Urologic Oncology: Seminars and Original Investigations ] (2014) ∎∎∎–∎∎∎
Original article
Low preoperative lymphocyte-monocyte ratio (LMR) represents a potentially poor prognostic factor in nonmetastatic clear cell renal cell carcinoma Georg C. Hutterer, M.D.a,*, Caroline Stoeckigt, M.D.b, Tatjana Stojakovic, M.D.c, Johanna Jesche, M.D.a, Katharina Eberhard, B.A., M.A.d, Karl Pummer, M.D.a, Richard Zigeuner, M.D.a, Martin Pichler, M.D.b,e a Department of Urology, Medical University of Graz, Graz, Austria Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria c Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria d Research Facility for Biostatistics, Medical University of Graz, Graz, Austria e Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX b
Received 26 February 2014; received in revised form 31 March 2014; accepted 3 April 2014
Abstract Objectives: To explore the potential prognostic significance of the lymphocyte-monocyte ratio (LMR) in patients with nonmetastatic renal cell carcinoma (RCC), as the LMR has been repeatedly proposed to have a negative effect on patient's survival in various hematological and solid cancers. However, findings about LMR's prognostic significance in RCC have not been reported yet. Methods and materials: We retrospectively evaluated the prognostic significance of the LMR in a cohort comprising 678 patients with nonmetastatic clear cell RCC, who were operated between 2000 and 2010 with curative radical or partial nephrectomy at a single tertiary academic center. Preoperative LMR was calculated 1 day before surgical intervention. Patients were categorized using an LMR cutoff of 3.0. Cancer-specific survival (CSS), metastasis-free survival, and overall survival were assessed using the Kaplan-Meier method. To evaluate the independent prognostic significance of the LMR, multivariate Cox regression models were applied. Additionally, the influence of the LMR on the predictive accuracy of the Leibovich prognosis score was determined using the Harrell concordance index (c-index) and decision curve analysis. Results: Low LMR was statistically significantly associated with older patients (Z65 y), high tumor grade (G3 þ G4), advanced pathologic T category (pT3 þ pT4), the presence of histologic tumor necrosis, and male gender (P o 0.05). Multivariate analysis identified a low LMR as an independent prognostic factor for patients' CSS (hazard ratio ¼ 2.33; 95% CI: 1.10–4.94; P ¼ 0.027). The estimated c-index was 0.83 using the Leibovich prognosis score and 0.86 when the LMR was added. Conclusions: Regarding CSS of patients with RCC, a decreased LMR represents an independent prognostic factor. Adding the LMR to well-established prognostic models, such as the Leibovich prognosis score, might improve their predictive ability. r 2014 Elsevier Inc. All rights reserved.
Keywords: Prognosis; Renal cell carcinoma; Validation study
1. Introduction
* Corresponding author. Tel.: þ43-316-385-82508; fax: þ43-316-38513550. E-mail address: [email protected] (G.C. Hutterer).
http://dx.doi.org/10.1016/j.urolonc.2014.04.001 1078-1439/r 2014 Elsevier Inc. All rights reserved.
Even if the worldwide incidence rates of renal cell carcinoma (RCC) show a slight increase within the last 3 decades [1], owing to the more widespread use of radiological imaging techniques, a migration toward small and organ-confined tumors has been observed [2,3]. The
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use of prognostic factors that can accurately predict clinical outcomes of patients with RCC is of paramount interest, not only for patients' individualized risk assessment but also for the possibility of comparing the results from international clinical multicenter trials [4]. Several prognostic models comprising different histologic RCC subtypes and regarding different end points have been established to better predict clinical outcomes of patients with RCC [4–8]. For instance, the Leibovich prognosis score integrates 5 clinicopathological features translated into a score and categorizes patients into 8 score categories, which are then assigned to one of 3 different risk groups [7]. According to this score, assignment of patients with RCC into the low-, intermediate-, or high-risk group can accurately assess the risk for the occurrence of metastatic disease after radical nephrectomy. The accuracy of these models might be further improved by the incorporation of different prognostic biomarkers [8]. The systemic inflammatory response, which is usually measured by surrogate peripheral blood-based parameters, such as C-reactive protein, neutrophil, or platelet count, has been shown to independently predict the clinical outcome of various human cancers [9]. Of these inflammatory parameters, an increased neutrophil-lymphocyte ratio has been proposed as an easily accessible and reliable marker to potentially predict survival of patients with RCC [9,10]. The role of tumor-associated macrophages, as well as of surrogate peripheral blood-based parameters, such as the absolute lymphocyte count/absolute monocyte count ratio (LMR) as a potential biomarker for predicting clinical outcomes of patients with cancer, including colorectal cancer, sarcoma, and lymphoid neoplasms, has been well documented [11–14]. By contrast, data about the prognostic role of the LMR as potential biomarker in patients with RCC are scarce. Thus, the aim of this study was to evaluate whether this parameter provides additional prognostic information to well-established clinicopathological parameters in patients with nonmetastatic clear cell RCC.
2. Methods and materials This retrospective analysis included data from 678 patients with consecutive nonmetastatic clear cell RCC who underwent curative radical or partial nephrectomy at the Department of Urology at the Medical University of Graz between January 2000 and December 2010. All analyses were restricted to clear cell RCC histologic subtype only. All of the clinicopathological data were retrieved from medical records from the Department of Urology, as well as from pathology reports from the Institute of Pathology at the same institution. As the TNM classification system for RCC changed during the observational period, pathologic T categories were uniformly adjusted according to the 7th edition of the TNM classification system [15]. Other documented clinicopathological parameters included histologic RCC subtype, tumor grade,
presence or absence (not quantitatively assessed) of histologic coagulative tumor necrosis (TN), and patients' age and gender. The laboratory data, including monocyte and lymphocyte counts, were obtained using preoperative exploration a day before surgical intervention. Patients' postoperative surveillance included routine clinical and laboratory examination; regarding imaging methods, x-rays of the chest and abdominal ultrasound were predominantly used, especially in patients with a low relapse risk (pT1, G1–2), whereas computed tomographic or magnetic resonance imaging was performed in all other patients as previously reported [16]. Follow-up evaluations were performed every 6 months for the first 5 years and annually thereafter for locally advanced tumors. In organ-confined cancers, imaging was performed twice in the first year after surgery and annually thereafter. No neoadjuvant or adjuvant treatment was administered. Dates of death were obtained from the central registry of the Austrian Bureau of Statistics. Cancer-specific survival (CSS) was defined as the time (in months) from date of surgery to a cancer-related death. Metastasis-free survival (MFS) was defined as the time (in months) from date of surgery to the recurrence of radiologically or histologically confirmed distant metastases. Overall survival (OS) was defined as the time (in months) from date of surgery to individuals' death of any cause. The study was approved by the local ethical committee (no. 24-330 ex 11/12) of the Medical University of Graz. For comparison of the pretreatment laboratory data with nonmalignant subjects, a control group consisting of patients without any known malignant disease (n ¼ 222) was added, who underwent surgical intervention for elective nonmalignant diseases, namely transurethral resection of the prostate because of benign prostate hyperplasia or ureterorenoscopic stone removal because of ureteral stone burden, during the years 2012/2013 at the Department of Urology of the Medical University of Graz. According to the patients' electronic medical records, a malignant disease was ruled out in all cases and pretreatment laboratory data (at the latest 1 wk before intervention) were equally available in all cases. 2.1. Statistical analyses The primary study end point was CSS, which was calculated from the date of diagnosis to the date of patients' cancer-related death. Secondary end points included OS (the time between diagnosis and death of any cause) and MFS (the time between diagnosis and occurrence of distant metastases). The ideal cutoff value for the continuously coded LMR was calculated by testing all possible cutoffs that would discriminate between survival and cancer-related death by receiver operating curve analysis as previously described [17]. The relationship between the LMR and clinicopathological parameters was studied by nonparametric tests. Patients' clinical end points were calculated using the Kaplan-Meier
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method and compared using the log-rank test. Backward stepwise multivariate Cox proportion analysis was performed to determine the influence of pathologic T category, grade, age, gender, and histologic TN on patients' CSS, MFS, and OS. Hazard ratios (HRs) estimated from the Cox analysis were reported as relative risks with corresponding 95% CIs. All patients were categorized according to the Leibovich prognosis score risk groups developed for M0 clear cell RCC [7]. The Harrell concordance index (c-index) was used for assessment of the prognostic accuracy of the model in multivariate analyses, as well as to compare the Leibovich prognosis score with the score supplemented by LMR. Decision curve analysis (DCA) was performed to determine the relative value of false-positive and falsenegative results as described by Vickers and Elkin [18]. All statistical analyses were performed using the Statistical Package for Social Sciences version 18.0 (SPSS Inc, Chicago, IL) and STATA 12 (StataCorp, College Station, TX). A 2-sided P o 0.05 was considered statistically significant.
