{"paper_id":"2c29a898-5cb9-4c8a-bc0d-90a8bc366e8f","body_text":"Enhancing First-Line TKI Efficacy in PD-L1-Positive EGFR-Mutated NSCLC: The Role of Antiangiogenic Agents | 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 Enhancing First-Line TKI Efficacy in PD-L1-Positive EGFR-Mutated NSCLC: The Role of Antiangiogenic Agents Xuanhong Jin, Yang Pan, Cheng cheng, Hangchen Shen, Chongya Zhai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872785/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: In individuals receiving treatment with epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs), those exhibiting positive PD-L1 expression might experience reduced progression-free survival (PFS). However, the effects on overall survival (OS) and the determination of efficacious treatment approaches are still not well-defined. Methods: In our retrospective study, we examined data from 201 NSCLC patients with advanced EGFR mutations, treated at two centers of Shaw Hospital in Zhejiang, China. This analysis covered a period from January 1, 2013, to April 30, 2023. Results: Patients with PD-L1 positivity exhibited a markedly shorter average PFS (9.2 months compared to 18.0 months, P<0.001) and OS (43.3 months versus 69.1 months, P=0.0011) relative to those without PD-L1 expression. This difference in both PFS and OS remained statistically significant even after adjusting for multiple factors (P<0.001 for PFS and P=0.002 for OS). In the PD-L1-positive cohort, introducing antiangiogenic therapy in the first line of treatment significantly extended both PFS (increasing from 8.6 to 25.7 months, P=0.03) and OS (from 29.7 to 53.5 months, P=0.026). Post-first-line TKI therapy, 39.3% of PD-L1-positive patients and 56.1% of PD-L1-negative patients developed the T790M mutation (P=0.157), with no notable difference in PFS from second-line TKI treatments between the groups (9.3 vs. 14.7 months, P=0.16). Additionally, subsequent immunotherapy markedly prolonged OS in the PD-L1-positive group (from 42 to 68.4 months, P=0.046). However, for PD-L1-negative patients, neither antiangiogenic therapy nor later-line immunotherapy demonstrated significant benefits in PFS or OS. Conclusion: Individuals exhibiting positive PD-L1 status generally experience reduced PFS and OS. Implementing antiangiogenic treatments or subsequent combined immunotherapy has shown effectiveness in enhancing outcomes for these patients. NSCLC EGFR TKIs PD-L1 Antiangiogenic Therapy Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), accounting for approximately 50% of cases in Asians and 25% in Caucasians ( 1 ). These mutations are particularly prevalent in East Asian lung adenocarcinoma patients, females, and non-smokers ( 2 ). The introduction of EGFR tyrosine kinase inhibitors (TKIs) has transformed the therapeutic approach for advanced NSCLC patients with EGFR mutations, establishing them as the primary treatment option and significantly enhancing survival ( 3 ). Despite the development of third-generation TKIs targeting the T790M resistance mutation ( 4 ), early resistance continues to be a major issue for many patients. Current research is focused on understanding the causes of this resistance and finding effective ways to counter it. In the precision medicine era, the use of immune checkpoint inhibitors (ICIs) has significantly altered the approach to treating NSCLC ( 5 ). However, in patients with EGFR-mutant NSCLC, PD-L1 expression is generally lower ( 6 ), which correlates with less effective responses to ICIs in this subset ( 7 ). Current meta-analyses indicate that patients with PD-L1 positivity tend to have shorter progression-free survival (PFS) when treated with first-line EGFR TKIs ( 8 ). This is further evidenced by various studies examining the relationship between PFS, PD-L1 expression, and the use of third-generation TKIs ( 9 , 10 , 11 ). The link between PD-L1 expression and overall survival (OS) in EGFR mutation-positive patients remains unclear. Some research suggests that PD-L1 positivity could be indicative of a poorer prognosis in these patients ( 12 , 13 ). There is also research that points to PD-L1 positivity being associated with a reduced occurrence of the T790M mutation following first-line TKI therapy ( 14 , 15 ), which might affect the selection and effectiveness of subsequent second-line treatments. Identifying the particular subgroups in which PD-L1 status significantly influences the efficacy of first-line TKI therapy, as well as assessing optimal therapies for PD-L1 positive patients with EGFR mutations, are areas where research findings are currently limited. This study looks retrospectively at the prognostic differences between PD-L1 positive and negativepatients with EGFR mutations who received TKI therapy. It evaluates the impact on various subgroups and the possible influence of different treatment combinations. Methods Research Design and Participant Selection This retrospective study was conducted at two facilities of Sir Run Run Shaw Hospital at Zhejiang University (Qingchun Hospital and Xiasha Hospital). It was ethically approved by the Ethics Committee of Sir Run Run Shaw Hospital, affiliated with the Zhejiang University School of Medicine, under the reference number 2024-2002-01. This approval ensured compliance with the ethical guidelines stipulated in the Declaration of Helsinki. Due to the retrospective nature of the research, the requirement for informed consent was waived by the Ethics Committee of Sir Run Run Shaw Hospital. This research involved 201 lung cancer patients, enrolled between January 1, 2013, and April 30, 2023. The criteria for inclusion were: ( 1 ) Histologically verified NSCLC, ( 2 ) Stage III or IV lung cancer classification as per the 8th edition of the AJCC staging system, ( 3 ) Detection of an EGFR mutation, either an exon 21 L858R substitution or an exon 19 deletion, and ( 4 ) Initial treatment with TKIs. Exclusion criteria encompassed patients with alternate mutations or those who had undergone systemic treatment before starting TKI therapy. Data collection in this study covered various demographic and clinical factors, including age, sex, smoking status, Eastern Cooperative Oncology Group Performance Status (ECOG PS) scores, clinical stage, and initial presence of brain or bone metastases. The study also meticulously documented EGFR mutation status, any subsequent resistance mutations, and PD-L1 expression levels. Details regarding the treatment regimen were comprehensively recorded, focusing on the first-line therapy administered to participants. The study tracked PFS during and after the TKI treatment period, alongside OS post-TKI therapy. PFS was measured from the beginning of TKI treatment until disease progression or death, whereas OS was determined from the start of TKI therapy until death. PD-L1 Expression and EGFR Mutation Analysis PD-L1 immunohistochemistry (IHC) expression in tumor cells was determined using either the Ventana SP263 monoclonal antibody kit or the Dako 22C3 pharmDx kit. PD-L1 expression was categorized as positive when it was equal to or exceeded 1%, and as negative when it was below this threshold. For identifying EGFR mutations, our facility's pathology department, or a third-party genetic testing service accredited by the College of American Pathologists (CAP), conducts genetic alteration tests on tumor tissues or cells. These samples are typically obtained through biopsy, surgical resection, or the centrifugation of pleural effusion sediments. The testing methodologies employed include NGS (Illumina) and polymerase chain reaction (PCR) techniques. Statistical analyses methods For assessing differences in clinical characteristics between PD-L1 subgroups, the chi-square test was utilized. PFS and OS were estimated using the Kaplan-Meier method for survival analysis. Subgroup and interaction tests were conducted using the \"jstable\" function in R software. The Cox proportional hazards model was employed to evaluate survival time differences in PFS. For the creation of nomograms and calibration curves, the \"rms\" package in R was used. Receiver operating characteristic (ROC) curves were generated using the \"timeROC\" package. Statistical significance was determined using two-tailed tests, with P < 0.05 as the threshold for significance, except in interaction tests where P < 0.1 was considered significant. Results Patient characteristics The clinical characteristics of the 201 patients included in this study are described in Table 1 , the median follow-up of the patients was 28 (1.4–112) months, all patients had adenocarcinomas, 129 (64.1%) patients were negative for PD-L1 status while 72 (35.9%) patients were positive for PD-L1 expression, 154 (76.6%) of them used 22C3 antibody test while 47 (23.4%) patients were tested with SP263 antibody, the median age of the patients was 64 (39–86) years, 108 (53.7%) were males and 93 (46.3%) were females, 153 (76.1%) patients had no history of smoking, 193 (96%) patients had an ECOG score between 0–1, 173 (86%) patients were stage IV while 28 (14%) patients were stage III, 47 (23.4%) and 86 (42.8%) patients were diagnosed with brain metastases and bone metastases at baseline, respectively. Genetic analysis revealed that 100 patients (49.8%) had the 19DEL mutation, whereas 101 patients (50.2%) exhibited the L858R mutation. Table 1 Clinical characteristics for all patients. Characteristics PD-L1(-) (N = 129) PD-L1(+) (N = 72) P-value Gender Female 59 (45.7%) 34 (47.2%) 0.96 Male 70 (54.3%) 38 (52.8%) Age <=65 75 (58.1%) 35 (48.6%) 0.25 >65 54 (41.9%) 37 (51.4%) Smokinghistory No 101 (78.3%) 52 (72.2%) 0.43 Yes 28 (21.7%) 20 (27.8%) ECOG 0 55 (42.6%) 24 (33.3%) 0.43 1 69 (53.5%) 45 (62.5%) 2 5 (3.9%) 3 (4.2%) Stage III 23 (17.8%) 5 (6.9%) 0.054 IV 106 (82.2%) 67 (93.1%) Brain metastasis No 103 (79.8%) 51 (70.8%) 0.20 Yes 26 (20.2%) 21 (29.2%) Bone metastasis No 81 (62.8%) 34 (47.2%) 0.047 Yes 48 (37.2%) 38 (52.8%) IHC 22C3 102 (79.1%) 52 (72.2%) 0.35 SP263 27 (20.9%) 20 (27.8%) Mutation 19DEL 68 (52.7%) 32 (44.4%) 0.33 L858R 61 (47.3%) 40 (55.6%) TKI Generation 1 72 (55.8%) 29 (40.3%) 0.11 2 7 (5.4%) 5 (6.9%) 3 50 (38.8%) 38 (52.8%) ECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor. A total of 88 patients (43.7%) received a third-generation TKI as their first-line