C-reactive protein-albumin ratio is an independent prognostic predictor of tumor recurrence in stage IIIA-N2 lung adenocarcinoma patients

Lung Cancer 114 (2017) 62–67

Contents lists available at ScienceDirect

Lung Cancer journal homepage: www.elsevier.com/locate/lungcan

Research paper

C-reactive protein-albumin ratio is an independent prognostic predictor of tumor recurrence in stage IIIA-N2 lung adenocarcinoma patients

MARK

Yoshikane Yamauchia,e, Seyer Safia,e, Thomas Muleyb,e, Arne Warthc,e, Felix J.F. Herthd,e, ⁎ Hendrik Dienemanna,e, Hans Hoffmanna,e, Martin E. Eichhorna,e, a

Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Heidelberg, Germany Section translational research (STF), Thoraxklinik, Heidelberg University, Germany c Institute of Pathology, Heidelberg University, Heidelberg, Germany d Department of Pneumology and Critical Care Medicine, Thoraxklinik, Heidelberg University, Heidelberg, Germany e Translational Lung Research Center (TLRC), Heidelberg, Member of German Center for Lung Research (DZL), Germany b

A R T I C L E I N F O

A B S T R A C T

Keywords: Lung adenocarcinoma NSCLC stage IIIA C-reactive protein-albumin ratio Inflammation Nutrition Prognostic marker

Objective: To systematically evaluate the prognostic value of nutrition/inflammation-based markers for recurrence-free survival (RFS) in pN2-stage IIIA lung adenocarcinoma patients. Materials and methods: Data from 156 patients who had pathologically confirmed pN2-stage IIIA primary lung adenocarcinoma and received complete surgical resection from 2010 to 2014 were retrospectively analyzed. The data for Glasgow prognostic score (GPS), modified GPS (mGPS), high-sensitivity mGPS, C-reactive protein/ albumin ratio (CAR), neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and prognostic nutritional index were analyzed. Univariate and multivariate Cox proportional-hazards regression analyses were used to identify the prognostic factors associated with RFS. Results: The optimal cutoff value for the CAR was set at 0.6. A significant correlation was found between the CAR and RFS (P = 0.001) by univariate analysis. Multivariate analysis between RFS and the factors selected from univariate analysis showed that ECOG performance status, pneumonectomy, multi-level N2, and high CAR were independent predictors of RFS. Conclusion: The CAR was the best prognostic marker to predict tumor recurrence in pN2-stage IIIA lung adenocarcinoma patients among the 7 nutrition/inflammation-based markers. The preoperative CAR may identify patients with a high risk of postoperative tumor recurrence.

1. Introduction Although great advances have been made in treatment strategies, lung cancer is still the main cause of cancer-associated mortality all over the world. Approximately 20% of non-small cell lung cancer (NSCLC) patients are diagnosed at pathological stage IIIA, which is considered a locally advanced stage and has postoperative 5 year overall survival rates ranging from 24% to 40.9% [1–3]. Because of poor prognosis, trimodality therapy is considered an important strategy in order to reduce tumor recurrence and to improve recurrence-free survival (RFS) [4]. However, due to the heterogeneity of pN2-stage IIIA NSCLC patients and lack of clear evidence from randomized controlled trials treatment for these patients still remains controversial [5,6]. Therefore, methods to distinguish patients with better prognosis from those with worse prognosis are urgently required in pN2-stage IIIA disease.

⁎

It has been recognized that systemic inflammatory responses play an important role in the development of tumor, enhancement of local immunosuppression, and induction of invasion and metastatic spread [7–11]. In addition, it has already been reported that both the level of inflammatory components and malnutrition are correlated with poor survival [12]. Moreover, relationships between lung cancer and several nutrition/inflammation-based hematological biomarkers, such as Glasgow prognostic score (GPS) [13], modified GPS (mGPS) [14], highsensitivity mGPS (HS-mGPS) [15], C-reactive protein/albumin ratio (CAR) [16], neutrophil/lymphocyte ratio (NLR) [17], platelet/lymphocyte ratio (PLR) [18], and prognostic nutritional index (PNI) [19], have been revealed. However, few studies regarding these biomarkers in patients with pN2-stage IIIA NSCLC are available, and the prognostic values of these biomarkers remain uncertain. In this study, we therefore systematically evaluated the prognostic value of nutrition/inflammation-based markers, including GPS, mGPS,

Corresponding author at: Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, Roentgenstrasse 1, 69126, Heidelberg, Germany. E-mail address: [email protected] (M.E. Eichhorn).

http://dx.doi.org/10.1016/j.lungcan.2017.11.002 Received 22 August 2017; Received in revised form 29 October 2017; Accepted 2 November 2017 0169-5002/ © 2017 Published by Elsevier Ireland Ltd.

Lung Cancer 114 (2017) 62–67

Y. Yamauchi et al.

HS-mGPS, CAR, NLR, PLR, and PNI, in patients with resectable N2stage IIIA adenocarcinomas. Moreover, we also investigated the prognostic impact of the selected markers and compared them with other established co-factors.

All nutrition scores were calculated from the preoperative laboratory result, which was measured one to two days before surgery.

2. Materials and methods

RFS was the time interval from the date of surgery to the date when tumor recurrence was observed or to the date of death. RFS curves were plotted with the Kaplan-Meier method and differences were compared with log-rank test. Cox regression was used for univariate and multivariate analyses. Hazard ratio (HR) and 95% confidence interval (95% CI) were computed with the Cox proportional-hazards model. Variables that were significantly prognostic in the univariate analysis were selected for multivariate analysis. The optimal cutoff values for continuous prognostic indexes were determined with the method established by Budczies et al. described at http://molpath.charite.de/cutoff/ http://molpath.charite.de/cutoff/ [29]. This method fits Cox proportional hazard models to the dichotomized variable and the survival variable. The optimal cutoff is defined as the point with the most significant log-rank test split. All statistical analyses were performed with SPSS 23.0 (SPSS Inc., Chicago, IL, USA). A two-tailed P value < 0.05 was considered statistically significant.