3. Results Overall, a total of 678 patients with clear cell RCC were included into this study. Pathologic T category was pT1a in 334 (49.3%), pT1b in 117 (17.2%), pT2a in 32 (4.7%), pT2b in 5 (0.7%), pT3a in 170 (25.1%), pT3b in 16 (2.4%), pT3c in 2 (0.3%), and pT4 in 2 (0.3%) patients. Tumor grading was G1 in 170 (25.1%), G2 in 411 (60.6%), G3 in 92 (13.6%), and G4 in 5 (0.7%) cases. Overall, the presence of histologic TN was noted in 165 (24.3%) patients with a mean age of 63.8 ⫾ 12.0 years, a mean monocyte count of 0.58 ⫾ 0.42, a mean lymphocyte count of 1.58 ⫾ 0.58, and a median LMR of 2.9 (Table 1). Descriptive pretreatment data of the control group (n ¼ 222) are shown in Appendix Table S1. A Spearman correlation showed a moderate (R = 0.199), albeit statistically significant (P = 0.003), inverse correlation between the LMR and patients' age, which is consistent with the findings for the patients with RCC. Regarding patients' gender in the control group, no statistically significant difference could be observed (t test P = 0.125). The median values of the LMR in the (nonmalignant) control group were 2.80 (interquartile range: 2.00– 3.75) and in the RCC study cohort 2.88 (interquartile range: 2.16–4.00). Regarding the clear cell RCC study cohort, a cutoff value of 3.0 for the LMR was determined to be optimal to discriminate between patients' CSS, which prompted us to select this cutoff value for all subsequent analyses. Accordingly, we defined a low (o3.0) and high (Z3.0) LMR group. Overall, there were 337 (49.7%) patients with a high LMR and 341 (50.3%) patients with a low LMR. A low (o3.0) LMR was statistically significantly associated with older patients (Z65 y), high tumor grade (G3 þ G4),
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Table 1 Descriptive clinicopathological parameters of the study cohort comprising patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
No. (%)
Age at operation, y Mean ⫾ SD Median Range
63.8 ⫾ 11.97 65.0 20.0–88.0
Gender Male Female
405 (59.7) 273 (40.3)
Pathologic T category (TNM 2010) pT1a pT1b pT2a pT2b pT3a pT3b pT3c pT4
334 (49.3) 117 (17.2) 32 (4.7) 5 (0.7) 170 (25.1) 16 (2.4) 2 (0.3) 2 (0.3)
Tumor grade G1 G2 G3 G4
170 (25.1) 411 (60.6) 92 (13.6) 5 (0.7)
Presence of histologic tumor necrosis No Yes
513 (75.7) 165 (24.3)
Monocytes Mean ⫾ SD Median Range
0.58 ⫾ 0.42 0.50 0.10–9.20
Lymphocytes Mean ⫾ SD Median Range
1.58 ⫾ 0.58 1.50 0.10–5.00
Lymphocyte-monocyte ratio (LMR) Mean ⫾ SD Median Z3.0 o3.0
3.23 ⫾ 1.68 2.88 337 (49.7) 341 (50.3)
SD ¼ standard deviation.
advanced pathologic T category (pT3 þ pT4), the presence of histologic TN, and with male gender (all P o 0.05) (Table 2). To investigate whether the LMR is associated with the clinical outcome of patients with RCC, univariable and multivariable analyses for all 3 end points were performed. Mean follow-up was 44 (range: 0–130) months. Of 678 patients with clear cell RCC, 76 (11.2%) developed metastatic disease, of which 49 (7.2%) died of their advanced disease state. Overall, 123 (18.1%) patients died of various causes before their most recent follow-up visit. Among the 678 patients with RCC, metastatic disease was diagnosed in 29 of 398 (7.3%) patients with a high LMR and in 47 of 280 (16.8%) patients with a low LMR
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Table 2 Cross-table demonstrating a statistically significant association of the lymphocyte-monocyte ratio (LMR) with various clinicopathological parameters in the study cohort (n ¼ 678) Parameter
Lymphocyte-monocyte ratio (LMR)
Total
P-value
Z3.0
o3.0
Age at operation, y o65 Z65
183 (54.3%) 154 (45.7%)
138 (40.5%) 203 (59.5%)
321 (47.3%) 357 (52.7%)
o0.001
Tumor grade G1 þ G2 G3 þ G4
304 (90.2%) 33 (9.8%)
277 (81.2%) 64 (18.8%)
581 (85.7%) 97 (14.3%)
0.001
Pathologic T category (TNM 2010) pT1 þ pT2 pT3 þ pT4
263 (78.0%) 74 (22.0%)
225 (66.0%) 116 (34.0%)
488 (72.0%) 190 (28.0%)
o0.001
Presence of histologic tumor necrosis No Yes
268 (79.5%) 69 (20.5%)
245 (71.8%) 96 (28.2%)
513 (75.7%) 165 (24.3%)
0.020
Gender Male Female
184 (54.6%) 153 (45.4%)
221 (64.8%) 120 (35.2%)
405 (59.7%) 273 (40.3%)
0.007
(P o 0.001). Regarding survival, cancer-related and overall deaths occurred in 26 (4.0%) and 52 (13.1%) patients with high LMR and in 33 (11.8%) and 71 (25.4%) patients with low LMR (P o 0.001), respectively. Fig. 1 shows the Kaplan-Meier curves for patients' CSS (A), MFS (B), and OS (C) and reveals that a low LMR seems to represent a consistent factor for poor prognosis in patients with RCC (log-rank test P o 0.001 for all 3 tested end points). Univariate analysis identified age (Z65 vs. o65 y, P ¼ 0.030), high tumor grade (G3 þ G4 vs. G1 þ G2, P o 0.001), histologic TN (presence vs. absence, P o 0.001), advanced pathologic T category (pT2–4 vs. pT1, P o 0.001), and a low LMR (Z3.0 vs. o3.0,
P o 0.001) as prognosticators of poor outcomes for patients' CSS, whereas gender (male vs. female, P ¼ 0.111) was not statistically significantly associated with patients' CSS (Table 3). In multivariate analysis including age, gender, pathologic T category, tumor grade, presence of histologic TN, and LMR, we identified pathologic T category, tumor grade, and the presence of histologic TN as independent prognostic factors for CSS and MFS, whereby the LMR was statistically significantly associated with CSS (HR ¼ 2.33; 95% CI: 1.10–4.94; P ¼ 0.027; Table 3), albeit not with MFS (HR ¼ 1.59; 95% CI: 0.94–2.69; P ¼ 0.087; Table 4). Regarding OS, age (HR ¼ 2.06; 95% CI: 1.34– 3.03; P o 0.001), pathologic T category (HR ¼ 1.54; 95%
Fig. 1. Kaplan-Meier curves for cancer-specific (A), metastasis-free (B), and overall survival (C) of clear cell renal cell carcinoma patients categorized by the lymphocyte-monocyte ratio (LMR).
