The Predictive efficacy of tumor mutational burden for immune Checkpoint Inhibitors in non-small cell lung cancer: A systematic review and meta-analysis of randomized controlled trials

The Predictive efficacy of tumor mutational burden for immune Checkpoint Inhibitors in non-small cell lung cancer: A systematic review and meta-analysis of randomized controlled trials | 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 Article The Predictive efficacy of tumor mutational burden for immune Checkpoint Inhibitors in non-small cell lung cancer: A systematic review and meta-analysis of randomized controlled trials Min Ying, Mengyu Hu, Kegui Weng, Yanyan Long, Jing Chen, Qianqian Lei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7114203/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: To date, immune checkpoint inhibitors (ICIs) have become the mainstay of treatment for locally advanced or metastatic non-small cell lung cancer (NSCLC). However, a subset of patients derives poor benefits from ICIs, highlighting the need for reliable biomarkers to identify those who are more likely to respond to such therapies. This review aims to evaluate the role of tumor mutational burden (TMB) as a predictive biomarker in NSCLC patients treated with ICIs. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched from inception to April 2, 2024. The endpoints of interest included overall response rate (ORR), overall survival (OS), and progression-free survival (PFS). Results: A total of 13 randomized controlled trials were included. High TMB was associated with improved ORR [relative ratio (RR) 1.63, 95% confidence interval (CI) 1.24–2.14], longer PFS [hazard ratio (HR) 0.78, 95% CI 0.71–0.86] and longer OS (HR 0.86, 95% CI 0.76–0.98). Subgroup analysis revealed that high tissue TMB (tTMB) was significantly associated with better PFS and OS, whereas pooled results for both PFS and OS based on blood TMB (bTMB) did not reach statistical significance. Furthermore, in the high TMB group, first-line treatment with ICIs provided significant benefits for NSCLC, as demonstrated by improved ORR, PFS, and OS. Conclusions: These results suggest that tTMB could serve as a potential predictor of clinical benefit from ICIs, particularly in previously untreated NSCLC. However, the predictive value of bTMB requires further investigation and defining an optimal bTMB cutpoint remains a significant challenge. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology biomarker tumor mutational burden immune checkpoint inhibitors non-small cell lung cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers, with many patients presenting as locally advanced or metastatic stages at initial diagnosis [ 1 ]. In recent decades, immune checkpoint inhibitors (ICIs) have become one of the most effective treatments [ 2 ]. According to the KEYNOTE-001 trial, pembrolizumab monotherapy provided superior antitumor effects, especially for the programmed death ligand-1 (PD-L1)–expressing NSCLC, compared with chemotherapy [ 3 ]. However, not all patients with PD-L1-positive tumors respond well to ICIs and PD-L1 is not a perfect biomarker [ 4 ]. Therefore, it is urgent to identify additional biomarkers to select patients for ICIs. Tumor mutational burden (TMB) represents the quantity of somatic mutations in cancer cells that can be assessed in tissue (tTMB) or blood (bTMB) [ 5 ]. Tumor with high TMB may carry more neoantigens,, which have been reported to respond better to ICIs [ 6 ]. Higher TMB was first identified as being associated with a better response to ICIs in NSCLC in a retrospective trial [ 7 ]. In CheckMate 026, the overall response rate (ORR) in the nivolumab group was higher and the median progression free survival (PFS) was longer than the chemotherapy group for the high TMB NSCLC patients. However, there was no significant association between TMB and overall survival (OS) [ 8 ]. According to the CheckMate 227 trial, NSCLC with a high TMB treated with first-line nivolumab plus ipilimumab achieved significantly longer PFS [ 9 ]. In the five-year survival analysis in KEYNOTE-010, high TMB was also associated with a better response to pembrolizumab in previously treated PD-L1 positive advanced NSCLC [ 10 ]. However, despite the approval of TMB as a biomarker for pembrolizumab in all cancers [ 11 ], the predictive value of TMB varied across trials. In the study reported by Gettinger [ 12 ], high TMB was not related to a better outcome in patients treated with ICIs but was found to be a predictive biomarker for OS in the subgroup analysis of patients with no tumor PD-L1 expression. More recently, clinical trials suggested that there was no significant association between tTMB and survival benefits [ 13 , 14 ]. Beyond the discordant results, the need for adequate tissue specimens limits the application of tTMB detection and its use in monitoring cancer therapy. With the development of liquid biopsy techniques, detecting bTMB has been applied in clinical practice [ 15 ]. An agreement analysis conducted using an independent cohort revealed that tTMB and bTMB are positively correlated [ 16 ]. NSCLC patients with high bTMB were reported to obtain more survival benefits in several trials [ 16 – 18 ]. However, the results regarding the efficacy of bTMB were inconsistent. In NEPTUNE trial [ 19 ], NSCLC patients with high bTMB did not achieve longer OS and PFS from treatment with durvalumab plus tremelimumab. As the conclusions of several meta-analysis [ 20 – 22 ] varied, we performed the meta-analysis to explore the efficacy of the TMB in NSCLC treated with ICIs, based on the latest trials. Our objective was to clarify the role of TMB as a biomarker for ICIs. This manuscript was prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [ 23 ]. 2. Methods 2.1. Search strategy We performed systematic searches of PubMed, Embase, Web of Science, and Cochrane Library from inception to April 2, 2024. The search keywords included: “mutation burden”, “mutational burden”, “mutation load”, “mutational load”, “TMB”, “TML”, “non-small-cell lung cancer”, “non-small-cell lung carcinoma”, “non-small-cell lung neoplasm”, “non-small-cell lung tumor”, “NSCLC”, “pulmonary adenocarcinoma”, “lung adenocarcinoma”, “adenocarcinoma of the lung”, “lung squamous carcinoma”, “pulmonary squamous carcinoma”, “squamous cell lung carcinoma”, “squamous carcinoma of the lung”, “random” and “randomized”. The potentially available references from related trials or reviews were also searched. The detailed strategies are summarized in Table S1 . 2.2. Study selection The inclusion criteria were as follows: (1) randomized controlled trials on the role of TMB in NSCLC patients treated with ICIs; (2) adequate data on complete response(CR), partial response (PR), stable disease (SD) and progressive disease (PD) evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) [ 24 ], stratified by TMB expression, or sufficient information to estimate the hazard ratio (HR) and its 95% confidence interval (CI) for OS and PFS comparing high TMB versus low TMB. As regards the studies with overlapping patient population, the most recent investigation of the maximum number of individuals was selected for inclusion. 2.3. Data extraction and Quality assessment The following information was extracted from each retrieved article: first author, year of publication, country, sample size, age, experiment drugs, detection method, cutoff value, sample source, outcome, and data linking TMB expression to treatment outcome (i.e., CR, PR, CR + PR, SD, PD, and HR). Two authors (MY and MY Hu) extracted the data independently. Any disagreements were resolved through discussion with a third author to reach a consensus. The quality of all studies was assessed using the Newcastle Ottawa Scale (NOS) [ 25 ]. A total of up to 9 stars were assigned based on items in different categories. Studies awarded 7 or more stars were considered high quality. 