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. 2019 Aug;8(4):429–449. doi: 10.21037/tlcr.2019.08.04

The clinicopathological and prognostic significance of PD-L1 expression assessed by immunohistochemistry in lung cancer: a meta-analysis of 50 studies with 11,383 patients

Huijuan Li 1,2,#, Yangyang Xu 3,#, Bing Wan 4,#, Yong Song 1,2,3, Ping Zhan 1,2, Yangbo Hu 5, Qun Zhang 1,2, Fang Zhang 1,2, Hongbing Liu 1,2, Tianhong Li 6, Haruhiko Sugimura 7, Federico Cappuzzo 8, Dang Lin 9,✉, Tangfeng Lv 1,2,✉; written on behalf of AME Lung Cancer Collaborative Group
PMCID: PMC6749117  PMID: 31555517

Abstract

Background

We conducted a meta-analysis to systematically evaluate the relationship between programmed death-ligand 1 (PD-L1) expression and survival in patients with lung cancer.

Methods

The electronic databases PubMed, Embase, Cochrane, and Web of Science were searched up to January 2nd, 2018, for articles relating to PD-L1 expression detected by immunohistochemistry (IHC) and lung cancer patient prognosis.

Results

Fifty studies including 11,383 patients published between 2011 and 2017 were enrolled in this meta-analysis. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) suggested that PD-L1 IHC expression was related to poor overall survival (OS) (HR =1.45, 95% CI: 1.24–1.68). In subgroup analysis categorized according to sample type, cut-off value, ethnicity and TNM stage, the pooled results demonstrated inferior survival in the PD-L1 positive group when the PD-L1 expression was detected by resection specimens (P=0.000), 5% was taken as the cutoff value (P=0.000), the patients were in early stage (I–III) (P=0.000), and the geographic setting of the study was in Asia (P=0.000). Besides, patients with high PD-L1 expression had shorter OS in NSCLC (P=0.000), ADC (P=0.000), SCC (P=0.353) and LELC (P=0.810), while no significant difference was observed in SCLC (P=0.000). The pooled odds ratios (ORs) suggested that PD-L1 expression was associated with male (P<0.001), smoker (P<0.001), poor tumor differentiation (P=0.014), large tumor size (P=0.132), positive lymph nodal metastasis (P=0.002), EGFR wild-type status (P<0.001) and KRAS mutations (P=0.393). However, age (P=0.15) and ALK rearrangements (P=0.567) had no bearing on PD-L1 expression.

Conclusions

PD-L1 expression that is associated with several clinicopathological feactures may serve as a poor prognostic biomarker for patients with lung cancer.

Keywords: Lung cancer, meta-analysis, programmed cell death ligand 1, prognosis

Introduction

Lung cancer is the most lethal cancer and a major public health challenge both worldwide and in China (1,2). Most lung cancer patients are diagnosed at the advanced stage as lacking of specific symptoms at early stage. Even with multidisciplinary treatment, the long-term survival rate of lung cancer remains poor, and the overall five-year survival rate is merely 17% (3). In clinical practice, several independent prognostic factors like disease stage and performance status are valuable for guiding treatments for individual patients (4). Nevertheless, the discriminant value of most potential prognostic biological markers is insufficient, and molecular biomarkers that precisely identify patients at a high risk of poor prognosis urgently need to be discovered.

Programmed death 1 (PD-1), which belongs to the CD28 superfamily, is an inhibitory surface-receptor expressing on activated T, B, and natural killer (NK) cells, and regulates their proliferation and activation (5). Programmed cell death ligand 1 (PD-L1), which belongs to the B7 family, is the main ligand of PD-1 that is frequently upregulated in several kinds of human malignancies, including lung cancer (6,7). PD-L1 transmits inhibitory signals leading to apoptosis or exhaustion of activated T cells, differentiation of naive CD4+ T cells into regulatory T cells, and maintenance of suppressive functions of regulatory T cells by engaging its receptor PD-1. Consequently, blockade of PD-1/PD-L1 signaling has demonstrated clinical efficacy in multiple tumor types in recent clinical trials (8,9).

Though several studies have reported the relationship between PD-L1 expression and survival in patients with lung cancer, the data still remain inconsistent and conflicting. To address these issues, we carried out a comprehensive meta-analysis to quantitatively investigate the clinicopathological and prognostic significance of PD-L1 expression in patients with lung cancer.

Methods

Literature search

The electronic databases PubMed, Embase, Cochrane, and Web of Science were searched using the following keywords: (“PD-L1” or “B7-H1” or “CD274” or “programmed cell death ligand 1”) and (“lung cancer” or “lung neoplasms” or “pulmonary cancers”). The last search deadline was January 2nd, 2018.