treatment. Antiangiogenic therapy was used in 95 patients (47.3%), with 15 of these (15.9%) receiving it as a first-line treatment. Immunotherapy was administered to 27 patients (13.4%), None received it as their initial treatment. Patients with PD-L1-positive and PD-L1-negative conditions essentially share broadly similar clinical features. PD-L1-positive patients had higher baseline tumor burden, such as higher baseline stage (P = 0.054) and bone metastases (P = 0.047). Correlation Between PD-L1 Status and Patient Outcomes In the entire study group, the PFS was 15.1 months (ranging from 12.5 to 17.5 months), and the OS was 56.6 months (ranging from 44.9 to 72.9 months). Notably, patients with positive PD-L1 status demonstrated significantly reduced PFS and OS compared to those with negative PD-L1 status, with median PFS at 9.2 months versus 18.0 months (P < 0.0001) and median OS at 43.3 months versus 69.1 months (P = 0.0011) as illustrated in Fig. 1 . Extensive univariate and multivariate Cox regression analyses revealed a consistent and significant link between PD-L1 status and both PFS (P < 0.001) and OS (P = 0.002), as shown in Table 2 and Table 3 respectively. Moreover, the addition of combination antiangiogenic therapy was identified as an independent variable impacting both PFS (P = 0.003) and OS (P = 0.047). Additionally, the presence of brain metastasis at diagnosis was recognized as an independent factor affecting PFS with initial-line therapy (P = 0.024). Table 2 Univariable and multivariable analysis for progression-free survival (PFS) in all patients. Univariate analysis Multivariate analysis Characteristics HR (95%CI) P-value HR (95%CI) P-value PD-L1 (-) Reference Reference (+) 2.29 (1.65–3.18) P < 0.001 2.10 (1.50–2.95) P < 0.001 Gender Female Reference - Male 1.00 (0.73–1.35) P = 0.979 - Age <=65 Reference - > 65 0.84 (0.62–1.14) P = 0.269 - Smokinghistory No Reference - Yes 1.01 (0.70–1.45) P = 0.965 - ECOG 0 Reference Reference 1–2 1.41 (1.03–1.95) P = 0.034 1.34 (0.96–1.85) P = 0.083 Stage III Reference Reference IV 1.91 (1.21–3.04) P = 0.006 1.55 (0.94–2.54) P = 0.085 Brain No Reference Reference Yes 1.83 (1.28–2.62) P < 0.001 1.55 (1.06–2.27) P = 0.024 Bone No Reference Reference Yes 1.46 (1.06–2.01) P = 0.019 1.17 (0.82–1.66) P = 0.390 IHC 22C3 Reference - SP263 0.77 (0.53–1.13) P = 0.178 - Mutation 19DEL Reference - L858R 1.09 (0.80–1.49) P = 0.588 - TKI Generation 1–2 Reference - 3 1.01 (0.73–1.39) P = 0.944 - 1stLine AntiAngiogenic No Reference Reference Yes 0.57 (0.37–0.88) P = 0.011 0.51 (0.32–0.79) P = 0.003 ECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor. Table 3 Univariable and multivariable analysis for overall survival (OS) in all patients. Univariate analysis Multivariate analysis Characteristics HR (95%CI) P-value HR (95%CI) P-value PD-L1 (-) Reference Reference (+) 2.21 (1.36–3.59) P = 0.001 2.15 (1.31–3.52) P = 0.002 Gender Female Reference - Male 1.35 (0.85–2.13) P = 0.207 - Age <=65 Reference - > 65 1.16 (0.74–1.82) P = 0.517 - Smokinghistory No Reference - Yes 1.29 (0.76–2.19) P = 0.353 - ECOG 0 Reference - 1–2 1.04 (0.66–1.64) P = 0.859 - Stage III Reference - IV 1.27 (0.68–2.35) P = 0.451 - Brain No Reference Reference Yes 1.62 (0.93–2.81) P = 0.087 1.39 (0.78–2.46) P = 0.265 Bone No Reference Reference Yes 1.68 (1.06–2.65) P = 0.028 1.47 (0.91–2.38) P = 0.114 IHC 22C3 Reference - SP263 0.85 (0.46–1.54) P = 0.586 - Mutation 19DEL Reference - L858R 1.13 (0.72–1.77) P = 0.585 - TKI Generation 1–2 Reference - 3 1.33 (0.78–2.28) P = 0.296 - AntiAngiogenic No Reference Reference Yes 0.61 (0.39–0.98) P = 0.040 0.62 (0.39–0.99) P = 0.047 Immunotherapy No Reference - Yes 0.66 (0.34–1.29) P = 0.226 - ECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor. Subgroup analyses We subsequently conducted subgroup analyses focusing on PFS and overall survival OS as the endpoint events (Fig. 2 ). For the PFS subgroup, almost all subgroups showed shorter PFS in PD-L1(+) patients. However, no significant survival differences were detected between PD-L1 (+) and (-) groups within the cohort undergoing first-line antiangiogenic combination therapy (PD-L1 (+) vs. (-): HR 1.27, P = 0.641; P for interaction = 0.096). In the OS subgroup, we found no survival differences between PD-L1 (+) and (-) groups across various patient categories. These categories included female patients, those aged 65 years or younger, individuals with a smoking history, patients at stage III, those with baseline brain metastases, patients tested with the SP263 antibody, those using third-generation TKIs, and patients undergoing antiangiogenic therapy or immunotherapy during their treatment course. Notably, this lack of difference was especially pronounced in patients with baseline brain metastases (PD-L1 (+) vs. (-): HR 1.04, P = 0.948; P for interaction = 0.063), those on antiangiogenic therapy (PD-L1 (+) vs. (-): HR 1.42, P = 0.341; P for interaction = 0.081), and patients receiving immunotherapy (PD-L1 (+) vs. (-): HR 0.78, P = 0.762; P for interaction = 0.084). PD-L1 status-related survival stratified by antiangiogenic therapy In the first-line treatment, the combination of antiangiogenic therapy with TKI therapy significantly improved PFS, extending it from 13.5 months to 22.8 months (P = 0.01) (Fig. 3 A). This combination therapy also notably enhanced OS throughout the course of treatment, increasing it from 46.3 months to 66.6 months (P = 0.038) (Fig. 3 B). When analyzing PD-L1 status within the patient group receiving antiangiogenic therapy, it was observed that this first-line combination therapy was particularly effective in improving PFS in PD-L1(+) patients, raising it from 8.6 to 25.7 months (P = 0.03) (Fig. 3 C). In the same group, the combination markedly boosted OS from 29.7 to 53.5 months (P = 0.026) (Fig. 3 D), achieving a median survival time comparable to that of PD-L1(-) patients. However, for PD-L1(-) patients specifically, no significant improvement in OS and PFS was observed (P > 0.05). PD-L1 status concerning second-Line TKI therapy and subsequent immunotherapy In our study, after excluding 32 non-progressing patients, 6 with initial T790M mutations, and 48 who didn't test for the mutation post-resistance, 59 of the remaining 115 patients (51.3%) developed a T790M mutation following resistance to first-line TKI therapy. Of these, 13 of 33 PD-L1(+) patients (39.3%) and 46 of 82 PD-L1(-) patients (56.1%) (P = 0.157) had the mutation. These patients were then treated with second-line TKIs, switching to a third-generation TKI if initially treated with a first-generation TKI. Our analysis showed slightly worse but not statistically significant second-line PFS for PD-L1(+) patients (9.3 vs. 14.7 months, P = 0.16) (Fig. 4 A). Additionally, immunotherapy significantly improved overall survival in PD-L1(+) patients from 42 to 68.4 months (P = 0.046), but not in PD-L1(-) patients (P = 0.85) (Fig. 4 B). Figure 4 . (A) Progression-Free Survival (PFS) undergoing second-line tyrosine kinase inhibitor (TKI) treatment, analyzed in relation to PD-L1 status. (B) Overall Survival (OS) by PD-L1 status with immunotherapy use. Discussion The patients harboring EGFR-sensitizing mutations are among those with the best prognosis in advanced NSCLC. However, some subgroups fare worse than others, such as patients who tested positive for both EGFR mutations and PD-L1 before initial therapy. In our retrospective analysis, we examined 201 patients with EGFR-mutated NSCLC receiving first-line TKI therapy across two centers at our institution. After a thorough evaluation of clinicopathological characteristics, we discovered that patients positive for PD-L1 exhibited a shorter PFS and OS compared to PD-L1-negative patients. These findings persisted even after adjustments for multiple factors. The prognostic significance of PD-L1 expression in EGFR-mutated NSCLC patients, particularly regarding PFS with TKI, remains an area of ongoing research. Most studies ( 10 , 11 , 16 , 17 , 18 , 19 ) and meta-analyses ( 8 ) have shown that patients with positive or high PD-L1 expression generally have poorer outcomes. This aligns with our findings, which bodes ill for this group of patients. Interestingly, we have discovered that those receiving first-line combined antiangiogenic therapy negated the impact of prognosis brought by PD-L1 expression, exhibited longer PFS in the PD-L1-positive subgroup. Concerning OS, the impact of PD-L1 expression warrants further investigation. A meta-analysis indicated a marginally worse prognosis for patients with high PD-L1 expression (P = 0.070) ( 8 ). Given the proximity of this value to statistical significance and the limited number of studies, we conducted a more detailed analysis of OS across different PD-L1 statuses. Our results revealed a generally worse OS for PD-L1-positive patients overall. However, in many subgroups, this difference was not statistically significant. Notable interactions were observed in subgroups with baseline brain metastases, those undergoing combination antiangiogenic therapy, and patients receiving subsequent immunotherapy. Indicating the benefit of anti-VEGF and immune checkpoint inhibitors in this specific patient group. Previous research has primarily focused on the prognostic implications of PD-L1 expression in patients with EGFR-mutated NSCLC. However, there's a significant gap in understanding how to improve outcomes for patients exhibiting high PD-L1 expression. Our study attempts to contribute to this area of research by exploring the potential efficacy of first-line TKI in combination therapy. We focused on the efficacy of combining first-line TKIs with antiangiogenic therapy, particularly in the context of PD-L1 expression. Our findings reveal that this combination therapy significantly enhances both PFS and OS in PD-L1-positive patients, effectively neutralizing the adverse prognostic effects typically associated with high PD-L1 expression. This improvement is likely due to the observed increase in VEGFA expression among PD-L1-positive lung adenocarcinomas ( 20 ), a phenomenon also noted in various other cancers ( 21 , 22 , 23 , 24 , 25 ). Intriguingly, antiangiogenic therapy appears to counteract the pro-angiogenic factors stimulated by PD-L1, particularly through the STAT signaling pathway in NSCLC cell lines ( 26 ). To our knowledge, this is the first study to stratify the effect of antiangiogenic therapy by PD-L1 expression in EGFR mutant population, and therefore, first-line TKI in combination with antiangiogenic therapy could be a preferable option in the clinic for patients with EGFR mutations who are initially tested positive for PD-L1 or have high expression. Some studies have suggested a correlation between PD-L1 status and the prevalence of T790M mutations ( 14 , 15 ). Our research supports this association, finding that PD-L1-positive patients had fewer T790M mutations. This may give more credit to 3rd generation TKI in the first line setting. Additionally, we observed no significant prognostic differences in PD-L1-positive patients treated with second-line TKIs, a result possibly influenced by factors such as sample size and treatment modalities. Jinfei Si et al. reported that patients treated with immune checkpoint inhibitors (ICIs) in combination with antiangiogenic therapy experienced longer PFS and OS compared to those treated with ICIs and chemotherapy ( 27 ). Yujing Li et al. found that subsequent immunotherapy significantly improved survival in EGFR-mutated patients with high PD-L1 expression after resistance to therapy ( 28 ). In alignment with these findings, our analysis of subsequent ICI treatment according to different PD-L1 statuses revealed that PD-L1-positive patients benefited from immunotherapy even in the presence of EGFR mutation. This supports the use of immunotherapy in patients with high PD-L1 expression and EGFR mutation following the failure of first-line TKI treatment, a benefit not observed in PD-L1-negative patients. In the field of PD-L1 expression and its prognostic relevance, the determination of a cut-off point remains a subject of debate. While a majority of previous studies have designated 50% as the threshold to differentiate between high and low PD-L1 expression ( 9 , 16 ), there is growing evidence of prognostic variances between PD-L1 positivity and negativity ( 17 , 29 , 30 , 31 ). A recent study has proposed that a 20% cut-off point might more accurately reflect these prognostic differences ( 10 ). This suggestion is particularly relevant in light of the substantial variability in PD-L1 expression detection caused by different antibodies and experimental conditions. Given that patients with EGFR mutations often exhibit very low PD-L1 expression ( 32 ), our study has chosen the 1% criterion. This decision aims to effectively address the heterogeneity issues arising from variations in PD-L1 detection methods, thereby improving the broad applicability and relevance of our findings in the context of diverse clinical scenarios. This study, while offering valuable insights, is subject to certain limitations. Firstly, its retrospective design and relatively small sample size may introduce a degree of selection bias, albeit unintentionally. Secondly, the longer survival duration observed in our patient cohort, as compared to other studies, might contribute to potential bias in the results. Lastly, to substantiate our findings more conclusively, we advocate prospective clinical trials designed to address the role of antiangiogenetic agents in patients with both EGFR mutations and PD-L1 expression. Such future research endeavors could provide more definitive evidence and further validate our conclusions. Conclusions This study underscores the crucial role of PD-L1 status in determining the prognosis and treatment outcomes in patients with EGFR-mutated NSCLC. The findings suggest that PD-L1 positivity is linked to shorter survival, but can be effectively countered by first-line antiangiogenic therapy and subsequent immunotherapy. These insights point towards the need for personalized treatment strategies based on PD-L1 expression and encourage further research to optimize therapeutic approaches for this patient group. Declarations Statements of Ethics and Participant Consent The study received ethical clearance from the Ethics Committee of Sir Run Run Shaw Hospital, part of the School of Medicine at Zhejiang University. Due to its retrospective design and the anonymization of personal data, the committee exempted the study from the need for informed consent. Data and Material Access Data supporting this study's conclusions can be obtained upon request from the corresponding author. Due to confidentiality and ethical considerations, this data is not openly accessible. Competing interests The authors declare that they have no competing interests Funding Not applicable Authors' contributions Xuanhong Jin : Conceptualization (lead); data curation (lead); writing – original draft (lead); writing – review and editing (lead). Yang Pan : Writing – review and editing (lead). Cheng Cheng : Writing – review and editing (equal). Hangchen Shen : Resources (equal). Chongya Zhai : Resources (equal. Kailai Yin : Writing – review and editing (equal). Xinyu Zhu : Methodology (equal). Hongming Pan : Conceptualization (lead); methodology (equal); supervision (lead); writing – review and editing (lead). Liangkun You : Methodology (lead); supervision (lead); writing – review and editing (lead). Acknowledgments The authors appreciate all the patients who participated in the study. References Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. 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J Thorac Oncol. 2018;13(11):1668-75.doi:10.1016/j.jtho.2018.07.016 Masuda K, Horinouchi H, Tanaka M, Higashiyama R, Shinno Y, Sato J, et al. Efficacy of anti-PD-1 antibodies in NSCLC patients with an EGFR mutation and high PD-L1 expression. J Cancer Res Clin Oncol. 2021;147(1):245-51.doi:10.1007/s00432-020-03329-0 Koh YW, Lee SJ, Han JH, Haam S, Jung J, Lee HW. PD-L1 protein expression in non-small-cell lung cancer and its relationship with the hypoxia-related signaling pathways: A study based on immunohistochemistry and RNA sequencing data. Lung Cancer. 2019;129:41-7.doi:10.1016/j.lungcan.2019.01.004 Koh YW, Han JH, Yoon DH, Suh C, Huh J. PD-L1 expression correlates with VEGF and microvessel density in patients with uniformly treated classical Hodgkin lymphoma. Ann Hematol. 2017;96(11):1883-90.doi:10.1007/s00277-017-3115-6 Shin SJ, Jeon YK, Kim PJ, Cho YM, Koh J, Chung DH, et al. Clinicopathologic Analysis of PD-L1 and PD-L2 Expression in Renal Cell Carcinoma: Association with Oncogenic Proteins Status. Ann Surg Oncol. 2016;23(2):694-702.doi:10.1245/s10434-015-4903-7 Fujii T, Hirakata T, Kurozumi S, Tokuda S, Nakazawa Y, Obayashi S, et al. VEGF-A Is Associated With the Degree of TILs and PD-L1 Expression in Primary Breast Cancer. In Vivo. 2020;34(5):2641-6.doi:10.21873/invivo.12082 Yu J, Zhuang A, Gu X, Hua Y, Yang L, Ge S, et al. Nuclear PD-L1 promotes EGR1-mediated angiogenesis and accelerates tumorigenesis. Cell Discov. 2023;9(1):33.doi:10.1038/s41421-023-00521-7 Yang Y, Xia L, Wu Y, Zhou H, Chen X, Li H, et al. Programmed death ligand-1 regulates angiogenesis and metastasis by participating in the c-JUN/VEGFR2 signaling axis in ovarian cancer. Cancer Commun (Lond). 2021;41(6):511-27.doi:10.1002/cac2.12157 Cavazzoni A, Digiacomo G, Volta F, Alfieri R, Giovannetti E, Gnetti L, et al. PD-L1 overexpression induces STAT signaling and promotes the secretion of pro-angiogenic cytokines in non-small cell lung cancer (NSCLC). Lung Cancer. 2023;187:107438.doi:10.1016/j.lungcan.2023.107438 Si J, Hao Y, Wei J, Xiang J, Xu C, Shen Q, et al. Clinical outcomes of immune checkpoint inhibitors to treat non-small cell lung cancer patients harboring epidermal growth factor receptor mutations. BMC Pulm Med. 2023;23(1):158.doi:10.1186/s12890-023-02466-9 Li Y, Jiang H, Qian F, Chen Y, Zhou W, Zhang Y, et al. Efficacy of ICI-based treatment in advanced NSCLC patients with PD-L1≥50% who developed EGFR-TKI resistance. Front Immunol. 2023;14:1161718.doi:10.3389/fimmu.2023.1161718 Kobayashi K, Seike M, Zou F, Noro R, Chiba M, Ishikawa A, et al. Prognostic Significance of NSCLC and Response to EGFR-TKIs of EGFR-Mutated NSCLC Based on PD-L1 Expression. Anticancer Res. 2018;38(2):753-62.doi:10.21873/anticanres.12281 Inomata M, Azechi K, Takata N, Hayashi K, Tokui K, Taka C, et al. Association of Tumor PD-L1 Expression with the T790M Mutation and Progression-Free Survival in Patients with EGFR-Mutant Non-Small Cell Lung Cancer Receiving EGFR-TKI Therapy. Diagnostics (Basel). 2020;10(12).doi:10.3390/diagnostics10121006 Hsu KH, Huang YH, Tseng JS, Chen KC, Ku WH, Su KY, et al. High PD-L1 expression correlates with primary resistance to EGFR-TKIs in treatment naïve advanced EGFR-mutant lung adenocarcinoma patients. Lung Cancer. 2019;127:37-43.doi:10.1016/j.lungcan.2018.11.021 Schoenfeld AJ, Rizvi H, Bandlamudi C, Sauter JL, Travis WD, Rekhtman N, et al. Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas. Ann Oncol. 