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. 2020 Jan 10;20:15. doi: 10.1186/s12935-020-1094-5

Prognostic role of pretreatment blood lymphocyte count in patients with solid tumors: a systematic review and meta-analysis

Jiawen Zhao 1,#, Weijia Huang 2,#, Yongxian Wu 1,#, Yihuan Luo 2, Bo Wu 1, Jiwen Cheng 1, Junqiang Chen 2, Deyun Liu 1, Chengyang Li 1,✉
PMCID: PMC6954501  PMID: 31938023

Abstract

Background

To evaluate the prognostic value of pretreatment lymphocyte counts with respect to clinical outcomes in patients with solid tumors.

Methods

Systematic literature search of electronic databases (Pubmed, Embase and Web of Science) up to May 1, 2018 was carried out by two independent reviewers. We included Eligible studies assessed the prognostic impact of pretreatment lymphocytes and had reported hazard ratios (HR) with 95% confidence intervals (CIs) for endpoints including overall survival (OS) and progression-free survival (PFS). Only English publications were included.

Results

A total of 42 studies comprising 13,272 patients were included in this systematic review and meta-analysis. Low pretreatment lymphocyte count was associated with poor OS (HR = 1.27, 95% CI 1.16–1.39, P < 0.001, I2 = 58.5%) and PFS (HR = 1.27, 95% CI 1.15–1.40, P < 0.001, I2 = 25.7%). Subgroup analysis disaggregated by cancer type indicated that low pretreatment lymphocytes were most closely associated with poor OS in colorectal cancer followed by breast cancer and renal cancer.

Conclusions

Low pretreatment lymphocyte count may represent an unfavorable prognostic factor for clinical outcomes in patients with solid tumors.

Keywords: Lymphocyte, Pretreatment, Prognosis, Solid tumor

Background

An increasing body of evidence suggests that immune status, an essential biological marker, is a key factor in carcinogenesis and cancer progression. Lymphocytes, such as those in the peripheral blood and tumor-infiltrating lymphocytes (TILs) constitute one of the most important effector mechanisms of anti-tumor immunity. Tumor cells are often surrounded by immune cells, especially lymphocytes. Tumor cells are distinguishable from healthy cells by the presence of tumor antigens which provide an immunological stimulus. Lymphocytes play an important role in anti-tumor immunity by inducing apoptosis and by suppressing the proliferation and migration of tumor cells [1–3]. High numbers of TILs were shown to be associated with inhibition of tumor progression and favorable prognosis in patients with hepatocellular carcinoma [4], colorectal cancers [5], and ovarian cancers [6]. Results of a meta-analysis suggest that TILs moderately influence the prognosis in diverse types of cancer; in particular, high number of intratumoral CD3+, CD4+ or CD8+ lymphocytes was associated with a lower risk of death and progression [2]. Numerous clinical studies have revealed that peripheral blood lymphopenia prior to initial treatment is associated with poor prognosis in various types of cancers, such as advanced carcinomas and sarcomas, cervical cancer, renal carcinoma, and bladder cancer [1, 7–9]. However, the inconsistent effect of pretreatment blood lymphocyte counts in patients with some publications cannot be ignored [10–15]. Moreover, the prognostic impact of lymphopenia in non-hematologic tumors has not been systematically analyzed. In order to reach a more reliable conclusion, a systematic review and meta-analysis to synthesize the evidence pertaining to pretreatment peripheral blood lymphocytes in patients with solid tumors is indispensable.

Materials and methods

Data sources and search strategy

The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were applied in the present study [16]. We conducted a systematic literature search in the PubMed, Web of Science, and Embase electronic databases to identify relevant studies published as of May 1, 2018. Combinations of the following keywords were used to retrieve articles: “lymphopenia”, “lymphocytosis”, “lymphocytes”, “tumor”, “carcinoma”, “cancer” and “prognosis” or “survival”.

Study selection criteria

Studies that qualified the following criteria were included: (1) original articles published in English language; (2) studies that enrolled patients with pathologically confirmed solid tumors who had not received any treatment; (2) lymphocyte counts were measured prior to the first treatment (surgery and/or chemotherapy or radiotherapy or palliative therapy); (3) pretreatment lymphocytes were reported as a dichotomous variable; (4) assessed the prognostic impact of pretreatment lymphocytes and had reported hazard ratio (HR) with 95% confidence interval (CI); at least provided Kaplan–Meier survival curves from which HRs and 95% CIs could be calculated.

In case of duplicate publications based on the same dataset, only the article with the largest sample size was included. Letters, reviews, case-reports, expert opinions and conference abstracts were excluded from the present study.

