Strong programmed cell death ligand-1 affect clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors

Strong programmed cell death ligand-1 affect clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Strong programmed cell death ligand-1 affect clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors Jiling Niu, Xuquan Jing, Qinhao Xu, Haoyu Liu, Yaru Tian, Zhengqiang Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3956319/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Third-generation tyrosine kinase inhibitors (TKIs) are the standard treatment for advanced epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma. In first/second generation EGFR-TKIs, strong programmed death ligand 1 (PD-L1) expression contributes to primary resistance, significantly affecting patient prognosis. Despite this, the relationship between PD-L1 expression levels and third-generation TKIs remains unclear. Patients and Methods: This retrospective cohort study reviewed patients with advanced NSCLC who received third-generation EGFR-TKIs as first-line systemic therapy at the Shandong Cancer Hospital between March 2019 and June 2022. The EGFR status of the patients was assessed using amplification refractory mutation system fluorescence quantitative polymerase chain reaction, and the PD-L1 expression level was evaluated using Dako 22 C3 immunohistochemical staining. The Kaplan–Meier method was used for survival analysis. Results Overall, 150 patients were included in this study. PD-L1 expression was negative (PD-L1 tumor proportion score < 1%) in 89 cases, weak (1–49%) in 42 cases, and strong (≥ 50%) in 19 cases. The median follow-up period for the entire cohort was 22.12 months (median progression-free survival [mPFS]: 24.33 months); the median overall survival was not reached. mPFS for patients with negative, weak, and strong PD-L1 expressions was 23.60, 26.12, and 16.60 months, respectively. The mPFS for strong PD-L1 expression was significantly shorter than that for with weak PD-L1 expression but was not associated with negativity, particularly in the 19DEL and 21L858R subgroups. PFS was significantly shorter in patients with strong PD-L1 expression in both subgroups (19DEL and 21L858R) than in those with weak PD-L1 expression. Conclusion Strong PD-L1 expression in tumor cells influenced the clinical outcomes of patients with advanced NSCLC treated with third-generation EGFR-TKIs. Stronger PD-L1 expression in TKI-treated patients with advanced first-line EGFR-mutated NSCLC was associated with worse PFS. NSCLC EGFR tyrosine kinase inhibitor programmed death-ligand 1 Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Lung cancer is the main contributor to cancer-related health issues, constituting approximately 80% of non-small cell lung cancer (NSCLC). Among NSCLC cases, adenocarcinoma comprises about 40%, followed by squamous cell carcinoma, which accounts for approximately 25% [ 1 , 2 ] . Epidermal growth factor receptor (EGFR) mutations, as the main driver mutation, account for approximately 30% of NSCLC cases and 50% of lung adenocarcinoma cases [ 3 – 5 ] . Third-generation tyrosine kinase inhibitors (TKIs) represent the established standard of care for advanced EGFR-positive lung adenocarcinoma [ 6 – 8 ] . PD-L1 is a ligand for programmed cell death protein 1 (PD-1), and the combination of PD-1 and PD-L1 transmits negative regulatory signals to T cells. This interaction impedes T cells from recognizing cancer cells, leading to the tumor’s “immune escape” [ 9 , 10 ] . The expression level of PD-L1 is an important predictor of the treatment efficacy of immune checkpoint inhibitors. However, in patients undergoing first/second generation EGFR-TKI treatment, high PD-L1 expression has been linked to primary resistance, significantly shortening the median progression-free-survival (mPFS) and median overall survival (mOS) [ 11 – 14 ] . Many studies have explored the association between PD-L1 expression levels and EGFR-TKI clinical efficacy, but few have focused on third-generation EGFR-TKIs; therefore, there are no clear, conclusive findings on the correlation between PD-L1 expression and third-generation TKI efficacy. This retrospective study aimed to further define the relationship between PD-L1 expression levels and the efficacy of first-line administration of third-generation EGFR TKIs in patients with advanced EGFR-mutant lung adenocarcinoma. 2 Patients and Methods 2.1 Study design and patients This retrospective study analyzed patients with advanced NSCLC who received third-generation EGFR-TKIs as first-line systemic therapy between March 2019 and June 2022 at the Shandong Cancer Hospital and Institute, affiliated with Shandong First Medical University (Fig. 1 ). Demographic characteristics and clinical data, including age, sex, smoking status, Eastern Cooperative Oncology Group Physical Status (ECOG PS), time to diagnosis, time to progression, tumor type, baseline EGFR mutation status, and EGFR-TKI treatment type, were extracted from electronic medical records. This study was approved by the Ethical Review Committee of the Affiliated Cancer Hospital of Shandong First Medical University and was conducted in accordance with the Declaration of Helsinki. 2.2 EGFR and PD-L1 Evaluation EGFR mutations were confirmed using amplification refractory mutation system fluorescence quantitative polymerase chain reaction in paraffin-embedded tissues. EGFR-sensitive mutations were those associated with EGFR-TKI sensitivity, including exon 19 deletions and exon 21 L858R. PD-L1 expression levels were determined using the Dako LINK 48 detection system (Agilent Technologies, Santa Clara, CA, USA). The tumor PD-L1 expression status was evaluated by determining the tumor proportion score (TPS) using 22 C3 antibodies. The TPS scores of the tumor sections were independently evaluated by at least two senior pathologists, categorizing PD-L1 expression into three groups (< 1%, negative expression; 1–49%, weak expression; ≥50%, strong expression) based on the TPS score. 2.3 Statistical analysis Correlations between baseline characteristics and subgroups of tumor PD-L1 expression were analyzed by χ 2 test or Fisher’s exact test. Progression-free survival (PFS) and overall survival (OS) were the primary endpoints of this study. PFS was defined as the time from the initiation of EGFR-TKI treatment to the detection of disease progression or death from any cause. OS was defined as the time from the initiation of EGFR-TKI therapy to death from any cause. PFS and OS were evaluated using the Kaplan–Meier method. The log-rank test was used to compare differences in survival curves between the two groups. Multivariate analysis, incorporating age, sex, smoking history, ECOG PS, brain metastasis status at diagnosis, and PD-L1 expression, was performed using logistic regression models. Statistical analyses were performed with SPSS statistical software version 26.0 (IBM Corporation, Armonk, NY, USA) and Prism software version 8.0.2 (GraphPad Software, Boston, MA, USA). All reported p-values were two-sided with a 95% confidence interval, and statistical significance was set at P ≤ 0.05. 3 Results 3.1 Characterization of PD-L1 expression in EGFR-mutated NSCLC A total of 150 patients who received third-generation EGFR-TKIs as first-line systemic therapy for advanced NSCLC between March 2019 and June 2022 at the Affiliated Cancer Hospital of Shandong First Medical University were enrolled in this study. The screening flowchart is shown in Fig. 1 . The demographic and clinical characteristics of the patients with EGFR mutations are presented in Table 1 . The median age was 61 years (range 34–92 years), 92 (61.33%) patients were female, 122 (81.33%) were never-smokers, 141 (94%) had an ECOG PS performance status < 2, and 145 (96.67) were pathologically diagnosed with adenocarcinoma. Furthermore, 81 (54%) patients had an exon 19 deletion, 69 (46%) had an exon 21 L858R mutation, 98 (65.33%) were treated with osimertinib, and 52 (34.66%) were treated with aumolertinib. Baseline brain, bone, and liver metastases were seen in 67 (44.67%), 74 (49.33%), and 23 (15.33%) patients, respectively. Table 1 Baseline characteristics of patients Characteristic Number of patients (%) Age(years), n (%) <65 95(63.33) ≥ 65 55(36.67) median (range) 61(34–92) Gender, n (%) Female 92(61.33) Male 58(38.67) Smoking history, n (%) Never smoker 122(81.33) Current/former smoker 28(18.67) ECOG PS, n (%) < 2 141(94.00) ≥ 2 9(6.00) Pathological type, n (%) adenocarcinoma 145(96.67) Other 5(3.33) EGFR mutation, n (%) Exon 19 deletion 81(54.00) Exon 21 L858R 69(46.00) First-line three generations EGFR-TKIs, n (%) Osimertinib 98(65.33) Almonertinib 