Inclusion and exclusion criteria

Two authors (H Li and Y Xu) determined study eligibility independently, and any discrepancies were resolved by consensus. Studies eligible for inclusion were gathered in accordance with the following criteria: (I) all patients were confirmed to have lung cancer by a pathology assessment; (II) PD-L1 protein expression was evaluated in the primary lung cancer tissues by IHC; (III) studies revealed a correlation between PD-L1 expression and prognosis of lung cancer; (IV) studies reported sufficient information about PD-L1 expression and clinicopathological parameters; (V) studies provided HR and it’s 95% CI for OS, or sufficient information to estimate them; (VI) all patients received no preoperative immunotherapy; (VII) when there was more than one study with the same patient population, only the most recent or the most complete study was included. The exclusion criteria included the followings: (I) reviews, case reports, editorials, conference abstracts, meta-analyses, in vivo or vitro studies, non-English articles; (II) studies with insufficient data to be extracted; (III) a sample size of fewer than 20 patients.

Data extraction

The following information was extracted from each included study: name of the first author, year of publication, study location, the number of patients, sample type (resection, biopsy, etc.), histology, TNM stage, IHC antibody, IHC counting method, cut-off value, the percent of PD-L1 positive, HR and 95% CI: for OS, clinicopathological parameters. If any data from the above categories were not reported directly, items were treated as “not applicable (NA)”. If the HRs and their 95% CIs were not reported explicitly, we estimated the values from Kaplan-Meier curves using the methods of Parmar (10).

Quality assessment and statistical analysis

The final eligible articles were evaluated independently by two authors (H Li and B Wan) according to the Newcastle-Ottawa Scale (NOS), and any discrepancies were resolved by consensus. The maximum possible NOS score is 9 points, and any study included which receives a score of more than 6 is rated as high quality (11). The pooled overall survival (OS) was used to assess the relationship between PD-L1 expression and prognosis, and the pooled odds ratios (ORs) were combined to investigate the correlation between PD-L1 expression and clinicopathological features. The heterogeneity was statistically tested by chi-squared test and I square (I2), and a chi-squared P value <0.1 or an I2 statistic >50% was defined as statistically significant heterogeneity (12). If significant heterogeneity was observed, we used a random-effects model for the following analysis, otherwise a fixed-effects model was applied. Moreover, the potential publication bias was assessed through Begg’s funnel plots (13). All of the statistical analyses were conducted using STATA version 12.0 (Stata Corporation, College Station, TX, USA) statistical software.

Results

Study selection and characteristics

The initial database searching yielded a total of 372 records eligible for inclusion. Through reviewing the titles or abstracts of the all articles, 304 articles were excluded in accordance with the exclusion criteria (reviews, case reports, comments, meta-analysis, in vivo/vitro studies, conference abstracts, non-English language, or having fewer than 20 patients). The full text of the remaining 68 articles were further reviewed in detail, and eventually, 50 studies fulfilling the inclusion criteria were included in this meta-analysis. A flowchart of study selection is shown in Figure 1.

Figure 1.

Figure 1

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Flow chart of study selection.

The major characteristics and technical information on PD-L1 immunohistochemistry (IHC) of the 50 eligible studies are shown in Tables 1 and 2, respectively. In total, 50 studies published between 2011 and 2017 were included in the pooled analysis, with 11,383 lung cancer patients from Australia, Canada, China, France, Germany, Italy, Japan, Korea, and the United States enrolled. The study cohort size ranged from 36 to 1,070 patients (median 228). Among the 50 studies, 24 focused on PD-L1 expression in non-small cell lung cancer (NSCLC) (7,14-36), 12 focused on adenocarcinoma (ADC) (37-48), 5 focused on squamous cell carcinoma (SCC) (49-53), 3 focused on small cell lung cancer (SCLC) (54-56), 2 focused on pulmonary lymphoepithelioma-like carcinoma (LELC) (57,58), 1 focused on pulmonary sarcomatoid carcinomas (SC) (59), 1 focused on high-grade neuroendocrine tumor (HGNET) (60), 1 focused on pulmonary pleomorphic carcinoma (PPC) (61), and 1 focused on pleomorphic, spindle cell and giant cell carcinoma of the lung (PSCGCC) (62). The expression of PD-L1 was found in 4,293 participants (37.7%), although the definitions of positive expressions of PD-L1 among the studies varied.

Table 1. Characteristics of the studies included in the meta-analysis.

