Prognostic value of neutrophil‐to‐lymphocyte ratio and platelet‐to‐lymphocyte ratio for breast cancer patients: An updated meta‐analysis of 17079 individuals

1Breast cancer (BC) is the most frequently diagnosed malignance among women and the one of the most common causes of cancer‐related death.1, 2 Clinical therapeutic strategies and prognosis of BC are based on tumor characteristics, patient factors and response to treatment. However, its high heterogeneity results in a broad range of clinical outcomes even among BC patients with similar clinical staging and pathologic grading.3 Tumor microenvironment, inflammation, and immune response have been reported to play important roles in tumor progression and prognosis.4, 5

2Recently, substantial evidence shown that inflammation‐based models, such as the systemic immune‐inflammation index, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR) and inflammation‐based index, were useful indicators for predicting the prognosis of various solid cancers.6, 7 NLR and PLR, two of the most frequently applied indicators, have been widely investigated for their value in predicting prognosis of BC patients. A majority of researches demonstrated elevated peripheral NLR and PLR were recognized as poor prognostic factors.8, 9 Nevertheless, there were still others revealed patients with elevated PLR were associated with better survival outcomes.10

3Therefore, this study was conducted to evaluate the prognostic value of NLR and PLR on overall survival (OS) and disease‐free survival (DFS) for BC patients. Moreover, by performing subgroup analyses, we quantified the effect of NLR and PLR in different subgroups.

4A comprehensive literature search of relevant studies was performed through the online medical databases PubMed, Embase, Web of Science, the Cochrane Library and Scopus. Studies focused on the correlation of BC and NLR as well as PLR were taken into retrievement. Search terms were confined to the following main words and Medical Subject Headings terms: “neutrophil”, “platelet”, “lymphocyte”, “neutrophil‐to‐lymphocyte”, “platelet‐to‐lymphocyte”, “breast cancer”, and “breast carcinoma”. Moreover, by using cross‐references from the references of primary selected studies and relevant studies, a backward search was also performed to ensure a comprehensive search. Literatures were restricted to those written in English. There was no restriction on geographical region. Two reviewers (Wanying Guo and Xin Lu) completed the electronic search independently.

5Reviewers screened the eligible studies based upon inclusion and exclusion criteria which were prespecified. The final decision on inclusion and exclusion criteria were approved by all the authors. Any disagreements between the two reviewers were solved and made final decision by the senior reviewer (Miao Deng).

6The criteria for inclusion were as following: Articles analyzed BC patients.Articles evaluated prognostic value of NLR or PLR.Articles assessed the OS or DFS of BC patients.

7Articles analyzed BC patients.

8Articles evaluated prognostic value of NLR or PLR.

9Articles assessed the OS or DFS of BC patients.

10The criteria for exclusion were as following: Articles not focused on the prognosis of BC patients.Articles concerned on neither pretreatment NLR nor PLR.Reviews or editorials.Case reports or conference abstracts.Articles without data of interest (OS or DFS).

11Articles not focused on the prognosis of BC patients.

12Articles concerned on neither pretreatment NLR nor PLR.

13Reviews or editorials.

14Case reports or conference abstracts.

15Articles without data of interest (OS or DFS).

16Articles with duplicate sample set, such as those published by same authors or departments, were picked out for further screening. In this case, only the articles with largest sample size or those published most recently were finally included in the present meta‐analysis. However, for those studies analyzed two or more independent sample sets such as training and validation cohorts, the cohorts were enrolled in this study and analyzed independently.

17The software of Endnote (version X8) was used for preliminary screening and sorting. Data were extracted from the enrolled literatures by two authors (Wanying Guo and Xin Lu) after reading full text intensively. The baseline information included full list of authors as well as affiliations, year of publication, geographical region, research centers, models of use, sample size, cut‐off values for NLR and PLR, mean or median ages, proportion of triple negative patients, indications for surgical treatment, follow‐up time, and treatment strategies. The hazard ratios (HRs) with 95% confidential intervals (CIs) were directly extracted from the tables or texts. However, in several studies, the HRs and 95% CIs were not shown directly. In such cases, the software of Engauge Digitizer (version 4.1) was used to extract HRs and 95% CIs by computing the Kaplan‐Meier graph.11, 12 The primary outcomes were pooled using the Cochrane Collaboration's Review Manager (version 5.3, Cochrane Collaboration, Oxford, UK).13 Random effect model was applied routinely only if no obvious heterogeneity was observed among the included literatures (I 2 < 40%). Heterogeneity within studies was explored by using the chi‐square test with a P value of 0.10 for significance. Moreover, the heterogeneities were quantified using the I 2 statistics. Sensitivity analyses of main outcomes were conducted by using the software of Stata (version 12.0).14 The publication bias was investigated using funnel plots. Moreover, the symmetry properties of funnel plot was examined by using Begg and Egger tests.15

18All the included studies were critically assessed for methodological quality by 2 researchers independently (Lu X and Guo WY) by using the Quality In Prognosis Studies tool. Each study was graded for the following domains: study participation, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis and reporting. The risk of bias for each domain is graded as low (−), moderate (±), or high (+).

