The impact of TP53 co-mutation on the clinical outcomes of advanced non-small cell lung cancer patients with EGFR sensitizing mutation: An updated systematic review and meta-analysis of prospective clinical trials and a single-center retrospective cohort study

The impact of TP53 co-mutation on the clinical outcomes of advanced non-small cell lung cancer patients with EGFR sensitizing mutation: An updated systematic review and meta-analysis of prospective clinical trials and a single-center retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of TP53 co-mutation on the clinical outcomes of advanced non-small cell lung cancer patients with EGFR sensitizing mutation: An updated systematic review and meta-analysis of prospective clinical trials and a single-center retrospective cohort study Wenshu Huang, Huiting Wei, Wei Jiang, Cuiyun Su, Yun Zhao, Jianbo He, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5354638/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose The currently updated research was conducted in order to determine the effect of TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR-sensitizing mutation. Methods We used the MINORS Methodological items for non-comparative single-arm clinical research, and for eligible randomized clinical trials (RCTs), we used the Cochrane risk of bias instrument. Outcomes including progression-free survival (PFS) and overall survival (OS) were extracted for further analysis. A total of 164 advanced non-small cell lung cancer (NSCLC) patients were enrolled in the study. The prognostic value of TP53 mutation status for PFS and OS was evaluated using Kaplan-Meier survival analysis and Cox's proportional hazards regression model. Results A pooled incidence of TP53 co-mutation was 49.6%. With regard to survival analysis, patients with no TP53 mutation showed a significantly longer PFS (HR = 0.66; 95% CI = 0.57–0.76; P < 0.0001), in the meantime, meta-analysis demonstrated a significantly shorter OS in patients harboring concurrent TP53 mutation treated with EGFRTKIs (HR = 0.61; 95% CI = 0.51–0.74; P < 0.0001). Mutations in exon 4 or 7 of TP53 served as independent worse prognostic factors for disease progression compared with no TP53 co-mutation (HR = 2.49, 95% CI: 1.56–3.97, P < 0.001) and the others TP53 mutation (HR = 2.38, 95% CI: 1.46–3.86, P < 0.001). Conclusion Individuals with advanced NSCLC who also had TP53 co-mutation had worse shorter PFS and OS. Particularly, TP53 exon 4 or exon 7 mutations suggest a poorer prognosis. advanced non-small cell lung cancer epidermal growth factor receptor TP53 coexistence of mutation meta-analysis retrospective cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction According to the World Health Organization's most current "Global Cancer Statistics 2020" statistics, lung cancer is the most common cancer-related cause of death globally, accounting for more than 10% of all malignant tumors [1] . The majority of lung cancer patients (80–85%) have non-small cell lung cancer (NSCLC), which is the most prevalent histological phenotype of lung cancer [2] . 70% of NSCLC patients with stages I to III are surgically curable [3] . Advanced non-small cell lung cancer patients had a 5% survival rate after five years [4] . Because they lack typical lung cancer symptoms, around 62 percent of non-small cell lung cancer patients are diagnosed with stage IV at their initial assessment [5] . The three main treatment options for non-small cell lung cancer are surgery, chemotherapy, and radiation. The treatment of the particular genotype NSCLC has made significant strides [6] . The epidermal growth factor receptor (EGFR) gene-activating mutation has been identified as the primary oncogenic factor in NSCLC. One of the most frequently seen causes of NSCLC is EGFR mutation, which is especially prevalent in Asian patients with NSCLC lung cancer. EGFR-sensitive mutations include deletion mutation in exon 19 and L858R point mutation in exon 21, both of which cause a marked increase in EGFR kinase activity [7] . Therefore, the detection of EGFR-sensitive mutation has become an important part of lung cancer treatment. The prognosis of patients with NSCLC who have EGFR mutation has significantly improved during the past ten years because of molecular targeted therapy [8]–10] . There are still some individuals who have initial non-response despite the fact that tyrosine kinase inhibitors (TKIs) that may target active EGFR and have excellent effectiveness in NSCLC with EGFR mutation are available, and extremely varied outcomes are seen in EGFR-TKI responders [11] . Due to the existence of non-responders and the variable prognosis of responders, it can't always be said that EGFR-TKI monotherapy is the best course of action for treating EGFR mutant NSCLC. To find additional mechanisms of TKI resistance, more research on the genetic profile of NSCLC is required. TP53 located on chromosome 17p13.1, is a pivotal intracellular tumor suppressor gene and plays a pivotal role in various biological processes, including regulation of the cell cycle, induction of apoptosis, differentiation of cells, repair of DNA damage, and regulation of angiogenesis [12] . The P53 protein, which is particularly efficient in suppressing tumor growth, is produced by the TP53 gene. The p53 pathway becomes active in response to DNA damage and either causes a brief interruption of the cell cycle to aid in repair or, in the event of irreversible damage, starts apoptosis [13] . A number of malignancies, including lung adenocarcinoma, typically have TP53 mutation (> 50%) [14] . Changes to the TP53 genetic structure are thought to have a key role in oncogene-driven lung cancer subgroups' clinical and molecular heterogeneity due to their significant effects on treatment resistance and genomic instability [15] . The results, particularly those related to the TP53 co-mutation [16]–[18] , implied that coexisting mutation may affect the effectiveness of EGFR-TKI. Although earlier research suggested that the TP53 co-mutation could be used to predict worse clinical outcomes in EGFR-TKIs treatment, almost all earlier meta-analysis studies on this topic were retrospective cohort studies [19]–[21] , leaving uncertainty in the epidemiological data. Retrospective studies don't have the same level of rigor as clinical trials when it comes to research design and outcomes analysis, thus the information clinical trials provide is more trustworthy. The influence of TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who have EGFR sensitizing mutation is a topic of increasing interest in clinical trials, the majority of which are still in progress. Similar information was provided in subgroup analysis by the few completed clinical investigations. Therefore, the purpose of the currently updated meta-analysis was to ascertain the effect of the co-mutation of TP53 on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutation in prospective clinical trials. In order to further support the impact of the TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutation, we simultaneously gathered a pertinent cohort from our institution and performed a retrospective analysis on it. Materials and methods A literature search of meta-analysis The most updated meta-analysis adhered to PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines [22] and had been registered with PROSPERO (ID: CRD42023462455) ( https://www.crd.york.ac.uk/prospero/ ). As of September 8, 2023, electronic searches for clinical trials were conducted in the databases PubMed, Embase, and Web of Science. The complete search we used for PubMed was supplied in Table S1 . Inclusion and exclusion criteria of meta-analysis The studies that were taken into consideration satisfied the following inclusion criteria: (1) Patients enrolled in prospective clinical studies with advanced non-small cell lung cancer verified by histopathology; (2) Patients receiving EGFR-TKIs as a therapy; (3) At least one set of survival and associated prognostic data was presented in the study. The following studies were omitted to lessen the chance of bias: (1) Patients in prospective clinical trials were not advanced non-small cell lung cancer; (2)Patients did not use EGFR-TKIs as treatment, such as chemotherapy or immunotherapy; (3) Not prospective clinical trials; (4) Lack of essential data or overlapping studies; (5) failure to consider animal experiments, cell research, reviews, meta-analyses, duplicates, case reports, or correspondence; Two investigators independently selected potential eligible papers using inclusion and exclusion criteria. Any arguments concerning the study's inclusion were resolved by these two or a third investigator. Data extraction and quality assessment of meta-analysis Two investigators independently retrieved the relevant data from each included study, and the quality of the investigations was assessed. Year of publication, first author, registration number, country, total number of patients, EGFR mutation, number of patients with TP53 mutation, method of TP53 detection, detected exons of TP53, samples for testing, trial phase, interventions of EGFR-TKIs and histology were a brief summary of the retrieved characteristics. Objective response rate (ORR), disease control rate (DCR), hazard ratio (HR), and 95% confidence interval (CI) of progression-free survival (PFS) and overall survival (OS) were among the outcomes that were also retrieved. Since most of the studies derived their data from subgroup analyses, for the literature that did not directly report HRs, we calculated HRs and 95% CIs by using the corresponding mean PFS or OS and log-rank p-values. For non-comparative single-arm clinical trials, we used the MINORS Methodological Items [23] and the Cochrane Risk of Bias Instrument [24] to evaluate the quality, whereas for eligible randomized clinical trials (RCTs), we utilized the Cochrane Risk of Bias Instrument [24] . The design of the retrospective cohort study Patients diagnosed with advanced NSCLC at the Guangxi Medical University Cancer Hospital between January 2016 and December 2022 were included for further screening, and the screening criteria for the cohort study were as follows: (1) Pathologically confirmed primary lung cancer, as defined by the 5th edition of the WHO Thoracic Tumor Classification; (2) clinical stage IV, as defined by the 8th TNM staging system of lung cancer; (3) Eastern Cooperative Oncology Group (ECOG) score for the physical status of 0–2 points; (4) tumor tissue specimens or peripheral blood cell-free tumor DNA (ctDNA) were used in our hospital or the referring institution for NGS (next-generation sequencing) in order to diagnose the patient, which detected the existence of classical EGFR mutation (Exon19 deletion and Exon21 L858R point mutations) and TP53 mutations as well as their mutational status; (5) First-line therapy with either EGFR-TKI monotherapy or EGFR-TKI combined chemotherapy. Exclusion criteria for the study were as follows: (1) adenocarcinoma with small cell lung cancer; (2) other types of EGFR mutation; (3) Follow-up information was unavailable and the cases with missing data. 164 patients were included in the final analysis. Patients in the TC group received EGFR-TKIs (gefitinib 250 mg once daily (qd), or erlotinib 150 mg, qd, or erlotinib 125 mg three times daily (tid), or afatinib 40 mg, qd, or osimertinib 80 mg, qd, or almonertinib 110 mg, qd, or furmonertinib 80 mg, qd) in combination with chemotherapy (primarily pemetrexed plus platinum) based on pemetrexed (500 mg/m2). Treatment is continued until the condition worsens or the side effects become intolerable. Short-term dose modifications or delays are permitted based on individual chemo response, and the precise number of cycles of chemotherapy can be altered based on drug efficacy and patient acceptability due to the severity of treatment-related side effects. After six cycles of chemotherapy, some patients additionally undergo EGFR-TKIs and pemetrexed maintenance treatment. Patients in the T group received treatment with EGFR-TKIs. The Next-Generation Sequencing of retrospective cohort study NGS was performed by Gene + Smart Laboratory (Beijing, China) and Geneseeq Technology (Nanjing, China). The 425-gene includes exons, fusion-related introns, alternative splicing regions, and specific microsatellite (MS) sites, with a total of about 1.28Mb base sites. We used 139 genes commonly found to be mutated in lung cancer, including point mutations, small fragment indels, gene fusions, copy number variations, and microsatellite analysis. The average sequencing depth was > 4500X. In this study, we obtained tissue specimens for molecular analysis from various sources including surgery and core needle biopsy, which were guided by computed tomography (CT) or Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) and EBUS Guide Sheath Transbronchial Lung Biopsy (EBUS-GS-TBLB). Tumor tissue, blood, cerebrospinal fluid, and pleural effusion were used to perform the molecular analyses. Data collection and assessment of retrospective cohort study For pertinent information, the database of medical records at the hospital was checked. Patients who had not visited the hospital for more than three months were called in order to follow up and gather pertinent information, such as the patient's tumor recurrence and prognosis. A CT scan was used to assess the effectiveness of the therapy and to analyze the tumor lesions both before and after treatment. The patient's medical records were used to gather all clinical and laboratory markers. Age, sex, smoking history, the ECOG-PS, histology, clinical stage, and metastases were clinical markers. The TP53 mutation and the EGFR mutation were laboratory markers. The four categories of complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) are used in Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 to determine efficacy. Disease control rate (DCR) equals CR + PR + SD; objective response rate (ORR) equals CR + PR. The research protocol's start date was used to compute progression-free survival (PFS), which was measured from that point until the time of the last follow-up or until the illness progressed. The patient's overall survival (OS) was measured as the interval from the start of anti-tumor medication therapy to death or the final follow-up. This study was approved by the ethical committee of the Guangxi Medical University Cancer Hospital, and all processes and information collection were conducted in accordance with the ethical standards set by the Research Committee of the hospital. Statistical Analysis Utilizing the statistical program Stata, evidence was synthesized. Stata (Stata Corp, USA) was used to calculate the relevant standard errors of these quasinormal distribution "rates" by entering the full clinical setting % for the main outcome and the complete list of research participants. The lower interval (LI) and upper interval (UI), which have a 95% confidence interval, may be justified using the "rates" and standard errors. The final component of the output was the pooled effect sizes (ES), which represented median "rates" and 95% confidence intervals (95% CI). To examine study heterogeneity, the I 2 statistic was utilized. Based on the I 2 values, which varied from 25 to 50%, 50 to 75%, and > 75%, studies were classified as having low, moderate, or high heterogeneity. Substantial heterogeneity was determined by the I 2 test as P < 0.05. For every pooled ES, random-effects models were utilized. The meta-regression and subgroup analyses could be done because of the abundance of data that was given. Sensitivity analysis was also carried out to evaluate the reliability and consistency of the combined data. Egger's tests also looked for a potential bias in favor of publications. Using the statistical tools R and EmpowerStats, all statistical analyses for the retrospective cohort research were carried out. In place of averages and standard deviations (SD), we utilized frequencies, percentages, or ratios for categorical variables. Comparisons between study protocol groups were evaluated using the χ2 or Fisher's exact test (for categorical variables). Kaplan-Meier survival curves were produced, and the log-rank test was used to assess differences. For both univariate and multivariate analyses, Cox regression was utilized. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox's proportional hazards regression model. We corrected for possibly confounding factors in the multivariate analysis. Statistically significant variables in univariate Cox regression analyses were included in the adjusted for multivariate analysis. To evaluate the relationship between various TP53 mutation and the risk of illness progression and mortality in various subgroups, subgroup analyses were utilized. P < 0.05 was considered statistically significant in each analysis. Results Study selection Searching the previously stated databases Pubmed (n = 633), Web of Science (n = 1159), and Embase (n = 2387) yielded a total of 4179 records. 1347 articles were disregarded due to redundancy. 2532 records that have no connection to the subject matter were removed. 