Titles and abstracts of articles retrieved on initial search were independently screened by two investigators (W.H. and Y.L.) to eliminate irrelevant articles. Full texts of the remaining articles were reviewed against the above criteria to identify eligible studies. In case of any disagreement between the two reviewers, the final decision was made by a third reviewer (J.Z.).

Data extraction and quality evaluation

Data pertaining to the following variables were independently extracted by two authors (W.H. and Y.L.): first author; publication year; region; study design; cancer type; sample size; disease stage; cut-off value; survival analysis; treatment details; and HR with corresponding 95% CI for OS and/or PFS. Survival outcomes obtained on multivariate analysis were accorded precedence over those obtained on univariate analysis.

Two investigators (W.H. and Y.L.) independently assessed the quality of each study according to the Newcastle–Ottawa Scale (NOS); any disagreement was resolved by consensus [17]. Newcastle–Ottawa Scale mainly includes selection, comparability, and evaluation of outcomes. On a scale of 0 to 9, a study with score of ≥ 6 was considered as a high-quality study. However, quality assessment was not an exclusion criterion for eligible studies.

Statistical analysis

We extracted the HRs and 95% CIs of the ratio for low pretreatment lymphocytes over high pretreatment lymphocytes from each eligible study for OS and/or PFS. The endpoints of survival were OS and/or PFS mainly because the two endpoints were frequently used in the included studies. Meta-analysis was performed to evaluate the prognostic effect of pretreatment lymphocytes in patients with solid tumors for each of the endpoints (OS/PFS). Extracted data were pooled using the Stata 12.0 (STATA Corporation, College Station, TX, USA). Cochrane Q test and the I2 statistic were used to test the heterogeneity among the studies included in the pooled analysis. In the absence of significant heterogeneity (P > 0.1 and I2 < 50%), the fixed effects model was used for pooled analysis [18]; otherwise, the random-effects model was used. Pooled HR > 1 was considered indicative of worse survival outcome of patients with low baseline lymphocytes. If the 95% CI did not overlap 1, the result was considered statistically significant. Subgroup analyses were performed to investigate the association of pretreatment lymphocyte counts with variables such as region, cancer type, disease stage, cut-off value, survival outcomes, and treatment scheme. Moreover, sensitivity analyses were performed by sequential elimination of one study at a time to explore its potential impact on the heterogeneity. We further used funnel plots and Egger’s test to examine the influence of publication bias on the pooled OS and PFS, respectively. All statistical tests were two-sided and P < 0.05 indicated statistical significance.

Results

Search and selection of studies

As illustrated in Fig. 1, a total of 2631 articles were retrieved on initial database search. Of these, 2507 articles were removed as irrelevant and duplicate articles. After full-text review, 75 were excluded due to lack of available information. Seven studies that reported lymphocytes count as a continuous variable were excluded. Finally, a total of 42 studies with a combined study population of 13,272 patients were considered eligible for inclusion [1, 7–15, 19–50]. The articles were published in the period from 2005 to 2018. The most common types of cancers in the included studies were lung cancer (n = 5), followed by nasopharyngeal cancer (n = 4) and renal cancer (n = 4). All the included studies had collected data retrospectively. Characteristics of included articles are described in Table 1.

Fig. 1.

Fig. 1

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Schematic illustration of the meta-analysis

Table 1.