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Table 3 Univariate and multivariate analysis of clinicopathological parameters for the prediction of cancer-specific survival (CSS) in patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis HR (95% CI)
Multivariate analysis P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 1.943 (1.066–3.544)
0.030
1 (Reference) 1.679 (0.912–3.093)
0.096
Gender Female Male
1 (Reference) 1.585 (0.899–2.794)
0.111
1 (Reference) 1.329 (0.736–2.399)
0.345
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 6.867 (3.633–12.982)
o0.001
1 (Reference) 3.998 (2.037–7.850)
o0.001
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 6.919 (3.893–12.297)
o0.001
1 (Reference) 2.413 (1.253–4.648)
0.008
Presence of histologic tumor necrosis No Yes
1 (Reference) 4.422 (2.499–7.826)
o0.001
1 (Reference) 2.491 (1.350–4.598)
0.004
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 3.691 (1.787–7.623)
o0.001
1 (Reference) 2.332 (1.100–4.942)
0.027
CI: 1.04–2.26; P ¼ 0.030), and tumor grade (HR ¼ 1.74; 95% CI: 1.08–2.83; P ¼ 0.024) reached independent predictor status, whereas the LMR did not (HR ¼ 1.37; 95% CI: 0.93–2.03; P ¼ 0.112; Table 5). Before analyzing the potential additional value ( ¼ gain in predictive
accuracy) of the LMR applied to the Leibovich prognosis score, patients were categorized into 3 risk groups (low, intermediate, and high risk) according to Leibovich et al. [7]. Regarding CSS, the c-index of the original Leibovich prognosis score was 0.83 compared with 0.86 when
Table 4 Univariate and multivariate analysis of clinicopathological parameters for the prediction of metastasis-free survival (MFS) in patients with clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis HR (95% CI)
Multivariate analysis P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 1.237 (0.786–1.948)
0.358
1 (Reference) 1.064 (0.671–1.687)
0.792
Gender Female Male
1 (Reference) 1.510 (0.962–2.369)
0.073
1 (Reference) 1.344 (0.846–2.134)
0.211
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 5.745 (3.559–9.273)
o0.001
1 (Reference) 3.560 (2.136–5.933)
o0.001
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 6.121 (3.857–9.714)
o0.001
1 (Reference) 2.615 (1.554–4.401)
o0.001
Presence of histologic tumor necrosis No Yes
1 (Reference) 3.775 (2.406–5.924)
o0.001
1 (Reference) 2.104 (1.294–3.423)
0.003
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 2.375 (1.436–3.928)
0.001
1 (Reference) 1.586 (0.936–2.690)
0.087
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Table 5 Univariate and multivariate analysis of clinicopathological parameters for the prediction of overall survival (OS) in patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis
Multivariate analysis
HR (95% CI)
P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 2.200 (1.499–3.229)
o0.001
1 (Reference) 2.059 (1.397–3.033)
o0.001
Gender Female Male
1 (Reference) 1.035 (0.721–1.485)
0.853
1 (Reference) 1.010 (0.699–1.460)
0.957
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 1.956 (1.367–2.799)
o0.001
1 (Reference) 1.535 (1.043–2.260)
0.030
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 2.506 (1.638–3.835)
o0.001
1 (Reference) 1.744 (1.075–2.829)
0.024
Presence of histologic tumor necrosis No Yes
1 (Reference) 1.446 (0.979–2.134)
0.064
1 (Reference) 1.172 (0.776–1.772)
0.451
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 1.696 (1.162–2.477)
0.006
1 (Reference) 1.373 (0.929–2.031)
0.112
LMR was supplemented. DCA demonstrated a higher predictive accuracy when LMR was added to the Leibovich prognosis score, supporting the superiority of the Leibovich prognosis score supplemented by LMR (Fig. 2).
Several other studies indicated that different tumorinfiltrating lymphocyte subtypes might play a meaningful role for the biological behavior of tumor cells in RCC. In a study by Kondo et al. [23], the authors demonstrated a favorable outcome for patients with RCC in cases, where
4. Discussion Despite enormous recent progress in the identification of genetic, epigenetic, and common molecular alterations in RCC has been made [19], the routine diagnostic and prognostic assessment of RCC currently still relies on pathological tissue examination and traditional clinicopathological prognostic variables [20,21]. The complexity of newly discovered molecular changes, as well as high costs of analyses, the time-consuming preparation required, and the lack of evidence demonstrating how these potential molecular markers might influence diagnostic or therapeutic decisions, have rendered none of the markers currently available for routine testing. Regularly used blood-based parameters, such as the neutrophil, monocyte, and lymphocyte counts, are relatively easy to assess without additional laborious efforts, making them attractive potential parameters for patients' improved individualized risk assessment in RCC [9,22]. For instance, beyond the neutrophil count only, an increased pretreatment neutrophil-lymphocyte ratio has been previously demonstrated as a poor prognostic factor for different human cancer types, including gastrointestinal, soft tissue sarcoma, nasopharyngeal, and lung cancer [9].
Fig. 2. Chart depicting a decision curve analysis (DCA) of the prognostic value of the original Leibovich prognosis score (model 3) compared with the score supplemented by the lymphocyte-monocyte ratio (LMR) (model 4) in patients with nonmetastatic clear cell renal cell carcinoma. Decision curve analyses were performed to determine the net benefit derived from the use of the 2010 TNM classification system for renal cell carcinoma, as described by Vickers and Elkin [18]. Decision curve analyses explore the theoretical relationship between the threshold probability of an event, e.g., metastatic disease, and the relative value of false-negative and falsepositive results to identify the value (net benefit) of a predictive model.
