. 2018;19(3):280-8.e4.29. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with nivolumab. International journal of clinical oncology. 2018;23(4):634-40.30. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA oncology. 2018;4(3):351-7.

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31. Maymani H, Hess K, Groisberg R, Hong DS, Naing A, Piha-Paul S, et al. Predicting outcomes in patients with advanced non-small cell lung cancer enrolled in early phase immunotherapy trials. Lung cancer (Amsterdam, Netherlands). 2018;120:137-41.32. Kiriu T, Yamamoto M, Nagano T, Hazama D, Sekiya R, Katsurada M, et al. The time-series behavior of neutrophil-to-lymphocyte ratio is useful as a predictive marker in non-small cell lung cancer. PloS one. 2018;13(2):e0193018.33. Khunger M, Patil PD, Khunger A, Li M, Hu B, Rakshit S, et al. Post-treatment changes in hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer patients. PloS one. 2018;13(10):e0197743.34. Inomata M, Hirai T, Seto Z, Tokui K, Taka C, Okazawa S, et al. Clinical Parameters for Predicting the Survival in Patients with Squamous and Non-squamous-cell NSCLC Receiving PD-1 Inhibitor Therapy. Pathology oncology research : POR. 2018.35. Facchinetti F, Veneziani M, Buti S, Gelsomino F, Squadrilli A, Bordi P, et al. Clinical and hematologic parameters address the outcomes of non-small-cell lung cancer patients treated with nivolumab. Immunotherapy. 2018;10(8):681-94.36. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung cancer (Amsterdam, Netherlands). 2017;111:176-81.37. Bagley SJ, Kothari S, Aggarwal C, Bauml JM, Alley EW, Evans TL, et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung cancer (Amsterdam, Netherlands). 2017;106:1-7.38. Ito A, Kondo S, Tada K, Kitano S. Clinical Development of Immune Checkpoint Inhibitors. Biomed Res Int. 2015;2015:605478.39. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-Line Nivolumab in Stage IV or Recurrent Non-Small-Cell Lung Cancer. The New England journal of medicine. 2017;376(25):2415-26.40. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351-7.41. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with nivolumab. Int J Clin Oncol. 2018.42. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2018.43. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for

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Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical Lung Cancer. 2018;19(3):280-8.e4.44. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non–Small-Cell Lung Cancer: A Prospective Observational Study. Clinical Lung Cancer. 2018.45. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018.46. Sacdalan DB, Lucero JA, Sacdalan DL. Prognostic utility of baseline neutrophil-to-lymphocyte ratio in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets and therapy. 2018;11:955-65.47. Jiang T, Bai Y, Zhou F, Li W, Gao G, Su C, et al. Clinical value of neutrophil-to-lymphocyte ratio in patients with non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors. Lung cancer (Amsterdam, Netherlands). 2019;130:76-83.48. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, et al. Neutrophils dominate the immune cell composition in non-small cell lung cancer. Nature communications. 2017;8:14381.

Figure Legends

Figure 1 Flow chart of study selection

Figure 2 Forest plot of the association between NLR and OS in patients with lung

cancer receiving immunotherapy

Figure 3 Subgroup analysis of the relationship NLR and OS in patients with lung


Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%:

Proportion of Squamous cell carcinoma;※: the data here shows the proportion of

patients whose NLR baseline levels exceeded the cutoff value; N: Nivolumab; P:

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Pembrolizumab; D: Durvalumab; E: Embrolizumab; A: Atezolizumab

Figure 4 Forest plot of the association between NLR and PFS in patients with lung


Figure 5 Subgroup analysis of the relationship NLR and PFS in patients with lung


Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%:

Proportion of Squamous cell carcinoma;※: 15 studies (20 researches) provided the

data for pretreatment NLR and PFS, and 5 of them also provided posttreatment NLR

and PFS; N: Nivolumab; P: Pembrolizumab; D: Durvalumab; E: Embrolizumab; A:

Atezolizumab

Figure 6 Sensitivity analysis on OS (A) and PFS (B)

Table

Table 1 The basic characteristic of enrolled studiesStudy Year Country Ethnicity Sample size MFP M/F NLR at baseline

Russo A 2018 Italy European 28 17 25/3 NMZer A 2018 America American 88 5.3 43/45 NLR＞4:56.8%Nakaya A 2018 Japan Asian 101 8.9 77/24 NLR≥3:46.5%Maymani H 2018 America American 74 12.3 36/38 NLR＞6:20.3%Mezquita L 2018 Europe European 161 12 100/61 NLR＞3:39%Diem S 2017 Europe European 52 NM 29/23 5.0(2.7-8.3) *Bagley SJ 2017 America American 175 NM 80/95 NLR≥5:58%Fukui T 2018 Japan Asian 52 10.9 37/15 NLR≥5:34.6%Park W 2018 America American 159 11.5 82/77 4.3(0.5-24.1) *Ren, F 2019 China Asian 147 2.6 94/53 NLR＞2.5:59.9%

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Pavan, A 2019 Italy European 184 56.3 125/59 NLR≥3:57.5%Passiglia, F 2019 Italy European 45 9.1 32/13 NLR＞3.3:51.1%Minami, S 2019 Japan Asian 76 NM 49/27 NLR≥6:14.5%Ichiki, Y. 2019 Japan Asian 44 4.83 38/6 NMDusselier, M. 2019 France European 59 NM 44/15 NLR＞5:62.7%Takeda, T. 2018 Japan Asian 30 NM 19/11 NLR＞5:30%Svaton, M 2018 Czech Republic European 120 NM 71/49 NLR＞3.8:50%Suh, Koung Jin 2018 Korea Asian 54 26.2 42/12 NLR＞5:14.8%Shiroyama, Takayuki 2018 Japan Asian 201 12.4 135/66 NLR＞4:39.3%Kiriu, T 2018 Japan Asian 19.00 NM 19 NLR＞5:31.6%Khunger, M 2018 America American 109 30 56/53 NLR≥5:50.5%Inomata, M 2018 Japan Asian 36 NM 27/9 NLR≥5:44.4%Facchinetti, F 2018 Italy European 54 12.6 45/9 NM