Main characteristics of the included studies in the meta-analysis

First author Year Region Disease site Disease stage Inclusion period No. of patients Age Treatment Analysis of survival Cut off value (× 109/L) Study design Follow-up (months) Outcome reported NOS score
Yang [19] 2018 ChinaA Hypopharyngeal cancer Non metastatic 2009–2014 197 NR Chemo + Resection Univariate 1.7 Retrospective 30.95b OS 8
Pang [20] 2018 ChinaA Hepatocellular cancer Non metastatic 2002–2016 470 52.2a Resection Univariate 0.7 Retrospective 29b OS 7
Margetts [21] 2018 ChinaA Hepatocellular cancer Mixed 2007–2013 585 60b Resection + Chemo Multivariate 1.3 Retrospective NR OS 7
Liu [22] 2018 ChinaA Nasopharyngeal cancer Mixed 2007–2012 413 45b Chemo Univariate 1.315 Retrospective NR OS, PFS 6
Zhaoc [14] 2017 ChinaA Advanced cancer Mixed 2013–2015 378 64b Palliative therapy Multivariate 0.8 Retrospective 14.83b OS 6
Zhaoc [14] 2017 ChinaA Advanced cancer Mixed 2013–2015 378 64b Palliative therapy Multivariate 1.1 Retrospective 14.83b OS 6
Zhaoc [14] 2017 ChinaA Advanced cancer Mixed 2013–2015 378 64b Palliative therapy Multivariate 1.5 Retrospective 14.83b OS 6
Zhao [14] 2017 ChinaA Advanced cancer Mixed 2013–2015 106 64b Palliative therapy Multivariate 0.8 Retrospective 16.97b OS 6
He [15] 2017 ChinaA Hepatocellular cancer Non metastatic 2007–2015 216 53b Chemo Univariate 0.8 Retrospective NR OS 8
Bobdey [23] 2017 IndiaA Oral cancer Mixed 2007–2008 471 50a Chemo Univariate 1.98 Retrospective 22b OS 6
Xu [24] 2017 ChinaA Glioblastoma Non metastatic 2010–2015 166 50b Resection Multivariate 1.9 Retrospective 14b OS 7
Zhang [25] 2017 ChinaA Gallbladder cancer Mixed 2001–2013 98 63a Resection Univariate 2.06 Retrospective NR OS 8
Sorensen [26] 2017 DenmarkNA MBDex Metastatic 2003–2013 270 64b Resection Multivariate 1.37 Retrospective 8.82b OS 6
Oh [27] 2017 KoreaA Colorectal cancer Mixed 2006–2011 261 65b Resection Univariate 1.83 Retrospective 78b OS 7
Wu [7] 2016 AmericaNA Cervical cancer Non metastatic 1998–2013 71 49a Chemo Multivariate 1.0 Retrospective 30.4b OS, PFS 8
Sun [28] 2016 ChinaA Gastric cancer Non metastatic 2000–2012 873 59b Resection Univariate 3 Retrospective 36b OS, PFS 8
Sun [29] 2016 ChinaA Nasopharyngeal cancer Non metastatic 2008–2011 251 46b Chemo Multivariate 1.5 Retrospective 41b OS,PFS 7
Kou [30] 2016 ChinaA Esophagus cancer Metastatic 2005–2013 215 58b Chemo Multivariate 1.0 Retrospective 120 OS 6
Joseph [9] 2016 UKNA Bladder cancer Non metastatic 2009–2014 131 68b Chemo Multivariate 1.5 Retrospective 17b OS 8
Eo [31] 2016 KoreaA Endometrial cancer Non metastatic 2005–2014 255 44b Resection Univariate 1.526 Retrospective 51.3b OS 7
d’Engremont [32] 2016 FranceNA Pancreatic cancer Non metastatic 2000–2010 390 NR Resection Multivariate 1.0 Retrospective 66.6b OS 6
Deng [33] 2016 ChinaA Gallbladder cancer Mixed 2002–2012 315 NR Resection Multivariate 1.5 Retrospective 9b OS 6
Cho [34] 2016 KoreaA Lung cancer Non metastatic 2001–2014 73 65a Radiotherapy Univariate 1.747 Retrospective 22b OS, PFS 7
Cho [35] 2016 KoreaA Cervical cancer Mixed 2001–2012 124 57b Chemoradiotherapy Multivariate 1.5 Retrospective 63b PFS 6
Berardi [36] 2016 ItalyNA Lung cancer Mixed 2009–2014 401 68a Chemo Univariate 1.5 Retrospective 80 OS, PFS 7
Zhou [50] 2016 ChinaA Gastric cancer Non metastatic 2006–2008 451 NR Resection Univariate 1.5 Retrospective 37.7b OS 6