2.4. Statistical analysis For the purpose of analyzing the possible relationships between demographic characteristics and TMB expression, odds ratio (OR) with a 95% CI was used. The correlation between TMB expression and ORR was measured by the pooled relative rate (RR) and its 95% CI. Survival benefits were evaluated using HR with 95%CI. We extracted the HRs with 95% CIs for OS and PFS in each study to further analysis. When HRs and CIs were not provided in the original articles, the exploitable survival analysis data were extracted from the survival curves of different treatment arms which had been scanned into Engauge Digitizer. The HRs were then estimated using the method reported by Tierney et al [ 26 ]. Statistical significance was defined as P 1 means more possibilities to harbor a high TMB expression. The primary endpoints of the meta-analysis were compared between high TMB and low TMB group. A correlation between higher ORR and TMB expression was established if RR > 1, indicating better response to the ICIs treatment. When the HR for OS or PFS was greater than 1, it reflected a higher risk reaching the end point of the study and less survival benefits for the high TMB group. Subgroup analyses were conducted by aggregating the studies. If both tTMB and bTMB data were available in the same study, the pooled analysis was performed using tTMB data. Heterogeneity was assessed by the Chi-square test and considered significant when P heterogeneity 50%. If the analysis showed high risk of heterogeneity, random-effect based model [ 27 ] was used. Otherwise, the Mantel and Haenszel fixed-effect model [ 28 ] was used. An evaluation of potential publication bias was tested by the funnel plot which was assessed by the method of Begg and Egger tests. Publication bias was deemed insignificant if P > 0.05. All the statistical tests were conducted with Review Manager 5.4 (The Cochrane Collaboration, Copenhagen, Denmark). 3. Results 3.1. Study characteristics Based on the search strategies, 590 relevant articles were yielded. After removing 323 duplicated articles according to the abstracts, 267 articles were taken into further consideration as indicated in the flowchart of study selection (Fig. 1 ). Finally, 13 randomized controlled trials [ 8 – 10 , 12 – 14 , 16 , 18 , 19 , 29 – 32 ] were included in the meta-analysis. Baseline characteristics of these eligible trials were summarized in Table 1 . The quality assessment was conducted and lumped into Table S2 . We also aggregated two studies [ 16 , 31 ] to evaluate the possible relationships between demographic characteristics and TMB expression (Fig. 2 ). A marginally statistically significant trend was observed for high TMB in male patients [OR = 1.43, (1.10, 1.86)] or non-Asian population [OR = 0.66, (0.49, 0.89)]. There was a marginally significant difference (P = 0.08) in TMB expression among smokers. Table 1 Main characteristics of the included trials Author Year Country Sample size Age Experiment drugs Detection method Cutoff Sample source Outcome Carbone 2017 Multiple areas 541 64 (29–89) Nivolumab WES 242mutations bTMB OS,PFS,ORR De Castro 2023 Multiple areas 512 64 (27–90) Durvalumab+ tremelimumab NGS 20mut/Mb bTMB OS,PFS Gandara (OAK) 2018 Multiple areas 287 NA Atezolizumab NGS 16mutations bTMB OS,PFS,ORR Gandara (POLAR) 2018 Multiple areas 797 NA Atezolizumab NGS 16mutations bTMB OS,PFS Garassino (Keynote 189) 2023 USA 293 64 (56–69) Pembrolizumab WES 175mut/exome tTMB OS,PFS,ORR Garassino (Keynote 407) 2023 USA 312 66 (60–71) Pembrolizumab WES 175mut/exome tTMB OS,PFS,ORR Gettinger 2021 USA 252 67.5 (41.8–90.3) Nivolumab+ ipilimumab NGS 10mut/Mb tTMB OS Herbst 2021 Multiple areas 1034 NA Pembrolizumab WES 175mut/exome tTMB OS,PFS,ORR Jiang 2022 China 389 NA Camrelizumab NGS 75%* tTMB + bTMB OS,PFS Leighl 2022 Multiple areas 301 64 (27–87) Durvalumab+ Tremelimumab NGS 20 mut/Mb bTMB OS,PFS,ORR Rizvi 2020 Multiple areas 488 NA Durvalumab+ tremelimumab NGS 20mut/Mb bTMB OS,PFS,ORR Wang 2023 China 465 NA Toripalimab NGS 10 mut/Mb tTMB OS,PFS Duan 2023 China 360 NA Tislelizumab NGS tTMB ≥ 10 mut/Mb bTMB ≥ 6 mut/Mb bTMB + tTMB PFS Hayashi 2022 Japan 102 NA Nivolumab NGS 6.2mut/Mb tTMB PFS Hellmann 2018 Multiple areas 299 NA Nivolumab+ Ipilimumab NGS 10mut/Mb tTMB PFS * Mutations that met a certain sequencing depth (100X for tissue samples and 500X for plasma samples) and VAF (5% for tissue samples and 0.7% for plasma samples) were chose as candidate mutations for bTMB analysis. Subsequently, bTMB was calculated based on the candidate mutations according to the following formula: bTMB = absolute mutation count* 1000000/panel exonic base number. 3.2. Relationship between TMB and ORR A total of 6 studies included 2,273 patients who were assessable for tumor response on the basis of nine groups (8,10,13,16,18,31). The ORRs in high and low TMB group of NSCLC patients were approximately 41.5% and 23.7%, respectively. The pooled RR for ORR was 1.63 (95% CI 1.24–2.14), further implying that high TMB for NSCLC treated with ICIs was associated with a better ORR (Fig. 3 ). No significant publication bias was found for ORR (Fig S1 ). In the light of the impact of sample sources on TMB, detection was conducted based on tTMB (Fig S2 A) and bTMB (Fig S3 A). The pooled RR were 1.31 (95%CI 0.92–1.86) and 1.94 (95%CI 1.58–2.39) for tTMB and bTMB subgroup, respectively. When ICIs were given as first line treatment (Fig S2 C) or second- or later-line therapy (Fig S3 F), high TMB was related to better response. With regard to the ORR to the patients treated with the combination of anti-PD-1/PD-L1 antibodies with chemotherapy (Fig S2 B), although the studies with relevant data showed consistent trends toward worse ORR among patients with low TMB, the result of pooled analysis showed no statistically significant difference between high TMB group and low TMB (RR 1.21, 95% CI 0.86–1.71). Compared to patients with low TMB expression (Fig S3 ), high TMB predicted significantly better ORR when treated with single-agent ICIs, anti-PD-1/PD-L1 antibodies, or combined with anti-cytotoxic T lymphocyte antigen 4 (anti-CTLA-4) antibodies. 3.3. Prognostic efficacy of TMB in NSCLC treated with ICIs As a result of pooled analysis, the HR for PFS was 0.78 (95% CI 0.71–0.86; P < 0.00001) (Fig. 4 ), indicating that NSCLC patients with high TMB can experience a significant prolongation of progression free survival times. There was no significant heterogeneity between studies (I 2 = 16%; P = 0.27; fixed-effect model). There was no significant publication bias (Fig S4 ). For tTMB, the pooled analysis of HR for PFS is 0.69 (95% CI 0.60–0.78), while bTMB failed to predict PFS benefits (HR 0.91, 95% CI 0.79–1.04). Significant survival benefits were observed for patients with high TMB treated with ICIs as first line therapy in the PFS analysis (HR 0.75, 95% CI 0.67–0.83) (Fig S5 E), but no such benefits were found in the PFS analysis of previously treated patients. Based on the subgroup analysis, high TMB was related to better PFS in NSCLC treated with anti-PD-1 therapy with or without chemotherapy and ICIs combined with anti-CTLA-4 (Figure S5 ). There was no significant difference in the subgroup analysis of patients treated with single immunotherapy or anti- PD-L1 antibodies (Fig S6 ). Similar to the analysis on PFS, the pooled HR for OS was 0.86 (95% CI 0.76–0.98; P = 0.02), which suggested that patients with high TMB (Fig. 5 A) or high tTMB (Fig. 5 C) had significantly longer OS. However, the analysis of bTMB showed no significant difference (Fig. 5 B). Our pooled results of OS showed statistically significant differences in the subgroup analysis of ICIs as first-line therapy, indicating that high TMB was associated with longer OS in the previous untreated patients (Fig. 6 F). The results of subgroup analyses indicated that the OS of patients with high TMB was longer than that of patients with low TMB, when treated with ICIs combined with anti-CTLA-4 (Fig. 6 B). However, there was no significant OS difference between the high TMB and low TMB groups when treated with single-agent ICIs, anti-PD-1/PD-L1 antibodies, with or without chemotherapy (Fig. 6 ). There was no evidence of publication bias in the analysis of OS (Fig S7 ). Further subgroup analysis based on type of sample revealed significantly improved PFS was observed in patients with high tTMB, whereas no significant PFS benefit was observed in those with high bTMB (Fig S8 - S9 ). In terms of OS, neither high tTMB nor high bTMB led to significant survival benefits (Fig S10 - S11 ). These findings indicate that tTMB exhibits better predictive value for PFS, while neither biomarkers showed significant predictive utility for OS. 4. Discussion Up to now, ICIs have been approved for a wide range of cancers including NSCLC, exerting greater