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high levels of lymphocytic attractant chemokines are expressed. Hotta et al. [24] postulated that the intratumoral CD45ROþ memory T-cell status represents a significant independent prognostic value, indicating that the adaptive immune response is functionally critical in human RCC. Cumulating data also indicated that T-regulatory cells, which are detectable by different markers such as CD4CD25 positivity and intracytoplasmic-Foxp3 expression, are involved in RCC progression and RCC prognosis [25,26]. Regarding the potential prognostic significance of the LMR in clear cell RCC, it has to be emphasized that to the best of our knowledge, no studies with a comparable sample size of patients have been published on this topic so far. In the patient cohort with RCC that was studied, the distribution between patients with a high (Z3.0) LMR and those with a low (o3.0) LMR was almost equal (337/ 49.7% vs. 341/50.3%), whereby a low LMR was statistically significantly associated with older patients, high tumor grade, advanced pathologic T category, presence of histologic TN, and male gender (all P o 0.05). In univariate analysis, a low LMR was found to represent a prognosticator of poor outcome for patients' CSS, as was the case in multivariate analysis, whereas a low LMR failed to demonstrate a statistically significant association with MFS, as well as with OS. In this context, several potential protumorigenic mechanisms of monocytes that drive the progression of RCC might be hypothesized. The density of tumor-associated macrophages in many cancers correlates with increased angiogenesis, tumor invasion, and poor prognosis [27]. Tumor-associated macrophages derive from circulating monocytes, which are selectively recruited to the tumor microenvironment by locally produced chemokines [28]. Thus, the circulating level of monocytes may reflect a surrogate for formation or presence of tumor-associated macrophages. Many macrophage-released soluble factors directly stimulate the growth of tumor cells and promote tumor cell migration and metastasis [28,29]. In addition, macrophages can produce enzymes and inhibitors that regulate the digestion of the extracellular matrix, hence favoring tumor invasion and migration [29]. Analyzing the potential additional value ( ¼ gain in predictive accuracy) of the LMR applied to the Leibovich prognosis score for clear cell RCC, we found that the c-index for CSS was 0.83 compared with 0.86 when LMR was supplemented, a finding that is supported by DCA. As with all retrospective studies, limitations of our study are inherent to the design, including the retrospective data collection. No data were available about the cause of death for calculation of patients' OS. In an attempt to control for homogeneity of the study population, we excluded patients with non–clear cell histology, hereditary RCC, patients with metachronous secondary RCC, and competitive invasive cancers originating from other sites if metastatic spread was not assessed through histologic examination. Regarding the slightly higher LMR median values in patients with clear
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cell RCC (2.88) compared with the nonmalignant control group (2.80), the observed difference is definitely too small to make the LMR appropriate as a highly specific tumor marker for RCC diagnosis. We believe it rather should be regarded as a potential prognostic indicator in this malignant condition, as the cellular mechanisms behind our observation remain speculative. Nonetheless, even considering these limitations, our data clearly indicate that an increased pretreatment LMR might represent an independent prognostic factor for CSS in patients with nonmetastatic clear cell RCC. 5. Conclusion An increased LMR seems to represent an independent predictor with respect to patients' CSS in nonmetastatic clear cell RCC. As the addition of the LMR improved the predictive accuracy of the Leibovich prognosis score, this parameter warrants further validation as a potential selection criterion for risk factor-stratified patient management in nonmetastatic RCC. Appendix. Supplementary Information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.urolonc.2014. 04.001.
References [1] Ljungberg B, Campbell SC, Choi HY, et al. The epidemiology of renal cell carcinoma. Eur Urol 2011;60:615–21. [2] Pichler M, Hutterer GC, Chromecki TF, et al. Renal cell carcinoma stage migration in a single European centre over 25 years: effects on 5- and 10-year metastasis-free survival. Int Urol Nephrol 2012;44: 997–1004. [3] Sun M, Thuret R, Abdollah F, et al. Age-adjusted incidence, mortality, and survival rates of stage-specific renal cell carcinoma in North America: a trend analysis. Eur Urol 2011;59:135–41. [4] Meskawi M, Sun M, Trinh QD, et al. A review of integrated staging systems for renal cell carcinoma. Eur Urol 2012;62:303–14. [5] Zisman A, Pantuck AJ, Dorey F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol 2001;19:1649–57. [6] Kattan MW, Reuter V, Motzer RJ, Katz J, Russo P. A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001;166:63–7. [7] Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer 2003;97:1663–71. [8] Crispen PL, Boorjian SA, Lohse CM, Leibovich BC, Kwon ED. Predicting disease progression after nephrectomy for localized renal cell carcinoma: the utility of prognostic models and molecular biomarkers. Cancer 2008;113:450–60. [9] Roxburgh CS, McMillan DC. Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol 2010;6:149–63. [10] Pichler M, Hutterer GC, Stoeckigt C, et al. Validation of the pretreatment neutrophil-lymphocyte ratio as a prognostic factor in a large
8
[11]
[12]
[13]
[14] [15]
[16]
[17]
[18] [19]
G.C. Hutterer et al. / Urologic Oncology: Seminars and Original Investigations ] (2014) 1–8 European cohort of renal cell carcinoma patients. Br J Cancer 2013;108:901–7. Stotz M, Pichler M, Absenger G, et al. The preoperative lymphocyte to monocyte ratio predicts clinical outcome in patients with stage III colon cancer. Br J Cancer 2014;110:435–40. Szkandera J, Gerger A, Liegl-Atzwanger B, et al. The lymphocyte/ monocyte ratio predicts poor clinical outcome and improves the predictive accuracy in patients with soft tissue sarcomas. Int J Cancer 2013; http://dx.doi.org/10.1002/ijc.28677. Porrata LF, Ristow K, Colgan JP, et al. Peripheral blood lymphocyte/ monocyte ratio at diagnosis and survival in classical Hodgkin's lymphoma. Haematologica 2012;97:262–9. Steidl C, Lee T, Shah SP, et al. Tumor-associated macrophages and survival in classic Hodgkin's lymphoma. N Engl J Med 2010;362:875–85. Novara G, Ficarra V, Antonelli A, et al. Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: are further improvements needed? Eur Urol 2010;58:588–95. Pichler M, Hutterer GC, Chromecki TF, et al. External validation of the Leibovich prognosis score for nonmetastatic clear cell renal cell carcinoma at a single European center applying routine pathology. J Urol 2011;186:1773–7. Absenger G, Szkandera J, Pichler M, et al. A derived neutrophil to lymphocyte ratio predicts clinical outcome in stage II and III colon cancer patients. Br J Cancer 2013;109:395–400. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565–74. Al-Ali BM, Ress AL, Gerger A, Pichler M. MicroRNAs in renal cell carcinoma: implications for pathogenesis, diagnosis, prognosis and therapy. Anticancer Res 2012;32:3727–32 [review].
[20] Ficarra V, Brunelli M, Cheng L, et al. Prognostic and therapeutic impact of the histopathologic definition of parenchymal epithelial renal tumors. Eur Urol 2010;58:655–68. [21] Sun M, Shariat SF, Cheng C, et al. Prognostic factors and predictive models in renal cell carcinoma: a contemporary review. Eur Urol 2011;60:644–61. [22] Jagdev SP, Gregory W, Vasudev NS, et al. Improving the accuracy of pre-operative survival prediction in renal cell carcinoma with Creactive protein. Br J Cancer 2010;103:1649–56. [23] Kondo T, Ito F, Nakazawa H, Horita S, Osaka Y, Toma H. High expression of chemokine gene as a favorable prognostic factor in renal cell carcinoma. J Urol 2004;171:2171–5. [24] Hotta K, Sho M, Fujimoto K, et al. Prognostic significance of CD45ROþ memory T cells in renal cell carcinoma. Br J Cancer 2011;105:1191–6. [25] Liotta F, Gacci M, Frosali F, et al. Frequency of regulatory T cells in peripheral blood and in tumour-infiltrating lymphocytes correlates with poor prognosis in renal cell carcinoma. BJU Int 2011;107: 1500–6. [26] Balkwill F. Cancer and the chemokine network. Nat Rev Cancer 2004;4:540–50. [27] Mantovani A, Schioppa T, Porta C, Allavena P, Sica A. Role of tumor-associated macrophages in tumor progression and invasion. Cancer Metastasis Rev 2006;25:315–22. [28] Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature 2008;454:436–44. [29] Coussens LM, Tinkle CL, Hanahan D, Werb Z. MMP-9 supplied by bone marrow-derived cells contributes to skin carcinogenesis. Cell 2000;103:481–90.