Author Year Patients source No. Tissues source Histology Stage Outcome HR estimation Prognostic value
Chen 2012 China 120 Surgical resections NSCLC I–III OS HR and 95% CI: 2.95 (1.63–4.38) Poor
Mao 2014 China 128 Surgical resections NSCLC I–III OS HR and 95% CI: 1.90 (1.09–3.30) Poor
Cha 2016 Korea 323 Surgical resections ADC I–IV OS HR and 95% CI: 2.70 (1.78-4.10) Poor
Toyokawa 2017 Japan 292 Surgical resections ADC I OS HR and 95% CI: 5.86 (2.66–12.91) Poor
Mu 2011 China 109 Surgical resections NSCLC I–III OS Survival curves: 1.78 (1.12–2.83) Poor
Schmidt 2015 Germany 321 Surgical resections NSCLC I–III OS HR and 95% CI: 0.95 (0.68–1.33) NA
Miao 2017 China 83 NA SCLC I–IV OS HR and 95% CI: 0.943 (0.57–1.56) Good
Jiang 2015 China 79 NA LELC I–IV OS HR and 95% CI: 3.44 (0.86–13.68) NA
Lin 2015 China 56 Surgical resections or biopsy specimens ADC IV OS HR and 95% CI: 0.26 (0.11–0.62) Good
Zhang 2014 China 143 Surgical resections ADC I–III OS K-M and 95% CI: 2.72 (1.29–5.73) Poor
Tang 2015 China 170 Surgical resections or biopsy specimens NSCLC IIIB–IV OS HR and 95% CI: 1.901 (0.953–3.790) NA
Ishii 2015 Japan 102 NA SCLC I–IV OS HR and 95% CI: 0.44 (0.24–0.80) Good
Yang 2014 Taiwan 163 Surgical resections ADC I OS K-M and 95% CI: 0.85 (0.21–3.44) NA
Yvorel 2017 France 36 Surgical resections PSCGCC I–IV OS K-M and 95% CI: 1.30 (0.4–4.27) Poor
Zhang 2017 China 84 Surgical resections SCC I–III OS HR and 95% CI: 2.49 (1.27–4.88) Poor
Inamura 2017 Japan 115 Surgical resections HGNET I–IV OS HR and 95% CI: 0.29 (0.11–0.61) Good
Takada 2017 Japan 499 Surgical resections NSCLC I–III OS HR and 95% CI: 2.08 (1.42–3.09) Poor
Shimoji 2016 Japan 220 Surgical resections NSCLC I–IV OS K-M and 95% CI: 2.42 (1.25–4.68) Poor
D’incecco 2015 Italy 123 NA NSCLC IV OS K-M and 95% CI: 0.70 (0.44–1.11) NA
Mori 2017 Japan 296 Surgical resections ADC NR OS HR and 95% CI: 2.59 (1.25–5.39) Poor
Chang 2017 Taiwan 186 Biopsies,surgery SCLC I–IV OS K-M and 95% CI: 2.90 (1.44–5.86) Poor
Igawa 2017 Japan 229 Surgical resections NSCLC I–III OS HR and 95% CI: 0.90 (0.60–1.35) NA
Okita 2017 Japan 91 Surgical resections NSCLC IA–IIIA OS HR and 95% CI: 3.32 (1.10–9.97) Poor
Sun 2016 Korea 1,070 Surgical resections NSCLC I–IV OS HR and 95% CI: 1.23 (1.00–1.51) Poor
Song 2016 China 385 Surgical resections ADC I–III OS HR and 95% CI: 1.79 (1.30–2.46) NA
Inamura 2016 Japan 268 Surgical resections ADC I–IV OS HR and 95% CI: 1.88 (1.25–2.74) Poor
Vieira 2016 France 75 Surgical resections SC I–IV OS HR and 95% CI: 1.07 (0.60–2.00) NA
Takada-a 2017 Japan 205 Surgical resections SCC I–III OS HR and 95% CI: 1.65 (1.08–2.54) Poor
Wu 2017 China 133 Surgical resections ADC I–IV OS HR and 95% CI: 3.39 (1.25–9.19) Poor
Pan 2017 China 329 Surgical resections NSCLC I–III OS K-M and 95% CI: 3.23 (0.80–13.12) NA
Tokito 2016 Japan 74 NA NSCLC III OS HR and 95% CI: 0.47 (0.37–1.53) NA
Cooper 2015 Australia 678 Surgical resections NSCLC I–III OS HR and 95% CI: 0.65 (0.45–0.85) Good
Guo 2017 China 128 NA SCC III–IV OS K-M and 95% CI: 2.29 (1.47–3.57) Poor
Zhou 2017 China 108 Surgical resections NSCLC I–IV OS HR and 95% CI: 2.57 (1.46–4.52) Poor
Ji 2017 China 100 Surgical resections NSCLC I–III OS HR and 95% CI: 2.21 (1.10–4.42) Poor
Huynh 2016 USA 261 Surgical resections ADC I–IV OS K-M and 95% CI: 1.65 (0.79–3.45) Poor
Kim 2015 Korea 331 Surgical resections SCC I–III OS K-M and 95% CI: 1.24(0.76–2.02) NA
Inoue 2016 Japan 654 Surgical resections NSCLC I–III OS HR and 95% CI: 1.23 (0.86–1.76) NA
Sorensen 2016 USA 204 Biopsy specimens NSCLC IV OS HR and 95% CI: 1.17 (0.83–1.65) NA
Teng 2016 China 126 Surgical resections NSCLC I OS HR and 95% CI: 1.00 (0.47–2.14) NA
Chang 2016 Taiwan 122 Surgical resections or Biopsy specimens PPC I–IV OS K-M and 95% CI: 1.54 (0.94–2.54) Poor
Fang 2015 China 113 Surgical resections LELC I–IV OS HR and 95% CI: 2.73 (0.76–9.81) NA
Ameratunga 2016 Australia 420 Surgical resections NSCLC I–III OS HR and 95% CI: 1.05 (0.62–1.78) NA
Ilie 2016 France 56 Surgical resections SCC I–IV OS K-M and 95% CI: 1.79 (0.28–11.44) NA
Chen 2016 China 48 Surgical resections NSCLC I–III OS K-M and 95% CI: 1.25 (0.75–2.08) NA
Tsao 2017 Canada 982 NA NSCLC I–IV OS HR and 95% CI: 1.01 (0.76–1.35) NA
Hirai 2017 Japan 94 Surgical resections ADC I OS HR and 95% CI: 2.81 (1.06–8.23) Poor
Yang 2017 China 178 Surgical resections NSCLC I–IV OS HR and 95% CI: 1.68 (0.83–3.40) NA
Azuma 2014 Japan 164 Surgical resections NSCLC I–III OS HR and 95% CI: 1.60 (1.08–2.38) Poor
Uruga 2017 USA 109 Surgical resections ADC II–III OS K-M and 95% CI: 0.68 (0.40–1.16) NA
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No., number of patients; NSCLC, non-small cell lung cancer; ADC, adenocarcinoma; SCC, squamous cell carcinoma; SCLC, small cell lung cancer; LELC, pulmonary lymphoepithelioma-like carcinoma; SC, sarcomatoid carcinomas; HGNET, high-grade neuroendocrine tumor; PPC, pulmonary pleomorphic carcinoma; PSCGCC, pleomorphic, spindle cell and giant cell carcinoma; OS, overall survival; HR, hazard ratio; K-M, Kaplan-Meier curve; NA, not available.