293 records were eliminated for the following reasons: (1) reply and comment sections; (2) reviews and meta-analyses; (3) non-prospective clinical trials; (4) non-advanced nonsmall cell lung cancer; and (5) lack of available outcome data. It was feasible to perform a quantitative synthesis for the final seven publications. The process of selection was illustrated in Fig. 1 . Characteristics of the included studies The present meta-analysis included a total of 7 studies [25]–[31] involving 1319 participants. Table 1 described the main study characteristics; Table 2 presented the survival results and Table S2 shown the details of gene alteration in the included studies. The studies were all published between 2014 and 2023. Four studies [25][26][29][31] were randomized clinical trials, and 3 studies [27][28][30] were non-comparative clinical trials. Table 1 Characteristics of clinical trials included in the meta-analysis Study Year Register number Country Patients included Patients with TP53 mutation Histology Phase Interventions Costa et al. [25] 2014 NCT00446225 France, Italy, Spain 82 23 NSCLC III Erlotinib Li et al. [26] 2021 NCT01024413 China 195 134 NSCLC III Elortinib, Gefitinib Wang et al. [27] 2021 ChiCTR2000029062 ChiCTR2000029059 China 69 33 NSCLC I/II Mefatinib Yu et al. [28] 2021 NCT02282267 China 180 115 N-Sq NSCLC II Gefitinib Zhao et al. [29] 2021 NCT02824458 China 311 145 N-Sq NSCLC III Apatinib + Gefitinib, Gefitinib Hayashi et al. [30] 2022 jRCTs051180009 Japan 46 22 NSCLC II Osimertinib + Afatinib Nishio et al. [31] 2023 NCT02411448 15-countries * 386 165 NSCLC III Ramucirumab + Erlotinib Note: 15-countries * :Canada, France, Germany, Greece, Hong Kong, Italy, Japan, Korea, Republic of China, Romania, Spain, Taiwan, Turkey, United Kingdom, United States. Table 2 Results of survival analyses of clinical trials included in the meta-analysis Progression Free Survival Overall Survival Study HR 95% CI P HR 95% CI P Costa et al. [25] 0.60* u 0.34 to 1.06* 0.079 0.71* u 0.40 to 1.28* 0.255 Li et al. [26] 0.74^ u 0.55 to 0.99^ 0.041 0.69^ u 0.40 to 1.28^ 0.011 Wang et al. [27] 0.74 u 0.41 to 1.34 0.315 0.48 u 0.25 to 0.95 0.067 Yu et al. [28] 0.66 u 0.48 to 0.89 0.007 0.54 u 0.40 to 0.74 < 0.001 Zhao et al. [29] 0.79^ u 0.57 to 1.09^ 0.153 - - - Hayashi et al. [30] 0.80^ u 0.48 to 1.33^ 0.380 - - - Nishio et al. [31] 0.51* u 0.42 to 0.69* - - - - Note: HR, hazard ratio; u, univariate result; CI: confidence intervals. HR: the hazard ratio of TP53 wild type vs. TP53 mutation. * Calculated result from data presented in article: HR and 95% CI for TP53 mutation vs. TP53 wild type have been given in the article. We subjected this value to 1/HR and 1/95% CI to derive values for analysis. ^ Calculated result from data presented in article: In studies not quoting the HRs or CIs, these were calculated from the presented data using two of the following parameters: the HR point estimate, the log-rank statistic or its P value, the O-E statistic (the difference between the number of observed and the number of expected events) or its variance. If these data were unavailable, the total number of events, the number of patients at risk in each group, and the log-rank statistic or its P value were used to derive an approximate estimate of the HR. Quality Assessment The four RCTs [25][26] [29] [31] were assessed using the Cochrane risk of bias method. They produced random sequences, did not show allocation concealment, offered comprehensive outcome data, and reported no selective outcome. If there was additional bias, it was unclear ( Figure S1 ). The quality of the non-comparative single-arm clinical trials was assessed using the MINORS Methodological components; Table S3 contains the details of the quality evaluation. Incidence of TP53 mutation The incidence of TP53 mutation was documented in each study that was examined, values ranged from 28.0 to 68.7% throughout the studies. The analysis found significant heterogeneity (I 2 = 92.1%, P < 0.0001) and a pooled incidence of 49.6% (95% CI: 39.6%-59.7%) ( Fig. 2 TP53) . Meta-regression was employed to assess potential sources of heterogeneity in greater detail. We chose five variables (region, phase, TP53 detection method, discovered TP53 exons, and testing samples) for the meta-regression analysis. The results of the meta-regression show that the p-values for each variable do not differ statistically significantly ( Figure S2 ). Associations between TP53 mutation and PFS Data from 7 trials that were suitable for analysis were used to determine the relationship between concurrent TP53 mutations and PFS. These investigations showed no statistically significant heterogeneity (I2 = 10.4%; P = 0.350). The PFS was noticeably longer in patients without TP53 mutations (HR = 0.66; 95% CI = 0.57–0.76; P < 0.0001) ( Fig. 2 A ) . Because there was no heterogeneity in the included studies, we did not conduct a meta-regression analysis. Subgroup analysis suggested that Europeans (HR = 0.52; 95% CI = 0.42–0.66; P = 0.608) and Asians (HR = 0.73; 95% CI = 0.63–0.86; P = 0.943) with no TP53 mutations were associated with a longer PFS, and the difference was statistically significant. In the meantime, we selected other 5 variables (phase, method of TP53 detection, detected exons of TP53, samples for testing, and intervention) for the subgroup analysis, detailed results were displayed in (Figure S3) Associations between TP53 mutation and OS. In total, 4 studies were included in the present analysis. Pooled results with a randomeffects model demonstrated a significantly shorter OS in patients harboring concurrent TP53 mutations treated with EGFRTKIs (HR = 0.61; 95% CI = 0.51–0.74; P P < 0.0001) ( Fig. 2 B ) . No statistically significant heterogeneity was observed among these studies (I 2 = 0.0%; P = 0.557). Subgroup analysis was subsequently performed (Figure S4) . Sensitivity Analysis of Meta-analysis Throughout the sensitivity analysis, one study was excluded at a time to see how it would impact the total outcomes. The results of the analysis showed that no single study had a substantial impact on any of the combined outcomes with 95% confidence intervals. This demonstrated the general validity of the meta-analysis's conclusions. The results of the sensitivity analysis are shown in Figure S5 . Publication Bias of meta-analysis The publication bias was calculated by employing Egger's tests. For the incidence (Egger's test: 0.214), the PFS (Egger's test: 0.354), and the OS (Egger's test: 0.776), we made the assumption that there was no publication bias. Figure S6 displayed the publishing bias funnel graphs. Baseline Characteristics of the Retrospective Cohort Study The baseline characteristics of the included patients are shown in Table 3 . The study population consisted of 57.9% (95/164) female and 42.1% (69/164) male patients, with 54.9% having a history of smoking. 34.1% were diagnosed with brain metastases, 52.4% with bone metastases, and 15.2% with liver metastases. The median age was 63 years old. The majority of patients (97.6%) were diagnosed with adenocarcinoma, while other histologic subtypes included 4 cases of squamous carcinoma. 53 (32.3%) of the 164 patients received EGFR-TKIs in addition to chemotherapy, while 111 (67.7%) received EGFR-TKI monotherapy. Gender, age, smoking history, ECOG-PS, histology, and metastatic status were all similar in each of these categories. Table 3 Baseline Demographics Total (N = 164) Exon 4 or 7 (N = 31) Others (N = 63) Wild Type (N = 70) P-value Age 0.539 ≤ 65 99(60.4%) 16 (51.6%) 39 (61.9%) 44 (62.9%) > 65 65(39.6%) 15 (48.4%) 24 (38.1%) 26 (37.1%) Gender 0.331 male 69(42.1%) 15 (48.4%) 22 (34.9%) 32 (45.7%) female 95(57.9%) 16 (51.6%) 41 (65.1%) 38 (54.3%) Smoking History 0.700 no 74(45.1%) 14 (45.2%) 26 (41.3%) 34 (48.6%) yes 90(54.9%) 17 (54.8%) 37 (58.7%) 36 (51.4%) ECOG.PS 0.186 0 31(18.9%) 5 (16.1%) 11 (17.5%) 15 (21.4%) 1 124(75.6%) 26 (83.9%) 45 (71.4%) 53 (75.7%) 2 9(5.5%) 0 (0.0%) 7 (11.1%) 2 (2.9%) Histology 0.144 adenocarcinoma 160(97.6%) 29 (93.5%) 63 (100.0%) 68 (97.1%) non-adenocarcinoma 4(2.4%) 2 (6.5%) 0 (0.0%) 2 (2.9%) Clinical Stage 0.394 IVA 54(32.9%) 7 (22.6%) 22 (34.9%) 25 (35.7%) IVB 110(67.1%) 24 (77.4%) 41 (65.1%) 45 (64.3%) EGFR Mutation 0.156 Exon19 del 94(57.3%) 13 (41.9%) 38 (60.3%) 43 (61.4%) Exon21 L858R 70(42.7%) 18 (58.1%) 25 (39.7%) 27 (38.6%) Brain Metastasis 0.141 no 108(65.9%) 19 (61.3%) 37 (58.7%) 52 (74.3%) yes 56(34.1%) 12 (38.7%) 26 (41.3%) 18 (25.7%) Bone Metastasis 0.278 no 78(47.6%) 16 (51.6%) 25 (39.7%) 37 (52.9%) yes 86(52.4%) 15 (48.4%) 38 (60.3%) 33 (47.1%) Liver Metastasis 0.136 no 139(84.8%) 27 (87.1%) 49 (77.8%) 63 (90.0%) yes 25(15.2%) 4 (12.9%) 14 (22.2%) 7 (10.0%) Treatment 0.162 T group 111(67.7%) 42 (60.0%) 24 (77.4%) 45 (71.4%) TC group 53(32.3%) 28 (40.0%) 7 (22.6%) 18 (28.6%) Baseline Genomic Characteristics Figure 3 depicted the baseline prevalence of TP53 mutation among our cohort patients. 57% (94/164) of patients had TP53 mutation, which was closed to the pooled incidence results of meta-analysis. The most frequently identified mutation occurred in exon 7 (14%) and exon 5 (14%), followed by exon 8, exon 6, exon 4, and exon 10. There is one intron 7, exon 3, IVS6 + 1, intron 10, and intron 4. No TP53 mutation were detected in exons 1, 9, or 11 in our cohort. The highest frequency subtype was substitution (82%). The remaining mutation types were: frameshift (11%), deletion (5%), duplication (1%), and insert-deletion (1%). Univariate and Multivariate Cox regression analyses A Cox proportional hazards model was employed to evaluate the prognostic significance of various factors in the patient population ( Table 4 ) . Multivariate Cox regression analysis was utilized to assess the independent prediction of TP53 co-mutation in terms of progression of the illness and death, avoiding the interaction of clinical characteristics parameters. The results showed that when we divided TP53 into 2 groups (patients with exon 4 or 7 mutation and the others), mutations in exon 4 or 7 of TP53 served as independent worse prognostic factors for disease progression compared with no TP53 co-mutation (HR = 2.49, 95% CI: 1.56–3.97,P < 0.001) and the others TP53 mutation (HR = 2.38, 95% CI: 1.46–3.86,P < 0.001). In terms of death, the results were not statistically significant in either univariate or multivariate Cox regression analyses. Table 4 Analysis of Prognostic Factors of patients: Univariate analysis and Multivariate analysis Progression-Free Survival (PFS) Overall Survival (OS) UA MA UA MA P value HR 95% CI