{"paper_id":"3b8a424f-c409-4d8a-b1d8-9cce044e70a6","body_text":"The efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer (AN-BELIEF study) | 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 The efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer (AN-BELIEF study) Jiale Wang, xinxin zhi, Jinzhang Chen, Bingzhong Zhang, Haitao Lan, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9292563/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 With the increasing progression of bevacizumab and the urgent need for anti-angiogenic strategies, we aimed to evaluate the efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer. Methods This was a retrospective real-world study. We enrolled bevacizumab-naïve patients and bevacizumab-pretreated patients from six hospitals during Jan 2018 and May 2024. Progression-free survival (PFS) was the primary outcome. Secondary outcomes included objective response rate (ORR), disease control rate (DCR) and safe outcome. Results A total of 1256 patients were screened, and 746 patients were included in the study. Firstly, we selected patients treated with anlotinib monotherapy. After propensity score matching (PSM), we divided them into beva-pretreated (n = 73) and beva-naïve group (n = 146), and no statistical differences were observed in PFS (6.43 vs. 7.60 months; HR 1.14, p = 0.334). So previous bevacizumab didn't affect the efficiency of anlotinib in non-squamous NSCLC. We further divided the bevacizumab-pretreated patients into three groups according to the post-treatment: anlotinib monotherapy (n = 108), anlotinib combination (n = 345), and other therapies (n = 119). After PSM, the PFS was similar between the anlotinib monotherapy (n = 93) and other treatment group (n = 93) (5.90 vs. 5.23 months; HR 0.96, p = 0.766), whereas the PFS was significantly longer in the anlotinib combination group (n = 297) than other treatment group (n = 99) (6.50 vs 5.23 months; HR 0.82, p = 0.048). Conclusion This study demonstrated that previous bevacizumab didn't affect the efficiency of anlotinib in non-squamous NSCLC. For beva-pretreated patients, combined with anlotinib was effective and well-tolerated. Further studies are warranted to confirm these results and explore the potential advantages of anlotinib. Anlotinib Bevacizumab Anti-angiogenic NSCLC Real-world Figures Figure 1 Figure 2 Figure 3 1. Introduction Lung cancer remained the leading cause of cancer-related mortality worldwide 1 . While targeted and systemic therapies have significantly improved survival for many patients, those who progress on these treatments continue to face poor outcomes and limited options 2 . Abnormal tumor vasculature in structure and function contributed to tumor growth, metastasis and treatment resistance 3 – 5 . Inhibition of the VEGF/VEGFR2 signaling pathway was an effective anti-angiogenic strategy, which could promote vascular normalization and improve treatment outcomes, playing an important role in advanced non-small cell lung cancer (NSCLC) 6 , 7 . Its combination with chemotherapy or immunotherapy had become a standard regimen as first-line, and tyrosine kinase inhibitors (TKIs) with antiangiogenic effects had been approved as post-line in NSCLC 8 – 11 . Among them, the most commonly used antiangiogenic agents were bevacizumab and anlotinib. Bevacizumab, a monoclonal antibody targeting vascular endothelial growth factor A (VEGFA), has emerged as a significant anti-angiogenic therapy in non-squamous NSCLC for decades 12 . Nevertheless, the efficacy was often limited by the development of resistance through multiple complex mechanisms, including autocrine VEGF signalling, hypoxia tolerance, and the recruitment of fibrocyte-like cells 13 – 15 , which led to rapid disease progression, only with further benefits of 2.7 months for bevacizumab-containing regimens in non-squamous NSCLC 10 . And the AvaALL study showed that bevacizumab across multiple lines for NSCLC didn’t significantly prolong overall survival 16 . Therefore, inevitable resistance and limited effects produced a huge bevacizumab-resistant population, leaving an urgent need for effective antiangiogenic treatment to address this growing clinical challenge. Anlotinib, a novel anti-angiogenic inhibitor with multi-targets including VEGFR, FGFR, PDGFR, and c-Kit, has received approvals for NSCLC based on the significant improvement of the ALTER0303 trial 11 , 17 . Furthermore, many clinical trials demonstrated the promising therapeutic potential of anlotinib in NSCLC when combined with other therapies, such as the CAMPASS study 18 , AUTOMAN study 19 . Preclinical evidence suggested that anlotinib could improve antitumor and antiangiogenic effects after bevacizumab resistance by inhibiting bevacizumab-induced high VEGF/VEGFR expression and blocking activation of the PI3K/AKT signaling pathway 20 . A subgroup analysis of the ALTER0303 trial further indicated that prior treatment with bevacizumab or endostatin did not compromise anlotinib's efficacy 21 . These findings highlighted anlotinib's potential to overcome anti-angiogenic resistance, but with only about ten beva-pretreated patients, warranting further systematic and large-scale investigation of the efficacy of anlotinib in bevacizumab-pretreated (bevac-pretreated) populations. Hence, we evaluated the efficacy and safety profile of anlotinib in patients with non-squamous NSCLC who have experienced progressed diseases of bevacizumab. Focusing on this challenging population, we provided valuable insights into the potential role of anlotinib as a subsequent line and its ability to overcome resistance to prior antiangiogenic treatments. 2. Methods 2.1 Study design and population This was a retrospective study, and real-world data were collected from six hospitals during January 2018 and May 2024. We enrolled bevacizumab-naïve (beva-naïve) patients only treated with anlotinib monotherapy subsequently and bevacizumab-pretreated patients with non-squamous NSCLC (due to radiographic progression or intolerance, treated with anlotinib or others subsequently). Patients were excluded if 1) they received anlotinib before progression of bevacizumab therapy; 2) received other anti-angiogenic tyrosinase inhibitors (TKI) after bevacizumab treatment, including apatinib, lenvatinib, sorafenib, regorafenib, and fruquintinib; 3) received localized therapies during the medication period, except localized palliative radiotherapy for bone metastases. Finally, according to the administration, patients were allocated to one of four groups: (1) beva-naive following anlotinib monotherapy; (2) beva-pretreated following anlotinib monotherapy; (3) beva-pretreated following other therapies; or (4) beva-pretreated following anlotinib combination (Fig. 1 ). 2.2 Procedures The study was a retrospective study based on electronic health record (HER) data, and the ethical committee exempted the patients of this study from signing the informed consent in writing. The data collection form (DCF) was generated by extracting the relevant data from the established HER information of each hospital, and all the data during the clinical treatment of the patients were collected after determining that the inclusion and exclusion criteria were met. This study did not involve prospective patient follow-up. 2.3 Outcomes PFS was the primary outcome. Secondary outcomes included objective response rate (ORR) and disease control rate (DCR). Safe outcome included adverse events such as bleeding, hypertension, myocardial ischemia, proteinuria, hand-foot syndrome (HFS), and gastrointestinal reactions. 2.4 Statistical analysis Quantitative indicators were assessed for normality. If the data did not meet the criteria for a normal distribution, the Wilcoxon rank-sum test was used to compare values between two groups. For data that satisfy normality, a t-test was applied to compare the indicators between the two groups. The statistical description for quantitative data included the mean, standard deviation, median, and the upper and lower quartiles. For qualitative or categorical indicators, the frequency and its corresponding percentage were provided in the statistical description. Comparisons of unordered categorical variables were conducted using the chi-square test or the exact probability method (Fisher's exact test). All statistical tests were two-sided. A p-value of less than or equal to 0.05 was considered statistically significant for the difference being evaluated. The group comparisons were conducted after using a propensity score matching (PSM) model based on sex, age, and number of treatment lines. 