Study SCC% Treatment lines Outcome Study design

Cut-off IO

Russo A 60.71% at least second line therapy OS/PFS RO 3 NZer A 17.05% at least second line therapy OS/PFS/DCR RO 4 NMNakaya A 36.63% at least second line therapy PFS/irAEs RO 3 NMaymani H 16.22% including first line therapy OS/PFS RO 6 N/P/DMezquita L 28.57% at least second line therapy OS/PFS RO 3 N/E/A/DDiem S 34.62% including first line therapy OS/PFS RO 5 NBagley SJ 24.00% at least second line therapy OS/PFS RO 5 NFukui T 30.77% at least second line therapy OS/PFS/irAEs PO 5 NPark W 24.53% including first line therapy OS/PFS RO 5 NRen, F 42.18% at least second line therapy OS/PFS RO 2.5 N/PPavan, A 32.07% including first line therapy OS/PFS/irAEs RO 3 N/P/APassiglia, F 44.44% at least second line therapy OS/TTP RO 3.3 NMinami, S 23.68% at least second line therapy OS/PFS RO 6 N/P/AIchiki, Y. 65.91% including first line therapy OS/PFS/irAEs RO NM N/PDusselier, M. 20.34% at least second line therapy OS RO 5 NTakeda, T. 30.00% at least second line therapy PFS RO 5 NSvaton, M 33.33% at least second line therapy OS/PFS RO 3.8 NSuh, Koung Jin 31.48% including first line therapy OS/PFS/irAEs RO 5 N/PShiroyama, Takayuki 30.35% at least second line therapy PFS/RR RO 4 N

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Kiriu, T 31.58% at least second line therapy OS/PFS/TTF RO 5 NKhunger, M 23.85% at least second line therapy OS RO 5 NInomata, M 44.44% at least second line therapy PFS RO 5 N/PFacchinetti, F 48.15% at least second line therapy OS/PFS/TTF/DP PO 4 N

Abbreviation: NLR: neutrophil to lymphocyte ratio; NM: not mentioned; M/F:

male/female; MFP: Median follow-up (month); SCC%: Proportion of Squamous cell

carcinoma; IO: immunotherapy; N: Nivolumab; P: Pembrolizumab; D: Durvalumab;

E: Embrolizumab; A: Atezolizumab; OS: overall survival; PFS: progress free

survival; DCR: disease control rate; irAEs: immune-related adverse events; RR:

response rate; TTF: treatment failure; RO: retrospective studies; PO: prospective

studies; NOS: Ottawa quality assessment Scale; *: the study just provided the median

number and the range of NLR at baseline.

Supporting information

S1File. PRISMA 2009 checklist.

S2 Search strategy. This file provides a full electronic search strategy for PubMed

S3 Figure1 Trim and fill analysis on the data of OS and PFS

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338x190mm (96 x 96 DPI)

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Figure 2 Forest plot of the association between NLR and OS in patients with lung cancer receiving immunotherapy

396x246mm (96 x 96 DPI)

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Subgroup analysis of the relationship NLR and OS in patients with lung cancer receiving immunotherapy Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%: Proportion of Squamous cell

carcinoma;※: the data here shows the proportion of patients whose NLR baseline levels exceeded the cutoff value; N: Nivolumab; P: Pembrolizumab; D: Durvalumab; E: Embrolizumab; A: Atezolizumab

154x99mm (300 x 300 DPI)

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Figure 4 Forest plot of the association between NLR and PFS in patients with lung cancer receiving immunotherapy

396x246mm (96 x 96 DPI)

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Subgroup analysis of the relationship NLR and PFS in patients with lung cancer receiving immunotherapy Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%: Proportion of Squamous cell carcinoma;※: 15 studies (20 researches) provided the data for pretreatment NLR and PFS, and 5 of them

also provided posttreatment NLR and PFS; N: Nivolumab; P: Pembrolizumab; D: Durvalumab; E: Embrolizumab; A: Atezolizumab

162x99mm (300 x 300 DPI)

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143x74mm (300 x 300 DPI)

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PRISMA 2009 Checklist

Section/topic # Checklist item Reported on page #

TITLE

Title 1 Identify the report as a systematic review, meta-analysis, or both. 1

ABSTRACT

Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.

2-3

INTRODUCTION

Rationale 3 Describe the rationale for the review in the context of what is already known. 4

Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).

5

METHODS

Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number.

5-6

Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered,

language, publication status) used as criteria for eligibility, giving rationale.

6

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

5

Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

5-6 and supplements

Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

5-8

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

6-7

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

6

Risk of bias in individual studies

12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

8

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). 7-8

Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I2) for each meta-analysis.

7-8

Page 1 of 2

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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

8

Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

8

RESULTS

Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

8-9

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

9-10

Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 11-12

Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

8-11

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 8-11

Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 11-12

Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). 12

DISCUSSION

Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

12-13

Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

15-16

Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 16

FUNDING

Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.

17

From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097

For more information, visit: www.prisma-statement.org.

Page 2 of 2

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Supplement Table1 Search strategy for meta-analysis of Correlation of the

neutrophil to lymphocyte ratio and clinical outcomes in lung cancer patients

receiving immunotherapy: a meta-analysis (PubMed via NLM)

Search terms: neutrophil to lymphocyte ratio and lung cancer patients with immunotherapy

Population: persons with lung cancer receiving immunotherapy

1 (((((Cancer of Lung) OR Pulmonary Neoplasms) OR Neoplasms, Lung) OR Lung Neoplasm) OR

Neoplasm, Lung) OR Neoplasms, Pulmonary) OR Neoplasm, Pulmonary) OR Pulmonary

Neoplasm) OR Lung Cancer) OR Cancer, Lung) OR Cancers, Lung) OR Lung Cancers) OR

Pulmonary Cancer) OR Cancer, Pulmonary) OR Cancers, Pulmonary) OR Pulmonary Cancers)

OR Cancer of the Lung) OR "Lung Neoplasms"[Mesh]))))) AND (("Immunotherapy"[Mesh])

AND Immunotherapies)

Intervention (Expose): neutrophil to lymphocyte ratio

2 ((NLR) OR (neutrophil to lymphocyte ratio) OR neutrophil lymphocyte ratio))

Combined sets

3 1 and 2

Limits

4 3 AND English [Language]

[Mesh] = Term from the Medline controlled vocabulary, including terms found below this term in the Mesh

hierarchy

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For peer review onlyAssociation of the neutrophil to lymphocyte ratio and

clinical outcomes in lung cancer patients receiving immunotherapy: a meta-analysis

Journal: BMJ Open

Manuscript ID bmjopen-2019-035031.R1


Date Submitted by the Author: 14-Feb-2020


<b>Primary Subject Heading</b>: Oncology

Secondary Subject Heading: Respiratory medicine



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1 Association of the neutrophil to lymphocyte ratio and clinical

2 outcomes in lung cancer patients receiving immunotherapy: a

3 meta-analysis

4

5 Jing Jin1, Lan Yang1, Dan Liu1, Wei-Min Li1*

6 1. Department of Pulmonary & Critical Care, West China Hospital, Sichuan

7 University, Chengdu 610041, China.

8 Running title: Neutrophil to lymphocyte ratio and lung cancer

9 Address correspondence to: Dr. Wei-Min Li, Department of Pulmonary & Critical

10 Care, West China Hospital, Sichuan University, Chengdu 610041, China. Tel: +86 (028)

11 85423998; E-mail: [email protected]

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1 Abstract：

2 Objectives: To explore the relationship between the pretreatment or posttreatment

3 neutrophil to lymphocyte ratio (NLR) and overall survival (OS)/ progression-free

4 survival (PFS) in lung cancer patients receiving immunotherapy.