P value P value HR 95% CI P value Age 0.036 0.63 0.44–0.90 0.011 0.512 Gender 0.395 0.561 Smoke 0.931 0.806 ECOG.PS 0.19 0.687 Histology 0.056 3.31 1.18–9.32 0.023 0.151 Clinical Stage 0.409 0.387 EGFR Mutation 0.626 0.048 1.55 1.04–2.33 0.033 Brain Metastasis 0.007 1.58 1.10–2.26 0.013 0.108 Bone Metastasis 0.563 0.901 Liver Metastasis 0.095 1.78 1.10–2.83 0.016 0.191 Treatment 0.264 0.634 TP53 mutation 0.723 Mutation vs. Wild 0.05 1.34* 0.95–1.91 0.970 0.85 0.84* 0.56–1.28 0.423 Exon 4/7 vs. others 0.0045 2.38* 1.46–3.86 <0.001 0.803 1.01* 0.55–1.87 0.965 Exon 4/7 vs. wild type 0.0004 2.49* 1.56–3.97 <0.001 0.723 0.85* 0.47–1.54 0.598 Abbreviations: UA = Univariate analysis; PFS = Progression-free survival; OS = Overall survival; MA = Multivariate analysis; HR = hazard ratio; CI = confidence interval. *Adjusted confounding covariates: Age, Histology, Brain Metastasis, Liver Metastasis. Table 5 EGFR/TP53 co-mutation patients treatment outcomes Exon4 or 7 N (%) Others N (%) Wild-type N (%) P value Response CR 0 0 0 PR 14 (45.16%) 40 (63.49%) 48 (68.57%) 0.078 SD 14 (45.16%) 21 (33.34%) 20 (28.57% 0.177 PD 3 (9.68%) 2 (3.17%) 2 (2.86%) 0.254 ORR 14 (45.16%) 40 (63.49%) 48 (68.57%) 0.078 DCR 28 (90.32%) 61 (96.83%) 68 (97.14%) 0.254 Discussion The TP53 gene encodes the P53 protein, which is a highly effective tumor suppressor that surveils the development of cancer cells. Upon sensing DNA damage, the p53 pathway activates and induces either a temporary halt in the cell cycle to facilitate repair or, in case of irreparable damage, initiates apoptosis [32] . TP53 mutation can lead to the loss of normal function of the p53 protein, cause a dominant negative effect, cause genome instability, and down-regulated apoptosis. There is an ongoing conversation on the prognostic significance of TP53 mutation for TKI therapy in EGFR mutant non-small cell lung cancer. A prior meta-analysis [19] , which included several studies, found that patients with NSCLCs carrying concurrent TP53 mutation have a significantly worse prognosis than those without TP53 mutation when treated with EGFR TKIs. The meta-analysis examined the relationship between concurrent TP53 mutation and the clinical outcomes of patients with EGFR mutant NSCLC treated with TKIs. Shorter PFS and OS were discovered to be related to concurrent TP53 mutation. However, the study included mostly retrospective clinical studies, which may have some bias. Seven prospective clinical studies with 1319 patients were included in our meta-analysis to examine the relationship between concurrent TP53 mutation and clinical outcomes. Patients without TP53 mutation had significantly longer PFS (HR=0.66; 95% CI=0.57 0.76; P0.0001), while patients with concurrent TP53 mutation who were treated with EGFR TKIs had significantly shorter OS (HR=0.61; 95% CI=0.51 0.74; P P0.0001). This outcome was in line with other meta-analyses, which had found that the prognosis was poorer for TP53 co-mutation. We simultaneously gathered a relevant cohort from our institution and conducted a retrospective study on it in order to further determine the influence of the TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutations. The prevalence of TP53 gene mutation in our group was 57% (94/164), which was consistent with other reports [33]–[34] . We categorized exons according to how frequently they underwent mutation and named "hot spot" exons that underwent a lot of mutation. Exons 5-8 in TP53 are often mutated, according to a number of studies [18][35] . Exon 7 (14%) and exon 5 (14%), followed by exon 8, exon 6, exon 4, and exon 10, were the most often found mutation in our investigation, which was in line with other findings. Several studies have endeavored to evaluate the predictive implications of distinct TP53 mutation. Jiao et al. disclosed that patients with TP53 mutation in exons 4 and 6, multiple exonic mutations, or mutations of unknown type, exhibited the poorest prognosis in patients harboring common sensitizing EGFR mutation [36] . Hou et al. identified TP53 non-missense mutation, non-destructive mutation, mutation in exons 6, 7, and non-DBD regions as unfavorable prognosticators in NSCLC patients who underwent first-generation EGFR-TKI treatment [37] . Conversely, Labbe et al. observed that NSCLC patients with TP53 missense mutation, instead of not non-missense mutation, were associated with significantly decreased PFS following EGFR-TKI treatment [38] . Two studies found exon 8 can acted as an indicator for predicting prognosis [28][39] . In addition, we identified that TP53 exon 4 or 7 mutation emerged as independent predictors of poor prognosis after treatment (mPFS = 9.37months; HR = 2.49, 95% CI: 1.56-3.97,P<0.001). Due to a diverse range of genetic mutations in cancer cells, it is not uncommon for different studies to produce slightly different results, especially when studying specific genetic mutations and their impact on patient prognosis. Differences in patient populations, variations in gene detection techniques, and treatment regimens can also contribute to inconsistencies. On the other hand, further investigation on TP53 mutation impacting on patient’s prognosis should be highly encouraged. For patients with TP53 co-mutation, using a combination strategy to improve the efficacy seems to be promising and attractive. A prospective clinical trial, NEJ009 [40] , revealed that gefitinib combined with chemotherapy regimen significantly improved PFS and PFS2 with an acceptable safety profile compared with gefitinib-alone. Wu et al. conducted a meta-analysis that included eight randomized trials with a total of 1349 patients, showing that compared to EGFR-TKI monotherapy, EGFR-TKI combined with chemotherapy improved OS, and PFS [41] . Regretfully, patients with TP53 co-mutation were not included in any of the two earlier studies' evaluations. A growing number of clinical research have reported on the efficacy of TP53 co-mutation as a result of increased scholarly attention to the condition. The majority of these studies have found that combination treatment is an effective means of improving the prognosis of patients with TP53 co-mutation. Nishio et al. published the RELAY study's findings [31] . The analysis of TP53 co-mutation data in the article revealed that patients with concurrent TP53 mutation had a shorter PFS than individuals whose tumors were TP53 wild-type. The combination of ramucirumab and erlotinib showed a longer PFS of 15.2 months and 10.6 months, respectively, when compared to erlotinib monotherapy. In order to determine whether EGFR-TKI with chemotherapy or monotherapy is more successful as the first-line treatment for patients with advanced EGFR-mutant lung adenocarcinomas who also have TP53 mutation, Yang et al. performed a retrospective study [42] . The findings indicated that those individuals might benefit better from EGFR-TKIs in addition to chemotherapy. Our data revealed that regardless of whether patients received monotherapy or combination therapy, patients were numerically very close to one another in terms of mPFS (10.57 months,95%CI:9.37-11.9 vs. 10.73 months,95%CI:9.37-15.9,P=0.68) and mOS (29.37 months,95%CI:23.23-35.13 vs. 29.30 months,95%CI:22.57-58.83,P=0.87). This treatment data from our study needs to be analyzed with additional caution because, in real-world clinical practice, the simultaneous use of both TKI and chemotherapy modalities cannot completely rule out the possibility of additive toxic effects, which could hasten patient deterioration and affect treatment outcomes. There was less data from prospective clinical studies on the use of combination therapy to treat patients with TP53 co-mutation, and there was still debate as to whether combination therapy was the optimal treatment modality for those patients. A large number of clinical studies are underway, and we look forward to data from future clinical studies that will give us a definitive answer. To sum up, our research offered several benefits: First and foremost, the fact that prospective clinical studies were included in our meta-analysis lends greater credibility to the results. Second, in order to guarantee the stability and dependability of the outcomes, we thoroughly statistically analyze the data. Not to mention, we did a preliminary comparison between the combination therapy model and the monotherapy model, as well as an examination of actual data from our facility to support that finding. The results are useful to doctors because they may be applied to develop more personalized treatment plans for different patients in a clinical setting. The current study was not without its flaws. Firstly, the meta-analysis had very few studies, and the studies were not dispersed equally across the population, therefore the findings drawn were not necessarily applicable to a larger population. Furthermore, because the research was conducted retrospectively and the cases were collected from a single site, it was simple to introduce selection bias and distort the associations that were discovered. Third, some inadequate statistical conclusions may have been drawn from the study's small sample size. To get more accurate results, further large-scale clinical trials from various populations in the