3. Results 3.1 Patient baseline characterization We screened 1,256 patients across 6 centers, and they were diagnosed with non-squamous NSCLC and treated with anlotinib or bevacizumab. According to the prespecified eligibility criteria, 213 patients were excluded because they had received anlotinib prior to bevacizumab (n = 81) or had been exposed to other antiangiogenic TKIs or local therapies (n = 132), leaving 1,043 patients for further assessment. We then excluded 297 patients due to insufficient radiologic evaluation, including those without baseline imaging before treatment initiation (n = 103) and those without twice post-treatment imaging assessments (n = 194). Ultimately, 746 patients were included in the final analytic cohort (Fig. 1 ). Within the included population, 174 patients were bevacizumab-naïve and all received anlotinib monotherapy, whereas 572 patients were bevacizumab-pretreated and were subsequently treated with anlotinib monotherapy (n = 108), other therapies (n = 119), or anlotinib-based combination regimens (n = 345) (Fig. 1 ). Given the heterogeneity in baseline characteristics, we performed propensity score matching (PSM) using age, sex, and treatment-line for all comparative analyses. Specifically, Comparison A assessed the impact of prior bevacizumab exposure on outcomes with subsequent anlotinib monotherapy using a 2:1 match (bevacizumab-naïve vs bevacizumab-pretreated: n = 146 vs n = 73). Comparisons B and C evaluated the effectiveness of anlotinib among bevacizumab-pretreated patients, including a 1:1 matched comparison of anlotinib monotherapy versus other therapies (n = 93 vs n = 93) and a 1:3 matched comparison of other therapies versus anlotinib combination therapy (n = 99 vs n = 297) (Fig. 1 ). a. Anlotinib monotherapy patients: Beva-naïve (n = 146) vs. Beva-pretreated (n = 73); b. Beva-pretreated patients: Anlotinib monotherapy (n = 93) vs. Other therapies (n = 93); c. Beva-pretreated patients: Anlotinib combination (n = 297) vs. Other therapies (n = 99); As shown in Table 1 , after propensity score matching, the clinical characteristics were well balanced between the beva-pretreated group (N = 73) and the beva-naive group (N = 146), with no statistically significant differences. The majority were both male with a mean age of 61 years. Most patients reported no smoking history and drinking history. Regarding comorbidities, hypertension was the most common condition (32.88% vs 24.66%; p = 0.199), followed by diabetes (16.44% vs 12.33%; p = 0.404), and hyperlipidemia (5.48% vs 11.64%; p = 0.144). Most patients had good performance status (ECOG 0–1: 74.0% vs 65.8%; p = 0.217). Advanced disease predominated in both groups, with stage IV accounting for 79.5% and 77.4%, respectively (p = 0.729). Anlotinib was mainly administered as second (39.7% vs 41.1%) or third (35.6% vs 41.1%) line therapy. The characteristics before matching were demonstrated at Supplemental Table 1 , with more hyperlipidemia in beva-naïve group (4.63% vs. 12.07%, p = 0.036). Table 1 Demographic and Clinical Characteristics in Patients treated with Anlotinib monotherapy after propensity score matching. Category Anlotinib monotherapy P value Beva-pretreated Beva-naïve (N = 73) (N = 146) Age; Mean (SD) 61.71 (10.13) 61.74 (11.13) 0.781 Sex 0.248 Male 37 (50.68%) 86 (58.90%) Female 36 (49.32%) 60 (41.10%) Smoking history 0.507 Yes 8 (10.96%) 12 (8.22%) No 65 (89.04%) 134 (91.78%) Drinking history 0.909 Yes 4 (5.48%) 6 (4.11%) No 69 (94.52%) 140 (95.89%) Comorbidity Hypertension 24 (32.88%) 36 (24.66%) 0.199 Diabetes 12 (16.44%) 18 (12.33%) 0.404 Coronary 2 (2.74%) 11 (7.53%) 0.266 Hyperlipidemia 4 (5.48%) 17 (11.64%) 0.144 ECOG 0.217 0–1 54 (74.0%) 96 (65.8%) 2–3 19 (26.0%) 50 (34.2%) TNM stage 0.729 Ⅲ 15 (20.5%) 33 (22.6%) Ⅳ 58 (79.5%) 113 (77.4%) Treatment line 0.465 2 29 (39.7%) 60 (41.1%) 3 26 (35.6%) 60 (41.1%) ≥ 4 18 (24.7%) 26 (17.8%) In bevacizumab-pretreated patients, a total of 572 patients were categorized by post-treatment as anlotinib monotherapy (n = 108), other therapies (n = 119), and anlotinib combination groups (n = 345) (Table 2 ). Overall, patients were older (mean age approximately 60 years across groups) and predominantly male, with a marginal imbalance in sex distribution (male: 52.78% vs 68.07% vs 63.19%; p = 0.051). Notably, the prevalence of smoking history was significantly lower in the anlotinib monotherapy group (12.04% vs. 34.45% vs.31.01%, p < 0.001). Similarly, drinking history was less common with anlotinib monotherapy (6.48% vs. 20.17% vs. 16.81%, p = 0.011). Comorbidity profiles were generally comparable across groups, with no statistically significant between-group differences. Those receiving anlotinib-based regimens were treated in the poster line (p = 0.013). For exploration of anlotinib monotherapy in beva-pretreated patients (Comparison B), the characterizations were well balanced by PSM except for more smoking and drinking patients in “other therapies” group ( Supplemental Table 2 ). For the efficiency of anlotinib combination in bevacizumab-pretreated patients (Comparison C), PSM resulted in 297 and 99 patients in each group respectively, without statistically significant differences in characteristics ( Supplemental Table 3 ). Table 2 . Demographic and Clinical Characteristics in Beva-pretreated Patients. Category Bevacizumab-pretreated P value Anlotinib monotherapy Other therapies Anlotinib combination (N = 108) (N = 119) (N = 345) Age; Mean (SD) 61.68 (9.61) 61.87 (8.31) 59.54 (10.64) 0.101 Sex 0.051 Male 57 (52.78%) 81 (68.07%) 218 (63.19%) Female 51 (47.22%) 38 (31.93%) 127 (36.81%) Smoking history < 0.001 Yes 13 (12.04%) 41 (34.45%) 107 (31.01%) No 95 (87.96%) 78 (65.55%) 238 (68.99%) Drinking history 0.011 Yes 7 (6.48%) 24 (20.17%) 58 (16.81%) No 101 (93.52%) 95 (79.83%) 287 (83.19%) Comorbidity Hypertension 35 (32.41%) 47 (39.50%) 144 (41.74%) 0.223 Diabetes 15 (13.89%) 18 (15.13%) 51 (14.78%) 0.963 Coronary 3 (2.78%) 3 (2.52%) 23 (6.67%) 0.100 Hyperlipidemia 5 (4.63%) 16 (13.45%) 43 (12.46%) 0.054 ECOG 0.568 0–1 78 (72.2%) 89 (74.8%) 266 (77.1%) 2–3 30 (27.8%) 30 (25.2%) 79 (22.9%) TNM stage 0.187 Ⅲ 19 (17.6%) 23 (19.3%) 86 (24.9%) Ⅳ 89 (82.4%) 96 (80.7%) 259 (75.1%) Treatment line 0.013 2 41 (38.0%) 66 (55.5%) 150 (43.5%) 3 41 (38.0%) 42 (35.3%) 140 (40.6%) ≥ 4 26 (24.1%) 11 (9.2%) 55 (15.9%) 3.2 Efficacy comparison Before PSM, beva-naïve patients got prolonged PFS than beva-pretreated patients when receiving anlotinib monotherapy ( 8.23 vs. 5.90 months; HR 1.41, 95% CI 1.12–1.77; p = 0.003; Supplemental Fig. 1A). The DCR rate was lower with beva-pretreated patients (59.3% vs. 72.4%; p = 0.022), and there was no significant difference for ORR (8.3% vs. 8.6%; p = 0.933, Supplemental Fig. 1B ). However, when mitigating bias by PSM, no significant difference in PFS was observed between the two groups (6.43 vs. 7.60 months; HR 1.14, 95% CI 0.88–1.47; p = 0.334; Fig. 2 A). as well as that of DCR (68.5% vs. 67.1%; p = 0.838; Fig. 2 B). and ORR (6.8% vs. 6.8%; p = 1.000; Fig. 2 B). Therefore, similar PFS and response after PSM indicated that the prior use of bevacizumab didn’t affect the efficiency of the following anlotinib. Furthermore, we decided to demonstrate the efficiency and safety of anlotinib in bevacizumab-pretreated populations. As shown in Fig. 3 A, there was no significant difference in PFS among these three groups (6.10 vs. 5.90 vs. 5.87 months; p = 0.421), as well as that of ORR (12.75% vs. 8.33% vs. 15.97%, p = 0.221, Fig. 3 B) and DCR (61.2% vs. 60.2% vs. 63.9%, Fig. 3 B). For exploration of anlotinib monotherapy in beva-pretreated patients, the anlotinib monotherapy group and the other therapies group showed no significant differences in PFS (5.90 vs. 5.23 months; HR 0.96, 95% CI 0.75–1.24; p = 0.766, Fig. 3 C). These results suggested that anlotinib monotherapy can achieve comparable PFS in bevacizumab-pretreated patients. Moreover, the anlotinib combination group demonstrated a significantly improved PFS (6.50 vs. 5.23 months; HR 0.82, 95% CI 0.67–1.00; p = 0.048; Fig. 3 D). Therefore, bevacizumab-pretreated patients will benefit more when combined with anlotinib. Meanwhile, subgroup analysis suggested that female patients (p = 0.026) aged over 65 (p = 0.028) with stage IV (p = 0.018) non-squamous NSCLC were more likely to benefit from the combination of anlotinib. However, no particular regimen was identified to obtain superior benefit from the combination ( Supplemental Fig. 2 ). 3.3 Safety As shown in Table 3 , among patients receiving anlotinib monotherapy, the overall adverse effects (AEs) profile was similar between the bevacizumab-naive (n = 108) and bevacizumab-pretreated (n = 174) groups. Gastrointestinal events were the most commonly observed AEs in both groups (5.6% vs 6.3%, p = 0.793). Notably, neurological events occurred more frequently in the bevacizumab-naive group than bevacizumab-pretreated group (4.6% vs 0.6%, p = 0.032). No statistically significant differences between them were detected for other AEs. Myocardial damage, kidney events, and subclinical hypothyroidism were not observed in either monotherapy subgroup. Within the bevacizumab-pretreated patients, comparing anlotinib monotherapy (n = 108), anlotinib combination (n = 345) was associated with significantly higher rates of allergy (1.9% vs. 7.5%; p = 0.032) and hand-foot syndrome (0.9% vs. 8.4%; p = 0.006). In the combination group, the most common AEs were gastrointestinal events (11.3%), hand-foot syndrome (8.4%), and allergy (7.5%). There was no significant difference observed in other AEs. Table 3 Adverse Events Without Propensity Score Matching. Adverse events Anlotinib Monotherapy P value Bevacizumab-pretreated P value Beva-naive Beva-pretreated Anlotinib Monotherapy Anlotinib Combination (n = 108) (n = 174) (n = 108) (n = 345) Gastrointestinal events 6 (5.6%) 11 (6.3%) 0.793 6 (5.6%) 39 (11.3%) 0.081 Allergy 2 (1.9%) 5 (2.9%) 0.711 2 (1.9%) 26 (7.5%) 0.032 Other neurological events 5 (4.6%) 1 (0.6%) 0.032 5 (4.6%) 8 (2.3%) 0.202 Respiratory events 5 (4.6%) 5 (2.9%) 0.514 5 (4.6%) 20 (5.8%) 0.643 Skeletal muscle and connective tissue events 3 (2.8%) 3 (1.7%) 0.678 3 (2.8%) 13 (3.8%) 0.772 Skin and mucosa events 1 (0.9%) 0 (0%) 0.383 1 (0.9%) 17 (4.9%) 0.087 Bleeding 4 (3.7%) 3 (1.7%) 0.434 4 (3.7%) 21 (6.1%) 0.344 Proteinuria 1 (0.9%) 0 (0%) 0.383 1 (0.9%) 5 (1.4%) 1.000 Hand-foot syndrome (HFS) 1 (0.9%) 0 (0%) 0.383 1 (0.9%) 29 (8.4%) 0.006 Myocardial damage 0 (0%) 0 (0%) - 0 (0%) 1 (0.3%) 1.000 Bone marrow suppression 1 (0.9%) 4 (2.3%) 0.652 1 (0.9%) 5 (1.4%) 1.000 Kidney events 0 (0%) 0 (0%) - 0 (0%) 1 (0.3%) 1.000 Subclinical hypothyroidism 0 (0%) 0 (0%) - 0 (0%) 2 (0.6%) 1.000 4. Discussion This is the first large study based on real-world data on the exploration of the efficacy and safety of anlotinib in non-squamous NSCLC after bevacizumab pretreatment. The results showed prior bevacizumab treatment had a limited impact on the efficacy of anlotinib monotherapy. However, compared to other therapies, anlotinib monotherapy could also achieve comparable results and anlotinib combination demonstrated significantly improved PFS in beva-pretreated patients, especially in elderly females. Expect increased HFS and allergy in anlotinib combination group, the safety profile remained consistent, with no significant differences