overall benefits than conventional platinum-based doublet chemotherapy in the first-line treatment in advanced NSCLC and serving as a good option for second-line treatment [ 33 , 34 ] for those with negative driver genes. The treatment efficacy varies in-dividually [ 35 ], highlighting the importance of screening potential biomarkers to identify the subgroup of patients with potential survival benefits. TMB, including tTMB and bTMB, has been tested in many samples. Based on the analysis of matched samples, tTMB and bTMB were correlated [ 30 , 31 ]. The association between TMB and epidemiological characteristics was evaluated in several studies. According to subgroups analysis across treatment arms in the OAK Study, high bTMB was more common in the male and smoking patients. There was no significant relationship between high bTMB and histology or race [ 16 ]. Nevertheless, in another study, baseline bTMB did not correlate with sex or smoking history [ 30 ]. Rizvi found that there were greater proportions of patients with smoking history and squamous histologic type in the high-TMB group (bTMB ≥ 20 mut/Mb or tTMB ≥ 10 mut/Mb) [ 31 ]. As far as we know, this study is the first meta-analysis focused on the relationship between high TMB and clinical characteristics. In this study, we found that higher TMB levels were more common in the male group and less common in the Asian group. The smoking NSCLC patients also presented the same trend to have high TMB. The expression of TMB in the squamous carcinoma group did not show significant differences compared to the non-squamous carcinoma group. However, since only two studies were included, more trials are needed to explore the epidemiological features further. The predictive value of TMB efficacy for ICIs in cancer was analyzed in trials and meta-analysis [ 36 , 37 ]. Wu et al. (36) reported that TMB was a powerful marker for predicting the effect of immunotherapy in cancer patients. In the subgroup analysis of NSCLC patients, TMB can be used as a predictor of immunotherapy efficacy, except for the OS. Cancer patients with high TMB were also reported to benefit from immunotherapy [ 37 ]. In the meta-analysis, TMB has been shown to be a marker for predicting the OS effectiveness of immunotherapy in NSCLC. Four early studies focused on NSCLC have shown that TMB may be associated with the efficacy of immunotherapy [ 20 , 38 – 40 ]. Ba et al. found that PD-1/PD-L1 inhibitors could improve ORR, PFS and OS in the high bTMB advanced NSCLC patients [ 38 ]. These results were confirmed in another study when ICIs were used as first-line therapy [ 39 ]. Nevertheless, there was no direct comparison between the high TMB and low TMB group in the two trials. Although high TMB served as predictive biomarker based on the analysis of ORR and PFS, the clinical implications of TMB for PD-1/PD-L1 inhibitors cannot be adequately predicted in terms of OS, based on the meta-analysis conducted in 2003 [ 22 ]. According to two other studies [ 20 , 40 ], TMB was a promising biomarker for ICIs therapy while the enrolled studies included retrospective trials. According to the latest meta-analysis, six randomized trials were included [ 21 ]. In light of the 13 randomized clinical trials enrolled in the analysis, four trials were published in 2023 [ 13 , 14 , 19 , 32 ]. Wang et al. reported that the OS benefits were similar in both TMB subgroups [ 32 ]. There was also evidence indicating that the test specimen for TMB could result in differential sensitivity to ICIs [ 20 ]. Differences in the time of detection such as pretreatment TMB and on-treatment TMB, may also lead to a potential bias in conclusions [ 30 ]. Considering the longstanding controversy on this topic, we conducted the meta-analysis based on high-quality studies. In our pooled analysis, ICIs were beneficial for patients with high TMB in terms of ORR, PFS and OS. However, the positive predictive role of high TMB for response to ICIs in NSCLC was not consistently observed in the subgroup analysis. The clinical implications of high bTMB could identify potential responders to ICIs, while the pooled analysis of the predictive value of PFS and OS did note reach statistical significance. In contrast, our results based on the pooled analysis of four datasets suggested that high tTMB did not appear to be associated with the outcomes of ICIs in terms of ORR, but it was related to better survival benefits in terms of both PFS and OS. In view of the available evidence, high TMB was associated with better response and survival in the first-line treatment subgroup and in patients who were treated with anti-PD-1/PD-L1 antibodies combined with anti-CTLA-4. A potential interpretation for the results might be that the predictive value of high TMB could be interrupted by other treatment strategies, as high TMB is related to greater tumor mutational loads which are associated with ICIs response [ 41 ]. Furthermore, according to the stratified analysis based on treatment, our results are consistent with those of Li, who reported that NSCLC patients with high TMB had better ORR when treated with PD-1/PD-L1 inhibitors [ 22 ]. The survival benefits were only noticed in the patients treated with PD-1 inhibitors in terms of PFS based on our results. ICIs combined with chemotherapy is suggested for driver-negative non-small cell lung cancer. The predictive value of TMB in patients receiving ICIs with chemotherapy was investigated by pooling together the response and survival outcomes. Meta-analysis of six datasets showed that patients with high TMB had favorable PFS, while the results of ORR and OS did not reach statistical significance, which may be due to the different mechanisms of chemotherapy. Although we conducted a comprehensive analysis of the value of TMB for ICIs treatment, there were several limitations. Firstly, we evolved only two studies with sufficient data on the relationship between TMB expression and clinicopathological characteristics. Therefore, selection of patients with high TMB based demographic features should be interpreted with caution. Moreover, only a few studies provided the exact HRs and 95% Cls when investigating the survival benefit. Most of the survival information was extracted from the survival curves which might limit precision. Last but not least, the TMB threshold value was not consistent across the enrolled trials due to the different analytical methods used in various trials, which necessitates further validation in prospective trials. 5. Conclusions In summary, the present meta-analysis provides evidence that high TMB expression is associated with greater clinical benefits from ICIs in NSCLC patients. This predictive value is more pronounced in patients who are previously untreated and those treated with ICIs alone. Compared with bTMB, tTMB is more suitable for selection of NSCLC patients who may benefit from ICIs. Abbreviations CR, complete response; HR, hazard ratio; ICIs, immune checkpoint inhibitors; NSCLC, non-small cell lung cancer; Newcastle-Ottawa Scale; OR, odds ratio; ORR, overall response rate; OS, overall survival; PD, progressive disease; PD-L1, programmed death ligand 1; PFS, progression-free survival; PR, partial response; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RECIST, Response Evaluation Criteria in Solid Tumors; RR, risk ratio, rate ratio; SD, stable disease; TMB, tumor mutational burden Declarations Conflict of interest All authors declare that they have no conflict of interest. Ethical approval Not applicable. Informed consent Not applicable. Funding This research was funded by Chongqing Science and Health Joint Medical Research Project (No. 2023GGXM002 to YZ Wu), National Natural Science Foundation Project (No. 82073347 to YZ Wu), Chongqing Talent Plan (No. cstc2022ycjh-bgzxm0208 to YZ Wu) and Chongqing Science and Health Joint Medical Research Project (No. 2023MSXM129 to QQ Lei). The APC was funded by YZ Wu. Author Contribution Min Ying: conceptualization, data curation, methodology, formal analysis, writing original draft.Meng-Yu Hu: data curation, methodology, formal analysis, review and editing.Qian-qian Lei: conceptualisation, review and editing.Yong-Zhong Wu: conceptualisation, funding acquisition.Ke-Gui Weng, Yan-Yan Long, Jing Chen: review and editing. Data Availability All data generated or analyzed during this study are included in this published article. References Wang, Z. et al. 