Wild [10] 2015 AmericaNA Pancreatic cancer Non metastatic 1997–2011 101 62b Chemo Univariate 1.0 Retrospective 10.1b OS 6
Santoni [11] 2015 ItalyNA Renal cancer Mixed 2005–2014 151 64a Chemo Univariate 1.5 Retrospective 51.6b OS, PFS 7
Rochet [12] 2015 AmericaNA Stage III Melanoma Non metastatic 2000–2010 153 59b Resection Multivariate 2.1 Retrospective 30b OS 7
Rochet [12] 2015 AmericaNA Stage IV Melanoma Metastatic 2000–2010 74 56b Resection Multivariate 1.9 Retrospective 24b OS 7
Mehrazin [37] 2015 AmericaNA Renal cancer Non metastatic 2000–2013 192 62a Resection Multivariate 1.3 Retrospective 38.7b OS 6
Ku [38] 2015 UKNA Urothelial cancer Non metastatic 1999–2011 419 65.1b Resection Multivariate 1.0 Retrospective 37.7b OS 7
Jin [39] 2014 ChinaA Nasopharyngeal cancer Metastatic 2006–2011 229 45b Chemo Multivariate 1.0 Retrospective 84 OS 7
Paikc [13] 2014 KoreaA Colorectal cancer Non metastatic 2006–2009 600 62.3a Resection Univariate 1.0 Retrospective 27,4b OS 8
Paikc [13] 2014 KoreaA Colorectal cancer Non metastatic 2006–2009 600 62.3a Resection Univariate 3.0 Retrospective 27,4b OS 8
Kumagai [40] 2014 JapanA Lung cancer Non metastatic 2007–2012 302 67b Resection Multivariate 1.4 Retrospective 33.4b OS 7
DeGiorgi [41] 2014 ItalyNA Renal cancer Metastatic 2006–2011 181 NR Chemo Multivariate 1.0 Retrospective NR OS, PFS 7
Zhang [42] 2013 ChinaA Lung cancer Mixed 1999–2006 142 57.5a Resection Multivariate 1.8 Retrospective NR OS 7
Saroha [8] 2013 AmericaNA Renal cancer Non metastatic 1994–2008 430 60.2a Resection Multivariate 1.3 Retrospective 33.5b OS 6
Manuel [43] 2012 FranceNA Breast cancer Metastatic NR 66 NR Chemo Univariate 1.0 Retrospective 18.8b OS 8
Manuel [43] 2012 FranceNA Pancreatic cancer Metastatic NR 67 NR Chemo Univariate 1.0 Retrospective 14.3b OS 8
Hec [44] 2012 ChinaA Nasopharyngeal cancer Non metastatic 2005–2007 1410 46.1a Chemo Multivariate 1.69 Retrospective 41b OS, PFS 7
Hec [44] 2012 ChinaA Nasopharyngeal cancer Non metastatic 2005–2007 1410 46.1a Chemo Multivariate 2.06 Retrospective 41b OS, PFS 7
Hec [44] 2012 ChinaA Nasopharyngeal cancer Non metastatic 2005–2007 1410 46.1a Chemo Multivariate 2.53 Retrospective 41b OS, PFS 6
DeGiorgi [45] 2012 AmericaNA Breast cancer Metastatic 2004–2008 195 54b Chemo Multivariate 1.0 Retrospective NR OS, PFS 7
Ceze [46] 2011 FranceNA Colorectal cancer Non metastatic 1999–2007 260 64.8a Chemo Multivariate 1.0 Retrospective 15b OS, PFS 6
Teramukaic [47] 2009 JapanA Lung cancer Mixed 2001–2005 388 65b Chemo Multivariate 1.082 Retrospective 18.9b OS, PFS 7
Teramukaic [47] 2009 JapanA Lung cancer Mixed 2001–2005 388 65b Chemo Multivariate 1.386 Retrospective 18.9b OS, PFS 7
Teramukaic [47] 2009 JapanA Lung cancer Mixed 2001–2005 388 65b Chemo Multivariate 1.821 Retrospective 18.9b OS, PFS 7
Ray-Coquard [1] 2009 FranceNA Breast cancer Metastatic NR 287 NR Chemo Multivariate 1.0 Retrospective 138 OS, PFS 8
Ray-Coquard [1] 2009 FranceNA Soft tissue sarcoma Metastatic NR 193 NR Chemo Multivariate 1.0 Retrospective 90 OS, PFS 8
LeScodan [48] 2007 FranceNA Brain metastases Metastatic 1998–2003 132 54.9b Chemo Multivariate 0.7 Retrospective 25b OS 7
Claude [49] 2005 FranceNA Brain metastases Metastatic 1991–2001 120 54b Radiotherapy Multivariate 0.7 Retrospective 67b OS 7
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NR not report, OS overall survival, PFS progression free survival, MBDex metastatic bone disease in the extremities