Table 2. Technical information on PD-L1 immunohistochemistry of the studies included in the meta-analysis.

Author Year IHC counting method Cut-off PD-L1 positive (%) Antibody
Company Source Type Clone
Chen 2012 Percentage of positive cells and staining intensity IRS ≥3 57.5% (69/120) Abcam, HK Rabbit PAB 236A/E7
Mao 2014 Percentage of positive cells and staining intensity IRS ≥2 72.7% (93/128) NA Mouse MAB 2H11
Cha 2016 Percentage of positive cells ≥5% 18.6% (60/323) Spring Bioscience, USA Rabbit MAB SP142
Toyokawa 2017 Percentage of positive cells ≥5% 16.1% (47/292) Ventana Medical Systems, USA Rabbit MAB SP142
Mu 2011 Percentage of positive cells and staining intensity Median value of all the H-scores 53.2% (58/109) NA NA MAB NA
Schmidt 2015 Percentage of positive cells and staining intensity ≥10% and Moderate or strong staining 24% (77/321) Cell Signaling, USA Rabbit MAB E1L3N
Miao 2017 Percentage of positive cells ≥5% 51.8% (43/83) SPRINGBIO, USA Mouse NA SP66
Jiang 2015 Percentage of positive cells ≥5% 63.3% (50/79) Abcam, UK Rabbit PAB NA
Lin 2015 Percentage of positive cells and staining intensity Mean value of all the H-scores 53.6% (30/56) Abcam, UK Rabbit PAB ab58810
Zhang 2014 Percentage of positive cells and staining intensity Median value of all the H-scores 49% (70/143) Sigma-Aldrich, USA Rabbit PAB SAB2900365
Tang 2015 Percentage of positive cells and staining intensity H-score ≥5 65.9% (112/170) Cell Signaling, USA Rabbit MAB E1L3N
Ishii 2015 Percentage of positive cells ≥5% 71.6% (73/102) Abcam, UK Rabbit MAB NA
Yang 2014 Percentage of positive cells and staining intensity >5% and moderate-to-strong staining 39.9% (65/163) Proteintech Group Inc., USA NA NA NA
Yvorel 2017 Percentage of positive cells ≥5% 75% (27/36) Cell Signaling, USA Rabbit MAB E1L3N
Zhang 2017 Percentage of positive cells and staining intensity ≥5% and weak or Moderate or strong staining 58.3% (49/84) Abcam, UK Rabbit MAB 28-8
Inamura 2017 Percentage of positive cells ≥5% 21% (25/115) Cell Signaling, USA Rabbit MAB E1L3N
Takada 2017 Percentage of positive cells ≥1% 37.9% (189/499) Spring Bioscience, USA Rabbit MAB SP142
Shimoji 2016 Percentage of positive cells and staining intensity H-score ≥5 32% (70/220) Cell Signaling, USA Rabbit MAB E1L3N
D’incecco 2015 Percentage of positive cells and staining intensity >5% and moderate-to-strong staining 55.3% (68/123) Abcam, UK Rabbit PAB ab58810
Mori 2017 Percentage of positive cells and staining intensity 50 PD-L1 score 36.1% (107/296) Abcam, UK Rabbit MAB EPR1611
Chang 2017 Percentage of positive cells and staining intensity ≥5% and moderate to strong staining 78% (145/186) Proteintech Group Inc., USA Rabbit PAB NA
Igawa 2017 Percentage of positive cells and staining intensity Median value of all the H-scores 52.4% (120/229) Ventana Medical Systems, USA Rabbit PAB SP263
Okita 2017 Percentage of positive cells and staining intensity H-score >100 14% (13/91) Spring Bioscience, USA Mouse MAB SP142
Sun 2016 Percentage of positive cells ≥1% 44.7% (478/1,070) Merck & Co, USA Mouse MAB 22C3
Song 2016 Percentage of positive cells ≥5% 48.3% (186/385) Proteintech Group Inc., USA Rabbit NA 66248-1-Ig