2.3. Statistical analysis

The Heidelberg University Hospital Institutional Review Board approved the retrospective collection and analysis of data obtained from medical records of patients included in this study (approval ID: 080/ 2006) and waived the requirement for individual informed patient consent. 2.1. Patient selection Patients were retrospectively selected from a prospectively maintained database of patients who underwent lung operations at Thoraxklinik Heidelberg from January 2010 to December 2014. The eligibility criteria for this study were the following: (1) pathologically confirmed primary lung adenocarcinoma; (2) pathological stage IIIAN2, according to the seventh edition of the International Union Against Cancer Staging System for Lung Cancer [20]; and (3) complete surgical resection (R0) as the primary treatment for lung cancer. Patients who received neoadjuvant treatment or those who had synchronous primary lung cancer were excluded. The lost to follow-up cases within 2 months after surgery were also excluded. A total of 156 patients met these criteria and were included in this study. In all patients systematic mediastinal lymph node dissection was performed according to the ESTS guideline [21]. All patients with p-stage IIIA disease were scheduled to receive adjuvant therapy, although some of the patients did not undergo this therapy due to comorbidities or patients’ refusal. Adjuvant chemotherapy was administered within 4–8 weeks after primary lung resection. These patients received four cycles of cisplatin (75 mg/m2) or carboplatin with vinorelbine as the standard adjuvant chemotherapy. The multi-level pN2 stage IIIA patients received sequential adjuvant chemoradiotherapy. All treatments were planned by the institutional tumor board. The patients were routinely followed-up at Thoraxklinik, Heidelberg University, every 3 months during the first year after surgery, every 6 months for the next 2 years and annually thereafter. Follow-up was maintained by the retrieval of follow-up medical records, which were stored in the outpatient department database, or the patients were followed by personal contact with our professional follow-up institution, which involved requests for information regarding tumor recurrences and survival status.

3. Results The baseline patient characteristics are shown in Table 1. A total of 156 patients were included in this study with 49% males and average age of 62 ± 10 years. The mean vital capacity (VC), mean forced Table 1 Clinicopathological characteristics of the patient cohort. characteristics gender age BMI lung function VC FEV1 FEV1/VC ratio

2.2. Definition of nutrition/inflammation-based prognostic scores The nutrition and inflammation-based prognostic scores in this study were defined according to previous reports [22–25]. The GPS was calculated as follows: patients with elevated C-reactive protein (CRP) (> 10 mg/L) and hypoalbuminemia (< 35 g/L) were assigned to a score of 2. Patients with one or no abnormal value were assigned to a score of 1 or 0, respectively. The mGPS was calculated as follows [26]: patients with elevated CRP (> 5 mg/L) and hypoalbuminemia (< 38 g/ L) were assigned to a score of 2. Patients with one or no abnormal value were assigned to a score of 1 or 0, respectively. The HS-GPS was calculated as follows: patients with elevated CRP (> 3 mg/L) and hypoalbuminemia (< 35 g/L) were assigned to a score of 2. Patients with one or no abnormal value were assigned to a score of 1 or 0, respectively [26–28]. The CAR was calculated from the serum CRP level (mg/ L) divided by the serum albumin level (g/L). NLR and PLR were calculated at each time point by simply dividing the absolute counts of the components mentioned above. PNI was calculated by the following formula: PNI = serum albumin (g/L) + 5 × lymphocyte count (/nL).

value male: female (y) (kg/m2)

77: 79 62 ± 10 26.7 ± 5.4

(l) (l/s) (%)

3.5 ± 1.0 2.6 ± 0.7 74.1 ± 10.2

adenocarcinoma subtype lepidic papillary solid acinar micropapillary invasive mucinous

2 10 55 63 24 2

operation type lobectomy bilobectomy pneumonectomy

131 10 15

tumor markers CEA CYFRA

(ng/ml) (ng/ml)

16.1 ± 44.0 5.6 ± 24.0

blood cell count/biochemistry CRP albumin neutrophils lymphocytes platelets

(mg/L) (g/L) (/nL) (/nL) (/nL)

18.4 ± 30.4 42.8 ± 3.8 5.9 ± 2.2 1.9 ± 0.7 309 ± 122

nutrition/inflammation-based scores CAR NLR PLR PNI Continuous variables are expressed as average ± SD.

0.48 ± 0.87 3.65 ± 2.1 188 ± 99 51.9 ± 5.4

Abbreviations: BMI Body Mass Index, VC Vital capacity, FEV forced expiratory volume, CEA carcinoembryonic antigen, CYFRA cytokeratin-19 fragment, CRP C-reactive protein, CAR CRP Albumin ratio, NLR Neutrophil lymphocyte ratio, PLR Platelet lymphocyte ratio, PNI Prognostic nutritional index.