5 Design: We searched several databases to collect relevant studies conducted until July

6 2019. We carefully reviewed the full text of the included publications and combined

7 the hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association

8 between the NLR and survival time in lung cancer patients receiving immunotherapy.

9 Data Sources: PubMed, the Cochrane Library, Embase and Web of Science

10 Eligibility Criteria: Studies reporting the prognostic value of the NLR in lung cancer

11 patients receiving immunotherapy were enrolled.

12 Data extraction and synthesis: Basic information on the articles and patients(NLR

13 cutoff value, NLR at baseline, and HRs with 95% CIs for OS and PFS) was extracted

14 by two authors independently. The pooled HRs of OS and PFS were synthesized

15 using the random effects or fixed effects model.

16 Results: Twenty-three studies with 2068 patients were enrolled. Among all patients,

17 1305 (64.0%) were males, and 643 (31.4%) were diagnosed with squamous cell

18 carcinoma. In a pooled analysis of OS and PFS from all studies, an elevated NLR

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1 predicted poor OS (HR=1.62; 95% CI: 1.41-1.87; P< 0.001) and PFS (HR=1.47; 95%

2 CI: 1.25-1.72; P< 0.0001). Subgroup analyses stratified showed that the posttreatment

3 NLR was not significantly related to OS and that patients in Asia had significantly

4 higher HRs than those in Europe and America. Furthermore, the proportion of

5 squamous cell carcinoma and baseline NLR could affect the prognostic value of the

6 NLR.

7 Conclusions: Our study found that an elevated NLR was associated with poor OS and

8 PFS in lung cancer patients receiving immunotherapy and that several clinical factors

9 might have an impact on the predictive value of the NLR in the survival of lung

10 cancer patients.

11 Strengths and limitations of this study

12 1. Verification of the prognostic value of the NLR in a large number of lung

13 cancer patients who received immunotherapy

14 2. Different clinical characteristics could affect the prognostic value of the NLR

15 3. High heterogeneity was present in this analysis

16 Keywords: NLR; systemic inflammation; prognostic markers; lung cancer

17

18

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1 Introduction:

2 Lung cancer is the most prevalent cancer and life-threatening malignancy

3 worldwide.(1) The pathogenesis of lung cancer is complicated, and the primary

4 treatments for lung cancer patients are surgery and chemotherapy. Unfortunately,

5 most patients with lung cancer are diagnosed at advanced stages, and the benefits

6 achieved from chemotherapy in advanced lung cancer patients are relatively small.

7 Recently, many studies have revealed that tumor cells can evade the antitumor

8 responses of T cells by controlling the combined responses of programmed cell death

9 protein 1 (PD-1) and programmed cell death ligand-1 (PD-L1).(2) Nivolumab,

10 pembrolizumab, atezolizumab, durvalumab, ipilimumab and tremelimumab have

11 successfully changed clinical experiences in lung cancer treatment.(3) Tumor

12 mutational burden,(4) neoantigens(5) and classical monocytes in the peripheral

13 blood(6) and PD-L1 expression on tumor cells in particular, (7) are effective

14 predictive biomarkers for immune checkpoint therapy in lung cancer. Systemic

15 inflammation in cancer patients is believed to influence the growth and migration of

16 tumors via certain inflammatory factors.(8) An elevated level of systemic

17 inflammation, including Glasgow prognostic score (GPS), neutrophil to lymphocyte

18 ratio (NLR), platelet to lymphocyte ratio (PLR), and C-reactive protein to albumin

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1 ratio (CAR), have been indicated to be associated with poor survival in patients with

2 solid tumors.(9-11) However, data on the prognostic value of the pretreatment NLR in

3 lung cancer patients receiving immunotherapy remain scarce and inconsistent.

4 Therefore, we reviewed available publications and conducted a meta-analysis to

5 explore the prognostic value of the pretreatment NLR for r overall survival (OS) and

6 progression-free survival (PFS) in clinical trials on lung cancer patients receiving

7 immunotherapy.

8 Materials and Methods

9 Patient and Public Involvement: No patient was involved

10 Search strategy

11 The PRISMA guidelines for a systematic review and meta-analysis were strictly

12 followed in this article (registration number PROSPERO: CRD42018104856). An

13 online search was conducted to identify relevant publications in the PubMed,

14 Cochrane Library, Web of Science and Embase databases. The following words were

15 used to search for studies on the associations between the pretreatment NLR and

16 survival time in patients with lung cancer published before July 2019: “pulmonary

17 neoplasms”, “neutrophil lymphocyte ratio”, “immunotherapy”, “programmed death

18 receptor-1”, and “immune checkpoint inhibitor”. A full electronic search strategy is

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1 provided in the supplementary information (Supplementary Table 1). Additional

2 studies were selected for a full-text review were selected by exploring the references

3 cited in the selected articles and relevant reviews. The articles were limited to the

4 English language, but there were no restrictions on the minimum number of patients.

5 Two authors (J Jin and L Yang) independently reviewed the titles and abstracts of the

6 retrieved articles to select the potentially relevant articles for a careful assessment.

7 Eligibility criteria

8 The inclusion criteria were as follows: 1) retrospective or prospective studies

9 published before July 2019; 2) all patients enrolled in the studies were diagnosed with

10 lung cancer by biopsy and received immunotherapy; 3) the value of the NLR was

11 calculated based on the level of neutrophils and lymphocytes; and 4) HRs and 95%

12 CIs were provided and data necessary to calculate them were reported.

13 The exclusion criteria were as follows: 1) review, meeting abstract, letter, or full text

14 unavailable in English; 2) nonhuman studies; and 3) research that did not provide the

15 value of the NLR.

16 Data extraction

17 From each study, the name of the study, first author, year of publication, study design,

18 number of patients, sex distribution, age, median follow-up time, histology, NLR

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1 cutoff value, NLR at baseline, line of therapy, drugs and HRs with 95% CIs for OS

2 and PFS were extracted by two authors (D Liu and L Yang). If univariate and

3 multivariate analysis results were simultaneously reported, only the multivariate

4 analysis results were extracted. Any disagreements between the authors were resolved

5 by a discussion and consensus. The most recent study was chosen when duplicate

6 studies occurred.