when compared to monotherapy. The results regarding the impact of bevacizumab on anlotinib should be considered with caution. Subgroup analyses of subsequent combination therapy indicate that the efficacy of anlotinib was influenced by factors such as age, disease stage, and sex. Therefore, the different PFS between beva-naïve group and beva-pretreated group may be attributable to confounding differences. Although only disparities of comorbidity were detected before matching, the PSM enhanced comparability between groups, thereby better revealing the treatment effect 22 . Moreover, anlotinib could improve PFS of beva-pretreated patients in ALTER0303 and other studies 21 , 23 , 24 . Therefore, we concluded that bevacizumab didn’t affect the efficacy of anlotinib. Some retrospective studies shown the same results, but predominantly in single-center or small sample size 23 , 24 . We explored that in the multicenter study with a large population and demonstrated the following efficiency of the combination. Our findings provided valuable insights into the use of anlotinib in patients who have progressed on bevacizumab-containing regimens. This was particularly important as it indicated that anlotinib may be a viable treatment option for patients who have developed resistance to bevacizumab. The superior PFS observed with anlotinib combination therapy compared to other therapies highlighted the potential of incorporating anlotinib for bevacizumab-pretreated patients, which could be attributed to anlotinib's multi-target mechanism 25 , 26 , inducing sustained vascular inhibition and positive immune infiltration 27 , overcoming resistance of bevacizumab by inhibition of angiogenic bypass 28 . However, the \"other therapies\" group is likely highly heterogeneous (e.g., chemotherapy, immunotherapy, best supportive care). This heterogeneity makes the comparison less definitive. Although subgroups by different treatments revealed similar effects, elderly female patients may benefit more from combination. Align with our studies, the ALTER0303 trial showed anlotinib's efficacy in advanced NSCLC who had progressed on multiple lines of therapy 11 . Zhang et al reported that concurrent use of anlotinib overcame acquired resistance to EGFR-TKI in advanced EGFR‐mutant NSCLC patients 29 . Anlotinib plus benmelstobart also demonstrated longer PFS compared with pembrolizumab in the CAMPASS study 18 (11.0 vs. 7.1 months). These results suggested the potential of anlotinib as combination therapy with TKI or immunotherapy, and the safety profile across treatment groups suggested that anlotinib can be safely administered in beva-pretreated patients. This is crucial for maintaining quality of life in patients who may have already experienced significant treatment-related toxicities. This study has several limitations. Firstly, this was a retrospective, non-randomised study, and selection bias is unavoidable due to the missing data. However, the heterogeneity of the patient population in terms of treatment line were well balanced by PSM, enhancing the validity of our comparisons. While PSM can balance observed covariates, it cannot account for unobserved confounders 30 , 31 . Furthermore, PSM can lead to a reduction in sample size, potentially limiting statistical power for some comparisons. Although we screened patients in 6 centers, the Caucasian population and adenocarcinoma were not included. Despite these limitations, our study provided valuable real-world evidence on the efficacy and safety of anlotinib in patients previously treated with bevacizumab. Future prospective studies focusing on specific tumor types and standardized treatment protocols would be beneficial to further validate these findings. In conclusion, this study demonstrated that anlotinib monotherapy showed comparable clinical efficacy to other therapies, and the combination of anlotinib significantly improved PFS compared to other treatments. Anlotinib-containing regimens may offer enhanced benefits in beva-pretreated patients. Further studies were warranted to confirm these results and explore the potential advantages of anlotinib in this setting. Declarations 5. Author Contributions JW and XZ designed this study and drafted the manuscript. JC and BZ contributed to the acquisition and analysis of data. HL, FJ and CJ collected the data. YL, FW, YJ, and AL analyzed the data. CJ, QF and SR revised the paper. All authors contributed to this article, and the manuscript has been read and approved by all the authors. 6. Funding This study was granted by the Natural Science Foundation of Shanghai (25ZR1401308) and the Tongji University Independent and Original Basic Research Project (No. 22120240374). China Health & Medical Development Foundation (Medical Research Project, chmdf2024-xrzx02-21) National Natural Science Foundation of China (No. 82373319, No. 82172869), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0520200, 2024ZD0520206), the Science and Technology Commission of Shanghai Municipality (24Y12800300), National Natural Science Foundation of China (NO. 82203046), Clinical Research Foundation of Shanghai Pulmonary Hospital （LYRC202405）. 7. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Front Oncol 12:875888. 10.3389/fonc.2022.875888 Wang L, He Z, Yang S et al (2019) The impact of previous therapy strategy on the efficiency of anlotinib hydrochloride as a third-line treatment on patients with advanced non-small cell lung cancer (NSCLC): a subgroup analysis of ALTER0303 trial. Transl Lung Cancer Res Oct 8(5):575–583. 10.21037/tlcr.2019.09.21 Kurz CF, Krzywinski M, Altman N (2024) Propensity score matching. Nature Methods . /10/01 2024;21(10):1770–1772. 10.1038/s41592-024-02405-4 Jiang F, Li J, Kong X, Sun P, Qu H (2022) Efficacy and safety evaluations of anlotinib in patients with advanced non-small cell lung cancer treated with bevacizumab. Front Pharmacol 13:973448. 10.3389/fphar.2022.973448 Zhong Q, Liu Z (2021) Efficacy and Safety of Anlotinib in Patients with Advanced Non-Small Cell Lung Cancer: A Real-World Study. Cancer Manag Res 13:4115–4128. 10.2147/cmar.S304838 Lin B, Song X, Yang D, Bai D, Yao Y, Lu N (2018) Anlotinib inhibits angiogenesis via suppressing the activation of VEGFR2, PDGFRβ and FGFR1. Gene . 15:77–86. 10.1016/j.gene.2018.02.026 Babina IS, Turner NC (2017) Advances and challenges in targeting FGFR signalling in cancer. Nat Rev Cancer May 17(5):318–332. 10.1038/nrc.2017.8 Tang H, You T, Ge H et al (2025) Autophagy inhibition improves the efficacy of anlotinib and PD-1 inhibitors in the treatment of NSCLC. J Immunother Cancer Sep 21(9). 10.1136/jitc-2024-010812 Lu J, Zhong H, Chu T et al (2019) Role of anlotinib-induced CCL2 decrease in anti-angiogenesis and response prediction for nonsmall cell lung cancer therapy. Eur Respir J Mar 53(3). 10.1183/13993003.01562-2018 Chen Y, Liu H, Hu N et al (2023) Survival benefit of anlotinib in T790M-positive non-small-cell lung cancer patients with acquired osimertinib resistance: A multicenter retrospective study and exploratory in vitro study. Cancer Med Aug 12(15):15922–15932. 10.1002/cam4.6232 Franchetti Y (2022) Use of Propensity Scoring and Its Application to Real-World Data: Advantages, Disadvantages, and Methodological Objectives Explained to Researchers Without Using Mathematical Equations. J Clin Pharmacol Mar 62(3):304–319. 10.1002/jcph.1989 Wan F (2025) Propensity Score Matching: should we use it in designing observational studies? BMC Med Res Methodol Jan 29(1):25. 10.1186/s12874-025-02481-w Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx SupplementaryFigure1.tif SupplementaryFigure2.tif 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-9292563\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":618677195,\"identity\":\"fbca285f-4d4f-4d04-9e77-4bdf04aa0218\",\"order_by\":0,\"name\":\"Jiale Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiale\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":618677196,\"identity\":\"0a5d5496-af32-497b-9cb2-8e1534dcb040\",\"order_by\":1,\"name\":\"xinxin zhi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"xinxin\",\"middleName\":\"\",\"lastName\":\"zhi\",\"suffix\":\"\"},{\"id\":618677197,\"identity\":\"a2670c62-d2a1-4fa9-87d7-d07d9b1e8ee7\",\"order_by\":2,\"name\":\"Jinzhang Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanfang Hospital, Southern Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinzhang\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":618677198,\"identity\":\"822678cc-1bfd-40c4-84b6-0915e0bbab6c\",\"order_by\":3,\"name\":\"Bingzhong Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen Memorial Hospital, Sun Yat-sen University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bingzhong\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":618677199,\"identity\":\"7f7d4713-4886-4824-8832-1d30905efefd\",\"order_by\":4,\"name\":\"Haitao Lan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sichuan Provincial People's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haitao\",\"middleName\":\"\",\"lastName\":\"Lan\",\"suffix\":\"\"},{\"id\":618677200,\"identity\":\"4039df56-efc7-42b6-8da6-46b5cd8b2271\",\"order_by\":5,\"name\":\"Feng Jiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Renji Hospital affiliated to Shanghai Jiaotong University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Feng\",\"middleName\":\"\",\"lastName\":\"Jiao\",\"suffix\":\"\"},{\"id\":618677201,\"identity\":\"36df935a-3a11-4456-a7be-8a428a5b6b33\",\"order_by\":6,\"name\":\"Yiwei Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiwei\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":618677202,\"identity\":\"ef3dd225-da5e-4748-a221-5d2b00f22e48\",\"order_by\":7,\"name\":\"Fengying Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fengying\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":618677203,\"identity\":\"392fb0f1-4f29-423a-8e6f-085b464fc47b\",\"order_by\":8,\"name\":\"Jia Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jia\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":618677204,\"identity\":\"2779e8b0-25f8-48dc-ad8e-94634e79134c\",\"order_by\":9,\"name\":\"Aiwu Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Aiwu\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":618677210,\"identity\":\"284e1bce-51f1-4968-97fe-b36dd4463a36\",\"order_by\":10,\"name\":\"Cheng