Skip to main content

Predictive value of pretreatment PD-L1 expression in EGFR-mutant non-small cell lung cancer: a meta-analysis

Abstract

Objective

To investigate the predictive value of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs).

Methods

We conducted a systemic search of PubMed, EMBASE, and the Cochrane Library from 1 January 2000 to 30 August 2020, to identify related studies. We combined the hazard ratio (HR) and 95% confidence interval (CI) to assess the correlation of PD-L1 expression with progression-free survival (PFS) and overall survival (OS). We assessed the quality of the included studies by the Newcastle–Ottawa Scale (NOS). We performed subgroup analyses based on immunohistochemistry (IHC) scoring system, IHC antibodies, sample size, countries, and survival analysis mode. Sensitivity analysis and evaluation of publication bias were also performed.

Results

Twelve studies including 991 patients met the criteria. The mean NOS score was 7.42 ± 1.19. Patients with high PD-L1 expression was associated with poorer PFS (HR = 1.90; 95% CI = 1.16–3.10; P = 0.011), while there was no association between PD-L1 expression and OS (HR = 1.19; 95% CI = 0.99–1.43; P = 0.070). Subgroup analysis prompted IHC scoring systems, IHC antibodies, and sample size have important effects on heterogeneity. The pooled results were robust according to the sensitivity analysis.

Conclusions

The result of this meta-analysis suggested that PD-L1 expression might be a predictive biomarker for EGFR-mutant non-small cell lung cancer treated with EGFR-TKIs.

Background

Lung cancer is the major cause of cancer-related mortality among both men and women worldwide, and non-small cell lung cancer (NSCLC) accounts for approximately 85% of reported cases [1, 2]. Nearly 80% of NSCLC patients are diagnosed at the advanced stage, and the prognosis of patients with advanced stage NSCLC is extremely poor [3]. Epidermal growth factor receptor (EGFR) mutation is one of the most common driver oncogenes in NSCLC, and targeted therapy on EGFR-activating mutations has achieved great benefits [4, 5]. In the first line of treatment, several large-scale phase three trials have shown the better efficacy of EGFR tyrosine kinase inhibitors (EGFR-TKIs) to standard platinum-based chemotherapy [6,7,8]. However, nearly all the patients treated with EGFR-TKIs developed resistance after the early response [9, 10].

In the past few years, the immune checkpoint inhibitors (ICIs), which target the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis, have led to a long-lasting response in some patients with NSCLC by prompting the exhausted tumor infiltrating lymphocytes [11]. However, a limited effect of PD-1/PD-L1 inhibitors in patients with EGFR-mutant NSCLC was reported by Gainor et al. [12]. Moreover, the expression of PD-L1 was generally lower in EGFR-mutated tumors than in EGFR wild-type tumors. This might be the reason for the poor response to immune checkpoint inhibitors in EGFR-mutated tumors [12, 13]. The expression of PD-L1 reveals the immunogenic nature of the tumor microenvironment. Therefore, it is probably related to clinical outcomes of the treatments other than ICIs. At present, several studies have investigated the predictive value of PD-L1 expression in EGFR-mutant NSCLC patients treated with EGFR-TKIs [14,15,16,17,18,19,20,21,22,23,24,25]. However, previous studies regarding this topic have yielded conflicting results because of different sample size, antibody clone for immunohistochemistry (IHC), and IHC scoring system applied. A meta-analysis study on this issue has also been performed in the past [26]. However, this study included fewer studies and did not conduct a subgroup analysis looking for possible reasons for the large heterogeneity of results in previous studies. Therefore, we designed and performed the current meta-analysis to further determine the predictive value of PD-L1 expression in NSCLC patients treated with EGFR-TKIs.

Methods

A meta-analysis does not require necessary patients’ consent or approval. We carried out this meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Supplementary Table S1) [27]. The protocol was registered in International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202140093).