2020;31(5):599-608.doi:10.1016/j.annonc.2020.01.065 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-3872785\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":268793177,\"identity\":\"8209ef87-83be-4ec1-a8ab-2339572a0bb2\",\"order_by\":0,\"name\":\"Xuanhong Jin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuanhong\",\"middleName\":\"\",\"lastName\":\"Jin\",\"suffix\":\"\"},{\"id\":268793178,\"identity\":\"4b656956-0494-44e7-b2e2-01cf4b358590\",\"order_by\":1,\"name\":\"Yang Pan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yang\",\"middleName\":\"\",\"lastName\":\"Pan\",\"suffix\":\"\"},{\"id\":268793179,\"identity\":\"596d951a-7b97-4035-ba69-14f220b2396f\",\"order_by\":2,\"name\":\"Cheng cheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cheng\",\"middleName\":\"\",\"lastName\":\"cheng\",\"suffix\":\"\"},{\"id\":268793180,\"identity\":\"c7a9ff6f-7906-46de-a17b-9c18e6c4d52f\",\"order_by\":3,\"name\":\"Hangchen Shen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hangchen\",\"middleName\":\"\",\"lastName\":\"Shen\",\"suffix\":\"\"},{\"id\":268793181,\"identity\":\"d596ff33-b12f-48d0-9471-7959a464b3a5\",\"order_by\":4,\"name\":\"Chongya Zhai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chongya\",\"middleName\":\"\",\"lastName\":\"Zhai\",\"suffix\":\"\"},{\"id\":268793182,\"identity\":\"1aa519cc-ec4c-425b-96b0-81967eded972\",\"order_by\":5,\"name\":\"Kailai Yin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kailai\",\"middleName\":\"\",\"lastName\":\"Yin\",\"suffix\":\"\"},{\"id\":268793183,\"identity\":\"b400c4a6-019d-4c0c-9cc3-407010d5f12f\",\"order_by\":6,\"name\":\"Xinyu Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinyu\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":268793184,\"identity\":\"2cc2b500-b4a1-4465-a2cd-3eb3d1c39e05\",\"order_by\":7,\"name\":\"Hongming Pan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACNhjJ+ICBB8RMIF4LswFpWkAMCQhNQIs5+9ljDz7uqI3mk26/VvlD5jADP3uOAcPPHbi1WPbkpRvOPHM8t03mTNkNCZ7DDJI9bwwYe8/g1mJwIMdMmrftWG6bRE7aDQOgFoMbOQbMjG14tJx/g9BSkADUYk9Qyw2wLTVALenHGA6AbJEgoMVyxhszyZltB0C2MEs28KTzSJx5VnCwF48Wc/4cM4mPbXW582ekP/z4s8dajr89eeODn/gcBqEOAzGPAQNjDyQyD+DWANdSB8TsDxgYfuBTOwpGwSgYBSMVAACiS0+ub2UdygAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hongming\",\"middleName\":\"\",\"lastName\":\"Pan\",\"suffix\":\"\"},{\"id\":268793185,\"identity\":\"46fc93b3-5fd9-416b-aa84-3ca254c654bd\",\"order_by\":8,\"name\":\"Liangkun You\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liangkun\",\"middleName\":\"\",\"lastName\":\"You\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-17 11:44:15\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3872785/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3872785/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":50168110,\"identity\":\"4139ea42-2d35-441b-8671-cc65fa92719c\",\"added_by\":\"auto\",\"created_at\":\"2024-01-25 15:21:20\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1799260,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Progression-Free Survival (PFS) and (B) Overall Survival (OS) undergoing first-line tyrosine kinase inhibitor (TKI) treatment, analyzed in relation to PD-L1 status.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3872785/v1/da57542c9fadb4a1a5c054da.jpg\"},{\"id\":50168108,\"identity\":\"755eb9b7-3ac8-4140-bf80-582effb315dd\",\"added_by\":\"auto\",\"created_at\":\"2024-01-25 15:21:20\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1069600,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSubgroup analysis of PD-L1 status impact on (A) Progression-Free Survival (PFS) and (B) Overall Survival (OS) across different subgroups in EGFR-Mutant lung cancer patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3872785/v1/bc3129f9a0482fd9f5e85b5d.jpg\"},{\"id\":50168109,\"identity\":\"e369b777-7c19-47f7-bace-6f309a2285b9\",\"added_by\":\"auto\",\"created_at\":\"2024-01-25 15:21:20\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4198471,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Progression-Free Survival (PFS) for first-line TKI with/without anti-angiogenics. (B) Overall Survival (OS) with anti-angiogenics across all treatments. (C-D) PFS and OS by PD-L1 status with anti-angiogenic use.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3872785/v1/9999a917e42365ee3ddab563.jpg\"},{\"id\":50168111,\"identity\":\"09095255-61a0-47e7-bb19-8f481efdca7a\",\"added_by\":\"auto\",\"created_at\":\"2024-01-25 15:21:20\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1985718,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Progression-Free Survival (PFS) undergoing second-line tyrosine kinase inhibitor (TKI) treatment, analyzed in relation to PD-L1 status. (B) Overall Survival (OS) by PD-L1 status with immunotherapy use.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3872785/v1/193f9b8fdcae5fad0602b2a3.jpg\"},{\"id\":50415284,\"identity\":\"1a205267-1a23-41b5-b5bf-def86024408e\",\"added_by\":\"auto\",\"created_at\":\"2024-01-31 08:07:32\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":821423,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3872785/v1/d7d0408f-5b09-4cea-bb01-774a825f0e3e.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Enhancing First-Line TKI Efficacy in PD-L1-Positive EGFR-Mutated NSCLC: The Role of Antiangiogenic Agents\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eMutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), accounting for approximately 50% of cases in Asians and 25% in Caucasians (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). These mutations are particularly prevalent in East Asian lung adenocarcinoma patients, females, and non-smokers (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). The introduction of EGFR tyrosine kinase inhibitors (TKIs) has transformed the therapeutic approach for advanced NSCLC patients with EGFR mutations, establishing them as the primary treatment option and significantly enhancing survival (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Despite the development of third-generation TKIs targeting the T790M resistance mutation (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e), early resistance continues to be a major issue for many patients. Current research is focused on understanding the causes of this resistance and finding effective ways to counter it.\\u003c/p\\u003e \\u003cp\\u003eIn the precision medicine era, the use of immune checkpoint inhibitors (ICIs) has significantly altered the approach to treating NSCLC (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). However, in patients with EGFR-mutant NSCLC, PD-L1 expression is generally lower (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e), which correlates with less effective responses to ICIs in this subset (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Current meta-analyses indicate that patients with PD-L1 positivity tend to have shorter progression-free survival (PFS) when treated with first-line EGFR TKIs (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). This is further evidenced by various studies examining the relationship between PFS, PD-L1 expression, and the use of third-generation TKIs (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). The link between PD-L1 expression and overall survival (OS) in EGFR mutation-positive patients remains unclear. Some research suggests that PD-L1 positivity could be indicative of a poorer prognosis in these patients (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). There is also research that points to PD-L1 positivity being associated with a reduced occurrence of the T790M mutation following first-line TKI therapy (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), which might affect the selection and effectiveness of subsequent second-line treatments.\\u003c/p\\u003e \\u003cp\\u003eIdentifying the particular subgroups in which PD-L1 status significantly influences the efficacy of first-line TKI therapy, as well as assessing optimal therapies for PD-L1 positive patients with EGFR mutations, are areas where research findings are currently limited. This study looks retrospectively at the prognostic differences between PD-L1 positive and negativepatients with EGFR mutations who received TKI therapy. It evaluates the impact on various subgroups and the possible influence of different treatment combinations.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eResearch Design and Participant Selection\\u003c/h2\\u003e \\u003cp\\u003eThis retrospective study was conducted at two facilities of Sir Run Run Shaw Hospital at Zhejiang University (Qingchun Hospital and Xiasha Hospital). It was ethically approved by the Ethics Committee of Sir Run Run Shaw Hospital, affiliated with the Zhejiang University School of Medicine, under the reference number 2024-2002-01. This approval ensured compliance with the ethical guidelines stipulated in the Declaration of Helsinki. Due to the retrospective nature of the research, the requirement for informed consent was waived by the Ethics Committee of Sir Run Run Shaw Hospital.\\u003c/p\\u003e \\u003cp\\u003eThis research involved 201 lung cancer patients, enrolled between January 1, 2013, and April 30, 2023. The criteria for inclusion were: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) Histologically verified NSCLC, (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) Stage III or IV lung cancer classification as per the 8th edition of the AJCC staging system, (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) Detection of an EGFR mutation, either an exon 21 L858R substitution or an exon 19 deletion, and (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e) Initial treatment with TKIs. Exclusion criteria encompassed patients with alternate mutations or those who had undergone systemic treatment before starting TKI therapy.\\u003c/p\\u003e \\u003cp\\u003eData collection in this study covered various demographic and clinical factors, including age, sex, smoking status, Eastern Cooperative Oncology Group Performance Status (ECOG PS) scores, clinical stage, and initial presence of brain or bone metastases. The study also meticulously documented EGFR mutation status, any subsequent resistance mutations, and PD-L1 expression levels.\\u003c/p\\u003e \\u003cp\\u003eDetails regarding the treatment regimen were comprehensively recorded, focusing on the first-line therapy administered to participants. The study tracked PFS during and after the TKI treatment period, alongside OS post-TKI therapy. PFS was measured from the beginning of TKI treatment until disease progression or death, whereas OS was determined from the start of TKI therapy until death.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePD-L1 Expression and EGFR Mutation Analysis\\u003c/h2\\u003e \\u003cp\\u003ePD-L1 immunohistochemistry (IHC) expression in tumor cells was determined using either the Ventana SP263 monoclonal antibody kit or the Dako 22C3 pharmDx kit. PD-L1 expression was categorized as positive when it was equal to or exceeded 1%, and as negative when it was below this threshold. For identifying EGFR mutations, our facility's pathology department, or a third-party genetic testing service accredited by the College of American Pathologists (CAP), conducts genetic alteration tests on tumor tissues or cells. These samples are typically obtained through biopsy, surgical resection, or the centrifugation of pleural effusion sediments. The testing methodologies employed include NGS (Illumina) and polymerase chain reaction (PCR) techniques.