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Toripalimab Plus Chemotherapy for Patients With Treatment-Naive Advanced Non-Small-Cell Lung Cancer: A Multicenter Randomized Phase III Trial (CHOICE-01). J. Clin. Oncol. 41 (3), 651–663 (2023). Brahmer, J. et al. Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. N Engl. J. Med. 373 (2), 123–135 (2015). Borghaei, H. et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. N Engl. J. Med. 373 (17), 1627–1639 (2015). Kagamu, H. Immunotherapy for non-small cell lung cancer. Respir Investig . 62 (2), 307–312 (2024). Wu, Y. et al. The Predictive Value of Tumor Mutation Burden on Efficacy of Immune Checkpoint Inhibitors in Cancers: A Systematic Review and Meta-Analysis. Front. Oncol. 9 , 1161 (2019). Cao, J. et al. The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis. Transl Oncol. 20 , 101375 (2022). Ba, H., Liu, L., Peng, Q., Chen, J. & Zhu, Y. D. The relationship between blood-based tumor mutation burden level and efficacy of PD-1/PD-L1 inhibitors in advanced non-small cell lung cancer: a systematic review and meta-analysis. BMC Cancer . 21 (1), 1220 (2021). Galvano, A. et al. The prognostic impact of tumor mutational burden (TMB) in the first-line management of advanced non-oncogene addicted non-small-cell lung cancer (NSCLC): a systematic review and meta-analysis of randomized controlled trials. ESMO Open. 6 (3), 100124 (2021). Ma, X., Zhang, Y., Wang, S. & Yu, J. Predictive value of tumor mutation burden (TMB) with targeted next-generation sequencing in immunocheckpoint inhibitors for non-small cell lung cancer (NSCLC). J. Cancer . 12 (2), 584–594 (2021). Chabanon, R. M. et al. Mutational Landscape and Sensitivity to Immune Checkpoint Blockers. Clin. Cancer Res. 22 (17), 4309–4321 (2016). Table S1 Table S1 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files FigS1.jpg FigS2.jpg FigS3.jpg FigS4.jpg FigS5.jpg FigS6.jpg FigS7.jpg FigS8.png FigS9.png FigS10.png FigS11.png FigureS111legends.doc TableS2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Editor invited by journal 24 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 22 Jul, 2025 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. 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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-7114203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499059146,"identity":"7e8c9327-bf33-48f7-9e07-175fd7b5d413","order_by":0,"name":"Min Ying","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Ying","suffix":""},{"id":499059147,"identity":"5a298bc6-22a9-4eb3-bb99-5d012b69a88d","order_by":1,"name":"Mengyu Hu","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Hu","suffix":""},{"id":499059148,"identity":"81c71ef8-c95c-4a15-a2a0-752d3484d142","order_by":2,"name":"Kegui Weng","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kegui","middleName":"","lastName":"Weng","suffix":""},{"id":499059151,"identity":"48c0993f-776e-4f29-80a8-6f73888f6caf","order_by":3,"name":"Yanyan Long","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Long","suffix":""},{"id":499059153,"identity":"f54c74c6-ca7d-485b-b862-6a56d8428369","order_by":4,"name":"Jing Chen","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":499059154,"identity":"1ebbf9f9-72f6-42a1-b8de-e8501c631823","order_by":5,"name":"Qianqian Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACZhBhAMTsDAwHPkDEDIjUAmQcnEGUFmS9zDzEaOE7zvzswZsCuzwGZuaDh21qrBMb2Ju3STDU3MGpRfIwm7nhHIPkYgZmtoTDOcfSExt4jpVJMBx7hlOLwWEGM2keA+bEBmYeg8O5DYcTGyRyzCQYGw7j0cL+DailHqiF/8NhS5AW+TeEtPCAbDkMsoXhMCPYFh78WiQP85RJzjE4ntjGzGZwsOdYunEbT1qxRcIx3Fr4zh/fJvHmT3ViP3vz4w8/aqxl+9kPb7zxoQa3FoYDQAyKDjYIlxnCSMCtAaGFAaZlFIyCUTAKRgE6AAAask4qjN/ShAAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Lei","suffix":""},{"id":499059156,"identity":"717ad552-68c9-4d32-b32b-1a6d70f1cbf3","order_by":6,"name":"Yongzhong Wu","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongzhong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-07-13 15:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7114203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7114203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89454085,"identity":"a5edd204-1b84-48b6-995c-1a20ad286f0e","added_by":"auto","created_at":"2025-08-20 06:45:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57956,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of literature search\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/cd0032cacc5cf9a527579c2d.jpg"},{"id":89454086,"identity":"3cf3c05e-3bf1-4701-a211-d808125e351a","added_by":"auto","created_at":"2025-08-20 06:45:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148950,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis on the association between demographic characteristics and TMB expression. (A) squamous cancer, (B) male, (C) smoker, (D) Asian\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/0030f6cd9596c05c97156a21.jpg"},{"id":89454087,"identity":"4c49a6eb-0929-4532-8264-9c169f2906cc","added_by":"auto","created_at":"2025-08-20 06:45:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96134,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of immune checkpoint inhibitors therapy for objective response rate (ORR) in high TMB group versus low TMB group.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/d868d306c1addba147721c1a.jpg"},{"id":89454088,"identity":"db81fc43-0183-4374-b865-3c32c45afe20","added_by":"auto","created_at":"2025-08-20 06:45:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200406,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of hazard ratio for progression free survival (A) Total, (B) Blood, (C) Tissue.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/e8cfa21045297c36b2e81dfa.jpg"},{"id":89456222,"identity":"8531e697-228f-466d-9584-3332e5da6e46","added_by":"auto","created_at":"2025-08-20 07:01:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":227525,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of hazard ratio for overall survival (A) Total, (B) Blood, (C) Tissue.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/f4a76a0bf088da535d2ac01f.jpg"},{"id":89454096,"identity":"34c2145b-e1a3-4435-8fce-8838add414ea","added_by":"auto","created_at":"2025-08-20 06:45:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":162622,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analysis of immune checkpoint inhibitors therapy for overall survival of (A) single immunotherapy, (B) double immunotherapy, (C) Immunotherapy plus chemotherapy, (D) PD-1, (E) PD-L1, (F) First-line, (G) more than first-line therapy in high TMB group versus low TMB group.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/df4803427e09b3c9f549c2cb.jpg"},{"id":89457775,"identity":"3ba7a520-b8e9-4bfc-b001-7872097b99b6","added_by":"auto","created_at":"2025-08-20 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06:53:14","extension":"doc","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":36352,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS111legends.doc","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/36b5530ebb09bd62cc30436d.doc"},{"id":89454110,"identity":"51b531f9-597d-43b2-ac21-dce96c3fbe16","added_by":"auto","created_at":"2025-08-20 06:45:14","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":21472,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7114203/v1/333c11fabbbf9d733e1b2afe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Predictive efficacy of tumor mutational burden for immune Checkpoint Inhibitors in non-small cell lung cancer: A systematic review and meta-analysis of randomized controlled trials","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers, with many patients presenting as locally advanced or metastatic stages at initial diagnosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent decades, immune checkpoint inhibitors (ICIs) have become one of the most effective treatments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the KEYNOTE-001 trial, pembrolizumab monotherapy provided superior antitumor effects, especially for the programmed death ligand-1 (PD-L1)\u0026ndash;expressing NSCLC, compared with chemotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, not all patients with PD-L1-positive tumors respond well to ICIs and PD-L1 is not a perfect biomarker [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is urgent to identify additional biomarkers to select patients for ICIs.