aMean; bmedian; cThe same patients sources in different cut-off values; AAsian; NANon-Asian

Relationship between pretreatment lymphocytes and survival outcomes

Overall survival

A total of 41 studies involving 45 cohorts (13,148 patients) investigated the association between pretreatment lymphocytes and OS. The median cut-off value of pretreatment lymphocytes in the included cohorts was 1.3425 (range: 0.7–3.0). In 16 articles, the HRs and 95% CIs were obtained on univariate analysis, while 25 articles had calculated HR on multivariate analysis. Overall, low pretreatment lymphocyte counts were associated with poor OS (HR = 1.27, 95% CI 1.16–1.39, P < 0.001) (Fig. 2). There was moderate heterogeneity among studies and thus a random-effects model was used (I2 = 58.5%). Subgroup analysis stratified by main clinical features (tumor type, cut-off value, survival analysis, and treatment) was performed. On subgroup analysis stratified by cancer type, low pretreatment lymphocytes were most closely associated with poor OS in colorectal cancer (n = 3, HR = 1.96, 95% CI 1.36–2.83, P < 0.001, I2 = 0), followed by breast cancer (n = 3, HR = 1.82, 95% CI 1.43–2.31, P < 0.001, I2 = 0), and renal cancer (n = 4, HR = 1.65, 95% CI 1.22–2.24, P = 0.001, I2 = 24.3%) (Table 2). On subgroup analysis stratified by pretreatment lymphocytes cut-off value, the largest effect size was observed in the cut-off value ≤ 1.0 subgroup (n = 17, HR = 1.46; 95% CI 1.21–1.77, P < 0.001, I2 = 67.6%); followed by the 1.0 ˂ cut-off ≤ 2.0 subgroup (n = 23, HR = 1.18; 95% CI 1.06–1.31, P = 0.004, I2 = 49.6%). Cut-off ˃ 2.0 subgroup was not associated with poor OS (n = 5, HR = 1.16; 95% CI 0.96–1.39, P = 0.121, I2 = 0). On subgroup analysis stratified by disease stage, both non-metastatic (n = 21, HR = 1.32, 95% CI 1.12–1.54, P ˂ 0.001, I2= 58.0%) and metastatic subgroups (n = 10, HR = 1.54, 95% CI 1.24–1.92, P ˂ 0.001, I2= 60.2%) were significantly associated with unfavorable OS. However, for the mixed subgroup (patients with both non-metastatic and metastatic disease), the pooled HR was 1.09 (n = 11, HR = 1.09, 95% CI 0.98–1.20, P = 0.107, I2 = 26.2%). No significant differences in survival outcomes were observed on subgroup analysis stratified by treatment or by type of survival analysis (univariate analysis vs. multivariate analysis). Further, sensitivity analysis showed that the pooled HRs for OS were not significantly affected by elimination of any individual study from the pooled analysis. The funnel plot was roughly symmetrical and Egger’s test showed no significant effect of publication bias on the results of the meta-analysis (P = 0.188 for OS).

Fig. 2.

Fig. 2

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Forest plots for the association between pretreatment lymphocyte and overall survival

Table 2.

Subgroup analysis of the meta-analysis for OS

Subgroup No. of studies No. of patients Pooled HR 95% CI P Heterogeneity test Statistical method
I2 P
Treatment
 Resection [8, 12, 13, 20, 24–28, 31–33, 37, 38, 40, 42, 50] 17 5861 1.30 1.08–1.55 0.004 61.5% <0.001 Random
 Chemo [1, 7, 9–11, 15, 22, 23, 29, 30, 36, 39, 41, 43–47] 18 5687 1.64 1.00–2.71 < 0.001 60.0% <0.001 Random
Analysis of survival
 Multivariate [1, 7–9, 12, 14, 21, 24, 26, 29, 30, 32, 33, 37–42, 44–49] 25 7612 1.31 1.16–1.47 < 0.001 63.6% <0.001 Random
 Univariate [10, 11, 13, 15, 19, 20, 22, 23, 25, 27, 28, 31, 34, 36, 43, 50] 16 5536 1.20 1.02–1.40 0.023 46.6% 0.016 Random
Cut-off value
 ≤ 1.0 [1, 7, 10, 13–15, 20, 30, 32, 38, 39, 41, 43, 45, 46, 48, 49] 17 4437 1.46 1.21–1.77 < 0.001 67.6% <0.001 Random
 1.0 to < 2.0 [8, 9, 11, 12, 14, 19, 21–24, 26, 27, 29, 31, 33, 34, 36, 40, 42, 44, 47, 50] 22 7646 1.18 1.06–1.31 0.004 49.6% 0.002 Random
 ≥ 2.0 [12, 13, 25, 28, 44] 5 4544 1.16 0.96–1.39 0.121 0.0% 0.760 Random
Disease site
 Colorectal cancer [13, 27, 46] 3 1121 1.96 1.36–2.83 < 0.001 0.0% 0.737 Random
 Breast cancer [1, 43, 45] 3 454 1.82 1.43–2.31 < 0.001 0.0% 0.509 Random
 Renal cancer [8, 11, 37, 41] 4 954 1.65 1.22–2.24 0.001 24.3% 0.265 Random
 Lung cancer [34, 36, 40, 42, 47] 5 1306 1.20 0.92–1.57 0.177 63.9% 0.011 Random
 Pancreatic cancer [10, 32, 43] 3 558 1.56 0.88–2.15 0.129 73.5% 0.023 Random
 Nasopharyngeal cancer [22, 29, 39, 44] 4 2303 1.23 1.03–1.46 0.017 0.0% 0.701 Random
 Gallbladder cancer [25, 33] 2 511 1.05 0.637–1.75 0.828 77.7% 0.034 Random
 Gastric cancer [28, 50] 2 1324 1.10 0.85–1.43 0.442 29.9% 0.232 Random
Disease stage
 Non metastatic [7–10, 12, 13, 15, 19, 20, 24, 28, 29, 31, 32, 34, 37, 38, 40, 44, 46, 50] 21 7437 1.32 1.12–1.54 < 0.001 58.0% 0.001 Random
 Metastatic [1, 12, 26, 30, 39, 41, 43, 45, 48, 49] 10 2108 1.54 1.24–1.92 < 0.001 60.2% 0.004 Random
 Mixed [11, 14, 21–23, 25, 27, 33, 36, 42, 47] 11 3603 1.09 0.98–1.20 0.107 26.2% 0.160 Random
Region
 Asian [13–15, 19–25, 27–31, 33, 34, 39, 40, 42, 44, 47, 50] (China, India, Korea, Japan) 23 8422 1.10 0.99–1.21 0.08 48.6% 0.001 Random
 Non-Asian [1, 7–12, 26, 32, 36–38, 41, 43, 45, 46, 48, 49] (Denmark, America, UK, France, Italy) 18 4726 1.27 1.16–1.39 < 0.001 32.0% 0.080 Random
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Progression-free survival