7 Quality assessment

8 The primary studies were assessed by the Newcastle-Ottawa quality assessment Scale

9 (NOS). The quality assessment was conducted by two independent researchers (J Jin

10 and D Liu). The studies in which the mark was between 6 and 9 points were regarded

11 as high-quality studies.

12 (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp)

13 Statistical analysis

14 The primary endpoints were the OS and PFS of lung cancer patients receiving

15 immunotherapy. PFS was defined as the time from the initial date of immunotherapy

16 to the date of progression or death. OS was calculated from the date of inclusion to

17 the time of death from any cause. HRs with 95% CIs were directly obtained from the

18 articles or estimated from the Kaplan-Meier curves according to the methods reported

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1 by Tierney et al.(12) We calculated the pooled HRs of OS and PFS using random

2 effects or fixed effects model. We performed the Q-test to assess between-study

3 heterogeneity and calculated the I² statistic, which expresses the percentage of the

4 total observed variability due to study heterogeneity. The heterogeneity between

5 studies was considered small if the I2 statistic was less than 50% and the P value for

6 the Q-test was less than 0.05. We performed a subgroup analysis to detect the source

7 of heterogeneity. In addition, we considered only subgroups that included more than

8 two studies. Publication bias was assessed by Egger’s and Begg’s test, and

9 significant publication bias was defined as a P<0.10.(13) The trim and fill method was

10 applied when significant publication bias was found to confirm the pooled results.

11 Sensitivity analyses were carried out by excluding each study individually from the

12 meta-analysis.(14) All statistical analyses were performed with R (Version: 3.5.2).

13 Result

14 The characteristics of the included studies

15 A total of 1102 studies were retrieved in this meta-analysis, and 279 studies were

16 selected for full-text review. In total, 23 studies with 2068 patients fulfilled the

17 inclusion and exclusion criteria, with publication dates ranging from 2017 to

18 2018.(15-37) The flow diagram of this study is shown in Figure 1. The sample size

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1 was between 19 and 201. Of these studies, 9 were conducted in Europe, (16, 17, 21,

2 24, 27, 30, 35, 36) 5 were conducted in America, (22, 28, 31, 33, 37) and the

3 remaining studies were conducted in Asia. Among all patients included, 1305 (64.0%)

4 were males, and 643 (31.4%) were diagnosed with squamous cell carcinoma. Twenty

5 studies explored the association between the NLR and OS; fifteen studies investigated

6 the relationship between the NLR and PFS. Additionally, 7 of 23 studies provided

7 data on the posttreatment NLR. (21, 23, 25, 28, 29, 32, 33) If the study provided data

8 about posttreatment NLR and OS, we treated it as an independent study in the

9 subsequent analysis. Six trials performed first-line therapy, (16, 19, 25, 28, 31, 36)

10 and the other trails performed second or additional-lines of therapy. Most patients

11 received nivolumab, a PD-1 inhibitor, as immunotherapy. The cutoff value of the

12 NLR was not the same in all studies; a value of 5 was used frequently, and the median

13 cutoff value for all enrolled publications was also 5. The NOS scores of the enrolled

14 studies ranged from 6-9. Detailed information on these studies is presented in Table 1.

15 Relationship between the NLR and OS in lung cancer patients receiving

16 immunotherapy

17 Twenty studies on a total of 1629 patients treated with immunotherapy provided the

18 NLR value or data that could be used to calculate the NLR and OS. Five of these

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1 studies provided data on the posttreatment NLR and OS. Data from a total of 23

2 studies were used to combine HRs and 95% CIs. In the pooled analysis of the NLR

3 and OS, we found that a higher NLR was associated with poorer OS, with high

4 heterogeneity (HR=1.62; 95% CI: 1.41-1.87; P< 0.001) (I2=81.7%, P< 0.001) (Figure

5 2). To detect the source of heterogeneity, we conducted a subgroup analysis on certain

6 clinical factors that may influence the final results, such as study design, the time at

7 which the NLR was determined, ethnicity, sex ratio, the proportion of patients with

8 squamous cell carcinoma (SCC%), the NLR at baseline, the treatment line, the

9 median follow-up time, sample size and the drug given for immunotherapy (Figure 3).

10 Interestingly, the association between the pretreatment NLR and OS showed a similar

11 trend to the pooled result (HR=1.87; 95% CI: 1.46-2.39; P < 0.001). However, the

12 posttreatment NLR was not significantly related to the OS in lung cancer patients

13 (HR=1.80; 95% CI: 0.81-4.00; P= 0.111). However, these results were still highly

14 heterogeneous (pretreatment: I2=79.80%, P< 0.001; posttreatment: I2=83.5%, P<

15 0.001). Furthermore, the NLR was significantly unrelated to the OS in studies in

16 which the proportion of patients with squamous cell carcinoma or whose baseline

17 NLR exceeded the cutoff value was greater than 50% (Figure 3).The subgroup

18 analysis stratified by ethnicity found that patients in Asia had significantly higher HR

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1 (HR=2.76; 95% CI: 1.88- 4.06) and less heterogeneity (I²=45.7%, P=0.091) than

2 those in Europe and America (Pinteraction=0.030) (Figure 3).

3 Relationship between the NLR and PFS in lung cancer patients receiving

4 immunotherapy

5 Data on the NLR and PFS of 1612 patients treated with immunotherapy in 20 studies

6 were extracted to obtain the pooled HR and 95% CI. Four of these studies provided

7 the posttreatment NLR and its relationship with PFS. The random effects model

8 revealed a significant association between an elevated NLR and PFS in lung cancer

9 patients receiving immunotherapy (HR=1.47; 95% CI: 1.25-1.72; P< 0.001) with high

10 heterogeneity (I2=72.5%, P< 0.001) (Figure 4). To detect the potential source of

11 heterogeneity in studies reporting PFS data, a subgroup analysis stratified by the

12 factors that affect the NLR was performed as previously described (Figure 5). Similar

13 to the relationship between the NLR and OS, the NLR was significantly unrelated to

14 the PFS in studies in which the proportion of patients with squamous cell carcinoma

15 was greater than 50% (Pinteraction=0.005). However, the pooled results for subgroups

16 based on other factors were not markedly changed with a low level of heterogeneity.