\u003c/p\u003e\u003cp\u003eTumor mutational burden (TMB) represents the quantity of somatic mutations in cancer cells that can be assessed in tissue (tTMB) or blood (bTMB) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Tumor with high TMB may carry more neoantigens,, which have been reported to respond better to ICIs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Higher TMB was first identified as being associated with a better response to ICIs in NSCLC in a retrospective trial [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In CheckMate 026, the overall response rate (ORR) in the nivolumab group was higher and the median progression free survival (PFS) was longer than the chemotherapy group for the high TMB NSCLC patients. However, there was no significant association between TMB and overall survival (OS) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. According to the CheckMate 227 trial, NSCLC with a high TMB treated with first-line nivolumab plus ipilimumab achieved significantly longer PFS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the five-year survival analysis in KEYNOTE-010, high TMB was also associated with a better response to pembrolizumab in previously treated PD-L1 positive advanced NSCLC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, despite the approval of TMB as a biomarker for pembrolizumab in all cancers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the predictive value of TMB varied across trials. In the study reported by Gettinger [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], high TMB was not related to a better outcome in patients treated with ICIs but was found to be a predictive biomarker for OS in the subgroup analysis of patients with no tumor PD-L1 expression. More recently, clinical trials suggested that there was no significant association between tTMB and survival benefits [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Beyond the discordant results, the need for adequate tissue specimens limits the application of tTMB detection and its use in monitoring cancer therapy. With the development of liquid biopsy techniques, detecting bTMB has been applied in clinical practice [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. An agreement analysis conducted using an independent cohort revealed that tTMB and bTMB are positively correlated [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. NSCLC patients with high bTMB were reported to obtain more survival benefits in several trials [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the results regarding the efficacy of bTMB were inconsistent. In NEPTUNE trial [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], NSCLC patients with high bTMB did not achieve longer OS and PFS from treatment with durvalumab plus tremelimumab.\u003c/p\u003e\u003cp\u003eAs the conclusions of several meta-analysis [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] varied, we performed the meta-analysis to explore the efficacy of the TMB in NSCLC treated with ICIs, based on the latest trials. Our objective was to clarify the role of TMB as a biomarker for ICIs. This manuscript was prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Search strategy\u003c/h2\u003e\u003cp\u003eWe performed systematic searches of PubMed, Embase, Web of Science, and Cochrane Library from inception to April 2, 2024. The search keywords included: \u0026ldquo;mutation burden\u0026rdquo;, \u0026ldquo;mutational burden\u0026rdquo;, \u0026ldquo;mutation load\u0026rdquo;, \u0026ldquo;mutational load\u0026rdquo;, \u0026ldquo;TMB\u0026rdquo;, \u0026ldquo;TML\u0026rdquo;, \u0026ldquo;non-small-cell lung cancer\u0026rdquo;, \u0026ldquo;non-small-cell lung carcinoma\u0026rdquo;, \u0026ldquo;non-small-cell lung neoplasm\u0026rdquo;, \u0026ldquo;non-small-cell lung tumor\u0026rdquo;, \u0026ldquo;NSCLC\u0026rdquo;, \u0026ldquo;pulmonary adenocarcinoma\u0026rdquo;, \u0026ldquo;lung adenocarcinoma\u0026rdquo;, \u0026ldquo;adenocarcinoma of the lung\u0026rdquo;, \u0026ldquo;lung squamous carcinoma\u0026rdquo;, \u0026ldquo;pulmonary squamous carcinoma\u0026rdquo;, \u0026ldquo;squamous cell lung carcinoma\u0026rdquo;, \u0026ldquo;squamous carcinoma of the lung\u0026rdquo;, \u0026ldquo;random\u0026rdquo; and \u0026ldquo;randomized\u0026rdquo;. The potentially available references from related trials or reviews were also searched. The detailed strategies are summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study selection\u003c/h2\u003e\u003cp\u003eThe inclusion criteria were as follows: (1) randomized controlled trials on the role of TMB in NSCLC patients treated with ICIs; (2) adequate data on complete response(CR), partial response (PR), stable disease (SD) and progressive disease (PD) evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], stratified by TMB expression, or sufficient information to estimate the hazard ratio (HR) and its 95% confidence interval (CI) for OS and PFS comparing high TMB versus low TMB. As regards the studies with overlapping patient population, the most recent investigation of the maximum number of individuals was selected for inclusion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data extraction and Quality assessment\u003c/h2\u003e\u003cp\u003eThe following information was extracted from each retrieved article: first author, year of publication, country, sample size, age, experiment drugs, detection method, cutoff value, sample source, outcome, and data linking TMB expression to treatment outcome (i.e., CR, PR, CR\u0026thinsp;+\u0026thinsp;PR, SD, PD, and HR). Two authors (MY and MY Hu) extracted the data independently. Any disagreements were resolved through discussion with a third author to reach a consensus. The quality of all studies was assessed using the Newcastle Ottawa Scale (NOS) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A total of up to 9 stars were assigned based on items in different categories. Studies awarded 7 or more stars were considered high quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\u003cp\u003eFor the purpose of analyzing the possible relationships between demographic characteristics and TMB expression, odds ratio (OR) with a 95% CI was used. The correlation between TMB expression and ORR was measured by the pooled relative rate (RR) and its 95% CI. Survival benefits were evaluated using HR with 95%CI. We extracted the HRs with 95% CIs for OS and PFS in each study to further analysis. When HRs and CIs were not provided in the original articles, the exploitable survival analysis data were extracted from the survival curves of different treatment arms which had been scanned into Engauge Digitizer. The HRs were then estimated using the method reported by Tierney et al [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For patients with any demographic character, OR\u0026thinsp;\u0026gt;\u0026thinsp;1 means more possibilities to harbor a high TMB expression. The primary endpoints of the meta-analysis were compared between high TMB and low TMB group. A correlation between higher ORR and TMB expression was established if RR\u0026thinsp;\u0026gt;\u0026thinsp;1, indicating better response to the ICIs treatment. When the HR for OS or PFS was greater than 1, it reflected a higher risk reaching the end point of the study and less survival benefits for the high TMB group. Subgroup analyses were conducted by aggregating the studies. If both tTMB and bTMB data were available in the same study, the pooled analysis was performed using tTMB data.\u003c/p\u003e\u003cp\u003eHeterogeneity was assessed by the Chi-square test and considered significant when P\u003csup\u003eheterogeneity\u003c/sup\u003e \u0026lt; 0.1 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%. If the analysis showed high risk of heterogeneity, random-effect based model [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used. Otherwise, the Mantel and Haenszel fixed-effect model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was used. An evaluation of potential publication bias was tested by the funnel plot which was assessed by the method of Begg and Egger tests. Publication bias was deemed insignificant if P\u0026thinsp;\u0026gt;\u0026thinsp;0.05. All the statistical tests were conducted with Review Manager 5.4 (The Cochrane Collaboration, Copenhagen, Denmark).