A total of 14 studies comprising of 18 cohorts (5147 patients) were included in the analysis of HRs for PFS. The median cut-off value for pretreatment lymphocytes was 1.50 (range: 1–3). In 9 articles, the HRs and 95% CIs were obtained by multivariable analysis; while 5 articles had calculated HRs and 95% CIs by univariate analysis. Overall, low pretreatment lymphocyte counts were significantly associated with worse PFS (Fig. 3). Owing to the lack of significant heterogeneity (I2 = 25.7%), the fixed-effects model was used for pooled analysis. On subgroup analysis stratified by cancer type, low pretreatment lymphocytes was most closely associated with poor PFS in patients with breast cancer (n = 2, HR = 1.76, 95% CI 1.42–2.20, P ˂ 0.001, I2 = 0) (Table 3). Likewise, the funnel plot was roughly symmetrical and Egger’s test revealed no significant influence of publication bias (P = 0.267 for PFS).

Fig. 3.

Fig. 3

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Forest plots for the association between pretreatment lymphocyte and progression-free survival

Table 3.

Subgroup analysis of the meta-analysis for PFS

Subgroup No. of studies No. of patients Pooled HR 95% CI P Heterogeneity test Statistical method
I2 P
Analysis of survival
 Multivariate 9 [1, 7, 29, 35, 41, 44–47] 2487 1.30 1.14–1.47 < 0.001 37.1% 0.080 Fixed
 Univariate 5 [11, 22, 28, 34, 36] 2660 1.19 1.01–1.40 0.036 0.0% 0.441 Fixed
Cut-off value
 ≤ 1.0 5 [1, 7, 41, 45, 46] 1187 1.55 1.32–1.82 < 0.001 0.0% 0.617 Fixed
 > 1.0 9 [11, 22, 28, 29, 34–36, 44, 47] 3960 1.11 0.99–1.24 0.053 0.0% 0.643 Fixed
Disease site
 Nasopharyngeal cancer 3 [22, 29, 44] 2074 1.31 1.12–1.53 0.001 0.0% 0.444 Fixed
 Breast cancer 2 [1, 45] 482 1.76 1.42–2.20 < 0.001 0.0% 0.820 Fixed
 Renal cancer 2 [11, 41] 332 1.15 0.84–1.59 0.36 0.0% 0.690 Fixed
Disease stage
 Non metastatic 6 [7, 28, 29, 34, 44, 46] 2814 1.34 1.14–1.56 < 0.001 0.0% 0.612 Fixed
 Metastatic 3 [1, 41, 45] 856 1.54 1.30–1.84 < 0.001 15.2% 0.316 Fixed
 Mixed 5 [11, 22, 35, 36, 47] 1477 1.10 0.97–1.24 0.138 0.0% 0.528 Fixed
Region
 Asian(China, Korea, Japan) 7 [22, 28, 29, 34, 35, 44, 47] 3408 1.20 1.07–1.34 0.002 20.2% 0.257 Fixed
 Non Asian(America, France, Italy) 7 [1, 7, 11, 36, 41, 45, 46] 1739 1.37 1.20–1.55 < 0.001 31.6% 0.176 Fixed
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Discussion