Literature searching strategy

From the establishment date of databases to 30 August 2020, we searched PubMed, Embase, Cochrane library and China National Knowledge Infrastructure (CNKI) with terms related to “non-small cell lung cancer,” “PD-L1,” “EGFR-TKIs,” and “prognosis.” The detailed search strategy for each database is presented in Supplementary Table S2. Besides, potentially eligible studies were also manually checked through the reference lists of included studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) patients were diagnosed with advanced NSCLC and treated with EGFR-TKIs alone; (2) the primary outcomes were progression-free survival (PFS) and/or overall survival (OS); (3) the relationship between PD-L1 expression and PFS/OS was described; (4) necessary survival data including hazard ratio (HR), 95% confidence interval (CI), or Kaplan-Meier survival curve was provided.

The exclusion criteria were (1) a previous history of chemotherapy or radiotherapy; (2) case reports, comments, corresponding letters, reviews, and meeting abstracts; (3) necessary survival data to calculate the HR with 95% CI was not provided.

Data extraction and assessment of the study quality

Each study was reviewed and evaluated by two independent researchers (ZYP and HHL). Disagreement was resolved through discussion with a third researcher (JDM). The following data were recorded: the name of the first author, publication year, origin of the study, study period, sample size, type of cancer, stage of cancer, detective methods, and grouping methods. The primary outcomes were the hazard ratio (HR) and 95% confidence interval (CI). Where necessary, we applied Engauge Digitizer (version 4.1; http://digitizer.sourceforge.net) to extract the HR and 95% CI from survival curves [28]. According to the method described by Afzal, HR of the lowest versus the highest level of PD-L1 expression was extracted when the levels of PD-L1 expression were divided into several groups [29]. The quality of included studies was assessed by the Newcastle–Ottawa Scale (NOS). NOS scores of ≥ 6 were identified as high-quality studies.

Statistical analysis

Stata (version 16.0; Stata Corporation, TX, USA) software was applied to analyze the extracted data. HR with 95% CI was used to assess the significance of PD-L1 expression on OS and PFS of the patients with NSCLC treated with EGFR-TKIs. I-square (I2) test was applied to assess the heterogeneity among the studies. Statistical significance was set at a P value of 0.05. A fixed-effect model was applied when the heterogeneity was considered to be insignificant (I2 < 50%), or else, a random effect model was applied. In addition, we performed a sensitivity analysis by removing included studies one-by-one to test whether the results were robust. We applied Egger’s test and Begg’s test to assess the possibility of publication bias.

Results

Characteristics of studies

The flowchart of the study selection is shown in Fig. 1. During the primary search, we retrieved a total of 299 studies. After removing duplicates and screening the titles and abstracts, 118 studies were selected for full review. Full texts of 118 candidate studies were carefully reviewed and 106 of them were excluded (Fig. 1). Eventually, we included 12 original studies published between 2015 and 2020 in this meta-analysis. The characteristics of all eligible studies are presented in Table 1. The mean NOS score of these included studies was 7.42 ± 1.19.

Fig. 1
figure1

Flow diagram of studies retrieved, screened, and selected for further analysis

Table 1 Characteristics of qualified records in meta-analysis

Association between PD-L1 expression and survival outcomes

A total of 12 studies involving 991 patients were included in this meta-analysis. The pooled results showed that higher PD-L1 expression was associated with poorer PFS (HR = 1.90; 95% CI = 1.16–3.10; P = 0.011), but with high heterogeneity (I2 = 88.2, P = 0.000) (Fig. 2a). However, the level of PD-L1 expression was not associated with OS (HR = 1.19; 95% CI = 0.99–1.43; P = 0.070) (Fig. 2b) in NSCLC patients treated with EGFR-TKIs.

Fig. 2
figure2

Forest plot of the association between PD-L1 expression and a progression-free survival and b overall survival. CI, confidence interval; HR, hazard ratio