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Study characteristics\u003c/h2\u003e\u003cp\u003eBased on the search strategies, 590 relevant articles were yielded. After removing 323 duplicated articles according to the abstracts, 267 articles were taken into further consideration as indicated in the flowchart of study selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Finally, 13 randomized controlled trials [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] were included in the meta-analysis. Baseline characteristics of these eligible trials were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The quality assessment was conducted and lumped into Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. We also aggregated two studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] to evaluate the possible relationships between demographic characteristics and TMB expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A marginally statistically significant trend was observed for high TMB in male patients [OR\u0026thinsp;=\u0026thinsp;1.43, (1.10, 1.86)] or non-Asian population [OR\u0026thinsp;=\u0026thinsp;0.66, (0.49, 0.89)]. There was a marginally significant difference (P\u0026thinsp;=\u0026thinsp;0.08) in TMB expression among smokers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMain characteristics of the included trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExperiment drugs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDetection method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSample source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64\u003c/p\u003e\u003cp\u003e(29\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNivolumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e242mutations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDe Castro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (27\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDurvalumab+\u003c/p\u003e\u003cp\u003etremelimumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGandara\u003c/p\u003e\u003cp\u003e(OAK)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAtezolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGandara\u003c/p\u003e\u003cp\u003e(POLAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAtezolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGarassino\u003c/p\u003e\u003cp\u003e(Keynote 189)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (56\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePembrolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175mut/exome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGarassino\u003c/p\u003e\u003cp\u003e(Keynote 407)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66 (60\u0026ndash;71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePembrolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175mut/exome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGettinger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.5 (41.8\u0026ndash;90.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNivolumab+\u003c/p\u003e\u003cp\u003eipilimumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHerbst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePembrolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175mut/exome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJiang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCamrelizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e75%*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u0026thinsp;+\u0026thinsp;bTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeighl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (27\u0026ndash;87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDurvalumab+\u003c/p\u003e\u003cp\u003eTremelimumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20 mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRizvi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDurvalumab+\u003c/p\u003e\u003cp\u003etremelimumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS,ORR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eToripalimab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOS,PFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTislelizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003etTMB\u0026thinsp;\u0026ge;\u0026thinsp;10 mut/Mb\u003c/p\u003e\u003cp\u003ebTMB\u0026thinsp;\u0026ge;\u0026thinsp;6 mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ebTMB\u0026thinsp;+\u0026thinsp;tTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHayashi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNivolumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.2mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHellmann\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNivolumab+\u003c/p\u003e\u003cp\u003eIpilimumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10mut/Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003etTMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e* Mutations that met a certain sequencing depth (100X for tissue samples and 500X for plasma samples) and VAF (5% for tissue samples and 0.7% for plasma samples) were chose as candidate mutations for bTMB analysis. Subsequently, bTMB was calculated based on the candidate mutations according to the following formula: \u003cem\u003ebTMB\u0026thinsp;=\u0026thinsp;absolute mutation count*\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e1000000/panel\u003c/span\u003e\u003cspan address=\"1000000/panel\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cem\u003eexonic base number.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Relationship between TMB and ORR\u003c/h2\u003e\u003cp\u003eA total of 6 studies included 2,273 patients who were assessable for tumor response on the basis of nine groups (8,10,13,16,18,31). The ORRs in high and low TMB group of NSCLC patients were approximately 41.5% and 23.7%, respectively. The pooled RR for ORR was 1.63 (95% CI 1.24\u0026ndash;2.14), further implying that high TMB for NSCLC treated with ICIs was associated with a better ORR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No significant publication bias was found for ORR (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the light of the impact of sample sources on TMB, detection was conducted based on tTMB (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA) and bTMB (Fig \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). The pooled RR were 1.31 (95%CI 0.92\u0026ndash;1.86) and 1.94 (95%CI 1.58\u0026ndash;2.39) for tTMB and bTMB subgroup, respectively. When ICIs were given as first line treatment (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC) or second- or later-line therapy (Fig \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eF), high TMB was related to better response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWith regard to the ORR to the patients treated with the combination of anti-PD-1/PD-L1 antibodies with chemotherapy (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB), although the studies with relevant data showed consistent trends toward worse ORR among patients with low TMB, the result of pooled analysis showed no statistically significant difference between high TMB group and low TMB (RR 1.21, 95% CI 0.86\u0026ndash;1.71). Compared to patients with low TMB expression (Fig \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), high TMB predicted significantly better ORR when treated with single-agent ICIs, anti-PD-1/PD-L1 antibodies, or combined with anti-cytotoxic T lymphocyte antigen 4 (anti-CTLA-4) antibodies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Prognostic efficacy of TMB in NSCLC treated with ICIs\u003c/h2\u003e\u003cp\u003eAs a result of pooled analysis, the HR for PFS was 0.78 (95% CI 0.71\u0026ndash;0.86; P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that NSCLC patients with high TMB can experience a significant prolongation of progression free survival times. There was no significant heterogeneity between studies (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;16%; P\u0026thinsp;=\u0026thinsp;0.27; fixed-effect model). There was no significant publication bias (Fig \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). For tTMB, the pooled analysis of HR for PFS is 0.69 (95% CI 