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses methods\\u003c/h2\\u003e \\u003cp\\u003eFor assessing differences in clinical characteristics between PD-L1 subgroups, the chi-square test was utilized. PFS and OS were estimated using the Kaplan-Meier method for survival analysis. Subgroup and interaction tests were conducted using the \\\"jstable\\\" function in R software. The Cox proportional hazards model was employed to evaluate survival time differences in PFS. For the creation of nomograms and calibration curves, the \\\"rms\\\" package in R was used. Receiver operating characteristic (ROC) curves were generated using the \\\"timeROC\\\" package. Statistical significance was determined using two-tailed tests, with P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 as the threshold for significance, except in interaction tests where P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 was considered significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient characteristics\\u003c/h2\\u003e \\u003cp\\u003eThe clinical characteristics of the 201 patients included in this study are described in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the median follow-up of the patients was 28 (1.4\\u0026ndash;112) months, all patients had adenocarcinomas, 129 (64.1%) patients were negative for PD-L1 status while 72 (35.9%) patients were positive for PD-L1 expression, 154 (76.6%) of them used 22C3 antibody test while 47 (23.4%) patients were tested with SP263 antibody, the median age of the patients was 64 (39\\u0026ndash;86) years, 108 (53.7%) were males and 93 (46.3%) were females, 153 (76.1%) patients had no history of smoking, 193 (96%) patients had an ECOG score between 0\\u0026ndash;1, 173 (86%) patients were stage IV while 28 (14%) patients were stage III, 47 (23.4%) and 86 (42.8%) patients were diagnosed with brain metastases and bone metastases at baseline, respectively. Genetic analysis revealed that 100 patients (49.8%) had the 19DEL mutation, whereas 101 patients (50.2%) exhibited the L858R mutation.\\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\\u003eClinical characteristics for all patients.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePD-L1(-)\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;129)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePD-L1(+)\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;72)\\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\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59 (45.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34 (47.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70 (54.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (52.8%)\\u003c/p\\u003e \\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\\u003eAge\\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;=65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75 (58.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35 (48.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e54 (41.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (51.4%)\\u003c/p\\u003e \\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\\u003eSmokinghistory\\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\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e101 (78.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (72.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28 (21.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (27.8%)\\u003c/p\\u003e \\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\\u003eECOG\\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\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e55 (42.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (33.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69 (53.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (62.5%)\\u003c/p\\u003e \\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\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (3.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (4.2%)\\u003c/p\\u003e \\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\\u003eStage\\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\\u003eIII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23 (17.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (6.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.054\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e106 (82.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (93.1%)\\u003c/p\\u003e \\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\\u003eBrain metastasis\\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\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e103 (79.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 (70.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26 (20.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (29.2%)\\u003c/p\\u003e \\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\\u003eBone metastasis\\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\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e81 (62.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34 (47.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e48 (37.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (52.8%)\\u003c/p\\u003e \\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\\u003eIHC\\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\\u003e22C3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102 (79.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (72.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSP263\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27 (20.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (27.8%)\\u003c/p\\u003e \\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\\u003eMutation\\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\\u003e19DEL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68 (52.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32 (44.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eL858R\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61 (47.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (55.6%)\\u003c/p\\u003e \\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\\u003eTKI Generation\\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\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72 (55.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29 (40.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 (5.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (6.9%)\\u003c/p\\u003e \\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\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50 (38.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (52.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eA total of 88 patients (43.7%) received a third-generation TKI as their first-line treatment. Antiangiogenic therapy was used in 95 patients (47.3%), with 15 of these (15.9%) receiving it as a first-line treatment. Immunotherapy was administered to 27 patients (13.4%), None received it as their initial treatment. Patients with PD-L1-positive and PD-L1-negative conditions essentially share broadly similar clinical features. PD-L1-positive patients had higher baseline tumor burden, such as higher baseline stage (P\\u0026thinsp;=\\u0026thinsp;0.054) and bone metastases (P\\u0026thinsp;=\\u0026thinsp;0.047).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCorrelation Between PD-L1 Status and Patient Outcomes\\u003c/h2\\u003e \\u003cp\\u003eIn the entire study group, the PFS was 15.1 months (ranging from 12.5 to 17.5 months), and the OS was 56.6 months (ranging from 44.9 to 72.9 months). Notably, patients with positive PD-L1 status demonstrated significantly reduced PFS and OS compared to those with negative PD-L1 status, with median PFS at 9.2 months versus 18.0 months (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) and median OS at 43.3 months versus 69.1 months (P\\u0026thinsp;=\\u0026thinsp;0.0011) as illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Extensive univariate and multivariate Cox regression analyses revealed a consistent and significant link between PD-L1 status and both PFS (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and OS (P\\u0026thinsp;=\\u0026thinsp;0.002), as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e respectively. Moreover, the addition of combination antiangiogenic therapy was identified as an independent variable impacting both PFS (P\\u0026thinsp;=\\u0026thinsp;0.003) and OS (P\\u0026thinsp;=\\u0026thinsp;0.047). Additionally, the presence of brain metastasis at diagnosis was recognized as an independent factor affecting PFS with initial-line therapy (P\\u0026thinsp;=\\u0026thinsp;0.024).\\u003c/p\\u003e \\u003cp\\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\\u003eUnivariable and multivariable analysis for progression-free survival (PFS) in all patients.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMultivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003ePD-L1\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(-)\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(+)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.29 (1.65\\u0026ndash;3.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.10 (1.50\\u0026ndash;2.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003e1.00 (0.73\\u0026ndash;1.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.979\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.84 (0.62\\u0026ndash;1.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.269\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmokinghistory\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.01 (0.70\\u0026ndash;1.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.965\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eECOG\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.41 (1.03\\u0026ndash;1.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.034\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.34 (0.96\\u0026ndash;1.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.083\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIII\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.91 (1.21\\u0026ndash;3.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.55 (0.94\\u0026ndash;2.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.085\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBrain\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.83 (1.28\\u0026ndash;2.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.55 (1.06\\u0026ndash;2.