17 Sensitivity analysis and Publication bias

18 We found high heterogeneity among studies in which the relationship between the

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1 pretreatment NLR, OS and PFS was analyzed. Therefore, we performed a sensitivity

2 analysis on all enrolled studies. The effect of each study set on the combined HRs was

3 evaluated by excluding each study individually from the meta-analysis. The results of

4 the sensitivity analysis showed that the pooled HRs for OS and PFS were robust in

5 our meta-analysis (Figure 6A and 6B). We also conducted a subgroup analysis

6 stratified by various factors to detect the source of heterogeneity. Begg’s test

7 presented no evidence of obvious publication bias in studies reporting the association

8 between the NLR and OS (P=0.673) or in those reporting the association between the

9 NLR and PFS (P= 0.074), but Egger’s test showed significant publication bias in

10 which both were reported (P<0.001 for both). Therefore, we performed a trim and fill

11 analysis on studies reporting the relationship between the NLR and OS/PFS.

12 However, the result was unchanged after eliminating the influence of publication bias

13 (OS: HR=1.40; 95% CI:1.22-1.60; P< 0.001, PFS: HR=1.33; 95% CI:1.14-1.56; P<

14 0.001, Supplementary Figure 1).

15 Discussion:

16 The results of our meta-analysis revealed the prognostic effect of both the

17 pretreatment and posttreatment NLR on OS and PFS in lung cancer patients receiving

18 immunotherapy. Twenty-three studies involving a total of 2049 lung cancer patients

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1 showed that an increased NLR was significantly associated with poor OS (HR=1.62;

2 95% CI: 1.41-1.87; P< 0.001) and PFS (HR=1.47; 95% CI: 1.25-1.72; P< 0.001).

3 Interestingly, the posttreatment NLR was not significantly associated with OS, and

4 patients in Asia had significantly higher HRs than those in Europe and America.

5 The immune checkpoint is a kind of mechanism that plays a protective role in the

6 human immune system and acts as a brake to prevent inflammatory damage caused by

7 the excessive activation of T cells.(38) Human anti-PD-1 IgG4 mAb is now widely

8 used and shows higher efficacy than standard therapies in lung cancer therapies. (39)

9 Despite a wide consensus on testing tumor tissues for PD-L1 expression, the human

10 anti-PD-1 IgG4 mAb is limited by its “unperfected dichotomy” across studies and

11 molecules; patients with low levels of PD-L1 expression have response rates of up to

12 17%, and roughly half of patients are “not-responders” despite having high tumor

13 levels of PD-L1. Several factors could affect the response and survival of patients

14 receiving immunotherapy.(39) In addition to tumor mutation loads and the expression

15 of tumor antigens, the status of systemic inflammation also plays an important role in

16 lung cancer patients receiving immunotherapy. Tumor-associated cytokines and the

17 relevant signaling pathways could be reflected by the level of systemic inflammation,

18 which has been proven to be associated with poor survival in patients with solid

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1 tumors.(8) Biomarkers such as NLR, PLR, GPS and modified GPS(mGPS) have been

2 used as prognostic factors in lung cancer.(9-11) In addition, the role of systemic

3 inflammation in patients receiving immunotherapy is particularly important for their

4 survival. Several studies have explored the effect of the pretreatment NLR on lung

5 cancer patients receiving immunotherapy.(31, 40-45) There are also two meta-

6 analyses concerning the pretreatment NLR and survival in patients with advanced

7 cancer. (46) (47) In summary, the NLR is a reliable prognostic factor for patients with

8 various cancer types.

9 Sacdalan D. B reported that a high NLR resulted in poor PFS in patients with several

10 kinds of cancers, such as melanoma, non-small-cell lung cancer (NSCLC) and

11 genitourinary cancer,(46) which was consistent with our results. However, only three

12 publications on lung cancer were enrolled in the previous meta-analysis, and a

13 nonsignificant association was discovered between the pretreatment NLR and OS was

14 discovered. In addition, two of the three studies included in the meta-analysis

15 previously mentioned only provided only abstracts, and we cloud not obtain more

16 details about those cohorts or study designs. Another meta-analysis conducted by Jiang

17 T also revealed a trend similar to ours, but the results of the subgroup analysis showed

18 that posttreatment NLR was significantly associated with poor OS and PFS, which is

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1 inconsistent with our result. We enrolled more research articles in our study. In addition,

2 we performed subgroup analyses stratified by additional clinical factors. Furthermore,

3 our results showed that the ethnicity, the NLR at baseline and the proportion of patients

4 with squamous cell carcinoma may affect the prognostic value of the NLR. However,

5 due to the high heterogeneity, the results must be interpreted with caution. We found

6 that patients in Asia had a significant higher HR than those in Europe and America in

7 the subgroup analysis of the relationship between the NLR and OS. Some studies

8 showed that neutrophils were the most abundant immune cell type identified in NSCLC

9 patients and accounted for nearly 20% of all CD45+ cells in patients from America.(48)

10 However, this result was not found in patients from Asia or Europe. The systemic

11 inflammatory response in different ethnicities might differ. Furthermore, we collected

12 baseline patient information, including the proportion of patients with squamous cell

13 carcinoma, from all studies, and our results showed that the histology of lung cancer

14 might have an impact on the prognostic value of the NLR. Many factors including

15 tumor mutation load and the expression of tumor antigens, affect patient response and

16 survival. (39) Patients with lung adenocarcinoma have a high EGFR mutation rate and

17 some studies revealed that patients with targetable oncogenes were associated with a

18 poor response to immunotherapy. (49) This may account for the results of our article.

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1 The current study had several limitations. First, high heterogeneity was present in this

2 analysis although we conducted sensitivity analyses on all studies. The results were

3 robust after eliminating each study from the analysis. In addition, we performed

4 subgroup analyses on certain possible impact factors to detect the source of

5 heterogeneity. Second, Egger’s test showed that obvious publication bias in the

6 current study. The pooled results should be treated with caution, although trim and fill

7 analysis testing indicated credibility for this study. Additionally, considering the high

8 heterogeneity after subgroup analysis, other factors might be responsible for the high

9 heterogeneity in this meta-analysis.

10 Conclusion:

11 Generally, our meta-analysis focused on the clinical prognostic agreement of the NLR

12 and OS and PFS in lung cancer patients. Importantly, given the limitations mentioned

13 above, these findings should be treated with caution in clinical practice. More

14 prospective cohort studies are needed to confirm our results.