Inamura 2016 Percentage of positive cells ≥5% 16% (43/268) Cell Signaling, USA Rabbit MAB E1L3N
Vieira 2016 Percentage of positive cells ≥5% 53% (40/75) NA Murine MAB 5H1
Takada-a 2017 Percentage of positive cells ≥1% 51.7% (106/205) Spring Bioscience, USA Rabbit MAB SP142
Wu 2017 Percentage of positive cells ≥25% 13.5% (18/133) Roche Ventana, USA Rabbit MAB SP263
Pan 2017 Percentage of positive cells and staining intensity 1+ to 3+ 14% (46/329) Dako Mouse MAB 22C3
Tokito 2016 Percentage of positive cells ≥5% 74.3% (55/74) Abcam, UK Rabbit MAB EPR1161
Cooper 2015 Percentage of positive cells ≥50% 7.4% (50/678) Merck, USA Mouse MAB 22C3
Guo 2017 Percentage of positive cells and staining intensity IRS ≥3 61.7% (79/128) Abcam, UK Rabbit PAB ab58810
Zhou 2017 Percentage of positive cells and staining intensity H-score ≥1 40.7% (44/108) Cell Signaling, USA Rabbit MAB E1L3N
Ji 2017 Percentage of positive cells and staining intensity >5% and staining intensity ≥2 40% (40/100) Abcam, USA Mouse PAB ab174838
Huynh 2016 Percentage of positive cells ≥5% 36.5% (95/261) Cell Signaling, USA Rabbit MAB E1L3N
Kim 2015 Percentage of positive cells and staining intensity 2+ or 3+ 26.9% (89/331) Cell Signaling, USA Rabbit MAB E1L3N
Inoue 2016 Percentage of positive cells and staining intensity H-score ≥5 30.7% (201/654) Cell Signaling, USA Rabbit MAB E1L3N
Sorensen 2016 Percentage of positive cells ≥1% 75% (153/204) Merck & Co, USA Mouse MAB 22C3
Teng 2016 Percentage of positive cells ≥5% 19.8% (25/126) Spring Bioscience, Canada NA NA M4424
Chang 2016 Percentage of positive cells ≥5% 70.5% (86/122) Proteintech Group Inc., USA NA NA NA
Fang 2015 Percentage of positive cells and staining intensity ≥5% 74.3% (84/113) Cell Signaling, USA Rabbit MAB E1L3N
Ameratunga 2016 Percentage of positive cells ≥50% 23.8% (100/420) Cell Signaling, USA Rabbit MAB E1L3N
Ilie 2016 NA NA 82% (46/56) Abcam, UK NA NA 28-8
Chen 2016 Percentage of positive cells and staining intensity Allred score ranges 1–8 64.6% (31/48) Abcam, USA Rabbit PAB ab58810
Tsao 2017 Percentage of positive cells ≥1% 32% (314/982) Cell Signaling, USA Rabbit MAB E1L3N
Hirai 2017 Percentage of positive cells ≥5% 16.0% (15/94) Cell Signaling, Japan Rabbit MAB E1L3N
Yang 2017 Percentage of positive cells ≥5% 39.9% (71/178) Cell Signaling, USA Rabbit MAB E1L3N
Azuma 2014 Percentage of positive cells and staining intensity H-score >30 50% (82/164) Lifespan Biosciences, USA Rabbit PAB NA
Uruga 2017 Percentage of positive cells ≥1% 51.4% (56/109) Cell Signaling, USA Rabbit MAB E1L3N
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Correlation between PD-L1 expression and prognosis

As shown in Figure 2, all 50 studies, comprising 11,383 patients, assessed the correlation between PD-L1 expression and OS. The pooled results (HR =1.45, 95% CI: 1.24–1.68) revealed that the overexpression of PD-L1 exhibited shorter OS in lung cancer, with a 45% increase in the risk for mortality. Meanwhile, a random-effects model was applied for this analysis, as significant heterogeneity was observed (P=0.000, I2=74.6%).

Figure 2.

Figure 2

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Forest plot describing the association between PD-L1 expression and OS of patients with lung cancer.