Subgroup analysis

To reveal the potential heterogeneity, subgroup analysis stratified by IHC scoring systems, sample size, IHC antibodies, countries, and survival analysis mode was applied. The results of subgroup analysis indicated that patients with higher PD-L1 expression had poorer PFS in the following subgroups: tumor proportional score (TPS) of scoring system subgroup (HR = 3.12; 95% CI = 1.28–3.89; P = 0.000; Fig. 3a), sample size larger than 100 patients (HR = 2.96; 95% CI = 1.72–5.09; P = 0.000; Fig. 4a), and 22C3 IHC antibodies (HR = 2.96; 95% CI = 1.94–4.1; P = 0.000; Fig. 5a). As for OS, the results of subgroup analysis indicated that patients with higher PD-L1 expression had poorer OS in the following subgroups: TPS scoring system subgroup (HR = 4.17; 95% CI = 2.70–6.42; P = 0.000; Fig. 3b), sample size larger than 100 group (HR = 4.17; 95% CI = 2.70–6.42; P = 0.000; Fig. 4b). The results of the subgroup analysis revealed IHC scoring systems, IHC antibodies, and sample size may contribute to the heterogeneity (Fig. 5). The detailed results of the subgroup analysis were summarized in Table 2.

Fig. 3
figure3

Subgroup analysis based on different scoring systems. a Stratified by the scoring system of PD-L1 with progression-free survival and b Stratified by the scoring system of PD-L1 expression with overall survival. CI, confidence interval; HR, hazard ratio; TPS, tumor proportional score; TC, tumor cells; H score is defined as the percentage of positively stained tumor cells multiplied by the intensity of staining

Fig. 4
figure4

Subgroup analysis based on different sample size. a Stratified by the sample size of included studies with progression-free survival and b stratified by the sample size of included studies with overall survival. CI, confidence interval; HR, hazard ratio

Fig. 5
figure5

Subgroup analysis based on different IHC antibodies. a Stratified by the IHC antibodies of PD-L1 with progression-free survival and b stratified by the IHC antibodies of PD-L1 expression with overall survival. IHC, immunohistochemistry; CI, confidence interval; HR, hazard ratio

Table 2 Pooled HR of PD-L1 expression (high vs. low level) for OS and PFS according to subgroup analyses

Sensitivity analysis

The results of the sensitivity analysis of OS and PFS were shown in Fig. 6. A sensitivity analysis was conducted by excluding each study from the meta-analysis at each time. The statistical results were steady, which indicated that the pooled results were robust.

Fig. 6
figure6

Sensitivity analysis of the association between pretreatment PD-L1 expression and PFS (a) or OS (b)

Publication bias

The Begg’s funnel plot (P = 0.929) (Fig. 7a) was symmetric, and the P value of Egger’s test was 0.174 (Fig. 7b), which both indicated no significant publication bias. The existence of bias among studies about OS was not estimated because of the insufficient research number (less than 10).

Fig. 7
figure7

Begg’s funnel plot and Egger’s funnel plot showed no publication bias among the included studies. a Begg’s funnel plot of PD-L1 expression and PFS (P = 0.929). b Egger’s funnel plot of PD-L1 expression and PFS (P = 0.174)

Discussion

Given that immune mechanisms are involved in EGFR-TKI resistance and that increased PD-L1 expression has been detected in the context of acquired resistance to EGFR-TKIs [30, 31], it is assumed that PD-L1 expression on tumor and immune cells may predict poor response to EGFR-TKI treatment in NSCLC patients with EGFR mutation. However, previous studies regarding this topic have not come to a unanimous conclusion [14, 19, 24]. Therefore, the present meta-analysis aimed to determine the predictive value of PD-L1 expression in NSCLC patients treated with EGFR-TKIs. In this meta-analysis, we found that NSCLC patients with higher PD-L1 expression were associated with poorer survival regarding PFS. However, there was no significant association between PD-L1 expression and OS, which suggested that the predictive value of pretreatment PD-L1 expression in non-small cell lung cancer treated with EGFR-TKIs needed to be further studied.

The subgroup analysis showed that higher PD-L1 expression was related to poor PFS in NSCLC patients treated with EGFR-TKIs. However, the PFS between high and low PD-L1 expression groups was similar in studies using different IHC scoring systems including moderate staining and H score. One of the possible reasons may be the insufficient sample size in the specific subgroup. In the subgroup analysis stratified by IHC scoring systems including TPS, moderate staining, tumor cells (TC), and H score, only TPS showed a robust result with low heterogeneity. Therefore, more studies should be performed to determine one reliable IHC scoring system and verify the results.