52 (34.66) Brain metastases at diagnosis, n (%) Metastasis 67(44.67) No metastasis 83(55.33) Bone metastases at diagnosis, n (%) Metastasis 74(49.33) No metastasis 76(50.67) liver metastases at diagnosis, n (%) Metastasis 23(15.33) No metastasis 127(84.67) EGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor; ECOG PS, eastern cooperative oncology group performance status. Of the 150 patients, 89 (59.33%) were negative for PD-L1 expression (TPS < 1%), 42 (28%) had weak expression (1–49%), and 19 (12.66%) had strong expression (≥ 50%). PD-L1 expression correlated with age, gender, ECOG PS, and presence of brain, bone, and liver metastases at diagnosis; however, the EGFR mutation type was irrelevant (Table 2 ). Table 2 Association between PD-L1 expression and clinicopathologic features PD-L1 expression Negative (TPS < 1%) (n = 89) Weak (TPS:1–49%) (n = 42) Strong (TPS ≥ 50%) (n = 19) P value Age(years) ,n (%) 0.184 <65 59(66.29%) 22(52.38%) 14(73.68%) ≥ 65 30(33.71%) 20(47.62%) 5(26.32%) Gender, n (%) 0.268 Female 54(60.67%) 29(69.05%) 9(47.37%) Male 35(39.33%) 13(30.95%) 10(52.63%) Smoking history, n (%) 0.010 Never smoker 73(82.02%) 38(90.48%) 11(57.89%) Current/former smoker 16(17.98%) 4 (9.52%) 8 (42.11%) ECOG PS, n (%) 0.896 < 2 83(93.26%) 40(95.24%) 18(94.74%) ≥ 2 6 (6.74%) 2(4.76%) 1(5.26%) EGFR mutation, n (%) 0.923 Exon 19 deletion 48(53.93%) 22(52.38%) 11(57.89%) Exon 21 L858R 41(46.07%) 20(47.62%) 8(42.11%) Brain metastases at diagnosis, n (%) 0.643 Metastasis 37(41.57%) 21(50.00%) 9(47.37%) No metastasis 52(58.43%) 21(50.00%) 10(52.63%) Bone metastases at diagnosis, n (%) 0.146 Metastasis 49(55.06%) 19(45.24%) 6(31.58%) No metastasis 40(44.94%) 23(54.76%) 13(68.42%) liver metastases at diagnosis, n (%) 0.368 Metastasis 16(17.98%) 6(14.29%) 1(5.26%) No metastasis 73(82.02%) 36(85.71%) 18(94.74%) No bone radiotherapy 66(74.16%) 33(78.57%) 15(78.95%) PD-L1, programmed death-ligand 1; TPS, tumor proportion score; EGFR, epidermal growth factor receptor; ECOG PS, eastern cooperative oncology group performance status 3.2 The relationship between PD-L1 expression and clinical prognosis. 3.2.1 Overall population The median follow-up for the entire cohort was 22.12 months (95% confidence interval (CI) 20.98–23.26 months), mPFS was 24.33 months, and mOS was not reached (Fig. 2 A). In addition, there was no significant difference in survival between the 19DEL and L858R patients (Fig. 2 B, 2 C). In the subgroup analysis, mPFS was 23.60 months (95% CI 16.61–30.60 months) for patients with negative PD-L1 expression, 26.12 months (95% CI 19.05–33.18 months) for patients with weak PD-L1 expression, and 16.60 months (95% CI 6.55–26.64 months) for patients with strong PD-L1 expression (log-rank: negative vs. weak PD-L1 expression, P = 0.103; negative vs. strong PD-L1 expression, P = 0.103; weak vs. strong PD-L1 expression, P = 0.001) (Fig. 2 D). This shows that the PFS of patients with strong PD-L1 expression was significantly shorter than that of patients with weak PD-L1 expression but was unrelated to negativity. For the OS of all three patients, mOS was achieved, and no clear correlation was observed (Fig. 2 E). In this study, PD-L1 expression levels were stratified by TPS = 1%, 25%, and 50% for prognostic analysis. The mPFS for PD-L1 < 1% vs. PD-L1 ≥ 1% was 23.60 months (95% CI 16.61–30.60 months) and 24.40 months (95% CI 22.44–26.35 months), respectively ( P = 0.397) (Fig. 2 F). For PD-L1 < 25% vs. PD-L1 ≥ 25%, mPFS was 24.40 months (95% CI 21.51–27.28 months) and 16.89 months (95% CI 6.87–26.92 months), respectively ( P = 0.024) (Fig. 2 G). For PD-L1 < 50% vs. PD-L1 ≥ 50%, mPFS was 25.49 months (95% CI 22.90–28.08 months) and 16.60 months (95% CI 6.55–26.64 months), respectively ( P = 0.015) (Fig. 2 H). In summary, a cutoff of 1 is not superior for PD-L1 expression and the efficacy of third-generation TKIs. Higher PD-L1 expression in TKI-treated patients with advanced EGFR-mutant NSCLC was associated with a worse prognosis. In the multivariate Cox regression model, PD-L1 expression remained a significant prognostic indicator of PFS when adjusted for age at diagnosis, sex, smoking history, ECOG PS, mutation type, TKI type, and brain, bone, and liver metastasis status (hazard ratio (HR) for weak vs. strong PD-L1 expression, 0.382; 95% CI 0.169–0.863, P = 0.021) (Table 3 ). Table 3 Multivariate analysis of clinicopathological features for progression-free survival Characteristic HR 95% CI P value Age(years) < 65 Reference ≥ 65 0.698 0.402–1.214 0.203 Gender Female Reference Male 1.141 0.606–2.149 0.682 Smoking history Never smoker Reference Current/former smoker 1.551 0.732–3.284 0.252 ECOG PS < 2 Reference ≥ 2 0.690 0.285–1.670 0.410 EGFR mutation Exon 21 L858R Reference Exon 19 deletion 0.776 0.474–1.272 0.315 First-line three generations EGFR-TKIs Osimertinib Reference Almonertinib 0.994 0.553–1.610 0.831 Brain metastases at diagnosis No metastasis Reference Metastasis 1.376 0.846–2.238 0.199 Bone metastases at diagnosis No metastasis Reference Metastasis 1.331 0.806–2.197 0.264 liver metastases at diagnosis No metastasis Reference Metastasis 1.768 0.948–3.299 0.07 PD-L1 expression Strong (≥ 50%) Reference Weak (1–49%) 0.382 0.169–0.863 0.021 Negative (< 1%) 0.576 0.292–1.136 0.111 EGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor; PD-L1, programmed death-ligand 1; ECOG PS, eastern cooperative oncology group performance status; CI, confidence interval; HR, hazard ratio. 3.2.2 19DEL and 21L858R subgroups We further analyzed the relationship between PD-L1 expression and clinical prognosis in the 19DEL and 21L858R subgroups. In the 19DEL subgroup analysis, the mPFS of negative, weak, and strong PD-L1 expression was 26.55 (95% CI 21.36–31.73 months), 33.52 (95% CI 22.03–45.01 months), and 16.60 months (95% CI 8.50–24.69 months), respectively. The three two-by-two P -values were negative vs. weak expression, P = 0.637), negative vs. strong expression, P = 0.095), and weak vs. strong expression, P = 0.047) (Fig. 3 A). In the 21L858R subgroup analysis, the mPFS for negative, weak, and strong PD-L1 expression was 17.06 (95% CI 8.46–25.66 months), 26.12 (95% CI 23.42–28.81 months), and 7.17 months (95% CI 0.00–23.12 months), respectively. The three two-by-two P -values were negative vs. weak expression, P = 0.047), negative vs. strong expression, P = 0.484), and weak vs. strong expression, P = 0.036) (Fig. 3 B). In summary, 19DEL and 21L858R patients with strong PD-L1 expression had significantly shorter PFS than those with weak PD-L1 expression, with negativity being an independent prognostic factor. No correlation with PD-L1 was observed for OS in both groups due to the short follow-up period (Fig. 3 C, 3 D). 3.2.3 Osimertinib and almonertinib Subgroups We further analyzed the relationship between PD-L1 expression and clinical prognosis in the osimertinib and almonertinib subgroups. In the osimertinib subgroup, mPFS for negative, weak, and strong PD-L1 expression was 23.54 (95% CI 18.52–34.57 months), 23.07 (95% CI 19.54–30.21 months), and 16.60 months (95% CI 5.52–27.99 months), respectively. The three two-by-two P values were negative vs. weak expression, P = 0.731), negative vs. strong expression, P = 0.032), and weak vs. strong expression, P = 0.096 (Fig. 4 A). In the almonertinib subgroup, the mPFS for negative, weak, and strong PD-L1 expression was 16.89 months (95% CI 13.33–20.46 months), NA, and 12.20 months, respectively. The P -values for the three comparisons were negative vs. weak expression, P = 0.002), negative vs. strong expression, P = 0.840), and weak vs. strong expression, P = 0.001) (Fig. 4 B). In summary, the PFS of patients with strong PD-L1 expression in the almonertinib subgroup was significantly shorter than that of patients with weak PD-L1 expression, with negativity being an independent prognostic factor. However, osimertinib showed a similar trend, but the results were not statistically different, probably because of the small sample size. No correlation with PD-L1 was observed for OS in both groups due to the short follow-up period (Fig. 4 C, 4 D). 