Ji\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Soochow University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cheng\",\"middleName\":\"\",\"lastName\":\"Ji\",\"suffix\":\"\"},{\"id\":618677211,\"identity\":\"0a655ab1-755b-4a0e-bd73-694f2ae7e12d\",\"order_by\":11,\"name\":\"Qiyu Fang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiyu\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":618677212,\"identity\":\"a3b5f20e-c9de-43ed-9e77-53395f1cf557\",\"order_by\":12,\"name\":\"Shengxiang Ren\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACAziLvbHxwQfStPAcbjacQZoWifQ2aQ5itJizn332mYfhnpy55MMGaQYGOzndBgJaLHvSjWfzMBQbW85ObDAuYEg2NjtAyGEH0piZeRgSEjfcTmxInsFwIHEbQS3nn4G11G+4ebDhMA9RWm5AbEkwuMHY2EyklmfMjHMYEgw3nElsZpxhQIxfzqcxM7xhSJA3OH78+Y8PFXZyBLWAABPvP7gJRCgHAcYfRCocBaNgFIyCEQoAa+U/TyB86KIAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Shanghai Pulmonary Hospital, School of Medicine, Tongji University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Shengxiang\",\"middleName\":\"\",\"lastName\":\"Ren\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-01 13:26:25\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9292563/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9292563/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106835534,\"identity\":\"79449ded-ab3e-4d4c-b66d-f30cf1783328\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 02:01:10\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2138782,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePatient Disposition. A total of 265,936 patients were screened and finally 746 patients were enrolled in our study. Based on whether they have used bevacizumab and the subsequent treatment regimens, the patients were divided in different groups for the comparison. All comparison performed propensity score matching by age, sex, and treatment-line. Abbreviation: \\u003csup\\u003e1\\u003c/sup\\u003eAnlo-monotherapy: anlotinib monotherapy; \\u003csup\\u003e2\\u003c/sup\\u003eAnlo-combination: anlotinib combination.\\u003csup\\u003e 3\\u003c/sup\\u003ePSM: propensity score matching.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/b7e625b469c471c9dafcf907.png\"},{\"id\":106960478,\"identity\":\"91625630-aced-4435-aa9e-1f2ce0d69ff7\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 09:21:21\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4990786,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImpact of Previous Bevacizumab on Anlotinib Efficacy. A: Kaplan-Meier curves of PFS between beva-pretreated (red) and beva-naïve groups (blue) after PSM by age, sex and treatment-line. The dashed line represented the median PFS. P value, hazard ratio, and median PFS were demonstrated in the upper right corner. B: Percentage of treatment response after PSM. The proportion was labeled in the corresponding segment. PD: progression diseases (red); SD: stable diseases (blue); PR: partial response (green).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/ce9a3245bc6cd18df5bdaa09.png\"},{\"id\":106835536,\"identity\":\"be5b4505-49e7-4c52-a93d-b6fb31315365\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 02:01:10\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":12344318,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe efficiency of anlotinib in beva-pretreated patients. A: Kaplan–Meier estimates of progression-free survival (PFS) among the anlotinib combination (green), anlotinib monotherapy (red), and other therapies group (blue) in the overall bevacizumab-pretreated cohort. B: Percentage of treatment response. presented as stacked bars with the proportion of each response category labeled within the corresponding segment. Response categories include partial response (PR, green), stable disease (SD, blue), and progressive disease (PD, red). C: Kaplan–Meier PFS comparison between anlotinib monotherapy (red) and other therapies (blue) after propensity score matching (PSM) by age, sex and treatment-line. D: Kaplan-Meier curves of PFS between the anlotinib combination (red line) and other therapies groups (blue line) after PSM. The dashed line represented the median PFS. P values were calculated using the log-rank test. Numbers at risk were provided below the plot. P value, hazard ratio, and median PFS (months) with 95% confidence intervals (CIs) were shown in the in the upper right corner. Abbreviations: PFS, progression-free survival; PSM, propensity score matching; HR, hazard ratio; CI, confidence interval; PR, partial response; SD, stable disease; PD, progressive disease.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/5eeb07d3f6faa506c0ce6517.png\"},{\"id\":108804552,\"identity\":\"ea7fd29a-06fe-46fc-8e4f-92803efcaf22\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:21:32\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":19110400,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/0a574b2d-5162-4f41-9739-952880e8ddc1.pdf\"},{\"id\":106835537,\"identity\":\"56348156-65a3-4333-a33a-bfdf97941ab1\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 02:01:10\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":29351,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/aa9a9765838e2204d5fb3748.docx\"},{\"id\":106960213,\"identity\":\"cc2704a8-30fe-4a94-bc69-799169b140cc\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 09:19:24\",\"extension\":\"tif\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1414462,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFigure1.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/1601b8e361a8c036ffca9220.tif\"},{\"id\":106835539,\"identity\":\"1dd45a6f-bc12-4abf-a62b-3923a24f1b47\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 02:01:10\",\"extension\":\"tif\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1319478,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFigure2.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9292563/v1/26dba398edeba0164c4e79c8.tif\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer (AN-BELIEF study)\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLung cancer remained the leading cause of cancer-related mortality worldwide\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. While targeted and systemic therapies have significantly improved survival for many patients, those who progress on these treatments continue to face poor outcomes and limited options\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. Abnormal tumor vasculature in structure and function contributed to tumor growth, metastasis and treatment resistance\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Inhibition of the VEGF/VEGFR2 signaling pathway was an effective anti-angiogenic strategy, which could promote vascular normalization and improve treatment outcomes, playing an important role in advanced non-small cell lung cancer (NSCLC)\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. Its combination with chemotherapy or immunotherapy had become a standard regimen as first-line, and tyrosine kinase inhibitors (TKIs) with antiangiogenic effects had been approved as post-line in NSCLC\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR9 CR10\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. Among them, the most commonly used antiangiogenic agents were bevacizumab and anlotinib.\\u003c/p\\u003e \\u003cp\\u003eBevacizumab, a monoclonal antibody targeting vascular endothelial growth factor A (VEGFA), has emerged as a significant anti-angiogenic therapy in non-squamous NSCLC for decades\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Nevertheless, the efficacy was often limited by the development of resistance through multiple complex mechanisms, including autocrine VEGF signalling, hypoxia tolerance, and the recruitment of fibrocyte-like cells\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e, which led to rapid disease progression, only with further benefits of 2.7 months for bevacizumab-containing regimens in non-squamous NSCLC\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. And the AvaALL study showed that bevacizumab across multiple lines for NSCLC didn\\u0026rsquo;t significantly prolong overall survival\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, inevitable resistance and limited effects produced a huge bevacizumab-resistant population, leaving an urgent need for effective antiangiogenic treatment to address this growing clinical challenge.\\u003c/p\\u003e \\u003cp\\u003eAnlotinib, a novel anti-angiogenic inhibitor with multi-targets including VEGFR, FGFR, PDGFR, and c-Kit, has received approvals for NSCLC based on the significant improvement of the ALTER0303 trial\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. Furthermore, many clinical trials demonstrated the promising therapeutic potential of anlotinib in NSCLC when combined with other therapies, such as the CAMPASS study\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e, AUTOMAN study\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e. Preclinical evidence suggested that anlotinib could improve antitumor and antiangiogenic effects after bevacizumab resistance by inhibiting bevacizumab-induced high VEGF/VEGFR expression and blocking activation of the PI3K/AKT signaling pathway\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. A subgroup analysis of the ALTER0303 trial further indicated that prior treatment with bevacizumab or endostatin did not compromise anlotinib's efficacy\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. These findings highlighted anlotinib's potential to overcome anti-angiogenic resistance, but with only about ten beva-pretreated patients, warranting further systematic and large-scale investigation of the efficacy of anlotinib in bevacizumab-pretreated (bevac-pretreated) populations.