63

Lung Cancer 114 (2017) 62–67

Y. Yamauchi et al.

Fig. 1. (a) Kaplan-Meier plot of RFS from 156 patients with pathological stage IIIA-N2 lung adenocarcinoma who underwent complete surgical resection. (b-h) Kaplan-Meier curves of RFS according to CAR (b), to NLR (c), to PLR (d), to PNI (e), to GPS (f), to mGPS (g) and to HS-GPS (h). (b) and (d) showed significant differences between high and low groups (p < 0.001 and p = 0.005, respectively).

expiratory volume in one second (FEV1), and mean FEV1/VC ratio were 3.5 L, 2.6 L, and 74.1%, respectively. The predominant subtype of adenocarcinoma was of acinar subtype (40%), followed by solid (35%), micropapillary (15%), and papillary (6%) subtypes. Lobectomy was the most frequent type of surgical operation (84%). The mean CEA and CYFRA levels were 16.1 ng/ml and 5.6 mg/ml, respectively. Mean values of CRP, albumin, neutrophils, lymphocytes, and platelets were 18.4 mg/L, 42.8 g/L, 5.9/nL, 1.9/nL, 309/nL, respectively. The median follow-up period was 17.2 months. Eighty-seven patients (56%) were found to have tumor recurrence. Regarding adjuvant therapy, 42 (27%) patients received chemotherapy, 55 (35%) received sequential chemoradiotherapy, 12 (8%) received radiotherapy, 34 (22%) patients received no adjuvant therapy. In 13 patients, adjuvant therapy status was unknown. The Kaplan-Meier curve for all patients in this cohort is shown in Fig. 1(a). The estimated median RFS of this cohort was 22.1 months. The 1 year, 2 year, and 3 year RFS rates were 68%, 47% and 40%, respectively. Table 2 shows the univariate analysis of nutrition/inflammationbased markers associated with RFS. The GPS, mGPS, HS-GPS exhibited no significant difference in any combination of the two groups. Regarding the CAR, the optimal threshold was assigned at 0.6. A significant difference was found between the high CAR group and low CAR group (p < 0.001). The thresholds for NLR and PLR were assigned at 2 and 105, respectively; however, none of the groups dichotomized by these parameters reached significant differences (p = 0.097 and p = 0.062, respectively). On the other hand, the threshold of PNI was assigned at 48, and the two dichotomized groups demonstrated a significant difference (p = 0.005). Fig. 1(b)–(h) show the RFS in the

Table 2 Univariate analysis of recurrent free survival in Nutrition/Inflammation index. Characteristics

Number

% patients

p value

HR

(95% CI)

0 1 2

90 53 4

61% 36% 3%

ref 0.120 0.859

1.43 1.12

0.91 0.28

– –

2.25 4.68

0 1 2

66 66 15

45% 45% 10%

ref 0.242 0.106

1.32 1.79

0.83 0.88

– –

2.10 3.63

1.35 1.29

0.73 0.29

– –

2.49 5.76

2.99

1.75

–

5.12

2.18

0.87

–

5.47

2.41

0.96

–

6.05

2.35

1.29

–

4.27

GPS

mGPS

HS-GPS 0 26 17% ref 1 119 80% 0.344 2 4 3% 0.742 CAR cutoff: 0.6 low 122 83% ref high 25 17% < 0.001* NLR cutoff: 2 low 16 16% ref high 84 84% 0.097 PLR cutoff: 105 low 17 17% ref high 83 83% 0.062 PNI cutoff: 48 high 74 79% ref low 20 21% 0.005* Some cases were dropped due to data unavailability

Abbreviations: HR Hazard ratio, CI Confidence Interval, GPS Glasgow Prognostic Score, mGPS modified GPS, HS-GPS high sentsitivity-GPS.

64

Lung Cancer 114 (2017) 62–67

Y. Yamauchi et al.

Table 3 Univariate analysis of recurrcence free survival in clinical and pathological factors. characteristics General condition Age ≥ 51 ≤ 50 BMI < 30 ≥ 30

Number

% patients

p

HR

(95% CI)

132 24

85% 15%

ref 0.033*

1.76

1.05

–

2.96

117 34

77% 23%

ref 0.99

1

0.61

–

1.63

130 26

83% 17%

ref 0.001*

2.42

1.44

–

4.07

48 108

31% 69%

ref 0.291

1.29

0.81

–

2.05

FEV1/VC rati > 70% ≤ 70%

94 28

77% 23%

ref 0.107

1.52

0.91

–

2.52

Operation lobectomy bilobectomy pneumonectomy

131 10 15

84% 6% 10%

ref 0.362 0.018*

1.48 2.10

0.64 1.13

– –

3.40 3.88

Pathological factors T stage pT1 pT2 pT3

29 83 44

18% 53% 28%

ref 0.092 < 0.001*

1.76 3.56

0.91 1.78

– –

3.41 7.09

Multi-level N2 Single-level N2 Multi-level N2

110 46

71% 29%

ref 0.001*

2.04

1.33

–

3.13

49% 51%

ref 0.046*

ECOG-PS 0 1 ASA-PS 1 or 2 3

Clinical factors CEA <5 75 ≥5 78 CYFRA < 1.7 55 ≥1.7 74 Some cases were dropped due to

Table 4 Multivariate analysis for recurrence free survival in selected factors. Factors

p

HR

(95% CI)

General condition Age ≤ 50 ECOG-PS = 1 pneumonectomy

0.482 0.005* 0.020*

1.36 3.41 3.26

0.58 1.45 1.20

– – –

3.22 8.03 8.84

Pathological factor pT3 Multi-level N2

0.607 0.004*

1.28 3.00

0.50 1.43

– –

3.27 6.29

0.514 0.093

1.29 1.87

0.60 0.90

– –

2.75 3.90

0.031* 0.217

4.20 1.72

1.14 0.73

– –

15.51 4.09

clinical factors CEA ≥ 5 CYFRA ≥ 1.7 Nuitrition/Inflammation index CAR ≥ 0.6 PNI ≤ 48

single-level N2 metastasis. The optimal cutoffs for CEA and CYFRA were 5 ng/ml and 1.7 ng/ml, respectively, and there were significant relationships between RFS and both tumor markers (p = 0.046 and p < 0.001, respectively). From these analyses, age, ECOG-PS, surgery type, p-T, grade of N2-level, CEA, and CYFRA were selected for multivariate analyses. Table 4 shows the results of multivariate analysis between RFS and the selected factors. From this analysis, ECOG-PS = 1, pneumonectomy for complete resection, multi-level N2, and high CAR were found to be significantly important factors for poor prognosis in terms of RFS (p = 0.005, p = 0.020, p = 0.004, and p = 0.031, respectively). 4. Discussion