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.024\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBone\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.46 (1.06\\u0026ndash;2.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.17 (0.82\\u0026ndash;1.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.390\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIHC\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e22C3\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSP263\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.77 (0.53\\u0026ndash;1.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMutation\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e19DEL\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eL858R\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.09 (0.80\\u0026ndash;1.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.588\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTKI Generation\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.01 (0.73\\u0026ndash;1.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.944\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1stLine AntiAngiogenic\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.57 (0.37\\u0026ndash;0.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.51 (0.32\\u0026ndash;0.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\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\\u003eUnivariable and multivariable analysis for overall survival (OS) in all patients.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMultivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHR (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003ePD-L1\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(-)\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(+)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.21 (1.36\\u0026ndash;3.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.15 (1.31\\u0026ndash;3.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.002\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003e1.35 (0.85\\u0026ndash;2.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.207\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.16 (0.74\\u0026ndash;1.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.517\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmokinghistory\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.29 (0.76\\u0026ndash;2.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.353\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eECOG\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.04 (0.66\\u0026ndash;1.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.859\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIII\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.27 (0.68\\u0026ndash;2.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.451\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBrain\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.62 (0.93\\u0026ndash;2.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.39 (0.78\\u0026ndash;2.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.265\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBone\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.68 (1.06\\u0026ndash;2.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.47 (0.91\\u0026ndash;2.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.114\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIHC\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e22C3\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSP263\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.85 (0.46\\u0026ndash;1.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.586\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMutation\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e19DEL\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eL858R\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.13 (0.72\\u0026ndash;1.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.585\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTKI Generation\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.33 (0.78\\u0026ndash;2.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.296\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAntiAngiogenic\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.61 (0.39\\u0026ndash;0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.62 (0.39\\u0026ndash;0.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eImmunotherapy\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNo\\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 \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.66 (0.34\\u0026ndash;1.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;0.226\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eECOG, Eastern Cooperative Oncology Group; TKI, tyrosine kinase inhibitor.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSubgroup analyses\\u003c/h2\\u003e \\u003cp\\u003eWe subsequently conducted subgroup analyses focusing on PFS and overall survival OS as the endpoint events (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For the PFS subgroup, almost all subgroups showed shorter PFS in PD-L1(+) patients. However, no significant survival differences were detected between PD-L1 (+) and (-) groups within the cohort undergoing first-line antiangiogenic combination therapy (PD-L1 (+) vs. (-): HR 1.27, P\\u0026thinsp;=\\u0026thinsp;0.641; P for interaction\\u0026thinsp;=\\u0026thinsp;0.096). In the OS subgroup, we found no survival differences between PD-L1 (+) and (-) groups across various patient categories. These categories included female patients, those aged 65 years or younger, individuals with a smoking history, patients at stage III, those with baseline brain metastases, patients tested with the SP263 antibody, those using third-generation TKIs, and patients undergoing antiangiogenic therapy or immunotherapy during their treatment course. Notably, this lack of difference was especially pronounced in patients with baseline brain metastases (PD-L1 (+) vs. (-): HR 1.04, P\\u0026thinsp;=\\u0026thinsp;0.948; P for interaction\\u0026thinsp;=\\u0026thinsp;0.063), those on antiangiogenic therapy (PD-L1 (+) vs. (-): HR 1.42, P\\u0026thinsp;=\\u0026thinsp;0.341; P for interaction\\u0026thinsp;=\\u0026thinsp;0.081), and patients receiving immunotherapy (PD-L1 (+) vs. (-): HR 0.78, P\\u0026thinsp;=\\u0026thinsp;0.762; P for interaction\\u0026thinsp;=\\u0026thinsp;0.084).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePD-L1 status-related survival stratified by antiangiogenic therapy\\u003c/h2\\u003e \\u003cp\\u003eIn the first-line treatment, the combination of antiangiogenic therapy with TKI therapy significantly improved PFS, extending it from 13.5 months to 22.8 months (P\\u0026thinsp;=\\u0026thinsp;0.01) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). This combination therapy also notably enhanced OS throughout the course of treatment, increasing it from 46.3 months to 66.6 months (P\\u0026thinsp;=\\u0026thinsp;0.038) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). When analyzing PD-L1 status within the patient group receiving antiangiogenic therapy, it was observed that this first-line combination therapy was particularly effective in improving PFS in PD-L1(+) patients, raising it from 8.6 to 25.7 months (P\\u0026thinsp;=\\u0026thinsp;0.03) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). In the same group, the combination markedly boosted OS from 29.7 to 53.5 months (P\\u0026thinsp;=\\u0026thinsp;0.026) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD), achieving a median survival time comparable to that of PD-L1(-) patients. However, for PD-L1(-) patients specifically, no significant improvement in OS and PFS was observed (P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePD-L1 status concerning second-Line TKI therapy and subsequent immunotherapy\\u003c/h2\\u003e \\u003cp\\u003eIn our study, after excluding 32 non-progressing patients, 6 with initial T790M mutations, and 48 who didn't test for the mutation post-resistance, 59 of the remaining 115 patients (51.3%) developed a T790M mutation following resistance to first-line TKI therapy. Of these, 13 of 33 PD-L1(+) patients (39.3%) and 46 of 82 PD-L1(-) patients (56.1%) (P\\u0026thinsp;=\\u0026thinsp;0.157) had the mutation. These patients were then treated with second-line TKIs, switching to a third-generation TKI if initially treated with a first-generation TKI. Our analysis showed slightly worse but not statistically significant second-line PFS for PD-L1(+) patients (9.3 vs. 14.7 months, P\\u0026thinsp;=\\u0026thinsp;0.16) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). Additionally, immunotherapy significantly improved overall survival in PD-L1(+) patients from 42 to 68.4 months (P\\u0026thinsp;=\\u0026thinsp;0.046), but not in PD-L1(-) patients (P\\u0026thinsp;=\\u0026thinsp;0.85) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. (A) Progression-Free Survival (PFS) undergoing second-line tyrosine kinase inhibitor (TKI) treatment, analyzed in relation to PD-L1 status. (B) Overall Survival (OS) by PD-L1 status with immunotherapy use.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe patients harboring EGFR-sensitizing mutations are among those with the best prognosis in advanced NSCLC. However, some subgroups fare worse than others, such as patients who tested positive for both EGFR mutations and PD-L1 before initial therapy. In our retrospective analysis, we examined 201 patients with EGFR-mutated NSCLC receiving first-line TKI therapy across two centers at our institution. After a thorough evaluation of clinicopathological characteristics, we discovered that patients positive for PD-L1 exhibited a shorter PFS and OS compared to PD-L1-negative patients. These findings persisted even after adjustments for multiple factors.\\u003c/p\\u003e \\u003cp\\u003eThe prognostic significance of PD-L1 expression in EGFR-mutated NSCLC patients, particularly regarding PFS with TKI, remains an area of ongoing research. Most studies (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e) and meta-analyses (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e) have shown that patients with positive or high PD-L1 expression generally have poorer outcomes. This aligns with our findings, which bodes ill for this group of patients. Interestingly, we have discovered that those receiving first-line combined antiangiogenic therapy negated the impact of prognosis brought by PD-L1 expression, exhibited longer PFS in the PD-L1-positive subgroup.