The use of different IHC antibodies, IHC scoring systems, and cutoff values may also affect the results and conclusions. This could explain why the existing data are ambiguous concerning whether tumor PD-L1 expression can predict the response to EGFR-TKI treatment. Six IHC antibodies (22C3 [21, 24, 25], SP263 [22], 28-8 [23], SP142 [17, 20], E1L3N [16, 18], and ab5880 [14, 15]) were used for the detection of PD-L1 expression and almost all the included studies used different antibodies. The subgroup analysis indicated that patients with higher PD-L1 expression had poorer PFS in the group of 22C3 IHC antibodies (HR = 2.96; 95% CI = 1.94–4.1; P = 0.000; Fig. 5a) and SP142 (HR = 4.86; 95% CI = 3.02–7.83; P = 0.000; Fig. 5a). Considering that 22C3 was the only approved companion diagnostic test for immune checkpoint inhibitors in lung cancer [32], it may be treated as a standard IHC antibody in the future. Given the inconsistent findings regarding the relationship between PD-L1 expression and responses to EGFR-TKIs, further validation should be conducted using standardized methods.

The use of different EGFR-TKIs may also be a reason for the inconsistent results. The detailed report of different kinds of EGFR-TKIs of the included studies was listed in Table 1. As we can see, most of the studies used different EGFR-TKIs, but few of them compared the predictive value of pretreatment PD-L1 expression in NSCLC with different EGFR-TKIs. Different kinds of EGFR-TKIs had various therapeutic effects and eventually led to different survival data. For example, the third generation TKI can overcome the resistance caused by T790M mutation and have better survival time than that of the first generation [33,34,35]. Therefore, different kinds of EGFR-TKIs should be considered as a mixed factor in future studies.

Several recent studies indicated that there were some mechanisms by which PD-L1 expression affects the prognosis of NSCLC patients treated with EGFR-TKIs. It has been reported that EGFR mutant NSCLC was more likely to exhibit an uninflamed phenotype with less immune cell infiltration, which suggested a lower likelihood of adaptive PD-L1 expression [36]. Therefore, higher PD-L1 expression in EGFR-mutated NSCLC might relate to the activation of oncogenes other than EGFR [37], which could lead to TKI resistance through bypass activation. On the other hand, immune cells within the tumor microenvironment may also account for EGFR-TKI resistance [24]. Therefore, we speculate that higher PD-L1 expression in NSCLC patients may lead to EGFR-TKI resistance through tumor microenvironment and eventually result in a poor prognosis for TKI treatment.

The sample size of patients included in a few studies was relatively small, and there was no prospective study in the present meta-analysis. A larger-scale, multicenter, prospective study focusing on the predictive value of PD-L1 expression in NSCLC patients treated with EGFR-TKI is encouraged in the future. Moreover, future studies could also attempt to explore the predictive value of pretreatment PD-L1 expression in NSCLC treated with EGFR-TKIs by different IHC antibodies, IHC scoring systems, and EGFR-TKIs.

Inevitably, the present meta-analysis had several potential limitations. First, although the search process was not restricted to languages, we searched only one native Chinese database in addition to the three general English databases, possibly ignoring studies from native databases in other languages. Second, some of the HRs with 95% CIs were evaluated using the Kaplan-Meier survival curves, which might be different from the original data. Third, the heterogeneity tests were significant in some of the pooled HRs of PFS and OS. The potential cause to explain the heterogeneity included the antibodies or IHC scoring systems used in different studies, the type of EGFR-TKIs, origin of the patients, and potential publication bias. Fourth, all the included studies were retrospective studies with relatively small sample size.

Conclusions

In conclusion, this meta-analysis demonstrated that PD-L1 expression might be a negative predictive biomarker for EGFR-mutant NSCLC patients treated with EGFR-TKIs. Further study with standardized detection antibody, IHC scoring system, and larger sample size is warranted to validate the conclusion.

Availability of data and materials

All the data used in this study can be obtained from the original articles.