\\u003c/p\\u003e \\u003cp\\u003eHence, we evaluated the efficacy and safety profile of anlotinib in patients with non-squamous NSCLC who have experienced progressed diseases of bevacizumab. Focusing on this challenging population, we provided valuable insights into the potential role of anlotinib as a subsequent line and its ability to overcome resistance to prior antiangiogenic treatments.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study design and population\\u003c/h2\\u003e \\u003cp\\u003eThis was a retrospective study, and real-world data were collected from six hospitals during January 2018 and May 2024. We enrolled bevacizumab-na\\u0026iuml;ve (beva-na\\u0026iuml;ve) patients only treated with anlotinib monotherapy subsequently and bevacizumab-pretreated patients with non-squamous NSCLC (due to radiographic progression or intolerance, treated with anlotinib or others subsequently). Patients were excluded if 1) they received anlotinib before progression of bevacizumab therapy; 2) received other anti-angiogenic tyrosinase inhibitors (TKI) after bevacizumab treatment, including apatinib, lenvatinib, sorafenib, regorafenib, and fruquintinib; 3) received localized therapies during the medication period, except localized palliative radiotherapy for bone metastases.\\u003c/p\\u003e \\u003cp\\u003eFinally, according to the administration, patients were allocated to one of four groups: (1) beva-naive following anlotinib monotherapy; (2) beva-pretreated following anlotinib monotherapy; (3) beva-pretreated following other therapies; or (4) beva-pretreated following anlotinib combination (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Procedures\\u003c/h2\\u003e \\u003cp\\u003eThe study was a retrospective study based on electronic health record (HER) data, and the ethical committee exempted the patients of this study from signing the informed consent in writing. The data collection form (DCF) was generated by extracting the relevant data from the established HER information of each hospital, and all the data during the clinical treatment of the patients were collected after determining that the inclusion and exclusion criteria were met. This study did not involve prospective patient follow-up.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Outcomes\\u003c/h2\\u003e \\u003cp\\u003ePFS was the primary outcome. Secondary outcomes included objective response rate (ORR) and disease control rate (DCR). Safe outcome included adverse events such as bleeding, hypertension, myocardial ischemia, proteinuria, hand-foot syndrome (HFS), and gastrointestinal reactions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eQuantitative indicators were assessed for normality. If the data did not meet the criteria for a normal distribution, the Wilcoxon rank-sum test was used to compare values between two groups. For data that satisfy normality, a t-test was applied to compare the indicators between the two groups. The statistical description for quantitative data included the mean, standard deviation, median, and the upper and lower quartiles. For qualitative or categorical indicators, the frequency and its corresponding percentage were provided in the statistical description. Comparisons of unordered categorical variables were conducted using the chi-square test or the exact probability method (Fisher's exact test). All statistical tests were two-sided. A p-value of less than or equal to 0.05 was considered statistically significant for the difference being evaluated. The group comparisons were conducted after using a propensity score matching (PSM) model based on sex, age, and number of treatment lines.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1 Patient baseline characterization\\u003c/h2\\u003e\\n \\u003cp\\u003eWe screened 1,256 patients across 6 centers, and they were diagnosed with non-squamous NSCLC and treated with anlotinib or bevacizumab. According to the prespecified eligibility criteria, 213 patients were excluded because they had received anlotinib prior to bevacizumab (n\\u0026thinsp;=\\u0026thinsp;81) or had been exposed to other antiangiogenic TKIs or local therapies (n\\u0026thinsp;=\\u0026thinsp;132), leaving 1,043 patients for further assessment. We then excluded 297 patients due to insufficient radiologic evaluation, including those without baseline imaging before treatment initiation (n\\u0026thinsp;=\\u0026thinsp;103) and those without twice post-treatment imaging assessments (n\\u0026thinsp;=\\u0026thinsp;194). Ultimately, 746 patients were included in the final analytic cohort (Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eWithin the included population, 174 patients were bevacizumab-na\\u0026iuml;ve and all received anlotinib monotherapy, whereas 572 patients were bevacizumab-pretreated and were subsequently treated with anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;108), other therapies (n\\u0026thinsp;=\\u0026thinsp;119), or anlotinib-based combination regimens (n\\u0026thinsp;=\\u0026thinsp;345) (Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Given the heterogeneity in baseline characteristics, we performed propensity score matching (PSM) using age, sex, and treatment-line for all comparative analyses. Specifically, Comparison A assessed the impact of prior bevacizumab exposure on outcomes with subsequent anlotinib monotherapy using a 2:1 match (bevacizumab-na\\u0026iuml;ve vs bevacizumab-pretreated: n\\u0026thinsp;=\\u0026thinsp;146 vs n\\u0026thinsp;=\\u0026thinsp;73). Comparisons B and C evaluated the effectiveness of anlotinib among bevacizumab-pretreated patients, including a 1:1 matched comparison of anlotinib monotherapy versus other therapies (n\\u0026thinsp;=\\u0026thinsp;93 vs n\\u0026thinsp;=\\u0026thinsp;93) and a 1:3 matched comparison of other therapies versus anlotinib combination therapy (n\\u0026thinsp;=\\u0026thinsp;99 vs n\\u0026thinsp;=\\u0026thinsp;297) (Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cspan\\u003e\\n \\u003cp\\u003ea. Anlotinib monotherapy patients: Beva-na\\u0026iuml;ve (n\\u0026thinsp;=\\u0026thinsp;146) vs. Beva-pretreated (n\\u0026thinsp;=\\u0026thinsp;73);\\u003c/p\\u003e\\n \\u003c/span\\u003e \\u003cspan\\u003e\\n \\u003cp\\u003eb. Beva-pretreated patients: Anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;93) vs. Other therapies (n\\u0026thinsp;=\\u0026thinsp;93);\\u003c/p\\u003e\\n \\u003c/span\\u003e \\u003cspan\\u003e\\n \\u003cp\\u003ec. Beva-pretreated patients: Anlotinib combination (n\\u0026thinsp;=\\u0026thinsp;297) vs. Other therapies (n\\u0026thinsp;=\\u0026thinsp;99);\\u003c/p\\u003e\\n \\u003c/span\\u003e\\n \\u003cp\\u003eAs shown in Table \\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, after propensity score matching, the clinical characteristics were well balanced between the beva-pretreated group (N\\u0026thinsp;=\\u0026thinsp;73) and the beva-naive group (N\\u0026thinsp;=\\u0026thinsp;146), with no statistically significant differences. The majority were both male with a mean age of 61 years. Most patients reported no smoking history and drinking history. Regarding comorbidities, hypertension was the most common condition (32.88% vs 24.66%; p\\u0026thinsp;=\\u0026thinsp;0.199), followed by diabetes (16.44% vs 12.33%; p\\u0026thinsp;=\\u0026thinsp;0.404), and hyperlipidemia (5.48% vs 11.64%; p\\u0026thinsp;=\\u0026thinsp;0.144). Most patients had good performance status (ECOG 0\\u0026ndash;1: 74.0% vs 65.8%; p\\u0026thinsp;=\\u0026thinsp;0.217). Advanced disease predominated in both groups, with stage IV accounting for 79.5% and 77.4%, respectively (p\\u0026thinsp;=\\u0026thinsp;0.729). Anlotinib was mainly administered as second (39.7% vs 41.1%) or third (35.6% vs 41.1%) line therapy. The characteristics before matching were demonstrated at\\u0026nbsp;\\u003cstrong\\u003eSupplemental Table\\u0026nbsp;1\\u003c/strong\\u003e, with more hyperlipidemia in beva-na\\u0026iuml;ve group (4.63% vs. 12.07%, p\\u0026thinsp;=\\u0026thinsp;0.036).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u0026nbsp;\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDemographic and Clinical Characteristics in Patients treated with Anlotinib monotherapy after propensity score matching.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCategory\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eAnlotinib monotherapy\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eBeva-pretreated\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eBeva-na\\u0026iuml;ve\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;73)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;146)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge; Mean (SD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e61.71 (10.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e61.74 (11.