prospective setting and more meta-analysis are required. Conclusion The current updated meta-analysis suggested that individuals with advanced non-small cell lung cancer who also had TP53 co-mutation had worse shorter PFS and OS. Particularly, TP53 exon 4 or exon 7 mutations suggest a poorer prognosis. Declarations Funding This work was carried out with the supports of Beijing Xisike Clinical Oncology Research Foundation (No. Y-2019AZQN-04532) and Guangxi Medical and health key discipline construction project. Competing Interests The authors have no relevant financial or non-financial interests to disclose Author Contributions The data's statistical analysis was carried out by Wenshu Huang, who also prepared the Chinese text. Huiting Wei and Jiang Wei contributed to the translation of the text, analysis, and creation of several of the illustrations. Cuiyun Su, Yun Zhao, Jianbo He and Liping Tan got the patients' fully informed permission before treating them, giving them care, and giving them their clinical information. Shubin Chen provided some helpful feedback and made changes to the manuscript. Shaozhang Zhou directed and conceptualized the research procedure; Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request Ethics Approval The Guangxi Medical University Cancer Hospital 's ethical committee gave its approval for this study. All processes and information collection for this study followed the ethical standards of the Research Committee of the Guangxi Medical University Cancer Hospital Consent to participate Informed consent was obtained from all individual participants included in the study Consent to publish Our manuscripts do not contain any personal data of any kind (including any personal details, images or videos) References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal J, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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pooled incidence of TP53 co-mutation and for studies on TP53 co-mutation of PFS(A) and OS(B).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/cf2f941d25fa7ee25b0cf937.jpg"},{"id":69441317,"identity":"2e122bf6-cc1a-44c3-a29c-03b88f4901d4","added_by":"auto","created_at":"2024-11-20 11:21:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72050,"visible":true,"origin":"","legend":"\u003cp\u003eTP53 mutation types and subtypes.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/3fe1cabe42b17fce7f9138e7.jpg"},{"id":69441313,"identity":"ed6b11b8-0b6f-4cfb-8223-16af181f99d2","added_by":"auto","created_at":"2024-11-20 11:21:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1061844,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier (KM) curves of PFS of patients with TP53 co-mutation.(A1)\u003c/p\u003e\n\u003cp\u003eTP53 mutation vs. TP53 wild-type.(B1)Others TP53 mutation vs. TP53 wild-type.(C1)\u003c/p\u003e\n\u003cp\u003eexon 4 or 7 of TP53 mutation vs. TP53 wild-type.(D1) exon 4 or 7 of TP53 mutation vs. others TP53 mutation. And Kaplan-Meier (KM) curves of OS of patients with TP53 co-mutation.(A2)\u003c/p\u003e\n\u003cp\u003eTP53 mutation vs. TP53 wild-type.(B2)Others TP53 mutation vs. TP53 wild-type.(C2)\u003c/p\u003e\n\u003cp\u003eexon 4 or 7 of TP53 mutation vs. TP53 wild-type.(D2) exon 4 or 7 of TP53 mutation vs. others TP53 mutation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/1a0edffac99347b9014128a8.jpg"},{"id":69441316,"identity":"5f41215e-279f-463c-9014-c3f479e5a033","added_by":"auto","created_at":"2024-11-20 11:21:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1076712,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of disease progression and death.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/cacc8dc54ff819987883293b.jpg"},{"id":69442761,"identity":"108f88d3-0e33-4b72-b716-30db465cced1","added_by":"auto","created_at":"2024-11-20 11:29:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70345,"visible":true,"origin":"","legend":"\u003cp\u003eClinical outcomes of patients.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/f2014ed3136028f57f744f30.jpg"},{"id":72484491,"identity":"8d9dc4d6-9c42-4ed1-812c-39cc0f185d33","added_by":"auto","created_at":"2024-12-27 18:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4526795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/9e008d00-8139-4f78-b6e5-bae2211d0e41.pdf"},{"id":69441311,"identity":"1569bb48-a079-4b5e-b5be-49c04935a516","added_by":"auto","created_at":"2024-11-20 11:21:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":343671,"visible":true,"origin":"","legend":"","description":"","filename":"SuppInfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-5354638/v1/0a98c7bb5f05eec53327ccec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of TP53 co-mutation on the clinical outcomes of advanced non-small cell lung cancer patients with EGFR sensitizing mutation: An updated systematic review and meta-analysis of prospective clinical trials and a single-center retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the World Health Organization's most current \"Global Cancer Statistics 2020\" statistics, lung cancer is the most common cancer-related cause of death globally, accounting for more than 10% of all malignant tumors\u003csup\u003e[1]\u003c/sup\u003e. The majority of lung cancer patients (80\u0026ndash;85%) have non-small cell lung cancer (NSCLC), which is the most prevalent histological phenotype of lung cancer\u003csup\u003e[2]\u003c/sup\u003e. 70% of NSCLC patients with stages I to III are surgically curable\u003csup\u003e[3]\u003c/sup\u003e. Advanced non-small cell lung cancer patients had a 5% survival rate after five years\u003csup\u003e[4]\u003c/sup\u003e. Because they lack typical lung cancer symptoms, around 62 percent of non-small cell lung cancer patients are diagnosed with stage IV at their initial assessment \u003csup\u003e[5]\u003c/sup\u003e. The three main treatment options for non-small cell lung cancer are surgery, chemotherapy, and radiation. The treatment of the particular genotype NSCLC has made significant strides \u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe epidermal growth factor receptor (EGFR) gene-activating mutation has been identified as the primary oncogenic factor in NSCLC. One of the most frequently seen causes of NSCLC is EGFR mutation, which is especially prevalent in Asian patients with NSCLC lung cancer. EGFR-sensitive mutations include deletion mutation in exon 19 and L858R point mutation in exon 21, both of which cause a marked increase in EGFR kinase activity \u003csup\u003e[7]\u003c/sup\u003e. Therefore, the detection of EGFR-sensitive mutation has become an important part of lung cancer treatment. The prognosis of patients with NSCLC who have EGFR mutation has significantly improved during the past ten years because of molecular targeted therapy\u003csup\u003e[8]\u0026ndash;10]\u003c/sup\u003e. There are still some individuals who have initial non-response despite the fact that tyrosine kinase inhibitors (TKIs) that may target active EGFR and have excellent effectiveness in NSCLC with EGFR mutation are available, and extremely varied outcomes are seen in EGFR-TKI responders\u003csup\u003e[11]\u003c/sup\u003e. Due to the existence of non-responders and the variable prognosis of responders, it can't always be said that EGFR-TKI monotherapy is the best course of action for treating EGFR mutant NSCLC. To find additional mechanisms of TKI resistance, more research on the genetic profile of NSCLC is required.\u003c/p\u003e \u003cp\u003eTP53 located on chromosome 17p13.1, is a pivotal intracellular tumor suppressor gene and plays a pivotal role in various biological processes, including regulation of the cell cycle, induction of apoptosis, differentiation of cells, repair of DNA damage, and regulation of angiogenesis\u003csup\u003e[12]\u003c/sup\u003e. The P53 protein, which is particularly efficient in suppressing tumor growth, is produced by the TP53 gene. The p53 pathway becomes active in response to DNA damage and either causes a brief interruption of the cell cycle to aid in repair or, in the event of irreversible damage, starts apoptosis\u003csup\u003e[13]\u003c/sup\u003e. A number of malignancies, including lung adenocarcinoma, typically have TP53 mutation (\u0026gt;\u0026thinsp;50%)\u003csup\u003e[14]\u003c/sup\u003e. Changes to the TP53 genetic structure are thought to have a key role in oncogene-driven lung cancer subgroups' clinical and molecular heterogeneity due to their significant effects on treatment resistance and genomic instability \u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results, particularly those related to the TP53 co-mutation\u003csup\u003e[16]\u0026ndash;[18]\u003c/sup\u003e, implied that coexisting mutation may affect the effectiveness of EGFR-TKI. Although earlier research suggested that the TP53 co-mutation could be used to predict worse clinical outcomes in EGFR-TKIs treatment, almost all earlier meta-analysis studies on this topic were retrospective cohort studies\u003csup\u003e[19]\u0026ndash;[21]\u003c/sup\u003e, leaving uncertainty in the epidemiological data. Retrospective studies don't have the same level of rigor as clinical trials when it comes to research design and outcomes analysis, thus the information clinical trials provide is more trustworthy. The influence of TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who have EGFR sensitizing mutation is a topic of increasing interest in clinical trials, the majority of which are still in progress. Similar information was provided in subgroup analysis by the few completed clinical investigations.