4 Discussion Currently, the third-generation TKIs osimertinib, almonertinib, and furmonertinib have a more optimized structure with increased efficacy and reduced toxicity compared with first-generation TKIs and have become the standard of care for first-line treatment of EGFR mutation-positive advanced NSCLC. The FLAURA, AENEAS, and FURLONG studies have confirmed that the first-line application of third-generation TKIs achieved a significant benefit in PFS and OS compared with the 1st/2nd generation, with mPFS exceeding 20 months and mOS exceeding 33 months [ 6 – 8 , 15 ] . The correlation between PD-L1, a crucial immune checkpoint inhibitor efficacy predictor, and EGFR-TKI efficacy has received considerable attention. Previous studies in patients receiving 1st/2nd generation TKIs demonstrated that high PD-L1 expression leads to primary resistance and significantly shortens mPFS and mOS [ 11 – 13 , 16 ] . However, few such studies have been conducted, and no definitive conclusions have been drawn. Therefore, this study aimed to analyze the role of PD-L1 in patients with EGFR mutations receiving first-line treatment with third-generation TKIs. Our retrospective analysis showed that high PD-L1 expression was associated with poor PFS in patients with advanced NSCLC. To the best of our knowledge, this is the largest retrospective study to date on the correlation between PD-L1 expression and the efficacy of first-line third-generation TKI in advanced NSCLC. In the analysis of the FLAURA study, the osimertinib arm exhibited an mPFS of 18.4 months with a 79% ORR in patients with advanced primary EGFR-mutated NSCLC with PD-L1 ≥ 1%. Conversely, PD-L1-negative patients showed an mPFS of 18.9 months with an 85% ORR. We concluded that the clinical outcomes in advanced EGFR-mutant NSCLC were not affected by PD-L1 expression status [ 17 ] . However, the FLAURA authors did not analyze the impact of different PD-L1 expression levels on clinical efficacy. In our study, we similarly found that PD-L1 bounding to 1 was not a prognostic indicator for advanced EGFR-mutant NSCLC. In our study, mPFS was 23.60 months in patients with advanced primary EGFR-mutated NSCLC with PD-L1 ≥ 1% and 24.40 months in PD-L1-negative patients ( P = 0.397). The mPFS for PD-L1 < 25% vs. PD-L1 ≥ 25% was 24.40 months (95% CI 21.51–27.28 months), and 16.89 months (95% CI 6.87–26.92 months; P = 0.024). The mPFS for PD-L1 < 50% vs. PD-L1 ≥ 50% was 25.49 months (95% CI 22.90–28.08 months), and 16.60 months (95% CI 6.55–26.64 months; P = 0.015).In a multicenter prospective clinical study utilizing osimertinib, Yoshimura et al. showed that PFS was shorter in patients with strong PD-L1 expression than in those with weak expression + negative PD-L1 (mPFS: 5.0 vs. 17.4; P < 0.001) [ 18 ] . Similarly, Hsu et al. reached the same conclusion that patients with strong PD-L1 expression had shorter PFS and OS than patients with weak expression + negative PD-L1 (mPFS: 9.7 vs. 26.5; P = 0.009), (25.4 vs. NR; P = 0.021) [ 19 ] . In summary, for PD-L1 expression and the efficacy of three-generation TKIs, a cutoff value of 1 is not a better demarcation index. Patients with advanced NSCLC and high PD-L1 expression have a poorer prognosis after treatment with EGFR-TKIs. Differences were considered statistically significant. Preclinical studies have reported that EGFR activation induces PD-L1 expression, promoting immune escape. EGFR-TKIs significantly downregulate PD-L1 expression in EGFR-mutant NSCLC cells [ 20 , 21 ] . In addition to MET activation, EGFR mutations may upregulate PD-L1 expression through the p-ERK 1/2/p-c-Jun and JAK-STAT pathways, with MUC 16 mutation frequency associated with high PD-L1 expression [ 12 , 22 , 23 ] . Notably, activating the JAK-STAT pathway may play a role in primary resistance to EGFR-TKIs [ 12 ] . This may account for the relatively poor prognostic outcomes in patients with high PD-L1 expression. Increasing clinical evidence suggests that high PD-L1 expression could predict primary resistance to targeted therapies. Some studies have hypothesized that higher PD-L1 expression in patients with NSCLC leads to poor prognosis for TKI therapy through the tumor microenvironment. Although the mechanism is not fully understood, PD-L1 expression levels should be determined in clinical practice at initial diagnosis in patients with or without detectable driver gene variants. Subsequent immunotherapy after the failure of TKI treatment may be a viable treatment option for patients with high PD-L1 expression. Further research is needed to focus on the optimal treatment strategies to screen and develop personalized and precise treatment plans for the high PD-L1-expressing population with driver variants. Our study had some limitations. First, compared with prospective studies, this was a single-center retrospective study with unavoidable bias. Second, we studied only a Chinese population, limiting our finding’s generalizability. Third, we did not consider the effect of T790M mutation on prognosis. Fourth, we did not perform NGS to exclude the effects of coexisting tumor suppressor genes (including TP53 , RB1 , PTEN , and ARID1A ) on the PFS and OS of third-generation TKIs for EGFR-positive advanced NSCLC. Therefore, multicenter prospective clinical studies are required to explore the relationship between PD-L1 expression and the prognosis of patients treated with third-generation TKIs. 5 Conclusion In conclusion, this retrospective study showed that strong PD-L1 expression predicts poor response to EGFR-TKI therapy in patients with EGFR-mutated NSCLC. Thus, PD-L1 expression can be used as a reliable biomarker for EGFR-TKI therapy to predict patient survival. Declarations Statements and Declarations Funding: This study was supported by the National Natural Science Foundation of China (82103632), the Natural Science Foundation of Shandong Province (ZR2021QH245, ZR2022LZL008), and the Facilitating New Life Public Welfare Project (GX2DH04). Conflicts of Interest: All authors have seen and approved the final version of the manuscript being submitted. They warrant that the article is the author's original work, hasn't received prior publication, and isn't under consideration for publication elsewhere. And the authors declare that they have no competing interests. Availability of data and materials: All the data generated or analyzed during this study are included in this published article. The datasets used and/or analyzed during the current study are available from the corresponding author. Ethics approval and consent to participate: This study was approved by the Ethical Review Committee of the Affiliated Cancer Hospital of Shandong First Medical University and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all patients or their legal guardians. Consent to participate: Not applicable Consent for publication: Not applicable Code availability: Not applicable Author contributions: Concept and design: N.J., Z.H., and S.Y. .data management: N.J., J.X., and X.Q.. formal analysis: N.J., L.H., and T.Y.. survey: N.J., J.X., X.Q.. method: Y.Z., Z.H., and S.Y. . project management: Y.Z., Z.H., and S.Y. . resources: Y.Z., Z.H., and S.Y. .supervisors: Y.Z., Z.H., and S.Y.. Supervision: Y.Z., Z.H., and S.Y. . N.J. and J.X. wrote the main manuscript text.All authors have read and approved the final version of the manuscript. Acknowledgments: The authors thank all the patients and their fami-lies. References SCHABATH M B COTEML. Cancer Progress and Priorities: Lung Cancer [J]. Cancer Epidemiol Biomarkers Prev. 2019;28(10):1563–79. LEITER A, VELUSWAMY R R, WISNIVESKY JP. The global burden of lung cancer: current status and future trends [J]. Nat Rev Clin Oncol. 2023;20(9):624–39. SHI Y, AU J S, 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]. J Thorac Oncol. 2014;9(2):154–62. GOU L Y, WU YL. Prevalence of driver mutations in non-small-cell lung cancers in the People's Republic of China [J]. Lung Cancer (Auckl). 2014;5:1–9. TAN A C, TAN D S. W. Targeted Therapies for Lung Cancer Patients With Oncogenic Driver Molecular Alterations [J]. J Clin Oncol. 2022;40(6):611–25. CHENG Y, HE Y, LI W, et al. Osimertinib Versus Comparator EGFR TKI as First-Line Treatment for EGFR-Mutated Advanced NSCLC: FLAURA China, A Randomized Study [J]. Target Oncol. 2021;16(2):165–76. LU S, DONG X. AENEAS: A Randomized Phase III Trial of Aumolertinib Versus Gefitinib as First-Line Therapy for Locally Advanced or MetastaticNon-Small-Cell Lung Cancer With EGFR Exon 19 Deletion or L858R Mutations [J]. J Clin Oncol. 2022;40(27):3162–71. SHI Y, CHEN G, WANG X, et al. Furmonertinib (AST2818) versus gefitinib as first-line therapy for Chinese patients with locally advanced or metastatic EGFR mutation-positive non-small-cell lung cancer (FURLONG): a multicentre, double-blind, randomised phase 3 study [J]. Lancet Respir Med. 2022;10(11):1019–28. OHAEGBULAM K C ASSALA, LAZAR-MOLNAR E, et al. Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway [J]. Trends Mol Med. 2015;21(1):24–33. HACK S P, ZHU A X WANGY. Augmenting Anticancer Immunity Through Combined Targeting of Angiogenic and PD-1/PD-L1 Pathways: Challenges and Opportunities [J]. Front Immunol. 2020;11:598877. DONG Z Y SUS, XIE Z, 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]. J Thorac Oncol. 