43% ref 57% < 0.001* data unavailabilitY

1.55

1.01

–

2.39

2.76

1.70

–

4.49

In this study, we assessed the prognostic value of the GPS, mGPS, HS-GPS, CAR, NLR, PLR, and PNI in pN2-stage IIIA lung adenocarcinoma patients and found that CAR was the most valuable in predicting prognosis. Moreover, high CAR was an independent and significant risk factor for poor RFS after complete resection of tumors from these patients in addition to ECOG performance status 1, pneumonectomy and multiple-level N2 disease. Many studies have already investigated prognostic factors related to inflammation and nutrition [13–15,17–19]. However, for the purpose of increasing the number of cases for most of these studies, investigators collected data from patients with various histological backgrounds and with a wide variety of pathological stages over a long period of time. Indeed, there would not have been any issue if the studies had only focused on early-stage cases because the standard treatment protocol, i.e., complete resection, has not changed for a long time. However, in general, a prognostic marker is strongly desired for the group that is expected to have worse prognosis, especially patients with p-stage IIIA disease, for whom treatment strategies or adjuvant chemotherapy agents have changed frequently. Therefore, more sophisticated grouping methods should be used to explore valuable markers in order to acquire adequate result from analyses. For these reasons, we focused on patients with curatively resected pN2-stage IIIA lung adenocarcinoma over a period of only 5 years in this study. The median RFS in our study is 22 months. The median RFS of curatively resected p-stage IIIA non-small cell lung cancer patients has been reported from 15 to 22 months [31–33]; therefore, patients in our cohort exhibited similar results. Our study demonstrated that ECOG performance status, pneumonectomy and multi-level N2 were independent and significant factors associated with RFS in addition to CAR. Previous studies with multivariate analysis also reported significant correlation of these prognostic factors with overall survival in patients with curatively resected pN2stage IIIA NSCLC [34–38]. Our results reconfirmed the result of these studies.

stratified group according to these respective factors. From these data, the CAR and PNI were selected for multivariate analyses with other clinical and pathological co-factors. Table 3 shows the relationship between RFS and other clinical and pathological co-factors. The optimal cutoff of age was determined at 50 years, and there was significant difference between the younger and older patients (p = 0.033). The cutoff of body mass index (BMI) was assigned at 30 kg/m2 according to the WHO classification for obesity [30]. No significant relationship was found between RFS and BMI (p = 0.99). For 130 patients (83%) the ECOG performance status (ECOG-PS) score was 0, and that for the rest of the patients was 1. The two groups divided according to the ECOG-PS score showed a significant difference in RFS (p = 0.001). Regarding the ASA physical status classification (ASA-PS), 3 patients (2%) were categorized as class 1, 45 patients (29%) as class 2, and 108 patients (69%) as class 3. Class 1 and 2 were not significantly different from class 3 (p = 0.29). For the FEV1/VC ratio, the cutoff was assigned at 70%, which is one of the diagnostic criteria for chronic obstructive pulmonary disease and no significant difference in RFS was found between the two groups (p = 0.107). The dominant operation type was lobectomy (84%) followed by pneumonectomy (10%) and bilobectomy (6%). Patients with pneumonectomy had significantly worse RFS than those with lobectomy (p = 0.018). Regarding the pathological T stage, 29 patients (18%) were T1, 83 patients (53%) were T2, and 44 patients (28%) were T3. The patients with T3 stage disease exhibited significantly worse prognosis compared with patients with T1 stage disease (p < 0.001). Forty-seven patients (30%) had multi-level N2 metastasis and significantly poor prognosis in terms of RFS compared to patients with 65

Lung Cancer 114 (2017) 62–67

Y. Yamauchi et al.

considered in order to improve the nutrition/inflammation status. Second, a recommendation favoring more aggressive adjuvant treatment in high CAR patients compared to low CAR patients could be made. In conclusion, we found that CAR is the best prognostic marker for pN2-stage IIIA lung adenocarcinoma patients among 7 nutrition/inflammation-based markers. In addition, we also found that CAR may be useful to preoperatively detect poor prognosis.