15 Contributorship statement:

16 (I) Conception and design: W Li, J Jin and Lan Y; II) Administrative support: J Jin, W

17 Li; (III) Provision of study materials or patients: D Liu; (IV) Collection and assembly

18 of data: J Jin, D Liu; (V) Data analysis and interpretation: D Liu, Lan Y; (VI)

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1 Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

2 Competing interests

3 The authors have no conflicts of interest to declare

4 Funding

5 None

6 Data sharing statement:

7 All data in the current study are available in the published articles

8 Acknowledgments:

9 This work was supported by the Transformation Projects of Sci-Tech Achievements

10 of Sichuan Province (2016CZYD0001), the Sci-Tech Support Program of Science and

11 Technology Department of Sichuan Province (2016SZ0073), the National Major Sci-

12 Tech Project (2017ZX10103004-012) and the National Key Development Plan for

13 Precision Medicine Research (2017YFC0910004).

14 Compliance with Ethical Standards

15 Ethical approval: All procedures performed in the studies involving human

16 participants were in accordance with the ethical standards of the institutional and/or

17 national research committee and with the 1964 Helsinki Declaration and its later

18 amendment.

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1 Reference:

2 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29.3 2. Callahan MK, Postow MA, Wolchok JD. Targeting T Cell Co-receptors for Cancer Therapy. Immunity. 4 2016;44(5):1069-78.5 3. Chen YM. Immune checkpoint inhibitors for nonsmall cell lung cancer treatment. Journal of the 6 Chinese Medical Association Jcma. 2016;80(1):7.7 4. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 8 Inhibition. The New England journal of medicine. 2017;377(25):2500-1.9 5. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of

10 Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer 11 discovery. 2017;7(3):264-76.12 6. Goswami S, Basu S, Sharma P. A potential biomarker for anti-PD-1 immunotherapy. Nature 13 medicine. 2018;24(2):123-4.14 7. Marchetti A, Barberis M, Franco R, De Luca G, Pace MV, Staibano S, et al. Multicenter Comparison 15 of 22C3 PharmDx (Agilent) and SP263 (Ventana) Assays to Test PD-L1 Expression for NSCLC Patients to 16 Be Treated with Immune Checkpoint Inhibitors. Journal of thoracic oncology : official publication of the 17 International Association for the Study of Lung Cancer. 2017;12(11):1654-63.18 8. Hooper CE, Lyburn ID, Searle J, Darby M, Hall T, Hall D, et al. The South West Area Mesothelioma 19 and Pemetrexed trial: a multicentre prospective observational study evaluating novel markers of 20 chemotherapy response and prognostication. British journal of cancer. 2015;112(7):1175-82.21 9. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor 22 for small-cell lung cancer. OncoTargets and therapy. 2016;9:5761-70.23 10. Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, 24 et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer 25 development. Lung cancer (Amsterdam, Netherlands). 2016;97:28-34.26 11. Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Prognostic role of pretreatment 27 platelet/lymphocyte ratio in patients with non-small cell lung cancer. Wien Klin Wochenschr. 28 2016;128(17-18):635-40.29 12. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating 30 summary time-to-event data into meta-analysis. Trials. 2007;8(1):16.31 13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. 32 Biometrics. 1994;50(4):1088.33 14. O’Rourke K, Shea B, Wells GA. Meta-Analysis of Clinical Trials: Springer New York; 2001. 397-424 34 p.35 15. Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced

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1 non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). 2 OncoTargets and therapy. 2019;12:4235-44.3 16. Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral Blood Markers 4 Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with 5 Immune-Checkpoint Inhibitors. The oncologist. 2019.6 17. Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo V, Listi A, et al. Monitoring blood biomarkers 7 to predict nivolumab effectiveness in NSCLC patients. Therapeutic advances in medical oncology. 8 2019;11:1758835919839928.9 18. Minami S, Ihara S, Ikuta S, Komuta K. Gustave Roussy Immune Score and Royal Marsden Hospital

10 Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer. 11 World journal of oncology. 2019;10(2):90-100.12 19. Ichiki Y, Taira A, Chikaishi Y, Matsumiya H, Mori M, Kanayama M, et al. Prognostic factors of 13 advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point 14 inhibitors. Journal of thoracic disease. 2019;11(4):1117-23.15 20. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical lung cancer. 2019;20(3):208-14.e2.18 21. Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T, Melloni B, et al. Neutrophil-to-19 lymphocyte ratio evolution is an independent predictor of early progression of second-line nivolumab-20 treated patients with advanced non-small-cell lung cancers. PloS one. 2019;14(7):e0219060.21 22. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to 22 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 23 With Advanced Non-Small-Cell Lung Cancer. Clinical lung cancer. 2018;19(5):426-+.24 23. Takeda T, Takeuchi M, Saitoh M, Takeda S. Neutrophil-to-lymphocyte ratio after four weeks of 25 nivolumab administration as a predictive marker in patients with pretreated non-small-cell lung cancer. 26 Thoracic cancer. 2018;9(10):1291-9.27 24. Svaton M, Zemanova M, Skrickova J, Jakubikova L, Kolek V, Kultan J, et al. Chronic Inflammation 28 as a Potential Predictive Factor of Nivolumab Therapy in Non-small Cell Lung Cancer. Anticancer 29 research. 2018;38(12):6771-82.30 25. Suh KJ, Kim SH, Kim YJ, Kim M, Keam B, Kim TM, et al. Post-treatment neutrophil-to-lymphocyte 31 ratio at week 6 is prognostic in patients with advanced non-small cell lung cancers treated with anti-32 PD-1 antibody. Cancer Immunology Immunotherapy. 2018;67(3):459-70.33 26. Shiroyama T, Suzuki H, Tamiya M, Tamiya A, Tanaka A, Okamoto N, et al. Pretreatment advanced 34 lung cancer inflammation index (ALI) for predicting early progression in nivolumab-treated patients 35 with advanced non-small cell lung cancer. Cancer medicine. 2018;7(1):13-20.36 27. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia,

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1 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 2 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. Journal of cellular physiology. 3 2018;233(10):6337-43.4 28. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 5 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 6 lung cancer. 2018;19(3):280-8.e4.7 29. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 8 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 9 nivolumab. International journal of clinical oncology. 2018;23(4):634-40.