0.60\u0026ndash;0.78), while bTMB failed to predict PFS benefits (HR 0.91, 95% CI 0.79\u0026ndash;1.04). Significant survival benefits were observed for patients with high TMB treated with ICIs as first line therapy in the PFS analysis (HR 0.75, 95% CI 0.67\u0026ndash;0.83) (Fig \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eE), but no such benefits were found in the PFS analysis of previously treated patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the subgroup analysis, high TMB was related to better PFS in NSCLC treated with anti-PD-1 therapy with or without chemotherapy and ICIs combined with anti-CTLA-4 (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). There was no significant difference in the subgroup analysis of patients treated with single immunotherapy or anti- PD-L1 antibodies (Fig \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilar to the analysis on PFS, the pooled HR for OS was 0.86 (95% CI 0.76\u0026ndash;0.98; P\u0026thinsp;=\u0026thinsp;0.02), which suggested that patients with high TMB (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) or high tTMB (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) had significantly longer OS. However, the analysis of bTMB showed no significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Our pooled results of OS showed statistically significant differences in the subgroup analysis of ICIs as first-line therapy, indicating that high TMB was associated with longer OS in the previous untreated patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of subgroup analyses indicated that the OS of patients with high TMB was longer than that of patients with low TMB, when treated with ICIs combined with anti-CTLA-4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). However, there was no significant OS difference between the high TMB and low TMB groups when treated with single-agent ICIs, anti-PD-1/PD-L1 antibodies, with or without chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There was no evidence of publication bias in the analysis of OS (Fig \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther subgroup analysis based on type of sample revealed significantly improved PFS was observed in patients with high tTMB, whereas no significant PFS benefit was observed in those with high bTMB (Fig \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e-\u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). In terms of OS, neither high tTMB nor high bTMB led to significant survival benefits (Fig \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e-\u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003e). These findings indicate that tTMB exhibits better predictive value for PFS, while neither biomarkers showed significant predictive utility for OS.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUp to now, ICIs have been approved for a wide range of cancers including NSCLC, exerting greater overall benefits than conventional platinum-based doublet chemotherapy in the first-line treatment in advanced NSCLC and serving as a good option for second-line treatment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] for those with negative driver genes. The treatment efficacy varies in-dividually [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], highlighting the importance of screening potential biomarkers to identify the subgroup of patients with potential survival benefits.\u003c/p\u003e\u003cp\u003eTMB, including tTMB and bTMB, has been tested in many samples. Based on the analysis of matched samples, tTMB and bTMB were correlated [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The association between TMB and epidemiological characteristics was evaluated in several studies. According to subgroups analysis across treatment arms in the OAK Study, high bTMB was more common in the male and smoking patients. There was no significant relationship between high bTMB and histology or race [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nevertheless, in another study, baseline bTMB did not correlate with sex or smoking history [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Rizvi found that there were greater proportions of patients with smoking history and squamous histologic type in the high-TMB group (bTMB\u0026thinsp;\u0026ge;\u0026thinsp;20 mut/Mb or tTMB\u0026thinsp;\u0026ge;\u0026thinsp;10 mut/Mb) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As far as we know, this study is the first meta-analysis focused on the relationship between high TMB and clinical characteristics. In this study, we found that higher TMB levels were more common in the male group and less common in the Asian group. The smoking NSCLC patients also presented the same trend to have high TMB. The expression of TMB in the squamous carcinoma group did not show significant differences compared to the non-squamous carcinoma group. However, since only two studies were included, more trials are needed to explore the epidemiological features further.\u003c/p\u003e\u003cp\u003eThe predictive value of TMB efficacy for ICIs in cancer was analyzed in trials and meta-analysis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Wu et al. (36) reported that TMB was a powerful marker for predicting the effect of immunotherapy in cancer patients. In the subgroup analysis of NSCLC patients, TMB can be used as a predictor of immunotherapy efficacy, except for the OS. Cancer patients with high TMB were also reported to benefit from immunotherapy [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the meta-analysis, TMB has been shown to be a marker for predicting the OS effectiveness of immunotherapy in NSCLC. Four early studies focused on NSCLC have shown that TMB may be associated with the efficacy of immunotherapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Ba et al. found that PD-1/PD-L1 inhibitors could improve ORR, PFS and OS in the high bTMB advanced NSCLC patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These results were confirmed in another study when ICIs were used as first-line therapy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Nevertheless, there was no direct comparison between the high TMB and low TMB group in the two trials. Although high TMB served as predictive biomarker based on the analysis of ORR and PFS, the clinical implications of TMB for PD-1/PD-L1 inhibitors cannot be adequately predicted in terms of OS, based on the meta-analysis conducted in 2003 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. According to two other studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], TMB was a promising biomarker for ICIs therapy while the enrolled studies included retrospective trials. According to the latest meta-analysis, six randomized trials were included [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In light of the 13 randomized clinical trials enrolled in the analysis, four trials were published in 2023 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Wang et al. reported that the OS benefits were similar in both TMB subgroups [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. There was also evidence indicating that the test specimen for TMB could result in differential sensitivity to ICIs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Differences in the time of detection such as pretreatment TMB and on-treatment TMB, may also lead to a potential bias in conclusions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Considering the longstanding controversy on this topic, we conducted the meta-analysis based on high-quality studies.