\u003c/p\u003e \u003cp\u003eTherefore, the purpose of the currently updated meta-analysis was to ascertain the effect of the co-mutation of TP53 on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutation in prospective clinical trials. In order to further support the impact of the TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutation, we simultaneously gathered a pertinent cohort from our institution and performed a retrospective analysis on it.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eA literature search of meta-analysis\u003c/h2\u003e \u003cp\u003eThe most updated meta-analysis adhered to PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines\u003csup\u003e[22]\u003c/sup\u003e and had been registered with PROSPERO (ID: CRD42023462455) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.crd.york.ac.uk/prospero/\u003c/span\u003e\u003cspan address=\"https://www.crd.york.ac.uk/prospero/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As of September 8, 2023, electronic searches for clinical trials were conducted in the databases PubMed, Embase, and Web of Science. The complete search we used for PubMed was supplied in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria of meta-analysis\u003c/h3\u003e\n\u003cp\u003eThe studies that were taken into consideration satisfied the following inclusion criteria: (1) Patients enrolled in prospective clinical studies with advanced non-small cell lung cancer verified by histopathology; (2) Patients receiving EGFR-TKIs as a therapy; (3) At least one set of survival and associated prognostic data was presented in the study. The following studies were omitted to lessen the chance of bias: (1) Patients in prospective clinical trials were not advanced non-small cell lung cancer; (2)Patients did not use EGFR-TKIs as treatment, such as chemotherapy or immunotherapy; (3) Not prospective clinical trials; (4) Lack of essential data or overlapping studies; (5) failure to consider animal experiments, cell research, reviews, meta-analyses, duplicates, case reports, or correspondence; Two investigators independently selected potential eligible papers using inclusion and exclusion criteria. Any arguments concerning the study's inclusion were resolved by these two or a third investigator.\u003c/p\u003e\n\u003ch3\u003eData extraction and quality assessment of meta-analysis\u003c/h3\u003e\n\u003cp\u003eTwo investigators independently retrieved the relevant data from each included study, and the quality of the investigations was assessed. Year of publication, first author, registration number, country, total number of patients, EGFR mutation, number of patients with TP53 mutation, method of TP53 detection, detected exons of TP53, samples for testing, trial phase, interventions of EGFR-TKIs and histology were a brief summary of the retrieved characteristics. Objective response rate (ORR), disease control rate (DCR), hazard ratio (HR), and 95% confidence interval (CI) of progression-free survival (PFS) and overall survival (OS) were among the outcomes that were also retrieved. Since most of the studies derived their data from subgroup analyses, for the literature that did not directly report HRs, we calculated HRs and 95% CIs by using the corresponding mean PFS or OS and log-rank p-values. For non-comparative single-arm clinical trials, we used the MINORS Methodological Items \u003csup\u003e[23]\u003c/sup\u003e and the Cochrane Risk of Bias Instrument \u003csup\u003e[24]\u003c/sup\u003e to evaluate the quality, whereas for eligible randomized clinical trials (RCTs), we utilized the Cochrane Risk of Bias Instrument \u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eThe design of the retrospective cohort study\u003c/h3\u003e\n\u003cp\u003ePatients diagnosed with advanced NSCLC at the Guangxi Medical University Cancer Hospital between January 2016 and December 2022 were included for further screening, and the screening criteria for the cohort study were as follows: (1) Pathologically confirmed primary lung cancer, as defined by the 5th edition of the WHO Thoracic Tumor Classification; (2) clinical stage IV, as defined by the 8th TNM staging system of lung cancer; (3) Eastern Cooperative Oncology Group (ECOG) score for the physical status of 0\u0026ndash;2 points; (4) tumor tissue specimens or peripheral blood cell-free tumor DNA (ctDNA) were used in our hospital or the referring institution for NGS (next-generation sequencing) in order to diagnose the patient, which detected the existence of classical EGFR mutation (Exon19 deletion and Exon21 L858R point mutations) and TP53 mutations as well as their mutational status; (5) First-line therapy with either EGFR-TKI monotherapy or EGFR-TKI combined chemotherapy. Exclusion criteria for the study were as follows: (1) adenocarcinoma with small cell lung cancer; (2) other types of EGFR mutation; (3) Follow-up information was unavailable and the cases with missing data. 164 patients were included in the final analysis.\u003c/p\u003e \u003cp\u003ePatients in the TC group received EGFR-TKIs (gefitinib 250 mg once daily (qd), or erlotinib 150 mg, qd, or erlotinib 125 mg three times daily (tid), or afatinib 40 mg, qd, or osimertinib 80 mg, qd, or almonertinib 110 mg, qd, or furmonertinib 80 mg, qd) in combination with chemotherapy (primarily pemetrexed plus platinum) based on pemetrexed (500 mg/m2). Treatment is continued until the condition worsens or the side effects become intolerable. Short-term dose modifications or delays are permitted based on individual chemo response, and the precise number of cycles of chemotherapy can be altered based on drug efficacy and patient acceptability due to the severity of treatment-related side effects. After six cycles of chemotherapy, some patients additionally undergo EGFR-TKIs and pemetrexed maintenance treatment. Patients in the T group received treatment with EGFR-TKIs.\u003c/p\u003e\n\u003ch3\u003eThe Next-Generation Sequencing of retrospective cohort study\u003c/h3\u003e\n\u003cp\u003eNGS was performed by Gene\u0026thinsp;+\u0026thinsp;Smart Laboratory (Beijing, China) and Geneseeq Technology (Nanjing, China). The 425-gene includes exons, fusion-related introns, alternative splicing regions, and specific microsatellite (MS) sites, with a total of about 1.28Mb base sites. We used 139 genes commonly found to be mutated in lung cancer, including point mutations, small fragment indels, gene fusions, copy number variations, and microsatellite analysis. The average sequencing depth was \u0026gt;\u0026thinsp;4500X. In this study, we obtained tissue specimens for molecular analysis from various sources including surgery and core needle biopsy, which were guided by computed tomography (CT) or Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) and EBUS Guide Sheath Transbronchial Lung Biopsy (EBUS-GS-TBLB). Tumor tissue, blood, cerebrospinal fluid, and pleural effusion were used to perform the molecular analyses.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection and assessment of retrospective cohort study\u003c/h2\u003e \u003cp\u003eFor pertinent information, the database of medical records at the hospital was checked. Patients who had not visited the hospital for more than three months were called in order to follow up and gather pertinent information, such as the patient's tumor recurrence and prognosis. A CT scan was used to assess the effectiveness of the therapy and to analyze the tumor lesions both before and after treatment. The patient's medical records were used to gather all clinical and laboratory markers. Age, sex, smoking history, the ECOG-PS, histology, clinical stage, and metastases were clinical markers. The TP53 mutation and the EGFR mutation were laboratory markers.\u003c/p\u003e \u003cp\u003eThe four categories of complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) are used in Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 to determine efficacy. Disease control rate (DCR) equals CR\u0026thinsp;+\u0026thinsp;PR\u0026thinsp;+\u0026thinsp;SD; objective response rate (ORR) equals CR\u0026thinsp;+\u0026thinsp;PR. The research protocol's start date was used to compute progression-free survival (PFS), which was measured from that point until the time of the last follow-up or until the illness progressed. The patient's overall survival (OS) was measured as the interval from the start of anti-tumor medication therapy to death or the final follow-up.