Original article
Low preoperative lymphocyte-monocyte ratio (LMR) represents a potentially poor prognostic factor in nonmetastatic clear cell renal cell carcinoma Georg C. Hutterer, M.D.a,*, Caroline Stoeckigt, M.D.b, Tatjana Stojakovic, M.D.c, Johanna Jesche, M.D.a, Katharina Eberhard, B.A., M.A.d, Karl Pummer, M.D.a, Richard Zigeuner, M.D.a, Martin Pichler, M.D.b,e a Department of Urology, Medical University of Graz, Graz, Austria Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria c Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria d Research Facility for Biostatistics, Medical University of Graz, Graz, Austria e Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX b
Received 26 February 2014; received in revised form 31 March 2014; accepted 3 April 2014
Abstract Objectives: To explore the potential prognostic significance of the lymphocyte-monocyte ratio (LMR) in patients with nonmetastatic renal cell carcinoma (RCC), as the LMR has been repeatedly proposed to have a negative effect on patient's survival in various hematological and solid cancers. However, findings about LMR's prognostic significance in RCC have not been reported yet. Methods and materials: We retrospectively evaluated the prognostic significance of the LMR in a cohort comprising 678 patients with nonmetastatic clear cell RCC, who were operated between 2000 and 2010 with curative radical or partial nephrectomy at a single tertiary academic center. Preoperative LMR was calculated 1 day before surgical intervention. Patients were categorized using an LMR cutoff of 3.0. Cancer-specific survival (CSS), metastasis-free survival, and overall survival were assessed using the Kaplan-Meier method. To evaluate the independent prognostic significance of the LMR, multivariate Cox regression models were applied. Additionally, the influence of the LMR on the predictive accuracy of the Leibovich prognosis score was determined using the Harrell concordance index (c-index) and decision curve analysis. Results: Low LMR was statistically significantly associated with older patients (Z65 y), high tumor grade (G3 þ G4), advanced pathologic T category (pT3 þ pT4), the presence of histologic tumor necrosis, and male gender (P o 0.05). Multivariate analysis identified a low LMR as an independent prognostic factor for patients' CSS (hazard ratio ¼ 2.33; 95% CI: 1.10–4.94; P ¼ 0.027). The estimated c-index was 0.83 using the Leibovich prognosis score and 0.86 when the LMR was added. Conclusions: Regarding CSS of patients with RCC, a decreased LMR represents an independent prognostic factor. Adding the LMR to well-established prognostic models, such as the Leibovich prognosis score, might improve their predictive ability. r 2014 Elsevier Inc. All rights reserved.
Keywords: Prognosis; Renal cell carcinoma; Validation study
1. Introduction
* Corresponding author. Tel.: þ43-316-385-82508; fax: þ43-316-38513550. E-mail address: [email protected] (G.C. Hutterer).
http://dx.doi.org/10.1016/j.urolonc.2014.04.001 1078-1439/r 2014 Elsevier Inc. All rights reserved.
Even if the worldwide incidence rates of renal cell carcinoma (RCC) show a slight increase within the last 3 decades [1], owing to the more widespread use of radiological imaging techniques, a migration toward small and organ-confined tumors has been observed [2,3]. The
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use of prognostic factors that can accurately predict clinical outcomes of patients with RCC is of paramount interest, not only for patients' individualized risk assessment but also for the possibility of comparing the results from international clinical multicenter trials [4]. Several prognostic models comprising different histologic RCC subtypes and regarding different end points have been established to better predict clinical outcomes of patients with RCC [4–8]. For instance, the Leibovich prognosis score integrates 5 clinicopathological features translated into a score and categorizes patients into 8 score categories, which are then assigned to one of 3 different risk groups [7]. According to this score, assignment of patients with RCC into the low-, intermediate-, or high-risk group can accurately assess the risk for the occurrence of metastatic disease after radical nephrectomy. The accuracy of these models might be further improved by the incorporation of different prognostic biomarkers [8]. The systemic inflammatory response, which is usually measured by surrogate peripheral blood-based parameters, such as C-reactive protein, neutrophil, or platelet count, has been shown to independently predict the clinical outcome of various human cancers [9]. Of these inflammatory parameters, an increased neutrophil-lymphocyte ratio has been proposed as an easily accessible and reliable marker to potentially predict survival of patients with RCC [9,10]. The role of tumor-associated macrophages, as well as of surrogate peripheral blood-based parameters, such as the absolute lymphocyte count/absolute monocyte count ratio (LMR) as a potential biomarker for predicting clinical outcomes of patients with cancer, including colorectal cancer, sarcoma, and lymphoid neoplasms, has been well documented [11–14]. By contrast, data about the prognostic role of the LMR as potential biomarker in patients with RCC are scarce. Thus, the aim of this study was to evaluate whether this parameter provides additional prognostic information to well-established clinicopathological parameters in patients with nonmetastatic clear cell RCC.
2. Methods and materials This retrospective analysis included data from 678 patients with consecutive nonmetastatic clear cell RCC who underwent curative radical or partial nephrectomy at the Department of Urology at the Medical University of Graz between January 2000 and December 2010. All analyses were restricted to clear cell RCC histologic subtype only. All of the clinicopathological data were retrieved from medical records from the Department of Urology, as well as from pathology reports from the Institute of Pathology at the same institution. As the TNM classification system for RCC changed during the observational period, pathologic T categories were uniformly adjusted according to the 7th edition of the TNM classification system [15]. Other documented clinicopathological parameters included histologic RCC subtype, tumor grade,
presence or absence (not quantitatively assessed) of histologic coagulative tumor necrosis (TN), and patients' age and gender. The laboratory data, including monocyte and lymphocyte counts, were obtained using preoperative exploration a day before surgical intervention. Patients' postoperative surveillance included routine clinical and laboratory examination; regarding imaging methods, x-rays of the chest and abdominal ultrasound were predominantly used, especially in patients with a low relapse risk (pT1, G1–2), whereas computed tomographic or magnetic resonance imaging was performed in all other patients as previously reported [16]. Follow-up evaluations were performed every 6 months for the first 5 years and annually thereafter for locally advanced tumors. In organ-confined cancers, imaging was performed twice in the first year after surgery and annually thereafter. No neoadjuvant or adjuvant treatment was administered. Dates of death were obtained from the central registry of the Austrian Bureau of Statistics. Cancer-specific survival (CSS) was defined as the time (in months) from date of surgery to a cancer-related death. Metastasis-free survival (MFS) was defined as the time (in months) from date of surgery to the recurrence of radiologically or histologically confirmed distant metastases. Overall survival (OS) was defined as the time (in months) from date of surgery to individuals' death of any cause. The study was approved by the local ethical committee (no. 24-330 ex 11/12) of the Medical University of Graz. For comparison of the pretreatment laboratory data with nonmalignant subjects, a control group consisting of patients without any known malignant disease (n ¼ 222) was added, who underwent surgical intervention for elective nonmalignant diseases, namely transurethral resection of the prostate because of benign prostate hyperplasia or ureterorenoscopic stone removal because of ureteral stone burden, during the years 2012/2013 at the Department of Urology of the Medical University of Graz. According to the patients' electronic medical records, a malignant disease was ruled out in all cases and pretreatment laboratory data (at the latest 1 wk before intervention) were equally available in all cases. 2.1. Statistical analyses The primary study end point was CSS, which was calculated from the date of diagnosis to the date of patients' cancer-related death. Secondary end points included OS (the time between diagnosis and death of any cause) and MFS (the time between diagnosis and occurrence of distant metastases). The ideal cutoff value for the continuously coded LMR was calculated by testing all possible cutoffs that would discriminate between survival and cancer-related death by receiver operating curve analysis as previously described [17]. The relationship between the LMR and clinicopathological parameters was studied by nonparametric tests. Patients' clinical end points were calculated using the Kaplan-Meier
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method and compared using the log-rank test. Backward stepwise multivariate Cox proportion analysis was performed to determine the influence of pathologic T category, grade, age, gender, and histologic TN on patients' CSS, MFS, and OS. Hazard ratios (HRs) estimated from the Cox analysis were reported as relative risks with corresponding 95% CIs. All patients were categorized according to the Leibovich prognosis score risk groups developed for M0 clear cell RCC [7]. The Harrell concordance index (c-index) was used for assessment of the prognostic accuracy of the model in multivariate analyses, as well as to compare the Leibovich prognosis score with the score supplemented by LMR. Decision curve analysis (DCA) was performed to determine the relative value of false-positive and falsenegative results as described by Vickers and Elkin [18]. All statistical analyses were performed using the Statistical Package for Social Sciences version 18.0 (SPSS Inc, Chicago, IL) and STATA 12 (StataCorp, College Station, TX). A 2-sided P o 0.05 was considered statistically significant.