2018;13(11):1668–75. KANG M, PARK C, KIM S H, et al. Programmed death-ligand 1 expression level as a predictor of EGFR tyrosine kinase inhibitor efficacy in lung adenocarcinoma [J]. Transl Lung Cancer Res. 2021;10(2):699–711. LIU J, ITCHINS M, NAGRIAL A, et al. Relationship between PD-L1 expression and outcome in EGFR-mutant lung cancer patients treated with EGFR tyrosine kinase inhibitors [J]. Lung Cancer. 2021;155:28–33. HSU K H, HUANG Y H, TSENG JS, et al. High PD-L1 expression correlates with primary resistance to EGFR-TKIs in treatment naive advanced EGFR-mutant lung adenocarcinoma patients [J]. Lung Cancer. 2019;127:37–43. SORIA JC, OHE Y, VANSTEENKISTE J, et al. Osimertinib in Untreated EGFR-Mutated Advanced Non-Small-Cell Lung Cancer [J]. N Engl J Med. 2018;378(2):113–25. WANG PENGS, ZHANG R. EGFR-TKI resistance promotes immune escape in lung cancer via increased PD-L1 expression [J]. Mol Cancer. 2019;18(1):165. BROWN H, VANSTEENKISTE J, NAKAGAWA K, et al. Programmed Cell Death Ligand 1 Expression in Untreated EGFR Mutated Advanced NSCLC and Response to Osimertinib Versus Comparator in FLAURA [J]. J Thorac Oncol. 2020;15(1):138–43. YOSHIMURA A, YAMADA T, OKUMA Y, et al. Impact of tumor programmed death ligand-1 expression on osimertinib efficacy in untreated EGFR-mutated advanced non-small cell lung cancer: a prospective observational study [J]. Transl Lung Cancer Res. 2021;10(8):3582–93. HSU K H, TSENG J S, YANG T Y, et al. PD-L1 strong expressions affect the clinical outcomes of osimertinib in treatment naive advanced EGFR-mutant non-small cell lung cancer patients [J]. Sci Rep. 2022;12(1):9753. AKBAY E A, KOYAMA S, CARRETERO J, et al. Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors [J]. Cancer Discov. 2013;3(12):1355–63. AZUMA K, OTA K, KAWAHARA A, et al. Association of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung cancer [J]. Ann Oncol. 2014;25(10):1935–40. CHEN N, FANG W, ZHAN J, et al. Upregulation of PD-L1 by EGFR Activation Mediates the Immune Escape in EGFR-Driven NSCLC: Implication for Optional Immune Targeted Therapy for NSCLC Patients with EGFR Mutation [J]. J Thorac Oncol. 2015;10(6):910–23. SAIGI M, ALBURQUERQUE-BEJAR J J, MC LEER-FLORIN A, et al. MET-Oncogenic and JAK2-Inactivating Alterations Are Independent Factors That Affect Regulation of PD-L1 Expression in Lung Cancer [J]. Clin Cancer Res. 2018;24(18):4579–87. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3956319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272955986,"identity":"1b097fd4-7329-4584-b994-e59efe53ea65","order_by":0,"name":"Jiling Niu","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiling","middleName":"","lastName":"Niu","suffix":""},{"id":272955987,"identity":"05dc2b89-7f1c-4973-a357-7d1bace310e5","order_by":1,"name":"Xuquan Jing","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuquan","middleName":"","lastName":"Jing","suffix":""},{"id":272955988,"identity":"f624a09f-f72f-41c1-92fc-cac750c716a5","order_by":2,"name":"Qinhao Xu","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qinhao","middleName":"","lastName":"Xu","suffix":""},{"id":272955989,"identity":"bae4fff2-47b5-47f8-9a46-6891dfae4eb6","order_by":3,"name":"Haoyu Liu","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Liu","suffix":""},{"id":272955990,"identity":"78ad7805-6073-4c84-908d-e14a448ead56","order_by":4,"name":"Yaru Tian","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yaru","middleName":"","lastName":"Tian","suffix":""},{"id":272955991,"identity":"9edb43e8-3a0a-4b62-b694-a3afd45f824f","order_by":5,"name":"Zhengqiang Yang","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhengqiang","middleName":"","lastName":"Yang","suffix":""},{"id":272955992,"identity":"5baa7708-a6df-46d0-bfda-f9dbd87b8043","order_by":6,"name":"Hui Zhu","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhu","suffix":""},{"id":272955993,"identity":"a313fe99-5725-41b2-9162-90be4cacfbd5","order_by":7,"name":"Yulan Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACeWbmgw8+GPyv39/eQKQWw/a2ZMMZFcyMG3gOEGvNmTNq0jxngFokEojUwTgjh02Ct42N2Vzy8cYbDDU20QS1sEvkHraQbONhs5ydVmzBcCwtt4GwLXmJNwzbJHgYbueYSTA2HCasheFGjoFEYpuBBMPNM8RqOXPGSOLAmQQDgxs8RGoBB3JDxYEEyR6gXxKI8QsoKh//MTiQwM9+eOONDzU2RDgMCRgQHTVIWkjVMQpGwSgYBSMDAABg/0GU9OLZxgAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science","correspondingAuthor":true,"prefix":"","firstName":"Yulan","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-02-14 14:32:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3956319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3956319/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51248575,"identity":"36ce7abd-f3bc-4952-a0f4-4098830ec684","added_by":"auto","created_at":"2024-02-16 21:23:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337379,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3956319/v1/fcdb75b66938c4e7cd1c4629.jpg"},{"id":51248574,"identity":"6edb6fa5-569f-4802-8016-a65ed36243b9","added_by":"auto","created_at":"2024-02-16 21:23:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":491855,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3956319/v1/45467075e6ef4a6507828fb7.jpg"},{"id":51248828,"identity":"e95ad712-175a-475c-a12a-15aaf1712dd3","added_by":"auto","created_at":"2024-02-16 21:31:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":321869,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3956319/v1/d60c230fbdd63dcd185cf66a.jpg"},{"id":51248577,"identity":"16bb63f6-d20b-47d5-b938-b0279e1a5745","added_by":"auto","created_at":"2024-02-16 21:23:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":341142,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3956319/v1/aaff5a7c6cf6d4b35f513606.jpg"},{"id":52770368,"identity":"1466e509-de57-4499-b825-59f5b05f8a1f","added_by":"auto","created_at":"2024-03-15 14:33:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":705671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3956319/v1/d10c6908-4a24-40b9-966d-9b73b0fa2339.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Strong programmed cell death ligand-1 affect clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer is the main contributor to cancer-related health issues, constituting approximately 80% of non-small cell lung cancer (NSCLC). Among NSCLC cases, adenocarcinoma comprises about 40%, followed by squamous cell carcinoma, which accounts for approximately 25% \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Epidermal growth factor receptor (EGFR) mutations, as the main driver mutation, account for approximately 30% of NSCLC cases and 50% of lung adenocarcinoma cases \u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Third-generation tyrosine kinase inhibitors (TKIs) represent the established standard of care for advanced EGFR-positive lung adenocarcinoma \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePD-L1 is a ligand for programmed cell death protein 1 (PD-1), and the combination of PD-1 and PD-L1 transmits negative regulatory signals to T cells. This interaction impedes T cells from recognizing cancer cells, leading to the tumor\u0026rsquo;s \u0026ldquo;immune escape\u0026rdquo; \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The expression level of PD-L1 is an important predictor of the treatment efficacy of immune checkpoint inhibitors. However, in patients undergoing first/second generation EGFR-TKI treatment, high PD-L1 expression has been linked to primary resistance, significantly shortening the median progression-free-survival (mPFS) and median overall survival (mOS) \u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Many studies have explored the association between PD-L1 expression levels and EGFR-TKI clinical efficacy, but few have focused on third-generation EGFR-TKIs; therefore, there are no clear, conclusive findings on the correlation between PD-L1 expression and third-generation TKI efficacy.\u003c/p\u003e \u003cp\u003eThis retrospective study aimed to further define the relationship between PD-L1 expression levels and the efficacy of first-line administration of third-generation EGFR TKIs in patients with advanced EGFR-mutant lung adenocarcinoma.