Abbreviations

PD-L1:

Programmed death-ligand 1

NSCLC:

Non-small cell lung cancer

EGFR-TKIs:

Epidermal growth factor receptor tyrosine kinase inhibitors

ICIs:

Immune checkpoint inhibitors

HR:

Hazard ratio

CI:

Confidence interval

PFS:

Progression-free survival

OS:

Overall survival

NOS:

Newcastle–Ottawa scalefvalue

IHC:

Immunohistochemistry

EGFR:

Epidermal growth factor receptor

PD-1:

Programmed death-1

TPS:

Tumor proportional score

TC:

Tumor cell

References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. https://doi.org/10.3322/caac.21492.

    Article  PubMed  Google Scholar 

  2. 2.

    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87–108. https://doi.org/10.3322/caac.21262.

    Article  PubMed  Google Scholar 

  3. 3.

    Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman J, Chirieac LR, et al. Non-small cell lung cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017;15(4):504–35. https://doi.org/10.6004/jnccn.2017.0050.

    Article  PubMed  Google Scholar 

  4. 4.

    Shi Y, Au JS, Thongprasert S, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J Thorac Oncol. 2014;9(2):154–62. https://doi.org/10.1097/JTO.0000000000000033.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Rotow J, Bivona TG. Understanding and targeting resistance mechanisms in NSCLC. Nat Rev Cancer. 2017;17(11):637–58. https://doi.org/10.1038/nrc.2017.84.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 2012;13(3):239–46. https://doi.org/10.1016/S1470-2045(11)70393-X.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Wu YL, Zhou C, Hu CP, Feng J, Lu S, Huang Y, et al. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian patients with advanced non-small-cell lung cancer harbouring EGFR mutations (LUX-Lung 6): an open-label, randomised phase 3 trial. Lancet Oncol. 2014;15(2):213–22. https://doi.org/10.1016/S1470-2045(13)70604-1.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Zhou C, Wu YL, Chen G, Feng J, Liu XQ, Wang C, et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study. Lancet Oncol. 2011;12(8):735–42. https://doi.org/10.1016/S1470-2045(11)70184-X.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Blakely CM, Watkins TBK, Wu W, Gini B, Chabon JJ, McCoach CE, et al. Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers. Nat Genet. 2017;49(12):1693–704. https://doi.org/10.1038/ng.3990.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Wang J, Wang B, Chu H, Yao Y. Intrinsic resistance to EGFR tyrosine kinase inhibitors in advanced non-small-cell lung cancer with activating EGFR mutations. Onco Targets Ther. 2016;9:3711–26. https://doi.org/10.2147/OTT.S106399.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Santoni-Rugiu E, Melchior LC, Urbanska EM, et al. Intrinsic resistance to EGFR-tyrosine kinase inhibitors in EGFR-mutant non-small cell lung cancer: differences and similarities with acquired resistance. Cancers (Basel). 2019;11(7):923. https://doi.org/10.3390/cancers11070923.

  12. 12.

    Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, von Pawel J, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet. 2017;389(10066):255–65. https://doi.org/10.1016/S0140-6736(16)32517-X.

    Article  PubMed  Google Scholar 

  13. 13.

    Takada K, Toyokawa G, Tagawa T, Kohashi K, Shimokawa M, Akamine T, et al. PD-L1 expression according to the EGFR status in primary lung adenocarcinoma. Lung Cancer. 2018;116:1–6. https://doi.org/10.1016/j.lungcan.2017.12.003.

    Article  PubMed  Google Scholar 

  14. 14.

    D'Incecco A, Andreozzi M, Ludovini V, Rossi E, Capodanno A, Landi L, et al. PD-1 and PD-L1 expression in molecularly selected non-small-cell lung cancer patients. Br J Cancer. 2015;112(1):95–102. https://doi.org/10.1038/bjc.2014.555.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Lin C, Chen X, Li M, Liu J, Qi X, Yang W, et al. Programmed death-ligand 1 expression predicts tyrosine kinase inhibitor response and better prognosis in a cohort of patients with epidermal growth factor receptor mutation-positive lung adenocarcinoma. Clin Lung Cancer. 2015;16(5):e25–35. https://doi.org/10.1016/j.cllc.2015.02.002.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Tang Y, Fang W, Zhang Y, Hong S, Kang S, Yan Y, et al. The association between PD-L1 and EGFR status and the prognostic value of PD-L1 in advanced non-small cell lung cancer patients treated with EGFR-TKIs. Oncotarget. 2015;6(16):14209–19. https://doi.org/10.18632/oncotarget.3694.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Soo RA, Kim HR, Asuncion BR, Fazreen Z, Omar MFM, Herrera MC, et al. Significance of immune checkpoint proteins in EGFR-mutant non-small cell lung cancer. Lung Cancer. 2017;105:17–22. https://doi.org/10.1016/j.lungcan.2017.01.008.