3. Results Overall, a total of 678 patients with clear cell RCC were included into this study. Pathologic T category was pT1a in 334 (49.3%), pT1b in 117 (17.2%), pT2a in 32 (4.7%), pT2b in 5 (0.7%), pT3a in 170 (25.1%), pT3b in 16 (2.4%), pT3c in 2 (0.3%), and pT4 in 2 (0.3%) patients. Tumor grading was G1 in 170 (25.1%), G2 in 411 (60.6%), G3 in 92 (13.6%), and G4 in 5 (0.7%) cases. Overall, the presence of histologic TN was noted in 165 (24.3%) patients with a mean age of 63.8 ⫾ 12.0 years, a mean monocyte count of 0.58 ⫾ 0.42, a mean lymphocyte count of 1.58 ⫾ 0.58, and a median LMR of 2.9 (Table 1). Descriptive pretreatment data of the control group (n ¼ 222) are shown in Appendix Table S1. A Spearman correlation showed a moderate (R = 0.199), albeit statistically significant (P = 0.003), inverse correlation between the LMR and patients' age, which is consistent with the findings for the patients with RCC. Regarding patients' gender in the control group, no statistically significant difference could be observed (t test P = 0.125). The median values of the LMR in the (nonmalignant) control group were 2.80 (interquartile range: 2.00– 3.75) and in the RCC study cohort 2.88 (interquartile range: 2.16–4.00). Regarding the clear cell RCC study cohort, a cutoff value of 3.0 for the LMR was determined to be optimal to discriminate between patients' CSS, which prompted us to select this cutoff value for all subsequent analyses. Accordingly, we defined a low (o3.0) and high (Z3.0) LMR group. Overall, there were 337 (49.7%) patients with a high LMR and 341 (50.3%) patients with a low LMR. A low (o3.0) LMR was statistically significantly associated with older patients (Z65 y), high tumor grade (G3 þ G4),
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Table 1 Descriptive clinicopathological parameters of the study cohort comprising patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
No. (%)
Age at operation, y Mean ⫾ SD Median Range
63.8 ⫾ 11.97 65.0 20.0–88.0
Gender Male Female
405 (59.7) 273 (40.3)
Pathologic T category (TNM 2010) pT1a pT1b pT2a pT2b pT3a pT3b pT3c pT4
334 (49.3) 117 (17.2) 32 (4.7) 5 (0.7) 170 (25.1) 16 (2.4) 2 (0.3) 2 (0.3)
Tumor grade G1 G2 G3 G4
170 (25.1) 411 (60.6) 92 (13.6) 5 (0.7)
Presence of histologic tumor necrosis No Yes
513 (75.7) 165 (24.3)
Monocytes Mean ⫾ SD Median Range
0.58 ⫾ 0.42 0.50 0.10–9.20
Lymphocytes Mean ⫾ SD Median Range
1.58 ⫾ 0.58 1.50 0.10–5.00
Lymphocyte-monocyte ratio (LMR) Mean ⫾ SD Median Z3.0 o3.0
3.23 ⫾ 1.68 2.88 337 (49.7) 341 (50.3)
SD ¼ standard deviation.
advanced pathologic T category (pT3 þ pT4), the presence of histologic TN, and with male gender (all P o 0.05) (Table 2). To investigate whether the LMR is associated with the clinical outcome of patients with RCC, univariable and multivariable analyses for all 3 end points were performed. Mean follow-up was 44 (range: 0–130) months. Of 678 patients with clear cell RCC, 76 (11.2%) developed metastatic disease, of which 49 (7.2%) died of their advanced disease state. Overall, 123 (18.1%) patients died of various causes before their most recent follow-up visit. Among the 678 patients with RCC, metastatic disease was diagnosed in 29 of 398 (7.3%) patients with a high LMR and in 47 of 280 (16.8%) patients with a low LMR
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Table 2 Cross-table demonstrating a statistically significant association of the lymphocyte-monocyte ratio (LMR) with various clinicopathological parameters in the study cohort (n ¼ 678) Parameter
Lymphocyte-monocyte ratio (LMR)
Total
P-value
Z3.0
o3.0
Age at operation, y o65 Z65
183 (54.3%) 154 (45.7%)
138 (40.5%) 203 (59.5%)
321 (47.3%) 357 (52.7%)
o0.001
Tumor grade G1 þ G2 G3 þ G4
304 (90.2%) 33 (9.8%)
277 (81.2%) 64 (18.8%)
581 (85.7%) 97 (14.3%)
0.001
Pathologic T category (TNM 2010) pT1 þ pT2 pT3 þ pT4
263 (78.0%) 74 (22.0%)
225 (66.0%) 116 (34.0%)
488 (72.0%) 190 (28.0%)
o0.001
Presence of histologic tumor necrosis No Yes
268 (79.5%) 69 (20.5%)
245 (71.8%) 96 (28.2%)
513 (75.7%) 165 (24.3%)
0.020
Gender Male Female
184 (54.6%) 153 (45.4%)
221 (64.8%) 120 (35.2%)
405 (59.7%) 273 (40.3%)
0.007
(P o 0.001). Regarding survival, cancer-related and overall deaths occurred in 26 (4.0%) and 52 (13.1%) patients with high LMR and in 33 (11.8%) and 71 (25.4%) patients with low LMR (P o 0.001), respectively. Fig. 1 shows the Kaplan-Meier curves for patients' CSS (A), MFS (B), and OS (C) and reveals that a low LMR seems to represent a consistent factor for poor prognosis in patients with RCC (log-rank test P o 0.001 for all 3 tested end points). Univariate analysis identified age (Z65 vs. o65 y, P ¼ 0.030), high tumor grade (G3 þ G4 vs. G1 þ G2, P o 0.001), histologic TN (presence vs. absence, P o 0.001), advanced pathologic T category (pT2–4 vs. pT1, P o 0.001), and a low LMR (Z3.0 vs. o3.0,
P o 0.001) as prognosticators of poor outcomes for patients' CSS, whereas gender (male vs. female, P ¼ 0.111) was not statistically significantly associated with patients' CSS (Table 3). In multivariate analysis including age, gender, pathologic T category, tumor grade, presence of histologic TN, and LMR, we identified pathologic T category, tumor grade, and the presence of histologic TN as independent prognostic factors for CSS and MFS, whereby the LMR was statistically significantly associated with CSS (HR ¼ 2.33; 95% CI: 1.10–4.94; P ¼ 0.027; Table 3), albeit not with MFS (HR ¼ 1.59; 95% CI: 0.94–2.69; P ¼ 0.087; Table 4). Regarding OS, age (HR ¼ 2.06; 95% CI: 1.34– 3.03; P o 0.001), pathologic T category (HR ¼ 1.54; 95%
Fig. 1. Kaplan-Meier curves for cancer-specific (A), metastasis-free (B), and overall survival (C) of clear cell renal cell carcinoma patients categorized by the lymphocyte-monocyte ratio (LMR).