\u003c/p\u003e\u003cp\u003eIn our pooled analysis, ICIs were beneficial for patients with high TMB in terms of ORR, PFS and OS. However, the positive predictive role of high TMB for response to ICIs in NSCLC was not consistently observed in the subgroup analysis. The clinical implications of high bTMB could identify potential responders to ICIs, while the pooled analysis of the predictive value of PFS and OS did note reach statistical significance. In contrast, our results based on the pooled analysis of four datasets suggested that high tTMB did not appear to be associated with the outcomes of ICIs in terms of ORR, but it was related to better survival benefits in terms of both PFS and OS. In view of the available evidence, high TMB was associated with better response and survival in the first-line treatment subgroup and in patients who were treated with anti-PD-1/PD-L1 antibodies combined with anti-CTLA-4. A potential interpretation for the results might be that the predictive value of high TMB could be interrupted by other treatment strategies, as high TMB is related to greater tumor mutational loads which are associated with ICIs response [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, according to the stratified analysis based on treatment, our results are consistent with those of Li, who reported that NSCLC patients with high TMB had better ORR when treated with PD-1/PD-L1 inhibitors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The survival benefits were only noticed in the patients treated with PD-1 inhibitors in terms of PFS based on our results. ICIs combined with chemotherapy is suggested for driver-negative non-small cell lung cancer. The predictive value of TMB in patients receiving ICIs with chemotherapy was investigated by pooling together the response and survival outcomes. Meta-analysis of six datasets showed that patients with high TMB had favorable PFS, while the results of ORR and OS did not reach statistical significance, which may be due to the different mechanisms of chemotherapy.\u003c/p\u003e\u003cp\u003eAlthough we conducted a comprehensive analysis of the value of TMB for ICIs treatment, there were several limitations. Firstly, we evolved only two studies with sufficient data on the relationship between TMB expression and clinicopathological characteristics. Therefore, selection of patients with high TMB based demographic features should be interpreted with caution. Moreover, only a few studies provided the exact HRs and 95% Cls when investigating the survival benefit. Most of the survival information was extracted from the survival curves which might limit precision. Last but not least, the TMB threshold value was not consistent across the enrolled trials due to the different analytical methods used in various trials, which necessitates further validation in prospective trials.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, the present meta-analysis provides evidence that high TMB expression is associated with greater clinical benefits from ICIs in NSCLC patients. This predictive value is more pronounced in patients who are previously untreated and those treated with ICIs alone. Compared with bTMB, tTMB is more suitable for selection of NSCLC patients who may benefit from ICIs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCR, complete response; HR, hazard ratio; ICIs, immune checkpoint inhibitors; NSCLC, non-small cell lung cancer; Newcastle-Ottawa Scale; OR, odds ratio; ORR, overall response rate; OS, overall survival; PD, progressive disease; PD-L1, programmed death ligand 1; PFS, progression-free survival; PR, partial response; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RECIST, Response Evaluation Criteria in Solid Tumors; RR, risk ratio, rate ratio; SD, stable disease; TMB, tumor mutational burden\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by Chongqing Science and Health Joint Medical Research Project (No. 2023GGXM002 to YZ Wu), National Natural Science Foundation Project (No. 82073347 to YZ Wu), Chongqing Talent Plan (No. cstc2022ycjh-bgzxm0208 to YZ Wu) and Chongqing Science and Health Joint Medical Research Project (No. 2023MSXM129 to QQ Lei). The APC was funded by YZ Wu.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMin Ying: conceptualization, data curation, methodology, formal analysis, writing original draft.Meng-Yu Hu: data curation, methodology, formal analysis, review and editing.Qian-qian Lei: conceptualisation, review and editing.Yong-Zhong Wu: conceptualisation, funding acquisition.Ke-Gui Weng, Yan-Yan Long, Jing Chen: review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, Z. et al. Impact of pneumonitis from radiotherapy combined with immune checkpoint inhibitors therapy on tumor progression and survival in patients with non-small cell lung cancer. \u003cem\u003eFront. 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Cancer Res.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (17), 4309\u0026ndash;4321 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table S1","content":"\u003cp\u003eTable S1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"biomarker, tumor mutational burden, immune checkpoint inhibitors, non-small cell lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-7114203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7114203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eTo date, immune checkpoint inhibitors (ICIs) have become the mainstay of treatment for locally advanced or metastatic non-small cell lung cancer (NSCLC). However, a subset of patients derives poor benefits from ICIs, highlighting the need for reliable biomarkers to identify those who are more likely to respond to such therapies. This review aims to evaluate the role of tumor mutational burden (TMB) as a predictive biomarker in NSCLC patients treated with ICIs.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003ePubMed, Embase, Web of Science, and the Cochrane Library were systematically searched from inception to April 2, 2024. The endpoints of interest included overall response rate (ORR), overall survival (OS), and progression-free survival (PFS).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA total of 13 randomized controlled trials were included. High TMB was associated with improved ORR [relative ratio (RR) 1.63, 95% confidence interval (CI) 1.24\u0026ndash;2.14], longer PFS [hazard ratio (HR) 0.78, 95% CI 0.71\u0026ndash;0.86] and longer OS (HR 0.86, 95% CI 0.76\u0026ndash;0.98). Subgroup analysis revealed that high tissue TMB (tTMB) was significantly associated with better PFS and OS, whereas pooled results for both PFS and OS based on blood TMB (bTMB) did not reach statistical significance. Furthermore, in the high TMB group, first-line treatment with ICIs provided significant benefits for NSCLC, as demonstrated by improved ORR, PFS, and OS.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThese results suggest that tTMB could serve as a potential predictor of clinical benefit from ICIs, particularly in previously untreated NSCLC. However, the predictive value of bTMB requires further investigation and defining an optimal bTMB cutpoint remains a significant challenge.\u003c/p\u003e","manuscriptTitle":"The Predictive efficacy of tumor mutational burden for immune Checkpoint Inhibitors in non-small cell lung cancer: A systematic review and meta-analysis of randomized controlled trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 06:45:08","doi":"10.21203/rs.3.rs-7114203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T20:17:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36245313297775063990900187631163960113","date":"2026-04-25T03:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28036502381619547753284298185862999814","date":"2026-04-03T05:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235920643355097089990018475180488130206","date":"2026-02-20T06:06:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186672461395244346656769710454915365790","date":"2026-02-13T07:02:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T23:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207909860480779238851060656834066870516","date":"2026-02-12T23:07:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T22:44:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T22:41:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T10:52:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T15:13:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-22T15:09:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16ba2d28-6094-403f-8616-c199fa1ac739","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T20:17:54+00:00","index":88,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53006487,"name":"Health sciences/Biomarkers"},{"id":53006488,"name":"Biological sciences/Cancer"},{"id":53006489,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-20T06:45:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 06:45:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7114203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7114203","identity":"rs-7114203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}