To the best of our knowledge, this is the first systematic review and meta-analysis that comprehensively summarizes the association between lymphocyte count and cancer survival. Current meta-analysis included a total of 42 studies with a combined study population of 13,272 patients and provides evidence that low lymphocyte counts are associated with shorter OS and PFS in patients with non-hematologic tumors. There was moderate heterogeneity among studies in the analysis of OS (I2 = 58.5%) but not that of PFS (I2 = 25.7%). Subsequently, on subgroup analysis by tumor location, the highest effect size with respect to OS was observed in patients with colorectal cancer followed by those with breast cancer and renal cancer. Intriguingly, we found a significant reduction in heterogeneity in subgroups of patients with colorectal cancer (I2 = 0), breast cancer (I2 = 0) and renal cancer (I2 = 24.3%) although moderate heterogeneity was observed (I2 = 58.5%) in the pooled analysis. Moreover, when stratified by disease stage in the analysis of OS and PFS, low lymphocyte count was an adverse prognostic factor in both non-metastatic and metastatic subgroups. This suggests that lymphocytes are involved in several stages of cancer development. Moreover, the negative prognostic effect on OS and PFS was consistent in subgroups stratified by cut-off value and type of survival analysis.

Patients with pretreatment lymphopenia have significantly worse survival than those of patients with normal lymphocyte counts in the context of several malignancies [1, 7–9]. Lymphocytes are known to play a role in cellular and humoral anti-tumor immune responses. Activated and proliferating lymphocytes play a role in cytotoxic cell death and inhibit tumor cell proliferation and migration. Chew et al. observed lymphocyte recruitment and proliferation in tumor areas devoid of tumor cell proliferation and rich in tumor cell apoptosis [4]. Therefore, lymphopenia may reflect poor host immunity against cancer and a favorable microenvironment for tumor growth. The underlying mechanism of pretreatment lymphopenia in solid tumors has not been fully clarified and is probably multifactorial. It is widely believed that lymphopenia may result from increased lymphocyte apoptosis and/or altered lymphocyte homeostasis. Kim et al. demonstrated that increased expression of Fas ligand (FasL) in tumor cells mediated apoptosis of TILs as well as circulating lymphocytes, which conferred immune privilege to tumors [51]. Increased numbers of apoptotic peripheral T lymphocytes have been detected in patients with gastric cancer [52]. Over-production of immunosuppressive cytokines such as transforming growth factor (TGF-β) and IL-10 by tumor cells specially during tumor growth may suppress different effector pathways of the immune response [53, 54]. Exposure to TGF-β reduced the expressions of apoptotic activators (such as perforin and granzyme A and B) on cytotoxic T cells that infiltrated the tumor tissues. Additionally, tumor growth increases the recruitment of CD4+ regulatory T cells that secrete IL-10 and TGF-β and suppress effector CD8+ T cell responses [55]. IL-10 exerts an inhibitory effect on major histocompatibility complex (MHC) class I antigen presentation. Dummer et al. observed excessive expression of immunosuppressive factor IL-I0 in metastatic lesions and in cultured cells from metastases; they inferred that this cytokine plays a key role in tumor progression [56]. Although numerous studies previously focused on T-cell-mediated immunity, B cells play an equally prominent role in modulating anti-tumor immune responses and in carcinogenesis. B cells are classically known for their role as producers of antibodies. Tumor-infiltrating B cells have relation to improved survival in cervical cancer and non-small cell lung cancer [57, 58]. Results from these clinical observations suggest that the potential mechanisms underlying B-cell anti-tumor immunity may involve tumor-infiltrating B cells could recruit and retain T cells at the tumor site, thus facilitating and sustaining T-cell responses that inhibit tumor development. Moreover, tumor-infiltrating B cells may function as antigen-presenting cells to aid in anti-tumor immunity [57, 59]. Thus, it may be possible to generate more amplified and prolonged immune responses at the tumor site by promoting cooperative interactions of B cells and T cells. Collectively, these findings suggest that lymphopenia may be a result of cancer-induced immune suppression that drives tumor progression.