Serum CRP, known as an acute-phase protein, is synthesized by hepatocytes, and this process is regulated by pro-inflammatory cytokines, particularly IL-6. It has been reported that CRP is a reliable prognostic marker for systemic inflammation, and there is a significant positive correlation between serum levels of CRP and progression of non-small cell lung cancer [39]. In addition, it has already been demonstrated that the presence of a systemic inflammatory response is accompanied with a decrease in serum albumin concentration and the progressive loss of weight and lean tissue, resulting in poor performance and increasing mortality in cancer patients [40,41]. Therefore, both CRP and serum albumin concentrations are strongly related to systemic inflammation. The CAR was primarily proposed by Fairclough et al. as a tracking and triggering tool for the prompt identification of seriously ill patients in acute medical wards [42]. Ranzani et al. indicated the CAR as an independent marker for the prediction of 90 day mortality of septic patients [43]. Moreover, some recent studies have demonstrated that the CAR is a promising prognostic factor in cancer, including hepatocellular carcinoma [44], small cell lung cancer [16], colorectal cancer [45], gastric cancer [46], pancreatic cancer [47–49], nasopharyngeal carcinoma [50], and esophageal cancer [51,52]. However, the optimal cutoff for this marker has not been determined yet. The cutoff used in previous publications varied broadly (0.0271-2), although many cutoff values, including ours, are approximately at 0.5. Therefore, we need to collect more information about the CAR in different clinical settings to find the universal and optimal cutoff. Although the CAR has been found to be an important prognostic factor, it is clear that surgery should not be completely avoided in pstage IIIA patients with high CAR, because it has already been demonstrated that p-stage IIIA patients have better prognosis with surgery than without it [53]. With this perspective in mind, our results raise the question of how to improve the preoperative immune and nutritional status, in other words, how to decrease the CAR. The CAR consists of two factors, CRP and albumin; therefore, the decrease in CRP and increase in serum albumin is required to decrease the CAR. As mentioned before, these two factors are strongly related to systemic inflammation. Thus, the preoperative attenuation of systemic inflammation could be an option, such as preoperative treatment with antibiotics if infection is the reason for inflammation. In addition, preoperative intervention may also be another option. The North American Surgical Nutrition Summit released the recommendation that the provision of nutrition is always the first choice for nutrition after a major elective surgery. They also mentioned that the perioperative administration of a pharmaconutrition formula containing arginine, fish oil, and nucleotides improves the nutritional status of patients undergoing major upper or lower gastrointestinal (GI) surgeries [54]. Moreover, Mocellin et al. also demonstrated that supplementation with eicosapentaenoic acid and docosahexaenoic acid in fish oil decreased the CAR in patients with colorectal cancer, resulting in the prevention of weight loss [55]. Although the situation is different between GI surgery and thoracic surgery, supplementation may be an option for preoperative intervention, especially for the patients identified to have worse prognosis, such as pN2-stage IIIA lung adenocarcinoma patients with low CAR. Simmons et al. have shown prognostic significance of mGPS in advanced lung cancer [56]. We could not confirm the result in our investigation. One possible reason for this discrepancy can be different mGPS calculation methods that have been applied. However, the mGPS according to the definition applied by Simmons et al. failed to enable prognostic stratification in our cohort (data not shown), too. The most plausible reason for this discrepancy may be related to different patient inclusion criteria. In order to remove all possible confounding factors, we have selected a patient cohort which is much more homogenous compared to previous reports. From the results of this study, two possible therapeutic strategies can be derived and implemented in patient care: first, the preoperative supplement of immunonutritional agents in high CAR patients can be