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.781\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSex\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e37 (50.68%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e86 (58.90%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e36 (49.32%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e60 (41.10%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmoking history\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.507\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e8 (10.96%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e12 (8.22%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e65 (89.04%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e134 (91.78%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDrinking history\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.909\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e4 (5.48%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e6 (4.11%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e69 (94.52%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e140 (95.89%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComorbidity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e24 (32.88%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e36 (24.66%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.199\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDiabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e12 (16.44%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e18 (12.33%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.404\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eCoronary\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2 (2.74%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e11 (7.53%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.266\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHyperlipidemia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e4 (5.48%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e17 (11.64%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.144\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eECOG\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.217\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0\\u0026ndash;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e54 (74.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e96 (65.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u0026ndash;3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e19 (26.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e50 (34.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTNM stage\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.729\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e15 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e33 (22.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eⅣ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e58 (79.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e113 (77.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTreatment line\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.465\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e29 (39.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e60 (41.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e26 (35.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e60 (41.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e18 (24.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e26 (17.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eIn bevacizumab-pretreated patients, a total of 572 patients were categorized by post-treatment as anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;108), other therapies (n\\u0026thinsp;=\\u0026thinsp;119), and anlotinib combination groups (n\\u0026thinsp;=\\u0026thinsp;345) (Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Overall, patients were older (mean age approximately 60 years across groups) and predominantly male, with a marginal imbalance in sex distribution (male: 52.78% vs 68.07% vs 63.19%; p\\u0026thinsp;=\\u0026thinsp;0.051). Notably, the prevalence of smoking history was significantly lower in the anlotinib monotherapy group (12.04% vs. 34.45% vs.31.01%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Similarly, drinking history was less common with anlotinib monotherapy (6.48% vs. 20.17% vs. 16.81%, p\\u0026thinsp;=\\u0026thinsp;0.011). Comorbidity profiles were generally comparable across groups, with no statistically significant between-group differences. Those receiving anlotinib-based regimens were treated in the poster line (p\\u0026thinsp;=\\u0026thinsp;0.013). For exploration of anlotinib monotherapy in beva-pretreated patients (Comparison B), the characterizations were well balanced by PSM except for more smoking and drinking patients in \\u0026ldquo;other therapies\\u0026rdquo; group (\\u003cstrong\\u003eSupplemental Table\\u0026nbsp;2\\u003c/strong\\u003e). For the efficiency of anlotinib combination in bevacizumab-pretreated patients (Comparison C), PSM resulted in 297 and 99 patients in each group respectively, without statistically significant differences in characteristics (\\u003cstrong\\u003eSupplemental Table\\u0026nbsp;3\\u003c/strong\\u003e).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\u0026nbsp;\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eTable \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Demographic and Clinical Characteristics in Beva-pretreated Patients.\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c6\\\" namest=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCategory\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBevacizumab-pretreated\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c6\\\" namest=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAnlotinib monotherapy\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOther therapies\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAnlotinib combination\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c6\\\" namest=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(N\\u0026thinsp;=\\u0026thinsp;108)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(N\\u0026thinsp;=\\u0026thinsp;119)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(N\\u0026thinsp;=\\u0026thinsp;345)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge; Mean (SD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e61.68 (9.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e61.87 (8.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e59.54 (10.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.101\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSex\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.051\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e57 (52.78%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e81 (68.07%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e218 (63.19%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e51 (47.22%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e38 (31.93%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e127 (36.81%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmoking history\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e13 (12.04%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e41 (34.45%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e107 (31.01%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e95 (87.96%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e78 (65.55%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e238 (68.99%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDrinking history\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.011\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e7 (6.48%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e24 (20.17%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e58 (16.81%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e101 (93.52%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e95 (79.83%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e287 (83.19%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComorbidity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e35 (32.41%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e47 (39.50%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e144 (41.74%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.223\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDiabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e15 (13.89%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e18 (15.13%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e51 (14.78%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eCoronary\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3 (2.78%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e3 (2.52%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e23 (6.67%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHyperlipidemia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5 (4.63%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e16 (13.45%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e43 (12.46%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.054\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eECOG\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.568\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0\\u0026ndash;1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e78 (72.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e89 (74.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e266 (77.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u0026ndash;3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e30 (27.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e30 (25.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e79 (22.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTNM stage\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.187\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e19 (17.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e23 (19.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e86 (24.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eⅣ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e89 (82.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e96 (80.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e259 (75.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTreatment line\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.013\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e41 (38.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e66 (55.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e150 (43.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e41 (38.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e42 (35.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e140 (40.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e26 (24.