\u003c/p\u003e \u003cp\u003e This study was approved by the ethical committee of the Guangxi Medical University Cancer Hospital, and all processes and information collection were conducted in accordance with the ethical standards set by the Research Committee of the hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eUtilizing the statistical program Stata, evidence was synthesized. Stata (Stata Corp, USA) was used to calculate the relevant standard errors of these quasinormal distribution \"rates\" by entering the full clinical setting % for the main outcome and the complete list of research participants. The lower interval (LI) and upper interval (UI), which have a 95% confidence interval, may be justified using the \"rates\" and standard errors. The final component of the output was the pooled effect sizes (ES), which represented median \"rates\" and 95% confidence intervals (95% CI). To examine study heterogeneity, the I\u003csup\u003e2\u003c/sup\u003e statistic was utilized. Based on the I\u003csup\u003e2\u003c/sup\u003e values, which varied from 25 to 50%, 50 to 75%, and \u0026gt;\u0026thinsp;75%, studies were classified as having low, moderate, or high heterogeneity. Substantial heterogeneity was determined by the I\u003csup\u003e2\u003c/sup\u003e test as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For every pooled ES, random-effects models were utilized. The meta-regression and subgroup analyses could be done because of the abundance of data that was given. Sensitivity analysis was also carried out to evaluate the reliability and consistency of the combined data. Egger's tests also looked for a potential bias in favor of publications.\u003c/p\u003e \u003cp\u003eUsing the statistical tools R and EmpowerStats, all statistical analyses for the retrospective cohort research were carried out. In place of averages and standard deviations (SD), we utilized frequencies, percentages, or ratios for categorical variables. Comparisons between study protocol groups were evaluated using the χ2 or Fisher's exact test (for categorical variables). Kaplan-Meier survival curves were produced, and the log-rank test was used to assess differences. For both univariate and multivariate analyses, Cox regression was utilized. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox's proportional hazards regression model. We corrected for possibly confounding factors in the multivariate analysis. Statistically significant variables in univariate Cox regression analyses were included in the adjusted for multivariate analysis. To evaluate the relationship between various TP53 mutation and the risk of illness progression and mortality in various subgroups, subgroup analyses were utilized. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in each analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy selection\u003c/h2\u003e \u003cp\u003eSearching the previously stated databases Pubmed (n\u0026thinsp;=\u0026thinsp;633), Web of Science (n\u0026thinsp;=\u0026thinsp;1159), and Embase (n\u0026thinsp;=\u0026thinsp;2387) yielded a total of 4179 records. 1347 articles were disregarded due to redundancy. 2532 records that have no connection to the subject matter were removed. 293 records were eliminated for the following reasons: (1) reply and comment sections; (2) reviews and meta-analyses; (3) non-prospective clinical trials; (4) non-advanced nonsmall cell lung cancer; and (5) lack of available outcome data. It was feasible to perform a quantitative synthesis for the final seven publications. The process of selection was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the included studies\u003c/h2\u003e \u003cp\u003eThe present meta-analysis included a total of 7 studies\u003csup\u003e[25]\u0026ndash;[31]\u003c/sup\u003e involving 1319 participants. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e described the main study characteristics; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the survival results and \u003cb\u003eTable S2\u003c/b\u003e shown the details of gene alteration in the included studies. The studies were all published between 2014 and 2023. Four studies \u003csup\u003e[25][26][29][31]\u003c/sup\u003e were randomized clinical trials, and 3 studies\u003csup\u003e[27][28][30]\u003c/sup\u003ewere non-comparative clinical trials.\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\u003eCharacteristics of clinical trials included in the meta-analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\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\u003eRegister number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eincluded\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients with TP53 mutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInterventions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCosta et al.\u003csup\u003e[25]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCT00446225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrance,\u0026nbsp;\u0026nbsp;Italy,\u0026nbsp;\u0026nbsp;Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u003csup\u003e[26]\u003c/sup\u003e\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\u003eNCT01024413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eElortinib, Gefitinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003csup\u003e[27]\u003c/sup\u003e\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\u003eChiCTR2000029062\u003c/p\u003e \u003cp\u003eChiCTR2000029059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMefatinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu et al.\u003csup\u003e[28]\u003c/sup\u003e\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\u003eNCT02282267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN-Sq NSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao et al.\u003csup\u003e[29]\u003c/sup\u003e\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\u003eNCT02824458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN-Sq NSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eApatinib\u0026thinsp;+\u0026thinsp;Gefitinib, Gefitinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHayashi et al.\u003csup\u003e[30]\u003c/sup\u003e\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\u003ejRCTs051180009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOsimertinib\u0026thinsp;+\u0026thinsp;Afatinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNishio et al.\u003csup\u003e[31]\u003c/sup\u003e\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\u003eNCT02411448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15-countries\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRamucirumab\u0026thinsp;+\u0026thinsp;Erlotinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e15-countries\u003csup\u003e*\u003c/sup\u003e:Canada, France, Germany, Greece, Hong Kong, Italy, Japan, Korea, Republic of China, Romania, Spain, Taiwan, Turkey, United Kingdom, United States.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of survival analyses of clinical trials included in the meta-analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eProgression Free Survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCosta et al.\u003csup\u003e[25]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60*\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 to 1.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71*\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40 to 1.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u003csup\u003e[26]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74^\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55 to 0.99^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69^\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40 to 1.28^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003csup\u003e[27]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41 to 1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25 to 0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu et al.\u003csup\u003e[28]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 to 0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40 to 0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao et al.\u003csup\u003e[29]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79^\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 to 1.09^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHayashi et al.\u003csup\u003e[30]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80^\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 to 1.33^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNishio et al.\u003csup\u003e[31]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51*\u003csup\u003eu\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42 to 0.69*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; u, univariate result; CI: confidence intervals.\u003c/p\u003e \u003cp\u003eHR: the hazard ratio of TP53 wild type vs. TP53 mutation.\u003c/p\u003e \u003cp\u003e* Calculated result from data presented in article: HR and 95% CI for TP53 mutation vs. TP53 wild type have been given in the article. We subjected this value to 1/HR and 1/95% CI to derive values for analysis.\u003c/p\u003e \u003cp\u003e^ Calculated result from data presented in article: In studies not quoting the HRs or CIs, these were calculated from the presented data using two of the following parameters: the HR point estimate, the log-rank statistic or its P value, the O-E statistic (the difference between the number of observed and the number of expected events) or its variance. If these data were unavailable, the total number of events, the number of patients at risk in each group, and the log-rank statistic or its P value were used to derive an approximate estimate of the HR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuality Assessment\u003c/h2\u003e \u003cp\u003eThe four RCTs \u003csup\u003e[25][26] [29] [31]\u003c/sup\u003e were assessed using the Cochrane risk of bias method. They produced random sequences, did not show allocation concealment, offered comprehensive outcome data, and reported no selective outcome. If there was additional bias, it was unclear \u003cem\u003e(\u003c/em\u003e\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e\u003cem\u003e).