    Article  PubMed  Google Scholar 

  18. 18.

    Bai Y, Chen X, Hou L, Qian J, Jiang T, Zhou C, et al. PD-L1 expression and its effect on clinical outcomes of EGFR-mutant NSCLC patients treated with EGFR-TKIs. Cancer Biol Med. 2018;15(4):434–42. https://doi.org/10.20892/j.issn.2095-3941.2018.0223.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Kobayashi K, Seike M, Zou F, Noro R, Chiba M, Ishikawa A, et al. Prognostic significance of NSCLC and response to EGFR-TKIs of EGFR-mutated NSCLC based on PD-L1 expression. Anticancer Res. 2018;38(2):753–62. https://doi.org/10.21873/anticanres.12281.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Su S, Dong ZY, Xie Z, Yan LX, Li YF, Su J, et al. Strong programmed death ligand 1 expression predicts poor response and de novo resistance to EGFR tyrosine kinase inhibitors among NSCLC patients with EGFR mutation. J Thorac Oncol. 2018;13(11):1668–75. https://doi.org/10.1016/j.jtho.2018.07.016.

    Article  PubMed  Google Scholar 

  21. 21.

    Yoneshima Y, Ijichi K, Anai S, Ota K, Otsubo K, Iwama E, et al. PD-L1 expression in lung adenocarcinoma harboring EGFR mutations or ALK rearrangements. Lung Cancer. 2018;118:36–40. https://doi.org/10.1016/j.lungcan.2018.01.024.

    Article  PubMed  Google Scholar 

  22. 22.

    Hsu KH, Huang YH, Tseng JS, Chen KC, Ku WH, Su KY, et al. High PD-L1 expression correlates with primary resistance to EGFR-TKIs in treatment naive advanced EGFR-mutant lung adenocarcinoma patients. Lung Cancer. 2019;127:37–43. https://doi.org/10.1016/j.lungcan.2018.11.021.

    Article  PubMed  Google Scholar 

  23. 23.

    Matsumoto Y, Sawa K, Fukui M, Oyanagi J, Izumi M, Ogawa K, et al. Impact of tumor microenvironment on the efficacy of epidermal growth factor receptor-tyrosine kinase inhibitors in patients with EGFR-mutant non-small cell lung cancer. Cancer Sci. 2019;110(10):3244–54. https://doi.org/10.1111/cas.14156.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Yang CY, Liao WY, Ho CC, Chen KY, Tsai TH, Hsu CL, et al. Association between programmed death-ligand 1 expression, immune microenvironments, and clinical outcomes in epidermal growth factor receptor mutant lung adenocarcinoma patients treated with tyrosine kinase inhibitors. Eur J Cancer. 2020;124:110–22. https://doi.org/10.1016/j.ejca.2019.10.019.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Yoon BW, Chang B, Lee SH. High PD-L1 expression is associated with unfavorable clinical outcome in EGFR-mutated lung adenocarcinomas treated with targeted therapy. Onco Targets Ther. 2020;13:8273–85. https://doi.org/10.2147/OTT.S271011.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Lee CC, Soon YY, Leong CN, Koh WY, Tey J. Impact of programmed death-ligand 1 expression on the patients of stage IV non-small cell lung cancer harboring epidermal growth factor receptor mutation: a systematic review and meta-analysis. Acta Oncol. 2020;59(12):1430–7. https://doi.org/10.1080/0284186X.2020.1807600.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339(jul21 1):b2700. https://doi.org/10.1136/bmj.b2700.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8(1):16. https://doi.org/10.1186/1745-6215-8-16.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Afzal S, Bojesen SE, Nordestgaard BG. Low 25-hydroxyvitamin D and risk of type 2 diabetes: a prospective cohort study and metaanalysis. Clin Chem. 2013;59(2):381–91. https://doi.org/10.1373/clinchem.2012.193003.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Han JJ, Kim DW, Koh J, et al. Change in PD-L1 expression after acquiring resistance to gefitinib in EGFR-mutant non-small-cell lung cancer. Clin Lung Cancer. 2016;17(4):263–270.e262.