\\u003c/p\\u003e \\u003cp\\u003eConcerning OS, the impact of PD-L1 expression warrants further investigation. A meta-analysis indicated a marginally worse prognosis for patients with high PD-L1 expression (P\\u0026thinsp;=\\u0026thinsp;0.070) (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Given the proximity of this value to statistical significance and the limited number of studies, we conducted a more detailed analysis of OS across different PD-L1 statuses. Our results revealed a generally worse OS for PD-L1-positive patients overall. However, in many subgroups, this difference was not statistically significant. Notable interactions were observed in subgroups with baseline brain metastases, those undergoing combination antiangiogenic therapy, and patients receiving subsequent immunotherapy. Indicating the benefit of anti-VEGF and immune checkpoint inhibitors in this specific patient group.\\u003c/p\\u003e \\u003cp\\u003ePrevious research has primarily focused on the prognostic implications of PD-L1 expression in patients with EGFR-mutated NSCLC. However, there's a significant gap in understanding how to improve outcomes for patients exhibiting high PD-L1 expression. Our study attempts to contribute to this area of research by exploring the potential efficacy of first-line TKI in combination therapy. We focused on the efficacy of combining first-line TKIs with antiangiogenic therapy, particularly in the context of PD-L1 expression. Our findings reveal that this combination therapy significantly enhances both PFS and OS in PD-L1-positive patients, effectively neutralizing the adverse prognostic effects typically associated with high PD-L1 expression. This improvement is likely due to the observed increase in VEGFA expression among PD-L1-positive lung adenocarcinomas (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e), a phenomenon also noted in various other cancers (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). Intriguingly, antiangiogenic therapy appears to counteract the pro-angiogenic factors stimulated by PD-L1, particularly through the STAT signaling pathway in NSCLC cell lines (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). To our knowledge, this is the first study to stratify the effect of antiangiogenic therapy by PD-L1 expression in EGFR mutant population, and therefore, first-line TKI in combination with antiangiogenic therapy could be a preferable option in the clinic for patients with EGFR mutations who are initially tested positive for PD-L1 or have high expression.\\u003c/p\\u003e \\u003cp\\u003eSome studies have suggested a correlation between PD-L1 status and the prevalence of T790M mutations (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Our research supports this association, finding that PD-L1-positive patients had fewer T790M mutations. This may give more credit to 3rd generation TKI in the first line setting. Additionally, we observed no significant prognostic differences in PD-L1-positive patients treated with second-line TKIs, a result possibly influenced by factors such as sample size and treatment modalities.\\u003c/p\\u003e \\u003cp\\u003eJinfei Si et al. reported that patients treated with immune checkpoint inhibitors (ICIs) in combination with antiangiogenic therapy experienced longer PFS and OS compared to those treated with ICIs and chemotherapy (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). Yujing Li et al. found that subsequent immunotherapy significantly improved survival in EGFR-mutated patients with high PD-L1 expression after resistance to therapy (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). In alignment with these findings, our analysis of subsequent ICI treatment according to different PD-L1 statuses revealed that PD-L1-positive patients benefited from immunotherapy even in the presence of EGFR mutation. This supports the use of immunotherapy in patients with high PD-L1 expression and EGFR mutation following the failure of first-line TKI treatment, a benefit not observed in PD-L1-negative patients.\\u003c/p\\u003e \\u003cp\\u003eIn the field of PD-L1 expression and its prognostic relevance, the determination of a cut-off point remains a subject of debate. While a majority of previous studies have designated 50% as the threshold to differentiate between high and low PD-L1 expression (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e), there is growing evidence of prognostic variances between PD-L1 positivity and negativity (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). A recent study has proposed that a 20% cut-off point might more accurately reflect these prognostic differences (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). This suggestion is particularly relevant in light of the substantial variability in PD-L1 expression detection caused by different antibodies and experimental conditions. Given that patients with EGFR mutations often exhibit very low PD-L1 expression (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e), our study has chosen the 1% criterion. This decision aims to effectively address the heterogeneity issues arising from variations in PD-L1 detection methods, thereby improving the broad applicability and relevance of our findings in the context of diverse clinical scenarios.\\u003c/p\\u003e \\u003cp\\u003eThis study, while offering valuable insights, is subject to certain limitations. Firstly, its retrospective design and relatively small sample size may introduce a degree of selection bias, albeit unintentionally. Secondly, the longer survival duration observed in our patient cohort, as compared to other studies, might contribute to potential bias in the results. Lastly, to substantiate our findings more conclusively, we advocate prospective clinical trials designed to address the role of antiangiogenetic agents in patients with both EGFR mutations and PD-L1 expression. Such future research endeavors could provide more definitive evidence and further validate our conclusions.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study underscores the crucial role of PD-L1 status in determining the prognosis and treatment outcomes in patients with EGFR-mutated NSCLC. The findings suggest that PD-L1 positivity is linked to shorter survival, but can be effectively countered by first-line antiangiogenic therapy and subsequent immunotherapy. These insights point towards the need for personalized treatment strategies based on PD-L1 expression and encourage further research to optimize therapeutic approaches for this patient group.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStatements of Ethics and Participant Consent\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study received ethical clearance from the Ethics Committee of Sir Run Run Shaw Hospital, part of the School of Medicine at Zhejiang University. Due to its retrospective design and the anonymization of personal data, the committee exempted the study from the need for informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData and Material Access\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData supporting this study\\u0026apos;s conclusions can be obtained upon request from the corresponding author. Due to confidentiality and ethical considerations, this data is not openly accessible.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eXuanhong Jin\\u003c/strong\\u003e: Conceptualization (lead); data curation (lead); writing \\u0026ndash; original draft (lead); writing \\u0026ndash; review and editing (lead). \\u003cstrong\\u003eYang Pan\\u003c/strong\\u003e: Writing \\u0026ndash; review and editing (lead). \\u003cstrong\\u003eCheng Cheng\\u003c/strong\\u003e: Writing \\u0026ndash; review and editing (equal). \\u003cstrong\\u003eHangchen Shen\\u003c/strong\\u003e: Resources (equal). \\u003cstrong\\u003eChongya Zhai\\u003c/strong\\u003e: Resources (equal. \\u003cstrong\\u003eKailai Yin\\u003c/strong\\u003e: Writing \\u0026ndash; review and editing (equal). \\u003cstrong\\u003eXinyu Zhu\\u003c/strong\\u003e: Methodology (equal). \\u003cstrong\\u003eHongming Pan\\u003c/strong\\u003e: Conceptualization (lead); methodology (equal); supervision (lead); writing \\u0026ndash; review and editing (lead). \\u003cstrong\\u003eLiangkun You\\u003c/strong\\u003e: Methodology (lead); supervision (lead); writing \\u0026ndash; review and editing (lead).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors appreciate all the patients who participated in the study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eLynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129-39.doi:10.1056/NEJMoa040938\\u003c/li\\u003e\\n\\u003cli\\u003eShi Y, Au JS, Thongprasert S, Srinivasan S, Tsai CM, Khoa MT, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J Thorac Oncol. 2014;9(2):154-62.doi:10.1097/jto.0000000000000033\\u003c/li\\u003e\\n\\u003cli\\u003e. !!! INVALID CITATION !!! [3]\\u003c/li\\u003e\\n\\u003cli\\u003eMok TS, Wu YL, Ahn MJ, Garassino MC, Kim HR, Ramalingam SS, et al. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N Engl J Med. 2017;376(7):629-40.doi:10.1056/NEJMoa1612674\\u003c/li\\u003e\\n\\u003cli\\u003eMok TSK, Wu YL, Kudaba I, Kowalski DM, Cho BC, Turna HZ, et al. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet. 2019;393(10183):1819-30.doi:10.1016/s0140-6736(18)32409-7\\u003c/li\\u003e\\n\\u003cli\\u003eSoo RA, Lim SM, Syn NL, Teng R, Soong R, Mok TSK, et al. Immune checkpoint inhibitors in epidermal growth factor receptor mutant non-small cell lung cancer: Current controversies and future directions. Lung Cancer. 2018;115:12-20.doi:10.1016/j.lungcan.2017.11.009\\u003c/li\\u003e\\n\\u003cli\\u003eGainor JF, Shaw AT, Sequist LV, Fu X, Azzoli CG, Piotrowska Z, et al. EGFR Mutations and ALK Rearrangements Are Associated with Low Response Rates to PD-1 Pathway Blockade in Non-Small Cell Lung Cancer: A Retrospective Analysis. Clin Cancer Res. 2016;22(18):4585-93.doi:10.1158/1078-0432.Ccr-15-3101\\u003c/li\\u003e\\n\\u003cli\\u003ePeng Z, Lin H, Zhou K, Deng S, Mei J. Predictive value of pretreatment PD-L1 expression in EGFR-mutant non-small cell lung cancer: a meta-analysis. World J Surg Oncol. 