\u003c/p\u003e"},{"header":"2 Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and patients\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed patients with advanced NSCLC who received third-generation EGFR-TKIs as first-line systemic therapy between March 2019 and June 2022 at the Shandong Cancer Hospital and Institute, affiliated with Shandong First Medical University (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Demographic characteristics and clinical data, including age, sex, smoking status, Eastern Cooperative Oncology Group Physical Status (ECOG PS), time to diagnosis, time to progression, tumor type, baseline EGFR mutation status, and EGFR-TKI treatment type, were extracted from electronic medical records. This study was approved by the Ethical Review Committee of the Affiliated Cancer Hospital of Shandong First Medical University and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 EGFR and PD-L1 Evaluation\u003c/h2\u003e \u003cp\u003eEGFR mutations were confirmed using amplification refractory mutation system fluorescence quantitative polymerase chain reaction in paraffin-embedded tissues. EGFR-sensitive mutations were those associated with EGFR-TKI sensitivity, including exon 19 deletions and exon 21 L858R. PD-L1 expression levels were determined using the Dako LINK 48 detection system (Agilent Technologies, Santa Clara, CA, USA). The tumor PD-L1 expression status was evaluated by determining the tumor proportion score (TPS) using 22 C3 antibodies. The TPS scores of the tumor sections were independently evaluated by at least two senior pathologists, categorizing PD-L1 expression into three groups (\u0026lt;\u0026thinsp;1%, negative expression; 1\u0026ndash;49%, weak expression; \u0026ge;50%, strong expression) based on the TPS score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eCorrelations between baseline characteristics and subgroups of tumor PD-L1 expression were analyzed by χ\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test. Progression-free survival (PFS) and overall survival (OS) were the primary endpoints of this study. PFS was defined as the time from the initiation of EGFR-TKI treatment to the detection of disease progression or death from any cause. OS was defined as the time from the initiation of EGFR-TKI therapy to death from any cause. PFS and OS were evaluated using the Kaplan\u0026ndash;Meier method. The log-rank test was used to compare differences in survival curves between the two groups. Multivariate analysis, incorporating age, sex, smoking history, ECOG PS, brain metastasis status at diagnosis, and PD-L1 expression, was performed using logistic regression models. Statistical analyses were performed with SPSS statistical software version 26.0 (IBM Corporation, Armonk, NY, USA) and Prism software version 8.0.2 (GraphPad Software, Boston, MA, USA). All reported p-values were two-sided with a 95% confidence interval, and statistical significance was set at P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characterization of PD-L1 expression in EGFR-mutated NSCLC\u003c/h2\u003e \u003cp\u003eA total of 150 patients who received third-generation EGFR-TKIs as first-line systemic therapy for advanced NSCLC between March 2019 and June 2022 at the Affiliated Cancer Hospital of Shandong First Medical University were enrolled in this study. The screening flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The demographic and clinical characteristics of the patients with EGFR mutations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 61 years (range 34\u0026ndash;92 years), 92 (61.33%) patients were female, 122 (81.33%) were never-smokers, 141 (94%) had an ECOG PS performance status\u0026thinsp;\u0026lt;\u0026thinsp;2, and 145 (96.67) were pathologically diagnosed with adenocarcinoma. Furthermore, 81 (54%) patients had an exon 19 deletion, 69 (46%) had an exon 21 L858R mutation, 98 (65.33%) were treated with osimertinib, and 52 (34.66%) were treated with aumolertinib. Baseline brain, bone, and liver metastases were seen in 67 (44.67%), 74 (49.33%), and 23 (15.33%) patients, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95(63.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(36.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedian (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(34\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(61.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(38.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122(81.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent/former smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(18.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141(94.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(6.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145(96.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR mutation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 19 deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(54.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 21 L858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(46.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-line three generations EGFR-TKIs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsimertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(65.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmonertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (34.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(44.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(55.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(49.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(50.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(15.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(84.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eEGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor; ECOG PS, eastern cooperative oncology group performance status.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the 150 patients, 89 (59.33%) were negative for PD-L1 expression (TPS\u0026thinsp;\u0026lt;\u0026thinsp;1%), 42 (28%) had weak expression (1\u0026ndash;49%), and 19 (12.66%) had strong expression (\u0026ge;\u0026thinsp;50%). PD-L1 expression correlated with age, gender, ECOG PS, and presence of brain, bone, and liver metastases at diagnosis; however, the EGFR mutation type was irrelevant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between PD-L1 expression and clinicopathologic features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003e(TPS\u0026thinsp;\u0026lt;\u0026thinsp;1%)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003cp\u003e(TPS:1\u0026ndash;49%)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003cp\u003e(TPS\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge(years) ,n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(66.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(52.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14(73.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(33.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(47.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5(26.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(60.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(69.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9(47.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(39.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(30.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10(52.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(82.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(90.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11(57.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent/former smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(17.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8 (42.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eECOG PS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(93.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(95.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e18(94.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1(5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEGFR mutation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExon 19 deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(53.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(52.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11(57.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExon 21 L858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(46.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(47.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8(42.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBrain metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(41.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9(47.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(58.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10(52.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBone metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(55.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(45.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6(31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(44.