G.C. Hutterer et al. / Urologic Oncology: Seminars and Original Investigations ] (2014) 1–8
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Table 3 Univariate and multivariate analysis of clinicopathological parameters for the prediction of cancer-specific survival (CSS) in patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis HR (95% CI)
Multivariate analysis P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 1.943 (1.066–3.544)
0.030
1 (Reference) 1.679 (0.912–3.093)
0.096
Gender Female Male
1 (Reference) 1.585 (0.899–2.794)
0.111
1 (Reference) 1.329 (0.736–2.399)
0.345
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 6.867 (3.633–12.982)
o0.001
1 (Reference) 3.998 (2.037–7.850)
o0.001
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 6.919 (3.893–12.297)
o0.001
1 (Reference) 2.413 (1.253–4.648)
0.008
Presence of histologic tumor necrosis No Yes
1 (Reference) 4.422 (2.499–7.826)
o0.001
1 (Reference) 2.491 (1.350–4.598)
0.004
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 3.691 (1.787–7.623)
o0.001
1 (Reference) 2.332 (1.100–4.942)
0.027
CI: 1.04–2.26; P ¼ 0.030), and tumor grade (HR ¼ 1.74; 95% CI: 1.08–2.83; P ¼ 0.024) reached independent predictor status, whereas the LMR did not (HR ¼ 1.37; 95% CI: 0.93–2.03; P ¼ 0.112; Table 5). Before analyzing the potential additional value ( ¼ gain in predictive
accuracy) of the LMR applied to the Leibovich prognosis score, patients were categorized into 3 risk groups (low, intermediate, and high risk) according to Leibovich et al. [7]. Regarding CSS, the c-index of the original Leibovich prognosis score was 0.83 compared with 0.86 when
Table 4 Univariate and multivariate analysis of clinicopathological parameters for the prediction of metastasis-free survival (MFS) in patients with clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis HR (95% CI)
Multivariate analysis P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 1.237 (0.786–1.948)
0.358
1 (Reference) 1.064 (0.671–1.687)
0.792
Gender Female Male
1 (Reference) 1.510 (0.962–2.369)
0.073
1 (Reference) 1.344 (0.846–2.134)
0.211
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 5.745 (3.559–9.273)
o0.001
1 (Reference) 3.560 (2.136–5.933)
o0.001
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 6.121 (3.857–9.714)
o0.001
1 (Reference) 2.615 (1.554–4.401)
o0.001
Presence of histologic tumor necrosis No Yes
1 (Reference) 3.775 (2.406–5.924)
o0.001
1 (Reference) 2.104 (1.294–3.423)
0.003
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 2.375 (1.436–3.928)
0.001
1 (Reference) 1.586 (0.936–2.690)
0.087
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Table 5 Univariate and multivariate analysis of clinicopathological parameters for the prediction of overall survival (OS) in patients with nonmetastatic clear cell renal cell carcinoma (n ¼ 678) Parameter
Univariate analysis
Multivariate analysis
HR (95% CI)
P-value
HR (95% CI)
P-value
Age at operation, y o65 Z65
1 (Reference) 2.200 (1.499–3.229)
o0.001
1 (Reference) 2.059 (1.397–3.033)
o0.001
Gender Female Male
1 (Reference) 1.035 (0.721–1.485)
0.853
1 (Reference) 1.010 (0.699–1.460)
0.957
Pathologic T category (TNM 2010) pT1 pT2–4
1 (Reference) 1.956 (1.367–2.799)
o0.001
1 (Reference) 1.535 (1.043–2.260)
0.030
Tumor grade G1 þ G2 G3 þ G4
1 (Reference) 2.506 (1.638–3.835)
o0.001
1 (Reference) 1.744 (1.075–2.829)
0.024
Presence of histologic tumor necrosis No Yes
1 (Reference) 1.446 (0.979–2.134)
0.064
1 (Reference) 1.172 (0.776–1.772)
0.451
Lymphocyte-monocyte ratio (LMR) Z3.0 o3.0
1 (Reference) 1.696 (1.162–2.477)
0.006
1 (Reference) 1.373 (0.929–2.031)
0.112
LMR was supplemented. DCA demonstrated a higher predictive accuracy when LMR was added to the Leibovich prognosis score, supporting the superiority of the Leibovich prognosis score supplemented by LMR (Fig. 2).
Several other studies indicated that different tumorinfiltrating lymphocyte subtypes might play a meaningful role for the biological behavior of tumor cells in RCC. In a study by Kondo et al. [23], the authors demonstrated a favorable outcome for patients with RCC in cases, where
4. Discussion Despite enormous recent progress in the identification of genetic, epigenetic, and common molecular alterations in RCC has been made [19], the routine diagnostic and prognostic assessment of RCC currently still relies on pathological tissue examination and traditional clinicopathological prognostic variables [20,21]. The complexity of newly discovered molecular changes, as well as high costs of analyses, the time-consuming preparation required, and the lack of evidence demonstrating how these potential molecular markers might influence diagnostic or therapeutic decisions, have rendered none of the markers currently available for routine testing. Regularly used blood-based parameters, such as the neutrophil, monocyte, and lymphocyte counts, are relatively easy to assess without additional laborious efforts, making them attractive potential parameters for patients' improved individualized risk assessment in RCC [9,22]. For instance, beyond the neutrophil count only, an increased pretreatment neutrophil-lymphocyte ratio has been previously demonstrated as a poor prognostic factor for different human cancer types, including gastrointestinal, soft tissue sarcoma, nasopharyngeal, and lung cancer [9].
Fig. 2. Chart depicting a decision curve analysis (DCA) of the prognostic value of the original Leibovich prognosis score (model 3) compared with the score supplemented by the lymphocyte-monocyte ratio (LMR) (model 4) in patients with nonmetastatic clear cell renal cell carcinoma. Decision curve analyses were performed to determine the net benefit derived from the use of the 2010 TNM classification system for renal cell carcinoma, as described by Vickers and Elkin [18]. Decision curve analyses explore the theoretical relationship between the threshold probability of an event, e.g., metastatic disease, and the relative value of false-negative and falsepositive results to identify the value (net benefit) of a predictive model.