Conflict of interest None declared. Acknowledgement This work was supported by Uehara foundation fellowship for research abroad and by institutional research funding. References [1] P. Goldstraw, K. Chansky, J. Crowley, R. Rami-Porta, H. Asamura, W.E.E. Eberhardt, A.G. Nicholson, P. Groome, A. Mitchell, V. Bolejack, PI, International association for the study of lung cancer staging and prognostic factors committee, advisory boards, international association for the study of lung cancer staging and prognostic factors committee advisory boards and participating institutions, the IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (Eighth) edition of the TNM classification for lung cancer, J. Thorac Oncol. 11 (2016) 39–51, http://dx.doi.org/10.1016/j.jtho. 2015.09.009. [2] T. Goya, H. Asamura, H. Yoshimura, H. Kato, K. Shimokata, R. Tsuchiya, Y. Sohara, T. Miya, E. Miyaoka, Prognosis of 6644 resected non-small cell lung cancers in Japan: a Japanese lung cancer registry study, Lung Cancer 50 (2005) 227–234, http://dx.doi.org/10.1016/j.lungcan.2005.05.021. [3] N. Sawabata, E. Miyaoka, H. Asamura, Y. Nakanishi, K. Eguchi, M. Mori, H. Nomori, Y. Fujii, M. Okumura, K. Yokoi, Japanese joint committee for lung cancer registration, Japanese lung cancer registry study of 11 663 surgical cases in 2004: demographic and prognosis changes over decade, J. Thorac. Oncol. 6 (2011) 1229–1235, http://dx.doi.org/10.1097/JTO.0b013e318219aae2. [4] K.S. Albain, R.S. Swann, V.W. Rusch, A.T. Turrisi, F.A. Shepherd, C. Smith, Y. Chen, R.B. Livingston, R.H. Feins, D.R. Gandara, W.A. Fry, G. Darling, D.H. Johnson, M.R. Green, R.C. Miller, J. Ley, W.T. Sause, J.D. Cox, Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial, Lancet 374 (2009) 379–386, http:// dx.doi.org/10.1016/S0140-6736(09)60737-6. [5] G.A. Silvestri, A.V. Gonzalez, M.A. Jantz, M.L. Margolis, M.K. Gould, L.T. Tanoue, L.J. Harris, F.C. Detterbeck, Methods for staging non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American college of chest physicians evidence-based clinical practice guidelines, Chest 143 (2013) e211S–e250S, http:// dx.doi.org/10.1378/chest.12-2355. [6] G. Massard, S. Renaud, J. Reeb, N. Santelmo, A. Olland, P.-E. Falcoz, N2-IIIA nonsmall cell lung cancer: a plea for surgery!, J. Thorac. Dis. 8 (2016) S849–S854, http://dx.doi.org/10.21037/jtd.2016.09.34. [7] J.W. Pollard, Tumour-educated macrophages promote tumour progression and metastasis, Nat. Rev. Cancer 4 (2004) 71–78, http://dx.doi.org/10.1038/nrc1256. [8] A. Mantovani, P. Allavena, A. Sica, F. Balkwill, Cancer-related inflammation, Nature 454 (2008) 436–444, http://dx.doi.org/10.1038/nature07205. [9] T.L. Whiteside, The tumor microenvironment and its role in promoting tumor growth, Oncogene 27 (2008) 5904–5912, http://dx.doi.org/10.1038/onc.2008. 271. [10] F. Colotta, P. Allavena, A. Sica, C. Garlanda, A. Mantovani, Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability, Carcinogenesis 30 (2009) 1073–1081, http://dx.doi.org/10.1093/carcin/bgp127. [11] E. Elinav, R. Nowarski, C.A. Thaiss, B. Hu, C. Jin, R.A. Flavell, Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms, Nat. Rev. Cancer 13 (2013) 759–771, http://dx.doi.org/10.1038/nrc3611. [12] M. Kovarik, M. Hronek, Z. Zadak, Clinically relevant determinants of body composition, function and nutritional status as mortality predictors in lung cancer patients, Lung Cancer 84 (2014) 1–6, http://dx.doi.org/10.1016/j.lungcan.2014.01. 020. [13] M. Yotsukura, T. Ohtsuka, K. Kaseda, I. Kamiyama, Y. Hayashi, H. Asamura, Value of the glasgow prognostic score as a prognostic factor in resectable non-Small cell lung cancer, J. Thorac. Oncol. 11 (2016) 1311–1318, http://dx.doi.org/10.1016/j. jtho.2016.04.029. [14] T. Kishi, Y. Matsuo, N. Ueki, Y. Iizuka, A. Nakamura, K. Sakanaka, T. Mizowaki, M. Hiraoka, Pretreatment modified glasgow prognostic score predicts clinical outcomes after stereotactic body radiation therapy for early-Stage non-Small cell lung cancer, Int. J. Radiat. Oncol. Biol. Phys. 92 (2015) 619–626, http://dx.doi.org/10. 1016/j.ijrobp.2015.02.018. [15] J. Osugi, S. Muto, Y. Matsumura, M. Higuchi, H. Suzuki, M. Gotoh, Prognostic impact of the high-sensitivity modified Glasgow prognostic score in patients with