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(54.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13(68.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eliver metastases at diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(17.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1(5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(82.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(85.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e18(94.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo bone radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(74.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(78.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e15(78.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePD-L1, programmed death-ligand 1; TPS, tumor proportion score; EGFR, epidermal growth factor receptor; ECOG PS, eastern cooperative oncology group performance status\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The relationship between PD-L1 expression and clinical prognosis.\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Overall population\u003c/h2\u003e \u003cp\u003eThe median follow-up for the entire cohort was 22.12 months (95% confidence interval (CI) 20.98\u0026ndash;23.26 months), mPFS was 24.33 months, and mOS was not reached (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, there was no significant difference in survival between the 19DEL and L858R patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In the subgroup analysis, mPFS was 23.60 months (95% CI 16.61\u0026ndash;30.60 months) for patients with negative PD-L1 expression, 26.12 months (95% CI 19.05\u0026ndash;33.18 months) for patients with weak PD-L1 expression, and 16.60 months (95% CI 6.55\u0026ndash;26.64 months) for patients with strong PD-L1 expression (log-rank: negative vs. weak PD-L1 expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103; negative vs. strong PD-L1 expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103; weak vs. strong PD-L1 expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This shows that the PFS of patients with strong PD-L1 expression was significantly shorter than that of patients with weak PD-L1 expression but was unrelated to negativity. For the OS of all three patients, mOS was achieved, and no clear correlation was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, PD-L1 expression levels were stratified by TPS\u0026thinsp;=\u0026thinsp;1%, 25%, and 50% for prognostic analysis. The mPFS for PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1% vs. PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1% was 23.60 months (95% CI 16.61\u0026ndash;30.60 months) and 24.40 months (95% CI 22.44\u0026ndash;26.35 months), respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.397) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). For PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;25% vs. PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;25%, mPFS was 24.40 months (95% CI 21.51\u0026ndash;27.28 months) and 16.89 months (95% CI 6.87\u0026ndash;26.92 months), respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). For PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;50% vs. PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50%, mPFS was 25.49 months (95% CI 22.90\u0026ndash;28.08 months) and 16.60 months (95% CI 6.55\u0026ndash;26.64 months), respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). In summary, a cutoff of 1 is not superior for PD-L1 expression and the efficacy of third-generation TKIs. Higher PD-L1 expression in TKI-treated patients with advanced EGFR-mutant NSCLC was associated with a worse prognosis.\u003c/p\u003e \u003cp\u003eIn the multivariate Cox regression model, PD-L1 expression remained a significant prognostic indicator of PFS when adjusted for age at diagnosis, sex, smoking history, ECOG PS, mutation type, TKI type, and brain, bone, and liver metastasis status (hazard ratio (HR) for weak vs. strong PD-L1 expression, 0.382; 95% CI 0.169\u0026ndash;0.863, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of clinicopathological features for progression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u0026ndash;1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.606\u0026ndash;2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent/former smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.732\u0026ndash;3.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.285\u0026ndash;1.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 21 L858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 19 deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u0026ndash;1.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-line three generations EGFR-TKIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsimertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmonertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553\u0026ndash;1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain metastases at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.846\u0026ndash;2.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone metastases at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.806\u0026ndash;2.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver metastases at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.948\u0026ndash;3.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong (\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak (1\u0026ndash;49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u0026ndash;0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.292\u0026ndash;1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eEGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor; PD-L1, programmed death-ligand 1; ECOG PS, eastern cooperative oncology group performance status; CI, confidence interval; HR, hazard ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.2.2 19DEL and 21L858R subgroups\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe further analyzed the relationship between PD-L1 expression and clinical prognosis in the 19DEL and 21L858R subgroups. In the 19DEL subgroup analysis, the mPFS of negative, weak, and strong PD-L1 expression was 26.55 (95% CI 21.36\u0026ndash;31.73 months), 33.52 (95% CI 22.03\u0026ndash;45.01 months), and 16.60 months (95% CI 8.50\u0026ndash;24.69 months), respectively. The three two-by-two \u003cem\u003eP\u003c/em\u003e-values were negative vs. weak expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.637), negative vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.095), and weak vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the 21L858R subgroup analysis, the mPFS for negative, weak, and strong PD-L1 expression was 17.06 (95% CI 8.46\u0026ndash;25.66 months), 26.12 (95% CI 23.42\u0026ndash;28.81 months), and 7.17 months (95% CI 0.00\u0026ndash;23.12 months), respectively. The three two-by-two \u003cem\u003eP\u003c/em\u003e-values were negative vs. weak expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), negative vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.484), and weak vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In summary, 19DEL and 21L858R patients with strong PD-L1 expression had significantly shorter PFS than those with weak PD-L1 expression, with negativity being an independent prognostic factor. No correlation with PD-L1 was observed for OS in both groups due to the short follow-up period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Osimertinib and almonertinib Subgroups\u003c/h2\u003e \u003cp\u003eWe further analyzed the relationship between PD-L1 expression and clinical prognosis in the osimertinib and almonertinib subgroups. In the osimertinib subgroup, mPFS for negative, weak, and strong PD-L1 expression was 23.54 (95% CI 18.52\u0026ndash;34.57 months), 23.07 (95% CI 19.54\u0026ndash;30.21 months), and 16.60 months (95% CI 5.52\u0026ndash;27.99 months), respectively. The three two-by-two \u003cem\u003eP\u003c/em\u003e values were negative vs. weak expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.731), negative vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), and weak vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the almonertinib subgroup, the mPFS for negative, weak, and strong PD-L1 expression was 16.89 months (95% CI 13.33\u0026ndash;20.46 months), NA, and 12.20 months, respectively. The \u003cem\u003eP\u003c/em\u003e-values for the three comparisons were negative vs. weak expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), negative vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.840), and