To investigate the sources of heterogeneity, subgroup analyses for OS were performed according to histology, TNM stage, sample type, cutoff value, ethnicity and PD-L1 IHC assay. Subgroup analyses according to histology revealed high PD-L1 expression significantly reduced the OS of NSCLC patients (HR =1.35, 95% CI: 1.13–1.61), ADC patients (HR =1.79, 95% CI: 1.22–2.64), SCC patients (HR =1.79, 95% CI: 1.39–2.32), and LELC patients (HR =3.04, 95% CI: 1.19–7.77), but there was no association of PD-L1 expression with survival in SCLC patients (HR =1.05, 95% CI: 0.39–2.78) (Figure 3). Moreover, subgroup analyses based on TNM stage showed that increased PD-L1 expression was negatively relevant to OS for lung cancer patients in stage I–IV (HR =1.48, 95% CI: 1.15–1.91). To further examine the effects of the different stages of lung cancer on survival, a subgroup analysis was conducted in patients with stage I–III and stage IV. The results revealed that increased PD-L1 expression was associated with poor prognosis for lung cancer patients in early stage I-III (HR =1.51, 95% CI: 1.23–1.86), but not in advanced stage IV (HR =0.66, 95% CI: 0.33–1.33) (Figure 4). When grouped according to the sample type, the pooled results demonstrated that using resection specimens to detect PD-L1 expression (HR =1.61, 95% CI: 1.37–1.90) was related to worse prognosis, when compared to using resection or biopsy specimens (HR =1.26, 95% CI: 0.54–2.98) and using biopsy specimens (HR =1.17, 95% CI: 0.83–1.65) (Figure 5). Furthermore, subgroup analyses based on cutoff value revealed patients with PD-L1 positive tumors had poor survival if 5% (HR =1.44, 95% CI: 1.03–2.03) was taken as the cutoff value, compared to 1% (HR =1.24, 95% CI: 0.97–1.59) or 50% (HR =0.79, 95% CI: 0.50–1.25) (Figure 6). When grouped by ethnicity, the pooled HRs revealed PD-L1 is a poor prognosis indicator in Asian patients 1.64 (95% CI: 1.38–1.94) compared to in non-Asian patients 0.93 (95% CI: 0.79–1.09) (Figure 7). Moreover, subgroup analyses according to PD-L1 IHC assay indicated that PD-L1 overexpression was associated with shorter OS when the SP142 antibody (HR =2.51, 95% CI: 1.75–3.61), the E1L3N antibody (HR =1.33, 95% CI: 1.05–1.67) or the 28-8 antibody (HR =2.40, 95% CI: 1.27–4.51) was used to assess PD-L1 expression. On the contrary, there was no significant association between PD-L1 expression and survival when ab58810 (HR =0.90, 95% CI: 0.41–1.96), 22C3 (HR =1.07, 95% CI: 0.72–1.59) or SP263 (HR =1.61, 95% CI: 0.44–5.85) antibody was used to assess PD-L1 expression (Figure 8).

Figure 3.

Figure 3

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to histology. OS, overall survival.

Figure 4.

Figure 4

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to TNM stage. OS, overall survival.

Figure 5.

Figure 5

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to sample acquisition method. OS, overall survival.

Figure 6.

Figure 6

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to cutoff value. OS, overall survival.

Figure 7.

Figure 7

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to ethnicity. OS, overall survival.

Figure 8.

Figure 8

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Forest plot describing subgroup analysis of the association between PD-L1 expression and OS according to PD-L1 IHC assay. OS, overall survival; IHC, immunohistochemistry.

Correlation between PD-L1 expression and clinicopathological features

Table 3 shows the main clinicopathological parameters. The combined results revealed that increased PD-L1 expression was associated with a male gender (OR =1.46, 95% CI: 1.24–1.71) (Figure S1), smoking history (OR =1.47, 95% CI: 1.18–1.83) (Figure S2), poor tumor differentiation (OR =2.25, 95% CI: 1.59–3.18) (Figure S3), large tumour size (OR =1.63, 95% CI: 1.35–1.98) (Figure S4), and positive lymph nodal metastasis (OR =1.29, 95% CI: 1.07–1.56) (Figure S5). However, no significant relationship was detected between PD-L1 expression and age (OR =1.27, 95% CI: 0.96–1.69) (Figure S6). To further understand the significance of PD-L1 expression, we also investigated the relevance of the expression of PD-L1 and major driver mutations including EGFR, ALK, and KRAS. In total, 22, 10, and 14 out of 50 studies demonstrated the relationship of PD-L1 expression to EGFR mutations (Figure S7), ALK rearrangements (Figure S8), and KRAS mutations (Figure S9) respectively. The pooled results showed that PD-L1 expression was related to EGFR wild-type status (OR =0.59, 95% CI: 0.40–0.86) and KRAS mutation (OR =1.45, 95% CI: 1.16–1.81), while no associations was identified between PD-L1 expression and ALK rearrangements (OR =1.00, 95% CI: 0.62–1.61). Heterogeneity was observed in the analysis of PD-L1 expression with gender (P=0.000, I2= 56.7%), smoking status (P=0.000, I2=67.3%), tumor differentiation (P=0.014, I2=52.2%), lymph nodal metastasis (P=0.002, I2=51.0%), EGFR mutation (P=0.000, I2=78.4%), so a random-effects model was applied. The other analyses above were conducted using a fixed-effects model.