19To identify the sources of heterogeneity, subgroup analyses were conducted according to etiologies, geographical region, cut‐off values, and tumor stage. Subgroup of triple‐negative included BC patients with negatively expressed estrogen receptor, progesterone receptor, and HER2, while subgroup of HER2‐positve enrolled BC patients with positive HER2 expression. Patients from different continents were classified into Asia, America, and Europe subgroups respectively. Patients with early stage and metastatic tumors were analyzed in different subgroups. It was difficult to subgroup analysis which based on therapeutic strategies because of most of BC patients received surgical treatment as well as other treatments like adjuvant chemotherapy, endocrine therapy or trastuzumab therapy.

20Literature research identified 1215 records: 115 from PubMed, 296 from Embase, 313 from Web of Science, 15 from the Cochrane Library, and 436 from Scopus. Meanwhile, 4 studies were identified through references. As shown in Figure 1, after screening titles and abstracts, 362 duplications and an additional 781 studies were excluded. Full text of the remaining 76 studies were read rigorously. Thirty‐seven more were excluded: 7 were published by same centers, 10 enrolled duplicate patients, 6 were conference abstracts and 14 were without survival data. Finally, a total of 39 studies with 17079 patients were included in the present meta‐analysis.8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 Among them, 35 studies with 159 39 BC patients analyzed the effectiveness of NLR and 15 studies with 7949 patients analyzed PLR. The characteristics of the 39 included studies were summarized in Table 1.

21Twenty‐eight studies comprising 142 64 BC patients investigated predicting role of NLR for OS. The pooled results shown that patients with elevated NLR prior treatment were associated worse prognosis compared to those with lower NLR (HR: 1.78; 95% CI: 1.49‐2.13; P < 0.001). Twenty‐seven cohort studies containing 115 04 patients explored the prognostic role of NLR in predicting DFS. The results demonstrated that patients with elevated NLR were associated with poor DFS, with a HR of 1.60 (95% CI: 1.42‐1.96; P < 0.001) (Figure 2).

22Results for subgroup analyses were shown in Table 2. The results of other subgroups displayed similar outcomes to overall result except subgroup of HER2‐positive and America. Six studies analyzed patients with HER2‐positively expressed showed that NLR was not significantly associated with OS (HR: 1.27; 95% CI: 0.94‐1.72; P = 0.12). In subgroup of America, the pooled result also showed no significant correlation between NLR with OS (HR: 1.91; 95% CI: 0.83‐4.37; P = 0.13). The results of subgroups for predicting DFS were similar to overall result. However, subgroup analyses based on tumor progression was unable to conduct due to insufficient data.

23As shown in Table 3, 12 studies with 6930 patients explored the prognostic role of PLR in predicting OS of patients with BC. The pooled outcome suggested that patients with higher PLR were associated with a significantly poor prognosis (HR: 1.32; 95% CI: 1.11‐1.57; P = 0.002). Eleven studies with 5013 patients investigated predicting role of PLR for DFS shown that patients with elevated PLR were associated with shorter DFS (HR: 1.43; 95% CI: 1.09‐1.86; P = 0.009) (Figure 3).

24Subgroup analyses shown there were no statistically significant correlation between PLR and OS in patients with HER2‐positive expression or patients with early stage tumor. Subgroup analysis based on cut‐off value suggested a value that more than 185 was better for PLR in predicting prognosis of BC patients (HR: 1.81; 95% CI: 1.33‐2.46; P < 0.001). In subgroup analyses for PLR in predicting DFS, no significant association was found (Table 3).

25The sensitivity analyses was conducted to investigate the stabilities of the pooled HRs of OS and DFS by omitting enrolled studies in turn. The results showed that the pooled HRs did not alter significantly after eliminating the included studies in sequence which suggested the steady of our findings (Figure 4).

26The funnel plots showed no obvious publication bias among the enrolled studies (studies distributed around the center line symmetrically). Moreover, 2 kinds of statistical tests further validated the dissymmetry of the funnel plot by using Egger's test and Begg's test (Table 4).