weak vs. strong expression, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In summary, the PFS of patients with strong PD-L1 expression in the almonertinib subgroup was significantly shorter than that of patients with weak PD-L1 expression, with negativity being an independent prognostic factor. However, osimertinib showed a similar trend, but the results were not statistically different, probably because of the small sample size. No correlation with PD-L1 was observed for OS in both groups due to the short follow-up period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eCurrently, the third-generation TKIs osimertinib, almonertinib, and furmonertinib have a more optimized structure with increased efficacy and reduced toxicity compared with first-generation TKIs and have become the standard of care for first-line treatment of EGFR mutation-positive advanced NSCLC. The FLAURA, AENEAS, and FURLONG studies have confirmed that the first-line application of third-generation TKIs achieved a significant benefit in PFS and OS compared with the 1st/2nd generation, with mPFS exceeding 20 months and mOS exceeding 33 months \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe correlation between PD-L1, a crucial immune checkpoint inhibitor efficacy predictor, and EGFR-TKI efficacy has received considerable attention. Previous studies in patients receiving 1st/2nd generation TKIs demonstrated that high PD-L1 expression leads to primary resistance and significantly shortens mPFS and mOS \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, few such studies have been conducted, and no definitive conclusions have been drawn. Therefore, this study aimed to analyze the role of PD-L1 in patients with EGFR mutations receiving first-line treatment with third-generation TKIs. Our retrospective analysis showed that high PD-L1 expression was associated with poor PFS in patients with advanced NSCLC. To the best of our knowledge, this is the largest retrospective study to date on the correlation between PD-L1 expression and the efficacy of first-line third-generation TKI in advanced NSCLC. In the analysis of the FLAURA study, the osimertinib arm exhibited an mPFS of 18.4 months with a 79% ORR in patients with advanced primary EGFR-mutated NSCLC with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1%. Conversely, PD-L1-negative patients showed an mPFS of 18.9 months with an 85% ORR. We concluded that the clinical outcomes in advanced EGFR-mutant NSCLC were not affected by PD-L1 expression status \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the FLAURA authors did not analyze the impact of different PD-L1 expression levels on clinical efficacy. In our study, we similarly found that PD-L1 bounding to 1 was not a prognostic indicator for advanced EGFR-mutant NSCLC. In our study, mPFS was 23.60 months in patients with advanced primary EGFR-mutated NSCLC with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;1% and 24.40 months in PD-L1-negative patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.397). The mPFS for PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;25% vs. PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;25% was 24.40 months (95% CI 21.51\u0026ndash;27.28 months), and 16.89 months (95% CI 6.87\u0026ndash;26.92 months; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024). The mPFS for PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;50% vs. PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% was 25.49 months (95% CI 22.90\u0026ndash;28.08 months), and 16.60 months (95% CI 6.55\u0026ndash;26.64 months; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015).In a multicenter prospective clinical study utilizing osimertinib, Yoshimura et al. showed that PFS was shorter in patients with strong PD-L1 expression than in those with weak expression\u0026thinsp;+\u0026thinsp;negative PD-L1 (mPFS: 5.0 vs. 17.4; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Similarly, Hsu et al. reached the same conclusion that patients with strong PD-L1 expression had shorter PFS and OS than patients with weak expression\u0026thinsp;+\u0026thinsp;negative PD-L1 (mPFS: 9.7 vs. 26.5; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), (25.4 vs. NR; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In summary, for PD-L1 expression and the efficacy of three-generation TKIs, a cutoff value of 1 is not a better demarcation index. Patients with advanced NSCLC and high PD-L1 expression have a poorer prognosis after treatment with EGFR-TKIs. Differences were considered statistically significant.\u003c/p\u003e \u003cp\u003ePreclinical studies have reported that EGFR activation induces PD-L1 expression, promoting immune escape. EGFR-TKIs significantly downregulate PD-L1 expression in EGFR-mutant NSCLC cells \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In addition to MET activation, EGFR mutations may upregulate PD-L1 expression through the p-ERK 1/2/p-c-Jun and JAK-STAT pathways, with MUC 16 mutation frequency associated with high PD-L1 expression \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Notably, activating the JAK-STAT pathway may play a role in primary resistance to EGFR-TKIs \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This may account for the relatively poor prognostic outcomes in patients with high PD-L1 expression.\u003c/p\u003e \u003cp\u003eIncreasing clinical evidence suggests that high PD-L1 expression could predict primary resistance to targeted therapies. Some studies have hypothesized that higher PD-L1 expression in patients with NSCLC leads to poor prognosis for TKI therapy through the tumor microenvironment. Although the mechanism is not fully understood, PD-L1 expression levels should be determined in clinical practice at initial diagnosis in patients with or without detectable driver gene variants. Subsequent immunotherapy after the failure of TKI treatment may be a viable treatment option for patients with high PD-L1 expression. Further research is needed to focus on the optimal treatment strategies to screen and develop personalized and precise treatment plans for the high PD-L1-expressing population with driver variants.\u003c/p\u003e \u003cp\u003eOur study had some limitations. First, compared with prospective studies, this was a single-center retrospective study with unavoidable bias. Second, we studied only a Chinese population, limiting our finding\u0026rsquo;s generalizability. Third, we did not consider the effect of T790M mutation on prognosis. Fourth, we did not perform NGS to exclude the effects of coexisting tumor suppressor genes (including \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, and \u003cem\u003eARID1A\u003c/em\u003e) on the PFS and OS of third-generation TKIs for EGFR-positive advanced NSCLC. Therefore, multicenter prospective clinical studies are required to explore the relationship between PD-L1 expression and the prognosis of patients treated with third-generation TKIs.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this retrospective study showed that strong PD-L1 expression predicts poor response to EGFR-TKI therapy in patients with EGFR-mutated NSCLC. Thus, PD-L1 expression can be used as a reliable biomarker for EGFR-TKI therapy to predict patient survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Natural Science Foundation of China (82103632), the Natural Science Foundation of Shandong Province (ZR2021QH245, ZR2022LZL008), and the Facilitating New Life Public Welfare Project (GX2DH04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eAll authors have seen and approved the final version of the manuscript being submitted. They warrant that the article is the author\u0026apos;s original work, hasn\u0026apos;t received prior publication, and isn\u0026apos;t under consideration for publication elsewhere. And the authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll the data generated or analyzed during this study are included in this published article. The datasets used and/or analyzed during the current study are available from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study was approved by the Ethical Review Committee of the Affiliated Cancer Hospital of Shandong First Medical University and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all patients or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eConcept and design: N.J., Z.H., and S.Y. .data management: N.J., J.X., and X.Q.. formal analysis: N.J., L.H., and T.Y.. survey: N.J., J.X., X.Q.. method: Y.Z., Z.H., and S.Y. . \u0026nbsp;project management: \u0026nbsp;Y.Z., Z.H., and S.Y. . resources: \u0026nbsp;Y.Z., Z.H., and S.Y. .supervisors: \u0026nbsp;Y.Z., Z.H., and S.Y.. Supervision: \u0026nbsp;Y.Z., Z.H., and S.Y. . N.J. and J.X. wrote the main manuscript text.All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors thank all the patients and their fami-lies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSCHABATH M B COTEML. Cancer Progress and Priorities: Lung Cancer [J]. Cancer Epidemiol Biomarkers Prev. 2019;28(10):1563\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLEITER A, VELUSWAMY R R, WISNIVESKY JP. The global burden of lung cancer: current status and future trends [J]. Nat Rev Clin Oncol. 