Table 3. Subgroup analyses of OR for the association between PD-L1 expression and clinicopathological features.

Clinicopathological features No. of studies Heterogeneity OR (95% CI)
P value I2 (%)
Gender (male vs. female) 47 0.000 56.70 1.46 (1.24–1.71)
Smoking status (yes vs. no) 33 0.000 67.30 1.47 (1.18–1.83)
Tumor differentiation (poor vs. moderate-well) 13 0.014 52.20 2.25 (1.59–3.18)
Tumor size (>3 vs. ≤3 cm) 19 0.132 27.30 1.63 (1.35–1.98)
Lymph nodal metastasis (N+ vs. N−) 25 0.002 51.00 1.29 (1.07–1.56)
Age (≥60 vs. <60) 10 0.150 32.30 1.27 (0.96–1.69)
EGFR mutation (EGFR+ vs. EGFR−) 22 0.000 78.40 0.59 (0.40–0.86)
ALK rearrangement (ALK+ vs. ALK−) 10 0.567 0.00 1.00 (0.62–1.61)
KRAS mutation (KRAS+ vs. KRAS−) 14 0.393 5.30 1.45 (1.16–1.81)
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OR, odds ratio.

Figure S1.

Figure S1

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Forest plots for the association between PD-L1 expression and gender.

Figure S2.

Figure S2

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Forest plots for the association between PD-L1 expression and smoking status.

Figure S3.

Figure S3

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Forest plots for the association between PD-L1 expression and tumor differentiation.

Figure S4.

Figure S4

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Forest plots for the association between PD-L1 expression and tumor size.

Figure S5.

Figure S5

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Forest plots for the association between PD-L1 expression and lymph nodal metastasis.

Figure S6.

Figure S6

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Forest plots for the association between PD-L1 expression and age.

Figure S7.

Figure S7

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Forest plots for the association between PD-L1 expression and EGFR mutation.

Figure S8.

Figure S8

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Forest plots for the association between PD-L1 expression and ALK rearrangement.

Figure S9.

Figure S9

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Forest plots for the association between PD-L1 expression and KRAS mutation.

Publication bias analysis

Begger’s funnel plot was employed to assess the publication bias in this meta-analysis; no publication bias was found in any of the studies, as evidenced by the symmetrical funnel plots (Figure 9).

Figure 9.

Figure 9

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Funnel plots for publication bias.

Discussion

So far, the prognostic significance of PD-L1 expression has attracted much attention with the application of PD-L1/PD-1 inhibitors in NSCLC. Some studies reported that NSCLC patients with high PD-L1 expression had shorter OS when compared to those with negative PD-L1 expression (15,63,64), while other studies showed that PD-L1 expression correlated with better prognosis (59,65). With the emergence of more latest clinical data, we combined 50 eligible studies comprising a total of 11,383 patients to evaluate the relationship between PD-L1 expression level and the prognosis of lung cancer patients.

In our study, the pooled results indicated that increased PD-L1 expression contributed to the poor survival of lung cancer patients, which is consistent with the study of Zhang et al. (64). The results of subgroup analyses revealed that patients with high PD-L1 expression had shorter OS in NSCLC, ADC, SCC and LELC, while no significant difference was observed in SCLC. Furthermore, PD-L1/PD-1 inhibitors have shown improved survival in patients with locally advanced and metastatic NSCLC (66,67). There have also been studies evaluating the use of immunotherapy in early stage of lung cancer (68). Thus, the prognostic significance of PD-L1 expression in the early stage of lung cancer has attracted extensive attention. In our meta-analysis, PD-L1 expression was negatively correlated with the prognosis of NSCLC patients in early stage (I–III) or Asian populations, while it may not serve as a prognostic factor for the survival of stage IV or non-Asian NSCLC patients. Moreover, in the previous meta-analyses, the effects of sample type and the cutoff value of PD-L1 positive expression were not analyzed. As surgical resections and biopsy specimens can be taken from different sites within the tumor, the expression of PD-L1 detected by IHC may also demonstrate heterogeneity. In our study, we found that PD-L1 expression detected by surgical resections was related to worse prognosis, while PD-L1 expression detected by biopsy specimens was not associated with shorter OS. Relative subgroup analyses were also performed to find uniform cutoff values. The pooled results suggested that patients with positive PD-L1 expression had decreased OS when studies used 5% as the cutoff value, while there was no significant difference when studies used 1% or 50% as the cutoff value. We also discovered that positive expression of PD-L1 by the SP142 antibody, the E1L3N antibody or the 28-8 antibody was associated with poor prognosis, while PD-L1 overexpression by the ab58810, 22C3 or SP263 antibody showed no predictive value. This result may be due to the diversity of PD-L1 IHC staining, the sensitivity of the antibody, multiple cut-off standards and different instrument platforms (69-71). As 22C3, 28-8, SP263, SP142 antibodies have been widely used in clinical trials, and recent harmonized studies have found that 22C3, 28-8 and SP263 assays are interchangeable, while SP142 is less sensitive than other assays, we tended to believe that PD-L1 antibody has no association with the prognosis of lung cancer patients. In a word, the conclusions of this subgroup analysis of PD-L1 IHC assay need to be treated with caution, and more clinical studies are needed to verify this view (69,72,73).