66

Lung Cancer 114 (2017) 62–67

Y. Yamauchi et al.

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

resectable non-small cell lung cancer, J. Cancer Res. Ther. 12 (2016) 945, http://dx. doi.org/10.4103/0973-1482.176168. T. Zhou, J. Zhan, S. Hong, Z. Hu, W. Fang, T. Qin, Y. Ma, Y. Yang, X. He, Y. Zhao, Y. Huang, H. Zhao, L. Zhang, Ratio of C-reactive protein/abumin is an inflammatory prognostic score for predicting overall survival of patients with small-cell lung cancer, Sci. Rep. 5 (2015) 10481, http://dx.doi.org/10.1038/srep10481. Y. Takahashi, M. Kawamura, T. Hato, M. Harada, N. Matsutani, H. Horio, Neutrophil-lymphocyte ratio as a prognostic marker for lung adenocarcinoma after complete resection, World J. Surg. 40 (2016) 365–372, http://dx.doi.org/10.1007/ s00268-015-3275-2. P. Sanchez-Salcedo, J.P. De-Torres, D. Martinez-Urbistondo, J. Gonzalez-Gutierrez, J. Berto, A. Campo, A.B. Alcaide, J.J. Zulueta, The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer development, Lung Cancer 97 (2016) 28–34, http://dx.doi.org/10.1016/j.lungcan.2016.04.010. F. Shoji, Y. Morodomi, T. Akamine, S. Takamori, M. Katsura, K. Takada, Y. Suzuki, T. Fujishita, T. Okamoto, Y. Maehara, Predictive impact for postoperative recurrence using the preoperative prognostic nutritional index in pathological stage I non-small cell lung cancer, Lung Cancer 98 (2016) 15–21, http://dx.doi.org/10. 1016/j.lungcan.2016.05.010. P. Goldstraw, J. Crowley, K. Chansky, D.J. Giroux, P.A. Groome, R. Rami-Porta, P.E. Postmus, V. Rusch, L. Sobin, The IASLC lung cancer staging project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours, J. Thorac. Oncol. 2 (2007) 706–714, http://dx.doi.org/10.1097/JTO.0b013e31812f3c1a. D. Lardinois, P. De Leyn, P. Van Schil, R.R. Porta, D. Waller, B. Passlick, M. Zielinski, T. Lerut, W. Weder, ESTS guidelines for intraoperative lymph node staging in non-small cell lung cancer, Eur. J. CardioThorac. Surg. 30 (2006) 787–792, http://dx.doi.org/10.1016/j.ejcts.2006.08.008. L.M. Forrest, D.C. McMillan, C.S. McArdle, W.J. Angerson, D.J. Dunlop, Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer, Br. J. Cancer 89 (2003) 1028–1030, http://dx.doi.org/10.1038/sj.bjc.6601242. D.C. McMillan, J.E.M. Crozier, K. Canna, W.J. Angerson, C.S. McArdle, Evaluation of an inflammation-based prognostic score (GPS) in patients undergoing resection for colon and rectal cancer, Int. J. Colorectal Dis. 22 (2007) 881–886, http://dx.doi. org/10.1007/s00384-006-0259-6. M.J. Proctor, P.G. Horgan, D. Talwar, C.D. Fletcher, D.S. Morrison, D.C. McMillan, Optimization of the systemic inflammation-based glasgow prognostic score: a glasgow inflammation outcome study, Cancer 119 (2013) 2325–2332, http://dx. doi.org/10.1002/cncr.28018. T. Onodera, N. Goseki, G. Kosaki, [Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients], Nihon Geka Gakkai Zasshi 85 (1984) 1001–1005 http://www. ncbi. nlm. nih. gov/pubmed /6438478 (accessed October 30 2016). K. Hirashima, M. Watanabe, H. Shigaki, Y. Imamura, S. Ida, M. Iwatsuki, T. Ishimoto, S. Iwagami, Y. Baba, H. Baba, Prognostic significance of the modified glasgow prognostic score in elderly patients with gastric cancer, J. Gastroenterol. 49 (2014) 1040–1046, http://dx.doi.org/10.1007/s00535-013-0855-5. T. Arigami, H. Okumura, M. Matsumoto, Y. Uchikado, Y. Uenosono, Y. Kita, T. Owaki, S. Mori, H. Kurahara, Y. Kijima, S. Ishigami, S. Natsugoe, Analysis of the fibrinogen and neutrophil-lymphocyte ratio in esophageal squamous cell carcinoma: a promising blood marker of tumor progression and prognosis, Medicine (Baltimore) 94 (2015) e1702, http://dx.doi.org/10.1097/MD.0000000000001702. T. Nozoe, T. Iguchi, A. Egashira, E. Adachi, A. Matsukuma, T. Ezaki, Significance of modified glasgow prognostic score as a useful indicator for prognosis of patients with gastric carcinoma, Am. J. Surg. 201 (2011) 186–191, http://dx.doi.org/10. 1016/j.amjsurg.2010.01.030. J. Budczies, F. Klauschen, B.V. Sinn, B. Győrffy, W.D. Schmitt, S. Darb-Esfahani, C. Denkert, Cutoff finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization, PLoS One 7 (2012) e51862, http:// dx.doi.org/10.1371/journal.pone.0051862. K.G. Alberti, P.Z. Zimmet, Definition, diagnosis and classification of diabetes mellitus and its complications part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation, Diabet. Med. 15 (1998) 539–553, http:// dx.doi.org/10.1002/(SICI)1096-9136(199807)15:7<539:AID-DIA668>3.0. CO;2-S. G. Qiang, C. Liang, Q. Yu, F. Xiao, Z. Song, Y. Tian, B. Shi, D. Liu, Y. Guo, Risk factors for recurrence after complete resection of pathological stage N2 non-small cell lung cancer, Thorac. Cancer 6 (2015) 166–171, http://dx.doi.org/10.1111/ 1759-7714.12159. S. Peters, W. Weder, U. Dafni, K.M. Kerr, L. Bubendorf, P. Meldgaard, K.J. O’Byrne, A. Wrona, J. Vansteenkiste, E. Felip, A. Marchetti, S. Savic, S. Lu, E. Smit, A.M. Dingemans, F.H. Blackhall, P. Baas, C. Camps, R. Rosell, R.A. Stahel, ETOP lungscape investigators, lungscape: resected non-small-cell lung cancer outcome by clinical and pathological parameters, J. Thorac. Oncol. 9 (2014) 1675–1684, http://dx.doi.org/10.1097/JTO.0000000000000320. V. Askoxylakis, J. Tanner, J. Kappes, H. Hoffmann, N.H. Nicolay, H. Rief, J. Debus, M. Thomas, M. Bischof, Trimodal therapy for stage III-N2 non-small-cell lung carcinoma: a single center retrospective analysis, BMC Cancer 14 (2014) 572, http:// dx.doi.org/10.1186/1471-2407-14-572. J.F. Vansteenkiste, P.R. De Leyn, G.J. Deneffe, G. Stalpaert, K.L. Nackaerts, T.E. Lerut, M.G. Demedts, Survival and prognostic factors in resected N2 non-small cell lung cancer: a study of 140 cases. Leuven lung cancer group, Ann. Thorac. Surg. 63 (1997) 1441–1450, http://dx.doi.org/10.1016/S0003-4975(97)00314-7. K. Suzuki, K. Nagai, J. Yoshida, M. Nishimura, K. Takahashi, Y. Nishiwaki, The prognosis of surgically resected N2 non-small cell lung cancer: the importance of