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e11 (9.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e55 (15.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2 Efficacy comparison\\u003c/h2\\u003e\\n \\u003cp\\u003eBefore PSM, beva-na\\u0026iuml;ve patients got prolonged PFS than beva-pretreated patients when receiving anlotinib monotherapy \\u003cstrong\\u003e(\\u003c/strong\\u003e8.23 vs. 5.90 months; HR 1.41, 95% CI 1.12\\u0026ndash;1.77; p\\u0026thinsp;=\\u0026thinsp;0.003; \\u003cstrong\\u003eSupplemental Fig.\\u0026nbsp;1A).\\u003c/strong\\u003e The DCR rate was lower with beva-pretreated patients (59.3% vs. 72.4%; p\\u0026thinsp;=\\u0026thinsp;0.022), and there was no significant difference for ORR (8.3% vs. 8.6%; p\\u0026thinsp;=\\u0026thinsp;0.933, \\u003cstrong\\u003eSupplemental Fig.\\u0026nbsp;1B\\u003c/strong\\u003e). However, when mitigating bias by PSM, no significant difference in PFS was observed between the two groups (6.43 vs. 7.60 months; HR 1.14, 95% CI 0.88\\u0026ndash;1.47; p\\u0026thinsp;=\\u0026thinsp;0.334; Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). as well as that of DCR (68.5% vs. 67.1%; p\\u0026thinsp;=\\u0026thinsp;0.838; Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). and ORR (6.8% vs. 6.8%; p\\u0026thinsp;=\\u0026thinsp;1.000; Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Therefore, similar PFS and response after PSM indicated that the prior use of bevacizumab didn\\u0026rsquo;t affect the efficiency of the following anlotinib. Furthermore, we decided to demonstrate the efficiency and safety of anlotinib in bevacizumab-pretreated populations.\\u003c/p\\u003e\\n \\u003cp\\u003eAs shown in Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, there was no significant difference in PFS among these three groups (6.10 vs. 5.90 vs. 5.87 months; p\\u0026thinsp;=\\u0026thinsp;0.421), as well as that of ORR (12.75% vs. 8.33% vs. 15.97%, p\\u0026thinsp;=\\u0026thinsp;0.221, Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) and DCR (61.2% vs. 60.2% vs. 63.9%, Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). For exploration of anlotinib monotherapy in beva-pretreated patients, the anlotinib monotherapy group and the other therapies group showed no significant differences in PFS (5.90 vs. 5.23 months; HR 0.96, 95% CI 0.75\\u0026ndash;1.24; p\\u0026thinsp;=\\u0026thinsp;0.766, Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). These results suggested that anlotinib monotherapy can achieve comparable PFS in bevacizumab-pretreated patients. Moreover, the anlotinib combination group demonstrated a significantly improved PFS (6.50 vs. 5.23 months; HR 0.82, 95% CI 0.67\\u0026ndash;1.00; p\\u0026thinsp;=\\u0026thinsp;0.048; Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Therefore, bevacizumab-pretreated patients will benefit more when combined with anlotinib. Meanwhile, subgroup analysis suggested that female patients (p\\u0026thinsp;=\\u0026thinsp;0.026) aged over 65 (p\\u0026thinsp;=\\u0026thinsp;0.028) with stage IV (p\\u0026thinsp;=\\u0026thinsp;0.018) non-squamous NSCLC were more likely to benefit from the combination of anlotinib. However, no particular regimen was identified to obtain superior benefit from the combination (\\u003cstrong\\u003eSupplemental Fig.\\u0026nbsp;2\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3 Safety\\u003c/h2\\u003e\\n \\u003cp\\u003eAs shown in Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, among patients receiving anlotinib monotherapy, the overall adverse effects (AEs) profile was similar between the bevacizumab-naive (n\\u0026thinsp;=\\u0026thinsp;108) and bevacizumab-pretreated (n\\u0026thinsp;=\\u0026thinsp;174) groups. Gastrointestinal events were the most commonly observed AEs in both groups (5.6% vs 6.3%, p\\u0026thinsp;=\\u0026thinsp;0.793). Notably, neurological events occurred more frequently in the bevacizumab-naive group than bevacizumab-pretreated group (4.6% vs 0.6%, p\\u0026thinsp;=\\u0026thinsp;0.032). No statistically significant differences between them were detected for other AEs. Myocardial damage, kidney events, and subclinical hypothyroidism were not observed in either monotherapy subgroup.\\u003c/p\\u003e\\n \\u003cp\\u003eWithin the bevacizumab-pretreated patients, comparing anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;108), anlotinib combination (n\\u0026thinsp;=\\u0026thinsp;345) was associated with significantly higher rates of allergy (1.9% vs. 7.5%; p\\u0026thinsp;=\\u0026thinsp;0.032) and hand-foot syndrome (0.9% vs. 8.4%; p\\u0026thinsp;=\\u0026thinsp;0.006). In the combination group, the most common AEs were gastrointestinal events (11.3%), hand-foot syndrome (8.4%), and allergy (7.5%). There was no significant difference observed in other AEs.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u0026nbsp;\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eAdverse Events Without Propensity Score Matching.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAdverse events\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eAnlotinib Monotherapy\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eBevacizumab-pretreated\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eBeva-naive\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eBeva-pretreated\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eAnlotinib Monotherapy\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003eAnlotinib Combination\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;108)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;174)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;108)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;345)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eGastrointestinal events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e6 (5.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e11 (6.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.793\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e6 (5.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e39 (11.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.081\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAllergy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2 (1.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e5 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.711\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e2 (1.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e26 (7.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.032\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther neurological events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5 (4.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e1 (0.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.032\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e5 (4.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e8 (2.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.202\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eRespiratory events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5 (4.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e5 (2.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.514\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e5 (4.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e20 (5.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.643\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSkeletal muscle and connective tissue events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3 (2.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e3 (1.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.678\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e3 (2.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e13 (3.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.772\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSkin and mucosa events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.383\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e17 (4.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBleeding\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e4 (3.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e3 (1.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.434\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e4 (3.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e21 (6.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e0.344\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eProteinuria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.383\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e5 (1.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eHand-foot syndrome (HFS)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.383\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e29 (8.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.006\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMyocardial damage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e1 (0.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBone marrow suppression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e4 (2.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.652\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e1 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e5 (1.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eKidney events\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e1 (0.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSubclinical hypothyroidism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\n \\u003cp\\u003e2 (0.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis is the first large study based on real-world data on the exploration of the efficacy and safety of anlotinib in non-squamous NSCLC after bevacizumab pretreatment. The results showed prior bevacizumab treatment had a limited impact on the efficacy of anlotinib monotherapy. However, compared to other therapies, anlotinib monotherapy could also achieve comparable results and anlotinib combination demonstrated significantly improved PFS in beva-pretreated patients, especially in elderly females. Expect increased HFS and allergy in anlotinib combination group, the safety profile remained consistent, with no significant differences when compared to monotherapy.