10 30. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 11 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 12 Non-Small Cell Lung Cancer. JAMA oncology. 2018;4(3):351-7.13 31. Maymani H, Hess K, Groisberg R, Hong DS, Naing A, Piha-Paul S, et al. Predicting outcomes in 14 patients with advanced non-small cell lung cancer enrolled in early phase immunotherapy trials. Lung 15 cancer (Amsterdam, Netherlands). 2018;120:137-41.16 32. Kiriu T, Yamamoto M, Nagano T, Hazama D, Sekiya R, Katsurada M, et al. The time-series behavior 17 of neutrophil-to-lymphocyte ratio is useful as a predictive marker in non-small cell lung cancer. PloS 18 one. 2018;13(2):e0193018.19 33. Khunger M, Patil PD, Khunger A, Li M, Hu B, Rakshit S, et al. Post-treatment changes in 20 hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer 21 patients. PloS one. 2018;13(10):e0197743.22 34. Inomata M, Hirai T, Seto Z, Tokui K, Taka C, Okazawa S, et al. Clinical Parameters for Predicting the 23 Survival in Patients with Squamous and Non-squamous-cell NSCLC Receiving PD-1 Inhibitor Therapy. 24 Pathology oncology research : POR. 2018.25 35. Facchinetti F, Veneziani M, Buti S, Gelsomino F, Squadrilli A, Bordi P, et al. Clinical and hematologic 26 parameters address the outcomes of non-small-cell lung cancer patients treated with nivolumab. 27 Immunotherapy. 2018;10(8):681-94.28 36. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) 29 and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer 30 (NSCLC) treated with nivolumab. Lung cancer (Amsterdam, Netherlands). 2017;111:176-81.31 37. Bagley SJ, Kothari S, Aggarwal C, Bauml JM, Alley EW, Evans TL, et al. Pretreatment neutrophil-to-32 lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell 33 lung cancer. Lung cancer (Amsterdam, Netherlands). 2017;106:1-7.34 38. Ito A, Kondo S, Tada K, Kitano S. Clinical Development of Immune Checkpoint Inhibitors. Biomed 35 Res Int. 2015;2015:605478.36 39. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-Line Nivolumab in Stage

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1 IV or Recurrent Non-Small-Cell Lung Cancer. The New England journal of medicine. 2017;376(25):2415-2 26.3 40. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 4 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 5 Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351-7.6 41. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 7 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 8 nivolumab. Int J Clin Oncol. 2018.9 42. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to

10 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 11 With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2018.12 43. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 13 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 14 Lung Cancer. 2018;19(3):280-8.e4.15 44. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non–Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical Lung Cancer. 2018.18 45. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, 19 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 20 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018.21 46. Sacdalan DB, Lucero JA, Sacdalan DL. Prognostic utility of baseline neutrophil-to-lymphocyte ratio 22 in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets and 23 therapy. 2018;11:955-65.24 47. Jiang T, Bai Y, Zhou F, Li W, Gao G, Su C, et al. Clinical value of neutrophil-to-lymphocyte ratio in 25 patients with non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors. Lung cancer (Amsterdam, 26 Netherlands). 2019;130:76-83.27 48. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, et al. Neutrophils dominate the immune 28 cell composition in non-small cell lung cancer. Nature communications. 2017;8:14381.29 49. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot AB, Mezquita L, et al. Immune checkpoint 30 inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the 31 IMMUNOTARGET registry. Ann Oncol. 2019;30(8):1321-8.

32

33 Figure Legends

34 Figure 1 Flow chart of study selection

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1 Figure 2 Forest plot of the association between the NLR and OS in patients with lung

2 cancer receiving immunotherapy

3 Figure 3 Subgroup analysis of the relationship between the NLR and OS in patients

4 with lung cancer receiving immunotherapy

5 Abbreviations: ICI: immune checkpoint inhibitor; M/F: male/female; SCC%:

6 proportion of patients with squamous cell carcinoma;※: the data here show the

7 proportion of patients whose baseline NLR exceeded the cutoff value; N: nivolumab;

8 P: pembrolizumab; D: durvalumab; E: embrolizumab; A: atezolizumab

9 Figure 4 Forest plot of the association between the NLR and PFS in patients with lung


11 Figure 5 Subgroup analysis of the relationship between the NLR and PFS in patients



14 proportion of patients with squamous cell carcinoma;※: 20 studies provided the data

15 on the pretreatment NLR and PFS, and 5 of them also provided the posttreatment

16 NLR and PFS; N: nivolumab; P: pembrolizumab; D: durvalumab; E: embrolizumab;

17 A: atezolizumab

18 Figure 6 Sensitivity analysis of OS (A) and PFS (B)

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1 Table

2 Table 1 The basic characteristics of the enrolled studiesStudy Year Country Ethnicity Sample size MFP M/F NLR at baseline

Diem S 2017 Europe European 52 NM 29/23 5.0(2.7-8.3) *Bagley SJ 2017 America American 175 NM 80/95 NLR≥5:58.0%Russo A 2018 Italy European 28 17 25/3 NMZer A 2018 America American 88 5.3 43/45 NLR＞4:56.8%Nakaya A 2018 Japan Asian 101 8.9 77/24 NLR≥3:46.5%Maymani H 2018 America American 74 12.3 36/38 NLR＞6:20.3%Mezquita L 2018 Europe European 161 12 100/61 NLR＞3:39.0%Fukui T 2018 Japan Asian 52 10.9 37/15 NLR≥5:34.6%Park W 2018 America American 159 11.5 82/77 4.3(0.5-24.1) *Takeda T 2018 Japan Asian 30 NM 19/11 NLR＞5:30.0%Svaton M 2018 Czech Republic European 120 NM 71/49 NLR＞3.8:50.0%Suh Koung Jin 2018 Korea Asian 54 26.2 42/12 NLR＞5:14.8%Shiroyama Takayuki 2018 Japan Asian 201 12.4 135/66 NLR＞4:39.3%Kiriu T 2018 Japan Asian 19.00 NM 19 NLR＞5:31.6%Khunger M 2018 America American 109 30 56/53 NLR≥5:50.5%Inomata M 2018 Japan Asian 36 NM 27/9 NLR≥5:44.4%Facchinetti F 2018 Italy European 54 12.6 45/9 NMRen F 2019 China Asian 147 2.6 94/53 NLR＞2.5:59.9%Pavan A 2019 Italy European 184 56.3 125/59 NLR≥3:57.5%Passiglia F 2019 Italy European 45 9.1 32/13 NLR＞3.3:51.1%Minami S 2019 Japan Asian 76 NM 49/27 NLR≥6:14.5%Ichiki Y 2019 Japan Asian 44 4.83 38/6 NMDusselier M 2019 France European 59 NM 44/15 NLR＞5:62.7%Study SCC% Treatment lines Outcome Study design NOS Cutoff IODiem S 34.6% including first line therapy OS/PFS RO 6 5 NBagley SJ 24.0% at least second-line therapy OS/PFS RO 6 5 NRusso A 60.7% at least second-line therapy OS/PFS RO 7 3 NZer A 17.1% at least second-line therapy OS/PFS/DCR RO 7 4 NMNakaya A 36.6% at least second-line therapy PFS/irAEs RO 6 3 NMaymani H 16.2% including first line therapy OS/PFS RO 7 6 N/P/DMezquita L 28.6% at least second line therapy OS/PFS RO 9 3 N/E/A/DFukui T 30.8% at least second-line therapy OS/PFS/irAEs PO 7 5 N