The identification of predictive biomarkers for immunotherapy may be valuable for treatment selection and cost saving as well as avoidance of toxicity and quality of time. Several studies have reported that high PD-L1 expression is associated with more clinical benefits in cancer patients treated with anti-PD-1 or anti-PD-L1 monoclonal antibodies (74). It is particularly vital to select patients who will likely benefit from immunotherapy through biomarker assessments and predict the prognosis of the disease in accordance with the goal of the individualized precision medicine. Our study investigated the relationship between PD-L1 expression and clinicopathological parameters, and the pooled results revealed that positive PD-L1 expression was more frequently seen in male, smokers, and patients with poor tumor differentiation, large tumour size, and/or positive lymph nodal metastasis. These patients are more likely to benefit from anti-PD-1/PD-L1 therapy, while the pooled subgroup results indicated no significant correlation between PD-L1 expression and age.

With more and more evidence revealing the relationship between PD-L1 expression and driver oncogene mutations, the association of EGFR mutations and PD-L1 expression in lung cancer is still controversial. Some studies revealed that PD-L1 was highly expressed in patients with EGFR mutations (17), some showed that PD-L1 had a higher positive rate in EGFR wild-type (45), and others indicated no association between PD-L1 expression and EGFR mutations (48). Our analysis showed that high PD-L1 expression was associated with EGFR wild-type. Calles A et al. reported that KRAS mutations were generally identified in NSCLC patients with significant smoking history that may be associated with high tumor mutation burden/a large number of tumor antigens leading to higher PD-L1 expression. In addition, PD-L1 is induced in tumor cells via Th1 pathway activation and IFN-γ secretion, which were associated with inflammatory response induced by smoking (75). Chen et al. (76) stated that PD-L1 was up-regulated by KRAS mutation through p-ERK signaling and KRAS-mediated upregulation of PD-L1 can induce apoptosis of CD3-positive T cells and immune escape in lung ADC cells. Our study observed increased PD-L1 expression was associated with KRAS mutations in lung cancer, which is consistent with the findings above. Moreover, we found no association between increased PD-L1 and ALK rearrangements. In a word, PD-L1 expression may be influenced by both intrinsic and extrinsic/acquired mechanisms and is possibly less stable than genomic changes such as amplification. A recent study has found that structural variation leads to a significant increase of aberrant PD-L1 transcripts (77). The monitoring of biological effects of PD-L1 may take several omics studies.

There are some limitations in our study. First, the number of studies for SCLC, LELC, and metastatic tumors (stage IV) included in this meta-analysis was relatively small. Thus, the prognostic role of PD-L1 expression in these lung cancer subtypes need to be further evaluated in large sample size. Second, different studies used different PD-L1 antibodies, staining methods, and cut-off values that might have affected the PD-L1 IHC results. It is necessary to use a single IHC assay to unify the detection of PD-L1 expression in tumor cells to obtain more accurate results. Third, we did not evaluate the expression of other predictive biomarkers such as PD-L1 expression on infiltrating immune cells in this study. Fourth, in some studies, the HRs and their 95% CIs were estimated from Kaplan-Meier curves as they were not reported directly, which may reduce the accuracy of the results.

Conclusions

Our meta-analysis demonstrated that high PD-L1 expression by IHC was significantly associated with poor OS for patients with lung cancer, especially for Asian patients with surgically resected, early stage I-III tumors and using 5% as the cutoff value. Moreover, positive PD-L1 expression was associated with male, smokers, poor tumor differentiation, large tumor size, positive lymph nodal metastasis, EGFR wild-type status, and KRAS mutations. These results may further help predicting the survival of lung cancer patients and screening appropriate patients for anti-PD-1/PD-L1 treatment.

Acknowledgments

Funding: This work was supported by grants from the National Natural Science Foundation of China (grant number 81401903, 81572937 and 81572273); China Postdoctoral Science Foundation 64th batch (Postdoctoral number: 45786); Jiangsu Provincial Postdoctoral Science Foundation in 2018; the Natural Science Foundation of Jiangsu province (grant number BK20180139 and BK20161386); Jiangsu Provincial Medical Youth Talent (grant number QNRC2016125), and the Nanjing Medical Science and Technology Development Project (No. ZKX17044), the Jiangsu Provincial Key Research and Development Program (No. BE2016721). The authors would like to thank Mari Mino-Kenudson at Massachusetts General Hospital, Boston, USA, for critical review of the manuscript.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Footnotes

Conflicts of Interest: The authors have no conflicts of interest to declare.

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