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

[53]

[54]

[55]

[56]

67

clinical N status, J. Thorac. Cardiovasc. Surg. 118 (1999) 145–153, http://dx.doi. org/10.1016/S0022-5223(99)70153-4. F. Tanaka, K. Yanagihara, Y. Otake, Y. Kawano, R. Miyahara, K. Takenaka, H. Katakura, S. Ishikawa, H. Ito, H. Wada, Prognostic factors in resected pathologic (p-) stage IIIA-N2, non-small-cell lung cancer, Ann. Surg. Oncol. 11 (2004) 612–618, http://dx.doi.org/10.1245/ASO.2004.07.013. M. Riquet, P. Bagan, F. Le Pimpec Barthes, E. Banu, F. Scotte, C. Foucault, A. Dujon, C. Danel, Completely resected non-small cell lung cancer: reconsidering prognostic value and significance of N2 metastases, Ann. Thorac. Surg. 84 (2007) 1818–1824, http://dx.doi.org/10.1016/j.athoracsur.2007.07.015. T. Tsitsias, A. Boulemden, K. Ang, A. Nakas, D.A. Waller, The n2 paradox: similar outcomes of pre- and postoperatively identified single-zone n2a positive non-smallcell lung cancer, Eur. J. Cardio Thoracic Surg. 45 (2014) 882–887, http://dx.doi. org/10.1093/ejcts/ezt478. Y. Kanoh, T. Abe, N. Masuda, T. Akahoshi, Progression of non-small cell lung cancer: diagnostic and prognostic utility of matrix metalloproteinase-2, C-reactive protein and serum amyloid A, Oncol. Rep. 29 (2013) 469–473, http://dx.doi.org/ 10.3892/or.2012.2123. D.C. McMillan, The systemic inflammation-based glasgow prognostic score: a decade of experience in patients with cancer, Cancer Treat. Rev. 39 (2013) 534–540, http://dx.doi.org/10.1016/j.ctrv.2012.08.003. C.S.D. Roxburgh, D.C. McMillan, Cancer and systemic inflammation: treat the tumour and treat the host, Br. J. Cancer 110 (2014) 1409–1412, http://dx.doi.org/10. 1038/bjc.2014.90. E. Fairclough, E. Cairns, J. Hamilton, C. Kelly, Evaluation of a modified early warning system for acute medical admissions and comparison with C-reactive protein/albumin ratio as a predictor of patient outcome, Clin. Med. (Northfield Il) 9 (2009) 30–33, http://dx.doi.org/10.7861/clinmedicine.9-1-30. O.T. Ranzani, F.G. Zampieri, D.N. Forte, L.C.P. Azevedo, M. Park, C-reactive protein/albumin ratio predicts 90-day mortality of septic patients, PLoS One 8 (2013), http://dx.doi.org/10.1371/journal.pone.0059321. A. Kinoshita, H. Onoda, N. Imai, A. Iwaku, M. Oishi, K. Tanaka, N. Fushiya, K. Koike, H. Nishino, M. Matsushima, The C-reactive protein/albumin ratio, a novel inflammation-based prognostic score predicts outcomes in patients with hepatocellular carcinoma, Ann. Surg. Oncol. 22 (2014) 803–810, http://dx.doi.org/10. 1245/s10434-014-4048-0. M. Shibutani, K. Maeda, H. Nagahara, Y. Iseki, T. Ikeya, K. Hirakawa, Prognostic significance of the preoperative ratio of C-reactive protein to albumin in patients with colorectal cancer, Anticancer Res. 36 (2016) 995–1001 (Accessed October 10, 2016), http://www.ncbi.nlm.nih.gov/pubmed/26976989. X. Liu, X. Sun, J. Liu, P. Kong, S. Chen, Y. Zhan, D. Xu, Preoperative C-reactive protein/albumin ratio predicts prognosis of patients after curative resection for gastric cancer, Transl. Oncol. 8 (2015) 339–345, http://dx.doi.org/10.1016/j. tranon.2015.06.006. M. Wu, J. Guo, L. Guo, Q. Zuo, The C-reactive protein/albumin ratio predicts overall survival of patients with advanced pancreatic cancer, Tumour Biol. 37 (2016) 12525–12533, http://dx.doi.org/10.1007/s13277-016-5122-y. K. Haruki, H. Shiba, Y. Shirai, T. Horiuchi, R. Iwase, Y. Fujiwara, K. Furukawa, T. Misawa, K. Yanaga, The C-reactive protein to albumin ratio predicts long-term outcomes in patients with pancreatic cancer after pancreatic resection, World J. Surg. 40 (2016) 2254–2260, http://dx.doi.org/10.1007/s00268-016-3491-4. J.M. Lee, H.S. Lee, J.J. Hyun, H.S. Choi, E.S. Kim, B. Keum, Y.S. Seo, Y.T. Jeen, H.J. Chun, S.H. Um, C.D. Kim, Prognostic value of inflammation-based markers in patients with pancreatic cancer administered gemcitabine and erlotinib, World J. Gastrointest. Oncol. 8 (2016) 555, http://dx.doi.org/10.4251/wjgo.v8. i7.555. Y. Zhang, G.-Q. Zhou, X. Liu, L. Chen, W.-F. Li, L.-L. Tang, Q. Liu, Y. Sun, J. Ma, Exploration and validation of C-reactive protein/albumin ratio as a novel inflammation-based prognostic marker in nasopharyngeal carcinoma, J. Cancer 7 (2016) 1406–1412, http://dx.doi.org/10.7150/jca.15401. X.L. Xu, H.Q. Yu, W. Hu, Q. Song, W.M. Mao, A novel inflammation-based prognostic score, the c-reactive protein/albumin ratio predicts the prognosis of patients with operable esophageal Squamous cell carcinoma, PLoS One 10 (2015) 1–13, http://dx.doi.org/10.1371/journal.pone.0138657. X. Wei, F. Wang, D. Zhang, M. Qiu, C. Ren, Y. Jin, Y. Zhou, D. Wang, M. He, L. Bai, F. Wang, H. Luo, Y. Li, R. Xu, A novel inflammation-based prognostic score in esophageal squamous cell carcinoma: the C-reactive protein/albumin ratio, BMC Cancer 15 (2015) 350, http://dx.doi.org/10.1186/s12885-015-1379-6. J. Hancock, J. Rosen, A. Moreno, A.W. Kim, F.C. Detterbeck, D.J. Boffa, Management of clinical stage IIIA primary lung cancers in the national cancer database, Ann. Thorac. Surg. 98 (2014) 424–432, http://dx.doi.org/10.1016/j. athoracsur.2014.04.067 (discussion 432). S.A. McClave, R. Kozar, R.G. Martindale, D.K. Heyland, M. Braga, F. Carli, J.W. Drover, D. Flum, L. Gramlich, D.N. Herndon, C. Ko, K.A. Kudsk, C.M. Lawson, K.R. Miller, B. Taylor, P.E. Wischmeyer, Summary points and consensus recommendations from the north american surgical nutrition summit, JPEN J. Parenter. Enteral Nutr. 37 (2013) 99S–105S, http://dx.doi.org/10.1177/ 0148607113495892. M.C. Mocellin, J. de A Pastore E Silva, C.D.Q. Camargo, M.E.S. de Fabre, S. Gevaerd, K. Naliwaiko, Y.M.F. Moreno, E.A. Nunes, E.B.S.D.M. Trindade, Fish oil decreases Creactive protein/albumin ratio improving nutritional prognosis and plasma fatty acid profile in colorectal cancer patients, Lipids 48 (2013) 879–888, http://dx.doi. org/10.1007/s11745-013-3816-0. C.P. Simmons, F. Koinis, M.T. Fallon, K.C. Fearon, J. Bowden, T.S. Solheim, B.H. Gronberg, D.C. McMillan, I. Gioulbasanis, B.J. Laird, Prognosis in advanced lung cancer – a prospective study examining key clinicopathological factors, Lung Cancer 88 (2015) 304–309, http://dx.doi.org/10.1016/j.lungcan.2015.03.020.