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Park W 24.5% including first line therapy OS/PFS RO 7 5 NTakeda T 30.0% at least second-line therapy PFS RO 6 5 NSvaton M 33.3% at least second-line therapy OS/PFS RO 7 3.8 NSuh Koung Jin 31.5% including first line therapy OS/PFS/irAEs RO 8 5 N/PShiroyama Takayuki 30.4% at least second-line therapy PFS/RR RO 7 4 NKiriu T 31.5% at least second-line therapy OS/PFS/TTF RO 7 5 NKhunger M 23.9% at least second-line therapy OS RO 6 5 NInomata M 44.4% at least second-line therapy PFS RO 6 5 N/PFacchinetti F 48.2% at least second-line therapy OS/PFS/TTF/DP PO 8 4 NRen F 42.2% at least second-line therapy OS/PFS RO 6 2.5 N/PPavan A 32.1% including first line therapy OS/PFS/irAEs RO 8 3 N/P/APassiglia F 44.4% at least second-line therapy OS/TTP RO 8 3.3 NMinami S 23.7% at least second-line therapy OS/PFS RO 9 6 N/P/AIchiki Y 65.9% including first line therapy OS/PFS/irAEs RO 7 NM N/PDusselier M 20.3% at least second-line therapy OS RO 8 5 N

1

2 Abbreviations: NLR: neutrophil to lymphocyte ratio; NM: not mentioned; M/F:

3 male/female; MFP: median follow-up (months); SCC%: proportion of patients with

4 squamous cell carcinoma; IO: immunotherapy; N: nivolumab; P: pembrolizumab; D:

5 durvalumab; E: embrolizumab; A: atezolizumab; OS: overall survival; PFS:

6 progression-free survival; DCR: disease control rate; irAEs: immune-related adverse

7 events; RR: response rate; TTF: time to treatment failure; RO: retrospective study;

8 PO: prospective study; NOS: Newcastle-Ottawa quality assessment Scale; *: the study

9 provided only the median NLR and range at baseline.

10 Supplementary information

11 S1: File. PRISMA 2009 checklist.

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1 S2: Search strategy. This file provides the full electronic search strategy for PubMed

2 S3: Figure 1.Trim and fill analysis of OS and PFS

3 S4: Professional editing certificaiton

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396x246mm (96 x 96 DPI)

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Figure 3 Subgroup analysis of the relationship between the NLR and OS in patients with lung cancer receiving immunotherapy

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396x246mm (96 x 96 DPI)

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Figure 5 Subgroup analysis of the relationship between the NLR and PFS in patients with lung cancer receiving immunotherapy

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143x74mm (300 x 300 DPI)

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Combined sets

3 1 and 2

Limits



hierarchy

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150x60mm (300 x 300 DPI)

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TITLE Title 1 Identify the report as a systematic review, meta-analysis, or both. 1

ABSTRACT Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility

criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.

2-3

INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known. 4


5

METHODS Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide

registration information including registration number. 5

Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

6


5


5-6 and supplements


5-8


6-7


6-7



8



7-8

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8


8

RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at

each stage, ideally with a flow diagram. 8-9


8-9



8-11




DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to

key groups (e.g., healthcare providers, users, and policy makers). 12-13


15-16


FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the

systematic review. 17



Page 2 of 2

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For peer review onlyAssociation of the neutrophil to lymphocyte ratio and

clinical outcomes in lung cancer patients receiving immunotherapy: a meta-analysis

Journal: BMJ Open

Manuscript ID bmjopen-2019-035031.R2


Date Submitted by the Author: 14-Apr-2020


<b>Primary Subject Heading</b>: Oncology

Secondary Subject Heading: Respiratory medicine



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1

1 Association of the neutrophil to lymphocyte ratio and clinical

2 outcomes in lung cancer patients receiving immunotherapy: a

3 meta-analysis

4

5 Jing Jin1, Lan Yang1, Dan Liu1, Wei-Min Li1*

6 1. Department of Pulmonary & Critical Care, West China Hospital, Sichuan

7 University, Chengdu 610041, China.

8 Running title: Neutrophil to lymphocyte ratio and lung cancer

9 Address correspondence to: Dr. Wei-Min Li, Department of Pulmonary & Critical

10 Care, West China Hospital, Sichuan University, Chengdu 610041, China. Tel: +86 (028)

11 85423998; E-mail: [email protected]

12 Article summary

13 Strengths and limitations of this study

14 1. Verification of the prognostic value of the NLR in a large number of lung

15 cancer patients who received immunotherapy

16 2. Different clinical characteristics could affect the prognostic value of the NLR

17 3. High heterogeneity was present in this analysis

18

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2

1 Abstract：

2 Objectives: To explore the relationship between the pretreatment or posttreatment

3 neutrophil to lymphocyte ratio (NLR) and overall survival (OS)/ progression-free

4 survival (PFS) in lung cancer patients receiving immunotherapy.

5 Design: We searched several databases to collect relevant studies conducted until July

6 2019. We carefully reviewed the full text of the included publications and combined

7 the hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association

8 between the NLR and survival time in lung cancer patients receiving immunotherapy.

9 Data Sources: PubMed, the Cochrane Library, Embase and Web of Science

10 Eligibility Criteria: Studies reporting the prognostic value of the NLR in lung cancer

11 patients receiving immunotherapy were enrolled.

12 Data extraction and synthesis: Basic information on the articles and patients (NLR

13 cutoff value, NLR at baseline, and HRs with 95% CIs for OS and PFS) was extracted

14 by two authors independently. The pooled HRs of OS and PFS were synthesized

15 using the random effects or fixed effects model.

16 Results: Twenty-three studies with 2068 patients were enrolled. Among all patients,

17 1305 (64.0%) were males, and 643 (31.4%) were diagnosed with squamous cell

18 carcinoma. In a pooled analysis of OS and PFS from all studies, an elevated NLR

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1 predicted poor OS (HR=1.62; 95% CI: 1.41-1.87; P< 0.001) and PFS (HR=1.47; 95%

2 CI: 1.25-1.72; P< 0.0001). Subgroup analyses stratified showed that the posttreatment

3 NLR was not significantly related to OS and that patients in Asia had significantly

4 higher HRs than those in Europe and America. Furthermore, the proportion of

5 squamous cell carcinoma and baseline NLR could affect the prognostic value of the

6 NLR.

7 Conclusions: Our study found that an elevated NLR was associated with poor OS and

8 PFS in lung cancer patients receiving immunotherapy and that several clinical factors

9 might have an impact on the predictive value of the NLR in the survival of lung

10 cancer patients.

11

12 Keywords: NLR; systemic inflammation; prognostic markers; lung cancer

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