2021;19(1):145.doi:10.1186/s12957-021-02254-x\\u003c/li\\u003e\\n\\u003cli\\u003eHsu KH, Tseng JS, Yang TY, Chen KC, Su KY, Yu SL, et al. PD-L1 strong expressions affect the clinical outcomes of osimertinib in treatment na\\u0026iuml;ve advanced EGFR-mutant non-small cell lung cancer patients. Sci Rep. 2022;12(1):9753.doi:10.1038/s41598-022-13102-7\\u003c/li\\u003e\\n\\u003cli\\u003eHamakawa Y, Agemi Y, Shiba A, Ikeda T, Higashi Y, Aga M, et al. Association of PD-L1 tumor proportion score \\u0026ge;20% with early resistance to osimertinib in patients with EGFR-mutated NSCLC. Cancer Med. 2023;12(17):17788-97.doi:10.1002/cam4.6405\\u003c/li\\u003e\\n\\u003cli\\u003eYoshimura A, Yamada T, Okuma Y, Fukuda A, Watanabe S, Nishioka N, 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. Transl Lung Cancer Res. 2021;10(8):3582-93.doi:10.21037/tlcr-21-461\\u003c/li\\u003e\\n\\u003cli\\u003eLin C, Chen X, Li M, Liu J, Qi X, Yang W, et al. Programmed Death-Ligand 1 Expression Predicts Tyrosine Kinase Inhibitor Response and Better Prognosis in a Cohort of Patients With Epidermal Growth Factor Receptor Mutation-Positive Lung Adenocarcinoma. Clin Lung Cancer. 2015;16(5):e25-35.doi:10.1016/j.cllc.2015.02.002\\u003c/li\\u003e\\n\\u003cli\\u003eLiu J, Itchins M, Nagrial A, Cooper WA, De Silva M, Barnet M, et al. Relationship between PD-L1 expression and outcome in EGFR-mutant lung cancer patients treated with EGFR tyrosine kinase inhibitors. Lung Cancer. 2021;155:28-33.doi:10.1016/j.lungcan.2021.03.004\\u003c/li\\u003e\\n\\u003cli\\u003eYang CY, Liao WY, Ho CC, Chen KY, Tsai TH, Hsu CL, et al. Association between programmed death-ligand 1 expression, immune microenvironments, and clinical outcomes in epidermal growth factor receptor mutant lung adenocarcinoma patients treated with tyrosine kinase inhibitors. Eur J Cancer. 2020;124:110-22.doi:10.1016/j.ejca.2019.10.019\\u003c/li\\u003e\\n\\u003cli\\u003eInomata M, Matsumoto M, Mizushima I, Seto Z, Hayashi K, Tokui K, et al. Association of Tumor PD-L1 Expression With Time on Treatment Using EGFR-TKIs in Patients With EGFR-Mutant Non-small Cell Lung Cancer. Cancer Diagn Progn. 2022;2(3):324-9.doi:10.21873/cdp.10112\\u003c/li\\u003e\\n\\u003cli\\u003eShiozawa T, Numata T, Tamura T, Endo T, Kaburagi T, Yamamoto Y, et al. Prognostic Implication of PD-L1 Expression on Osimertinib Treatment for EGFR-mutated Non-small Cell Lung Cancer. Anticancer Res. 2022;42(5):2583-90.doi:10.21873/anticanres.15736\\u003c/li\\u003e\\n\\u003cli\\u003eLei SY, Xu HY, Li HS, Yang YN, Xu F, Li JL, et al. Influence of PD-L1 expression on the efficacy of EGFR-TKIs in EGFR-mutant non-small cell lung cancer. Thorac Cancer. 2023;14(24):2327-37.doi:10.1111/1759-7714.15021\\u003c/li\\u003e\\n\\u003cli\\u003eSu S, Dong ZY, Xie Z, Yan LX, Li YF, Su J, et al. Strong Programmed Death Ligand 1 Expression Predicts Poor Response and De Novo Resistance to EGFR Tyrosine Kinase Inhibitors Among NSCLC Patients With EGFR Mutation. J Thorac Oncol. 2018;13(11):1668-75.doi:10.1016/j.jtho.2018.07.016\\u003c/li\\u003e\\n\\u003cli\\u003eMasuda K, Horinouchi H, Tanaka M, Higashiyama R, Shinno Y, Sato J, et al. Efficacy of anti-PD-1 antibodies in NSCLC patients with an EGFR mutation and high PD-L1 expression. J Cancer Res Clin Oncol. 2021;147(1):245-51.doi:10.1007/s00432-020-03329-0\\u003c/li\\u003e\\n\\u003cli\\u003eKoh YW, Lee SJ, Han JH, Haam S, Jung J, Lee HW. PD-L1 protein expression in non-small-cell lung cancer and its relationship with the hypoxia-related signaling pathways: A study based on immunohistochemistry and RNA sequencing data. Lung Cancer. 2019;129:41-7.doi:10.1016/j.lungcan.2019.01.004\\u003c/li\\u003e\\n\\u003cli\\u003eKoh YW, Han JH, Yoon DH, Suh C, Huh J. PD-L1 expression correlates with VEGF and microvessel density in patients with uniformly treated classical Hodgkin lymphoma. Ann Hematol. 2017;96(11):1883-90.doi:10.1007/s00277-017-3115-6\\u003c/li\\u003e\\n\\u003cli\\u003eShin SJ, Jeon YK, Kim PJ, Cho YM, Koh J, Chung DH, et al. Clinicopathologic Analysis of PD-L1 and PD-L2 Expression in Renal Cell Carcinoma: Association with Oncogenic Proteins Status. Ann Surg Oncol. 2016;23(2):694-702.doi:10.1245/s10434-015-4903-7\\u003c/li\\u003e\\n\\u003cli\\u003eFujii T, Hirakata T, Kurozumi S, Tokuda S, Nakazawa Y, Obayashi S, et al. VEGF-A Is Associated With the Degree of TILs and PD-L1 Expression in Primary Breast Cancer. In Vivo. 2020;34(5):2641-6.doi:10.21873/invivo.12082\\u003c/li\\u003e\\n\\u003cli\\u003eYu J, Zhuang A, Gu X, Hua Y, Yang L, Ge S, et al. Nuclear PD-L1 promotes EGR1-mediated angiogenesis and accelerates tumorigenesis. Cell Discov. 2023;9(1):33.doi:10.1038/s41421-023-00521-7\\u003c/li\\u003e\\n\\u003cli\\u003eYang Y, Xia L, Wu Y, Zhou H, Chen X, Li H, et al. Programmed death ligand-1 regulates angiogenesis and metastasis by participating in the c-JUN/VEGFR2 signaling axis in ovarian cancer. Cancer Commun (Lond). 2021;41(6):511-27.doi:10.1002/cac2.12157\\u003c/li\\u003e\\n\\u003cli\\u003eCavazzoni A, Digiacomo G, Volta F, Alfieri R, Giovannetti E, Gnetti L, et al. PD-L1 overexpression induces STAT signaling and promotes the secretion of pro-angiogenic cytokines in non-small cell lung cancer (NSCLC). Lung Cancer. 2023;187:107438.doi:10.1016/j.lungcan.2023.107438\\u003c/li\\u003e\\n\\u003cli\\u003eSi J, Hao Y, Wei J, Xiang J, Xu C, Shen Q, et al. Clinical outcomes of immune checkpoint inhibitors to treat non-small cell lung cancer patients harboring epidermal growth factor receptor mutations. BMC Pulm Med. 2023;23(1):158.doi:10.1186/s12890-023-02466-9\\u003c/li\\u003e\\n\\u003cli\\u003eLi Y, Jiang H, Qian F, Chen Y, Zhou W, Zhang Y, et al. Efficacy of ICI-based treatment in advanced NSCLC patients with PD-L1\\u0026ge;50% who developed EGFR-TKI resistance. Front Immunol. 2023;14:1161718.doi:10.3389/fimmu.2023.1161718\\u003c/li\\u003e\\n\\u003cli\\u003eKobayashi K, Seike M, Zou F, Noro R, Chiba M, Ishikawa A, et al. Prognostic Significance of NSCLC and Response to EGFR-TKIs of EGFR-Mutated NSCLC Based on PD-L1 Expression. Anticancer Res. 2018;38(2):753-62.doi:10.21873/anticanres.12281\\u003c/li\\u003e\\n\\u003cli\\u003eInomata M, Azechi K, Takata N, Hayashi K, Tokui K, Taka C, et al. Association of Tumor PD-L1 Expression with the T790M Mutation and Progression-Free Survival in Patients with EGFR-Mutant Non-Small Cell Lung Cancer Receiving EGFR-TKI Therapy. Diagnostics (Basel). 2020;10(12).doi:10.3390/diagnostics10121006\\u003c/li\\u003e\\n\\u003cli\\u003eHsu KH, Huang YH, Tseng JS, Chen KC, Ku WH, Su KY, et al. High PD-L1 expression correlates with primary resistance to EGFR-TKIs in treatment na\\u0026iuml;ve advanced EGFR-mutant lung adenocarcinoma patients. Lung Cancer. 2019;127:37-43.doi:10.1016/j.lungcan.2018.11.021\\u003c/li\\u003e\\n\\u003cli\\u003eSchoenfeld AJ, Rizvi H, Bandlamudi C, Sauter JL, Travis WD, Rekhtman N, et al. Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas. Ann Oncol. 2020;31(5):599-608.doi:10.1016/j.annonc.2020.01.065\\u003c/li\\u003e\\n\\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\":\"info@researchsquare.com\",\"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, TKIs, PD-L1, Antiangiogenic Therapy, Immunotherapy\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3872785/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3872785/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e In individuals receiving treatment with epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs), those exhibiting positive PD-L1 expression might experience reduced progression-free survival (PFS). However, the effects on overall survival (OS) and the determination of efficacious treatment approaches are still not well-defined.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eIn our retrospective study, we examined data from 201 NSCLC patients with advanced EGFR mutations, treated at two centers of Shaw Hospital in Zhejiang, China. This analysis covered a period from January 1, 2013, to April 30, 2023.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003ePatients with PD-L1 positivity exhibited a markedly shorter average PFS (9.2 months compared to 18.0 months, P\\u0026lt;0.001) and OS (43.3 months versus 69.1 months, P=0.0011) relative to those without PD-L1 expression. This difference in both PFS and OS remained statistically significant even after adjusting for multiple factors (P\\u0026lt;0.001 for PFS and P=0.002 for OS). In the PD-L1-positive cohort, introducing antiangiogenic therapy in the first line of treatment significantly extended both PFS (increasing from 8.6 to 25.7 months, P=0.03) and OS (from 29.7 to 53.5 months, P=0.026). Post-first-line TKI therapy, 39.3% of PD-L1-positive patients and 56.1% of PD-L1-negative patients developed the T790M mutation (P=0.157), with no notable difference in PFS from second-line TKI treatments between the groups (9.3 vs. 14.7 months, P=0.16). Additionally, subsequent immunotherapy markedly prolonged OS in the PD-L1-positive group (from 42 to 68.4 months, P=0.046). However, for PD-L1-negative patients, neither antiangiogenic therapy nor later-line immunotherapy demonstrated significant benefits in PFS or OS.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion: \\u003c/strong\\u003eIndividuals exhibiting positive PD-L1 status generally experience reduced PFS and OS. Implementing antiangiogenic treatments or subsequent combined immunotherapy has shown effectiveness in enhancing outcomes for these patients.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Enhancing First-Line TKI Efficacy in PD-L1-Positive EGFR-Mutated NSCLC: The Role of Antiangiogenic Agents\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-25 15:21:15\",\"doi\":\"10.21203/rs.3.rs-3872785/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"91df33b5-708b-4f4f-9ed8-d32905020f17\",\"owner\":[],\"postedDate\":\"January 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-01-31T07:59:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-01-25 15:21:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3872785\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3872785\",\"identity\":\"rs-3872785\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}