2023;20(9):624\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHI Y, AU J S, 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]. J Thorac Oncol. 2014;9(2):154\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGOU L Y, WU YL. Prevalence of driver mutations in non-small-cell lung cancers in the People's Republic of China [J]. Lung Cancer (Auckl). 2014;5:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTAN A C, TAN D S. W. Targeted Therapies for Lung Cancer Patients With Oncogenic Driver Molecular Alterations [J]. J Clin Oncol. 2022;40(6):611\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHENG Y, HE Y, LI W, et al. Osimertinib Versus Comparator EGFR TKI as First-Line Treatment for EGFR-Mutated Advanced NSCLC: FLAURA China, A Randomized Study [J]. Target Oncol. 2021;16(2):165\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLU S, DONG X. AENEAS: A Randomized Phase III Trial of Aumolertinib Versus Gefitinib as First-Line Therapy for Locally Advanced or MetastaticNon-Small-Cell Lung Cancer With EGFR Exon 19 Deletion or L858R Mutations [J]. J Clin Oncol. 2022;40(27):3162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHI Y, CHEN G, WANG X, et al. Furmonertinib (AST2818) versus gefitinib as first-line therapy for Chinese patients with locally advanced or metastatic EGFR mutation-positive non-small-cell lung cancer (FURLONG): a multicentre, double-blind, randomised phase 3 study [J]. Lancet Respir Med. 2022;10(11):1019\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOHAEGBULAM K C ASSALA, LAZAR-MOLNAR E, et al. Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway [J]. Trends Mol Med. 2015;21(1):24\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHACK S P, ZHU A X WANGY. Augmenting Anticancer Immunity Through Combined Targeting of Angiogenic and PD-1/PD-L1 Pathways: Challenges and Opportunities [J]. Front Immunol. 2020;11:598877.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDONG Z Y SUS, XIE Z, 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]. J Thorac Oncol. 2018;13(11):1668\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKANG M, PARK C, KIM S H, et al. Programmed death-ligand 1 expression level as a predictor of EGFR tyrosine kinase inhibitor efficacy in lung adenocarcinoma [J]. Transl Lung Cancer Res. 2021;10(2):699\u0026ndash;711.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU J, ITCHINS M, NAGRIAL A, et al. Relationship between PD-L1 expression and outcome in EGFR-mutant lung cancer patients treated with EGFR tyrosine kinase inhibitors [J]. Lung Cancer. 2021;155:28\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHSU K H, HUANG Y H, TSENG JS, et al. High PD-L1 expression correlates with primary resistance to EGFR-TKIs in treatment naive advanced EGFR-mutant lung adenocarcinoma patients [J]. Lung Cancer. 2019;127:37\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSORIA JC, OHE Y, VANSTEENKISTE J, et al. Osimertinib in Untreated EGFR-Mutated Advanced Non-Small-Cell Lung Cancer [J]. N Engl J Med. 2018;378(2):113\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG PENGS, ZHANG R. EGFR-TKI resistance promotes immune escape in lung cancer via increased PD-L1 expression [J]. Mol Cancer. 2019;18(1):165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBROWN H, VANSTEENKISTE J, NAKAGAWA K, et al. Programmed Cell Death Ligand 1 Expression in Untreated EGFR Mutated Advanced NSCLC and Response to Osimertinib Versus Comparator in FLAURA [J]. J Thorac Oncol. 2020;15(1):138\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYOSHIMURA A, YAMADA T, OKUMA Y, et al. Impact of tumor programmed death ligand-1 expression on osimertinib efficacy in untreated EGFR-mutated advanced non-small cell lung cancer: a prospective observational study [J]. Transl Lung Cancer Res. 2021;10(8):3582\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHSU K H, TSENG J S, YANG T Y, et al. PD-L1 strong expressions affect the clinical outcomes of osimertinib in treatment naive advanced EGFR-mutant non-small cell lung cancer patients [J]. Sci Rep. 2022;12(1):9753.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAKBAY E A, KOYAMA S, CARRETERO J, et al. Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors [J]. Cancer Discov. 2013;3(12):1355\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAZUMA K, OTA K, KAWAHARA A, et al. Association of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung cancer [J]. Ann Oncol. 2014;25(10):1935\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN N, FANG W, ZHAN J, et al. Upregulation of PD-L1 by EGFR Activation Mediates the Immune Escape in EGFR-Driven NSCLC: Implication for Optional Immune Targeted Therapy for NSCLC Patients with EGFR Mutation [J]. J Thorac Oncol. 2015;10(6):910\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAIGI M, ALBURQUERQUE-BEJAR J J, MC LEER-FLORIN A, et al. MET-Oncogenic and JAK2-Inactivating Alterations Are Independent Factors That Affect Regulation of PD-L1 Expression in Lung Cancer [J]. Clin Cancer Res. 2018;24(18):4579\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, EGFR tyrosine kinase inhibitor, programmed death-ligand 1","lastPublishedDoi":"10.21203/rs.3.rs-3956319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3956319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThird-generation tyrosine kinase inhibitors (TKIs) are the standard treatment for advanced epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma. In first/second generation EGFR-TKIs, strong programmed death ligand 1 (PD-L1) expression contributes to primary resistance, significantly affecting patient prognosis. Despite this, the relationship between PD-L1 expression levels and third-generation TKIs remains unclear.\u003c/p\u003e\u003ch2\u003ePatients and Methods:\u003c/h2\u003e \u003cp\u003e This retrospective cohort study reviewed patients with advanced NSCLC who received third-generation EGFR-TKIs as first-line systemic therapy at the Shandong Cancer Hospital between March 2019 and June 2022. The EGFR status of the patients was assessed using amplification refractory mutation system fluorescence quantitative polymerase chain reaction, and the PD-L1 expression level was evaluated using Dako 22 C3 immunohistochemical staining. The Kaplan\u0026ndash;Meier method was used for survival analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 150 patients were included in this study. PD-L1 expression was negative (PD-L1 tumor proportion score\u0026thinsp;\u0026lt;\u0026thinsp;1%) in 89 cases, weak (1\u0026ndash;49%) in 42 cases, and strong (\u0026ge;\u0026thinsp;50%) in 19 cases. The median follow-up period for the entire cohort was 22.12 months (median progression-free survival [mPFS]: 24.33 months); the median overall survival was not reached. mPFS for patients with negative, weak, and strong PD-L1 expressions was 23.60, 26.12, and 16.60 months, respectively. The mPFS for strong PD-L1 expression was significantly shorter than that for with weak PD-L1 expression but was not associated with negativity, particularly in the 19DEL and 21L858R subgroups. PFS was significantly shorter in patients with strong PD-L1 expression in both subgroups (19DEL and 21L858R) than in those with weak PD-L1 expression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eStrong PD-L1 expression in tumor cells influenced the clinical outcomes of patients with advanced NSCLC treated with third-generation EGFR-TKIs. Stronger PD-L1 expression in TKI-treated patients with advanced first-line EGFR-mutated NSCLC was associated with worse PFS.\u003c/p\u003e","manuscriptTitle":"Strong programmed cell death ligand-1 affect clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 21:23:50","doi":"10.21203/rs.3.rs-3956319/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3b11bbd-d649-4ee1-8c22-b13455df7b80","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T14:24:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 21:23:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3956319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3956319","identity":"rs-3956319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}