Neutrophil–lymphocyte ratio (NLR) has been identified as an independent prognostic factor in many solid tumors; a high NLR ratio was shown to be associated with inferior outcomes [60–62]. Nevertheless, it includes two potentially independent biological factors; high NLR indicates an increase in neutrophil and/or decreased total lymphocyte count. A meta-analysis of one hundred studies (combined n = 40,559) conducted by Templeton et al. revealed that high NLR is associated with adverse OS, CSS, PFS, or DFS in many solid tumors [63]. The prognostic impact of NLR may be explained by the association of high NLR with inflammation. However, at the same time, the authors admitted that the confounding effect of concurrent inflammatory conditions cannot be completely excluded because high NLR has also been shown to be of prognostic relevance in non-cancerous conditions such as acute pancreatitis [64] and cardiac events [65]. Joseph suggested that the prognostic value of high neutrophil–lymphocyte ratio may actually be driven by lymphocytopenia rather than neutrophilia in patients with bladder cancer [9]. Similar results have been reported elsewhere; lymphocyte count was shown to exert a stronger impact on the neutrophil-to-lymphocyte ratio in clear cell renal carcinoma and pancreatic cancer [8, 32]. Therefore, based on these observations, we evaluated the prognostic value of pretreatment peripheral blood lymphocyte counts with respect to clinical outcomes in patients with solid tumors.

Lymphocytopenia is not just a parameter related to cancer survival but may also reflect a biological mechanism that promotes tumor progression. Of note, adjunctive treatment for reversal of lymphopenia or to increase lymphocyte counts has also been proposed by some authors. Restoration of lymphocyte homeostasis may lead to activation of effector cytotoxic and helper T cells and result in a more potent antitumor immune response. IL-2 was used for treatment of patients with metastatic melanoma. Recombinant human IL-7 (rhIL-7) was shown to improve the immune function of patients with lymphopenia by promoting peripheral T cell expansion and suppressing the immunosuppressive network [66].

In view of the possible impact of different cut-off values of pretreatment lymphocytes on prognosis, we observed the largest effect size in the cut-off ≤ 1.0 subgroup; the next was the 1.0 < cut-off ≤ 2.0 subgroup. Nonetheless, the cut-off > 2.0 subgroup was not associated with poor OS. Similar results were obtained on subgroup analysis of PFS. Hence, a relatively lower pretreatment lymphocytes cut-off value may have a better discriminative prognostic value. However, optimal pretreatment lymphocytes cutoff value for various types of cancers needs further research.

Undoubtedly, our research has several limitations. First, our meta-analysis was based on HR and 95% CIs extracted from retrospective studies. Due to the inherent limitations of retrospective studies including heterogeneity with respect to data selection and analysis, our pooled data might be susceptible to biases and may be biased towards positive results. Second, moderate heterogeneity was observed in the analysis of OS and the sources of this heterogeneity remain unclear; however, no significant heterogeneity was observed in the analysis of PFS. This is likely attributable to inclusion of more than 40 cohorts comprising of 13,000 patients with different tumors and from various countries. As yet, we have not found any meta-analysis that determined the prognostic value of pretreatment lymphocytes in any malignancy. Our goal was to gain a comprehensive understanding of the prognostic value of lymphocytes in patients with solid tumors. Therefore, the moderate heterogeneity observed in the analysis of OS is reasonably expected. Third, in 16 out of the 42 studies, the HRs were calculated on univariate analysis. Compared with data from multivariate analysis, HR and 95% CI calculated on univariate analysis is more likely to lead to an overestimation of the prognostic value. Therefore, we conducted subgroup analysis of univariate analysis and multivariate analysis and the statistical significance was stable; moreover, the multivariate analysis subgroup even had a larger effect size.

Conclusion

Peripheral blood lymphocytes is a simple and routine index in clinical work. To the best our knowledge, we have not found any meta-analysis that determined the prognostic value of pretreatment lymphocytes in any malignancy. Our meta-analysis provides evidence that pretreatment lymphocyte might be a potential biomarker for survival in patients with solid tumors. However, the present meta-analysis was based on observational studies; we could not demonstrate a cause-effect relationship between pretreatment lymphocyte and survival in patients with solid tumors. Further prospective large-scale investigations are required to explore whether reversing lymphopenia can be a new target for cancer treatment and to increase the understanding of its role in disease pathogenesis.

Acknowledgements

None.

Abbreviations

HR

hazard ratio

CL

confidence interval

OS

overall survival

PFS

progression-free survival

NOS

Newcastle–Ottawa Scale

TILs

tumour-infiltrating lymphocytes

PRISMA

Preferred Reporting Items for Systematic Review and Meta-Analysis

FasL

Fas ligand

TGF-β

transforming growth factor

MHC

major histocompatibility complex

NLR

neutrophil–lymphocyte ratio

Authors’ contributions

JZ and WH collected, extracted performed quality assessment articles; WH and YL analyzed the data; JZ, JC, and WH conceived, designed this study and wrote the paper. CL, DL and JC reviewed the final manuscript. YW and BW revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81660125).

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declared no potential competing interests with respect to the research, authorship, publication of this article.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jiawen Zhao, Weijia Huang and Yongxian Wu contributed equally to this work and therefore share first authorship

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Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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