27Recently, more and more studies focused on correlation between inflammation and solid malignancies revealed that tumor initiation, progression, and metastasis were affected by host systemic inflammatory response as well as tumor microenvironment.4, 51, 52 Hence, we performed a meta‐analysis, including 39 studies comprising 17079 BC patients, to evaluate the prognostic role of NLR and PLR in predicting OS and DFS. A significant prognostic effect for NLR and PLR was found on OS and DFS after pooling results. High NLR level was associated with both poor OS as well as DFS for BC patients. Meanwhile, patients with elevated PLR were associated worse OS as well as higher risk of recurrence compared to those with decreased PLR level.

28Although the underlying molecular mechanisms have not been adequately illuminated. Neutrophil was considered to be related to cancer‐associated inflammation with the potential mechanism of responding to the ectopic interleukin‐8 released in tumor proliferation, progression and metastasis.53 Moreover, cancer‐associated cytokines like tumor necrosis factor‐α and interleukin‐6 contribute to neutrophilia in solid cancers.54, 55 Neutrophilia inhibits the cytotoxic activity of immune cells like lymphocytes, natural killer cells and T cells which would counteract the anti‐tumor immune response.56, 57 A high platelet counts was considered to be related to metastasis of BC cells with the mechanism of contribution to lysophosphatidic acid‐dependent metastasis.58 Platelets could also promote tumor angiogenesis and stroma formation by secreting vascular endothelial growth factor and facilitating migration of inflammatory cells.59, 60 Loi et al found elevated lymphocytic infiltration in BC was associated with favorable prognosis, especially in those node‐positive and HER2‐negative BC.61 The lymphocytes played an important role in cell‐mediated anti‐tumor immune responses and tumor immunological surveillance.62, 63, 64

29In view of the heterogeneity of BC, subgroup analyses according to different subtypes like triple‐negative and HER2‐positive. The results shown a greater magnitude of association between NLR and OS in triple‐negative BC patients than in HER2‐positive ones. And negative prognostic effect was found for NLR and PLR in HER2‐positive BC patients. A previous meta‐analysis performed by Zhang et al included 11 studies and 1 conference abstract to evaluate the prognostic value of PLR in BC.65 Their process of extracting data were not rigorous enough that several HRs with 95% were not consistent with original researches.10, 47 The pooled results of our study were more credible and stable because of more rigorous in data extraction and subgroup analyses. Nonetheless, future prospective studies with large sample size were in need to confirm our outcomes especially the prognostic effect of NLR and PLR on HER2‐positive BC patients. Our subgroup analyses also suggested that a cut‐off value no less than 185 for PLR in predicting OS was more preferable.

30Individualized therapy based on tumor‐associated biological characteristics and host circumstance was advocated in treatment strategies for BC. Several treatments such as surgical resection combined with adjuvant chemotherapy or neoadjuvant chemotherapy, endocrine therapy, and targeted therapy were optional for BC patients with different tumor stages. This study performed subgroup analysis based on tumor stage suggested comparative effect of NLR and PLR in predicting OS. The data were insufficient to conduct subgroup analysis based on treatment strategies.

31To our knowledge, this was the most comprehensive meta‐analysis with largest sample size to estimate the prognostic role of PLR as well as NLR for BC. However, there were still several limitations should be taken into consideration when interpreting our findings. First, although there was no obvious publication bias, all the included studies were retrospectively designed. High proportion of retrospective individual studies would give rise to inherent bias inevitably. It would be preferable for future studies to design and collect data prospectively. Second, in the subgroup analysis of pathology, only triple‐negative and HER2‐positive BC patients were enrolled. Furthermore, all the included studies were written in English which would result in potential publication bias. Finally, future international multi‐center studies with larger sample size were in need to confirm our results.

32Nevertheless, the present meta‐analysis was performed at an appropriate time as an adequate number of studies with sufficient data in a large patient cohort investigating the prognostic effect of NLR and PLR for BC patients have been accumulated, allowing evaluation through meta‐analysis. A meta‐analysis is considered to be a statistical inspection of scientific studies which is associated with higher evidence level than the individual studies themselves.66 Subgroup analyses were performed to minimize heterogeneity stemming from different BC‐specific subtypes, optional cut‐off values, geographical regions, and tumor stages.

33In conclusion, this study suggested that elevated NLR and PLR were associated with poor OS as well as high risk of recurrence for BC patients. Subgroup analyses confirmed negative prognostic value of NLR and PLR for HER2‐positive BC patients. As easily accessible parameters, NLR and PLR should be identified as useful biomarkers in the management of BC.

34The authors declare that there are no conflict of interest.

35Wanying Guo and Miao Deng contributed to the designation of this study. Wanying Guo and Xin Lu contributed to literature research. Qipeng Liu and Ting Zhang contributed to data extraction. Xin Lu, Peng Li and Weiqiang Qiao performed the statistical analysis. All the authors participated in drafting the manuscript.