\\u003c/p\\u003e \\u003cp\\u003eThe results regarding the impact of bevacizumab on anlotinib should be considered with caution. Subgroup analyses of subsequent combination therapy indicate that the efficacy of anlotinib was influenced by factors such as age, disease stage, and sex. Therefore, the different PFS between beva-na\\u0026iuml;ve group and beva-pretreated group may be attributable to confounding differences. Although only disparities of comorbidity were detected before matching, the PSM enhanced comparability between groups, thereby better revealing the treatment effect\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, anlotinib could improve PFS of beva-pretreated patients in ALTER0303 and other studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, we concluded that bevacizumab didn\\u0026rsquo;t affect the efficacy of anlotinib. Some retrospective studies shown the same results, but predominantly in single-center or small sample size\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e. We explored that in the multicenter study with a large population and demonstrated the following efficiency of the combination. Our findings provided valuable insights into the use of anlotinib in patients who have progressed on bevacizumab-containing regimens. This was particularly important as it indicated that anlotinib may be a viable treatment option for patients who have developed resistance to bevacizumab.\\u003c/p\\u003e \\u003cp\\u003eThe superior PFS observed with anlotinib combination therapy compared to other therapies highlighted the potential of incorporating anlotinib for bevacizumab-pretreated patients, which could be attributed to anlotinib's multi-target mechanism\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, inducing sustained vascular inhibition and positive immune infiltration\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e, overcoming resistance of bevacizumab by inhibition of angiogenic bypass\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. However, the \\\"other therapies\\\" group is likely highly heterogeneous (e.g., chemotherapy, immunotherapy, best supportive care). This heterogeneity makes the comparison less definitive. Although subgroups by different treatments revealed similar effects, elderly female patients may benefit more from combination. Align with our studies, the ALTER0303 trial showed anlotinib's efficacy in advanced NSCLC who had progressed on multiple lines of therapy\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. Zhang et al reported that concurrent use of anlotinib overcame acquired resistance to EGFR-TKI in advanced EGFR‐mutant NSCLC patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Anlotinib plus benmelstobart also demonstrated longer PFS compared with pembrolizumab in the CAMPASS study\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e (11.0 vs. 7.1 months). These results suggested the potential of anlotinib as combination therapy with TKI or immunotherapy, and the safety profile across treatment groups suggested that anlotinib can be safely administered in beva-pretreated patients. This is crucial for maintaining quality of life in patients who may have already experienced significant treatment-related toxicities.\\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. Firstly, this was a retrospective, non-randomised study, and selection bias is unavoidable due to the missing data. However, the heterogeneity of the patient population in terms of treatment line were well balanced by PSM, enhancing the validity of our comparisons. While PSM can balance observed covariates, it cannot account for unobserved confounders\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. Furthermore, PSM can lead to a reduction in sample size, potentially limiting statistical power for some comparisons. Although we screened patients in 6 centers, the Caucasian population and adenocarcinoma were not included. Despite these limitations, our study provided valuable real-world evidence on the efficacy and safety of anlotinib in patients previously treated with bevacizumab. Future prospective studies focusing on specific tumor types and standardized treatment protocols would be beneficial to further validate these findings.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, this study demonstrated that anlotinib monotherapy showed comparable clinical efficacy to other therapies, and the combination of anlotinib significantly improved PFS compared to other treatments. Anlotinib-containing regimens may offer enhanced benefits in beva-pretreated patients. Further studies were warranted to confirm these results and explore the potential advantages of anlotinib in this setting.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e5. Author Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJW and XZ designed this study and drafted the manuscript. JC and BZ contributed to the acquisition and analysis of data. HL, FJ and CJ collected the data. YL, FW, YJ, and AL analyzed the data. CJ, QF and SR revised the paper. All authors contributed to this article, and the manuscript has been read and approved by all the authors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e6. Funding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was granted by the Natural Science Foundation of Shanghai (25ZR1401308) and the Tongji University Independent and Original Basic Research Project (No. 22120240374). China Health \\u0026amp; Medical Development Foundation (Medical Research Project, chmdf2024-xrzx02-21) National Natural Science Foundation of China (No. 82373319, No. 82172869), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0520200, 2024ZD0520206), the Science and Technology Commission of Shanghai Municipality (24Y12800300), National Natural Science Foundation of China (NO. 82203046), Clinical Research Foundation of Shanghai Pulmonary Hospital\\u0026nbsp;（LYRC202405）.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e7. Conflict of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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BMC Med Res Methodol Jan 29(1):25. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12874-025-02481-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12874-025-02481-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"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\":\"Anlotinib, Bevacizumab, Anti-angiogenic, NSCLC, Real-world\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9292563/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9292563/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eWith the increasing progression of bevacizumab and the urgent need for anti-angiogenic strategies, we aimed to evaluate the efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis was a retrospective real-world study. We enrolled bevacizumab-na\\u0026iuml;ve patients and bevacizumab-pretreated patients from six hospitals during Jan 2018 and May 2024. Progression-free survival (PFS) was the primary outcome. Secondary outcomes included objective response rate (ORR), disease control rate (DCR) and safe outcome.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA total of 1256 patients were screened, and 746 patients were included in the study. Firstly, we selected patients treated with anlotinib monotherapy. After propensity score matching (PSM), we divided them into beva-pretreated (n\\u0026thinsp;=\\u0026thinsp;73) and beva-na\\u0026iuml;ve group (n\\u0026thinsp;=\\u0026thinsp;146), and no statistical differences were observed in PFS (6.43 vs. 7.60 months; HR 1.14, p\\u0026thinsp;=\\u0026thinsp;0.334). So previous bevacizumab didn't affect the efficiency of anlotinib in non-squamous NSCLC. We further divided the bevacizumab-pretreated patients into three groups according to the post-treatment: anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;108), anlotinib combination (n\\u0026thinsp;=\\u0026thinsp;345), and other therapies (n\\u0026thinsp;=\\u0026thinsp;119). After PSM, the PFS was similar between the anlotinib monotherapy (n\\u0026thinsp;=\\u0026thinsp;93) and other treatment group (n\\u0026thinsp;=\\u0026thinsp;93) (5.90 vs. 5.23 months; HR 0.96, p\\u0026thinsp;=\\u0026thinsp;0.766), whereas the PFS was significantly longer in the anlotinib combination group (n\\u0026thinsp;=\\u0026thinsp;297) than other treatment group (n\\u0026thinsp;=\\u0026thinsp;99) (6.50 vs 5.23 months; HR 0.82, p\\u0026thinsp;=\\u0026thinsp;0.048).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThis study demonstrated that previous bevacizumab didn't affect the efficiency of anlotinib in non-squamous NSCLC. For beva-pretreated patients, combined with anlotinib was effective and well-tolerated. Further studies are warranted to confirm these results and explore the potential advantages of anlotinib.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The efficacy and safety of anlotinib in bevacizumab-pretreated patients with non-squamous non-small cell lung cancer (AN-BELIEF study)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-14 02:01:05\",\"doi\":\"10.21203/rs.3.rs-9292563/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\":\"44222137-ee35-4e36-a245-dea8186a7dcd\",\"owner\":[],\"postedDate\":\"April 14th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Rejected\",\"date\":\"2026-05-05T03:15:18+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-05T03:24:50+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-14 02:01:05\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9292563\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9292563\",\"identity\":\"rs-9292563\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}