    CAS  Article  Google Scholar 

  31. 31.

    Haratani K, Hayashi H, Tanaka T, Kaneda H, Togashi Y, Sakai K, et al. Tumor immune microenvironment and nivolumab efficacy in EGFR mutation-positive non-small-cell lung cancer based on T790M status after disease progression during EGFR-TKI treatment. Ann Oncol. 2017;28(7):1532–9. https://doi.org/10.1093/annonc/mdx183.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. New Engl J Med. 2015;372(21):2018–28. https://doi.org/10.1056/NEJMoa1501824.

    Article  PubMed  Google Scholar 

  33. 33.

    Park K, Tan EH, O'Byrne K, Zhang L, Boyer M, Mok T, et al. Afatinib versus gefitinib as first-line treatment of patients with EGFR mutation-positive non-small-cell lung cancer (LUX-Lung 7): a phase 2B, open-label, randomised controlled trial. Lancet Oncol. 2016;17(5):577–89. https://doi.org/10.1016/S1470-2045(16)30033-X.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Wu YL, Cheng Y, Zhou X, Lee KH, Nakagawa K, Niho S, et al. Dacomitinib versus gefitinib as first-line treatment for patients with EGFR-mutation-positive non-small-cell lung cancer (ARCHER 1050): a randomised, open-label, phase 3 trial. Lancet Oncol. 2017;18(11):1454–66. https://doi.org/10.1016/S1470-2045(17)30608-3.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Soria JC, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee KH, et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med. 2018;378(2):113–25. https://doi.org/10.1056/NEJMoa1713137.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Dong ZY, Zhang JT, Liu SY, Su J, Zhang C, Xie Z, et al. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD-1 blockade in non-small cell lung cancer. Oncolmmunology. 2017;6(11):e1356145. https://doi.org/10.1080/2162402X.2017.1356145.

    Article  Google Scholar 

  37. 37.

    Ikeda S, Okamoto T, Okano S, Umemoto Y, Tagawa T, Morodomi Y, et al. PD-L1 Is Upregulated by simultaneous amplification of the PD-L1 and JAK2 genes in non-small cell lung cancer. J Thorac Oncol. 2016;11(1):62–71. https://doi.org/10.1016/j.jtho.2015.09.010.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None.

Funding

This work was supported by the grant from the National Natural Science Foundation of China (NSFC 81602025), and the 1.3.5 Project for Disciplines of Excellence (ZYJC18009), West China Hospital, Sichuan University. to Dr. Mei.

Author information

Affiliations

Authors

Contributions

ZYP, JDM, and SYD performed the experiment conception and design. ZYP, HHL, and KZ did the literature search. ZYP and HHL performed the data analysis. ZYP and JDM did the paper writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jiandong Mei.

Ethics declarations

Ethics approval and consent to participate

All analyses were based on previous published studies, thus no ethical approval and patient consent are required.

Consent for publication

None.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Additional file 1: Table S1

. Checklist of the PRISMA extension for meta-analysis. Table S2. Literature search criteria.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Peng, Z., Lin, H., Zhou, K. et al. Predictive value of pretreatment PD-L1 expression in EGFR-mutant non-small cell lung cancer: a meta-analysis. World J Surg Onc 19, 145 (2021). https://doi.org/10.1186/s12957-021-02254-x

Download citation

Keywords

  • Non-small cell lung cancer
  • Epidermal growth factor receptor
  • Programmed death-ligand 1
  • Prognosis