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Cancer Cell International

, 20:15 | Cite as

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

  • Jiawen Zhao
  • Weijia Huang
  • Yongxian Wu
  • Yihuan Luo
  • Bo Wu
  • Jiwen Cheng
  • Junqiang Chen
  • Deyun Liu
  • Chengyang LiEmail author
Open Access
Primary research

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 

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

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, 2, 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, 8, 9]. However, the inconsistent effect of pretreatment blood lymphocyte counts in patients with some publications cannot be ignored [10, 11, 12, 13, 14, 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, 8, 9, 10, 11, 12, 13, 14, 15, 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]. 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

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

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

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, 25, 26, 27, 28, 31, 32, 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, 10, 11, 15, 22, 23, 29, 30, 36, 39, 41, 43, 44, 45, 46, 47]

18

5687

1.64

1.00–2.71

< 0.001

60.0%

<0.001

Random

Analysis of survival

 Multivariate [1, 7, 8, 9, 12, 14, 21, 24, 26, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 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, 14, 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, 22, 23, 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, 8, 9, 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, 22, 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, 14, 15, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 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, 8, 9, 10, 11, 12, 26, 32, 36, 37, 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

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

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, 45, 46, 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, 35, 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

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, 8, 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, 61, 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.

Notes

Acknowledgements

None.

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).

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.

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Authors and Affiliations

  • 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
    Email author
  1. 1.Department of UrologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningChina
  2. 2.Department of Gastrointestinal SurgeryThe First Affiliated Hospital of Guangxi Medical UniversityNanningChina

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