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high levels of lymphocytic attractant chemokines are expressed. Hotta et al. [24] postulated that the intratumoral CD45ROþ memory T-cell status represents a significant independent prognostic value, indicating that the adaptive immune response is functionally critical in human RCC. Cumulating data also indicated that T-regulatory cells, which are detectable by different markers such as CD4CD25 positivity and intracytoplasmic-Foxp3 expression, are involved in RCC progression and RCC prognosis [25,26]. Regarding the potential prognostic significance of the LMR in clear cell RCC, it has to be emphasized that to the best of our knowledge, no studies with a comparable sample size of patients have been published on this topic so far. In the patient cohort with RCC that was studied, the distribution between patients with a high (Z3.0) LMR and those with a low (o3.0) LMR was almost equal (337/ 49.7% vs. 341/50.3%), whereby a low LMR was statistically significantly associated with older patients, high tumor grade, advanced pathologic T category, presence of histologic TN, and male gender (all P o 0.05). In univariate analysis, a low LMR was found to represent a prognosticator of poor outcome for patients' CSS, as was the case in multivariate analysis, whereas a low LMR failed to demonstrate a statistically significant association with MFS, as well as with OS. In this context, several potential protumorigenic mechanisms of monocytes that drive the progression of RCC might be hypothesized. The density of tumor-associated macrophages in many cancers correlates with increased angiogenesis, tumor invasion, and poor prognosis [27]. Tumor-associated macrophages derive from circulating monocytes, which are selectively recruited to the tumor microenvironment by locally produced chemokines [28]. Thus, the circulating level of monocytes may reflect a surrogate for formation or presence of tumor-associated macrophages. Many macrophage-released soluble factors directly stimulate the growth of tumor cells and promote tumor cell migration and metastasis [28,29]. In addition, macrophages can produce enzymes and inhibitors that regulate the digestion of the extracellular matrix, hence favoring tumor invasion and migration [29]. Analyzing the potential additional value ( ¼ gain in predictive accuracy) of the LMR applied to the Leibovich prognosis score for clear cell RCC, we found that the c-index for CSS was 0.83 compared with 0.86 when LMR was supplemented, a finding that is supported by DCA. As with all retrospective studies, limitations of our study are inherent to the design, including the retrospective data collection. No data were available about the cause of death for calculation of patients' OS. In an attempt to control for homogeneity of the study population, we excluded patients with non–clear cell histology, hereditary RCC, patients with metachronous secondary RCC, and competitive invasive cancers originating from other sites if metastatic spread was not assessed through histologic examination. Regarding the slightly higher LMR median values in patients with clear
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cell RCC (2.88) compared with the nonmalignant control group (2.80), the observed difference is definitely too small to make the LMR appropriate as a highly specific tumor marker for RCC diagnosis. We believe it rather should be regarded as a potential prognostic indicator in this malignant condition, as the cellular mechanisms behind our observation remain speculative. Nonetheless, even considering these limitations, our data clearly indicate that an increased pretreatment LMR might represent an independent prognostic factor for CSS in patients with nonmetastatic clear cell RCC. 5. Conclusion An increased LMR seems to represent an independent predictor with respect to patients' CSS in nonmetastatic clear cell RCC. As the addition of the LMR improved the predictive accuracy of the Leibovich prognosis score, this parameter warrants further validation as a potential selection criterion for risk factor-stratified patient management in nonmetastatic RCC. Appendix. Supplementary Information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.urolonc.2014. 04.001.
References [1] Ljungberg B, Campbell SC, Choi HY, et al. The epidemiology of renal cell carcinoma. Eur Urol 2011;60:615–21. [2] Pichler M, Hutterer GC, Chromecki TF, et al. Renal cell carcinoma stage migration in a single European centre over 25 years: effects on 5- and 10-year metastasis-free survival. Int Urol Nephrol 2012;44: 997–1004. [3] Sun M, Thuret R, Abdollah F, et al. Age-adjusted incidence, mortality, and survival rates of stage-specific renal cell carcinoma in North America: a trend analysis. Eur Urol 2011;59:135–41. [4] Meskawi M, Sun M, Trinh QD, et al. A review of integrated staging systems for renal cell carcinoma. Eur Urol 2012;62:303–14. [5] Zisman A, Pantuck AJ, Dorey F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol 2001;19:1649–57. [6] Kattan MW, Reuter V, Motzer RJ, Katz J, Russo P. A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001;166:63–7. [7] Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer 2003;97:1663–71. [8] Crispen PL, Boorjian SA, Lohse CM, Leibovich BC, Kwon ED. Predicting disease progression after nephrectomy for localized renal cell carcinoma: the utility of prognostic models and molecular biomarkers. Cancer 2008;113:450–60. [9] Roxburgh CS, McMillan DC. Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol 2010;6:149–63. [10] Pichler M, Hutterer GC, Stoeckigt C, et al. Validation of the pretreatment neutrophil-lymphocyte ratio as a prognostic factor in a large
8
[11]
[12]
[13]
[14] [15]
[16]
[17]
[18] [19]
G.C. Hutterer et al. / Urologic Oncology: Seminars and Original Investigations ] (2014) 1–8 European cohort of renal cell carcinoma patients. Br J Cancer 2013;108:901–7. Stotz M, Pichler M, Absenger G, et al. The preoperative lymphocyte to monocyte ratio predicts clinical outcome in patients with stage III colon cancer. Br J Cancer 2014;110:435–40. Szkandera J, Gerger A, Liegl-Atzwanger B, et al. The lymphocyte/ monocyte ratio predicts poor clinical outcome and improves the predictive accuracy in patients with soft tissue sarcomas. Int J Cancer 2013; http://dx.doi.org/10.1002/ijc.28677. Porrata LF, Ristow K, Colgan JP, et al. Peripheral blood lymphocyte/ monocyte ratio at diagnosis and survival in classical Hodgkin's lymphoma. Haematologica 2012;97:262–9. Steidl C, Lee T, Shah SP, et al. Tumor-associated macrophages and survival in classic Hodgkin's lymphoma. N Engl J Med 2010;362:875–85. Novara G, Ficarra V, Antonelli A, et al. Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: are further improvements needed? Eur Urol 2010;58:588–95. Pichler M, Hutterer GC, Chromecki TF, et al. External validation of the Leibovich prognosis score for nonmetastatic clear cell renal cell carcinoma at a single European center applying routine pathology. J Urol 2011;186:1773–7. Absenger G, Szkandera J, Pichler M, et al. A derived neutrophil to lymphocyte ratio predicts clinical outcome in stage II and III colon cancer patients. Br J Cancer 2013;109:395–400. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565–74. Al-Ali BM, Ress AL, Gerger A, Pichler M. MicroRNAs in renal cell carcinoma: implications for pathogenesis, diagnosis, prognosis and therapy. Anticancer Res 2012;32:3727–32 [review].
[20] Ficarra V, Brunelli M, Cheng L, et al. Prognostic and therapeutic impact of the histopathologic definition of parenchymal epithelial renal tumors. Eur Urol 2010;58:655–68. [21] Sun M, Shariat SF, Cheng C, et al. Prognostic factors and predictive models in renal cell carcinoma: a contemporary review. Eur Urol 2011;60:644–61. [22] Jagdev SP, Gregory W, Vasudev NS, et al. Improving the accuracy of pre-operative survival prediction in renal cell carcinoma with Creactive protein. Br J Cancer 2010;103:1649–56. [23] Kondo T, Ito F, Nakazawa H, Horita S, Osaka Y, Toma H. High expression of chemokine gene as a favorable prognostic factor in renal cell carcinoma. J Urol 2004;171:2171–5. [24] Hotta K, Sho M, Fujimoto K, et al. Prognostic significance of CD45ROþ memory T cells in renal cell carcinoma. Br J Cancer 2011;105:1191–6. [25] Liotta F, Gacci M, Frosali F, et al. Frequency of regulatory T cells in peripheral blood and in tumour-infiltrating lymphocytes correlates with poor prognosis in renal cell carcinoma. BJU Int 2011;107: 1500–6. [26] Balkwill F. Cancer and the chemokine network. Nat Rev Cancer 2004;4:540–50. [27] Mantovani A, Schioppa T, Porta C, Allavena P, Sica A. Role of tumor-associated macrophages in tumor progression and invasion. Cancer Metastasis Rev 2006;25:315–22. [28] Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature 2008;454:436–44. [29] Coussens LM, Tinkle CL, Hanahan D, Werb Z. MMP-9 supplied by bone marrow-derived cells contributes to skin carcinogenesis. Cell 2000;103:481–90.