\u003c/em\u003e The quality of the non-comparative single-arm clinical trials was assessed using the MINORS Methodological components; \u003cb\u003eTable S3\u003c/b\u003e contains the details of the quality evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of TP53 mutation\u003c/h2\u003e \u003cp\u003eThe incidence of TP53 mutation was documented in each study that was examined, values ranged from 28.0 to 68.7% throughout the studies. The analysis found significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;92.1%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and a pooled incidence of 49.6% (95% CI: 39.6%-59.7%) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eTP53)\u003c/b\u003e. Meta-regression was employed to assess potential sources of heterogeneity in greater detail. We chose five variables (region, phase, TP53 detection method, discovered TP53 exons, and testing samples) for the meta-regression analysis. The results of the meta-regression show that the p-values for each variable do not differ statistically significantly \u003cem\u003e(\u003c/em\u003e\u003cb\u003eFigure S2\u003c/b\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between TP53 mutation and PFS\u003c/h2\u003e \u003cp\u003eData from 7 trials that were suitable for analysis were used to determine the relationship between concurrent TP53 mutations and PFS. These investigations showed no statistically significant heterogeneity (I2\u0026thinsp;=\u0026thinsp;10.4%; P\u0026thinsp;=\u0026thinsp;0.350). The PFS was noticeably longer in patients without TP53 mutations (HR\u0026thinsp;=\u0026thinsp;0.66; 95% CI\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.76; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Because there was no heterogeneity in the included studies, we did not conduct a meta-regression analysis. Subgroup analysis suggested that Europeans (HR\u0026thinsp;=\u0026thinsp;0.52; 95% CI\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.66; P\u0026thinsp;=\u0026thinsp;0.608) and Asians (HR\u0026thinsp;=\u0026thinsp;0.73; 95% CI\u0026thinsp;=\u0026thinsp;0.63\u0026ndash;0.86; P\u0026thinsp;=\u0026thinsp;0.943) with no TP53 mutations were associated with a longer PFS, and the difference was statistically significant. In the meantime, we selected other 5 variables (phase, method of TP53 detection, detected exons of TP53, samples for testing, and intervention) for the subgroup analysis, detailed results were displayed in \u003cb\u003e(Figure S3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between TP53 mutation and OS.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn total, 4 studies were included in the present analysis. Pooled results with a randomeffects model demonstrated a significantly shorter OS in patients harboring concurrent TP53 mutations treated with EGFRTKIs (HR\u0026thinsp;=\u0026thinsp;0.61; 95% CI\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.74; P P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. No statistically significant heterogeneity was observed among these studies (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%; P\u0026thinsp;=\u0026thinsp;0.557). Subgroup analysis was subsequently performed \u003cb\u003e(Figure S4)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis of Meta-analysis\u003c/h2\u003e \u003cp\u003eThroughout the sensitivity analysis, one study was excluded at a time to see how it would impact the total outcomes. The results of the analysis showed that no single study had a substantial impact on any of the combined outcomes with 95% confidence intervals. This demonstrated the general validity of the meta-analysis's conclusions. The results of the sensitivity analysis are shown in \u003cb\u003eFigure S5\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias of meta-analysis\u003c/h2\u003e \u003cp\u003eThe publication bias was calculated by employing Egger's tests. For the incidence (Egger's test: 0.214), the PFS (Egger's test: 0.354), and the OS (Egger's test: 0.776), we made the assumption that there was no publication bias. \u003cb\u003eFigure S6\u003c/b\u003e displayed the publishing bias funnel graphs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of the Retrospective Cohort Study\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the included patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The study population consisted of 57.9% (95/164) female and 42.1% (69/164) male patients, with 54.9% having a history of smoking. 34.1% were diagnosed with brain metastases, 52.4% with bone metastases, and 15.2% with liver metastases. The median age was 63 years old. The majority of patients (97.6%) were diagnosed with adenocarcinoma, while other histologic subtypes included 4 cases of squamous carcinoma. 53 (32.3%) of the 164 patients received EGFR-TKIs in addition to chemotherapy, while 111 (67.7%) received EGFR-TKI monotherapy. Gender, age, smoking history, ECOG-PS, histology, and metastatic status were all similar in each of these categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExon 4 or 7\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWild Type\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99(60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65(39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95(57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74(45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90(54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG.PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124(75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (83.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160(97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68 (97.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54(32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110(67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon19 del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon21 L858R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70(42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108(65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56(34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78(47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86(52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139(84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (87.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111(67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Genomic Characteristics\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicted the baseline prevalence of TP53 mutation among our cohort patients. 57% (94/164) of patients had TP53 mutation, which was closed to the pooled incidence results of meta-analysis. The most frequently identified mutation occurred in exon 7 (14%) and exon 5 (14%), followed by exon 8, exon 6, exon 4, and exon 10. There is one intron 7, exon 3, IVS6\u0026thinsp;+\u0026thinsp;1, intron 10, and intron 4. No TP53 mutation were detected in exons 1, 9, or 11 in our cohort. The highest frequency subtype was substitution (82%). The remaining mutation types were: frameshift (11%), deletion (5%), duplication (1%), and insert-deletion (1%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and Multivariate Cox regression analyses\u003c/h2\u003e \u003cp\u003eA Cox proportional hazards model was employed to evaluate the prognostic significance of various factors in the patient population \u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e. Multivariate Cox regression analysis was utilized to assess the independent prediction of TP53 co-mutation in terms of progression of the illness and death, avoiding the interaction of clinical characteristics parameters. The results showed that when we divided TP53 into 2 groups (patients with exon 4 or 7 mutation and the others), mutations in exon 4 or 7 of TP53 served as independent worse prognostic factors for disease progression compared with no TP53 co-mutation (HR\u0026thinsp;=\u0026thinsp;2.49, 95% CI: 1.56\u0026ndash;3.97,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the others TP53 mutation (HR\u0026thinsp;=\u0026thinsp;2.38, 95% CI: 1.46\u0026ndash;3.86,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of death, the results were not statistically significant in either univariate or multivariate Cox regression analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Prognostic Factors of patients: Univariate analysis and Multivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eProgression-Free Survival (PFS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eOverall Survival (OS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG.PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u0026ndash;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04\u0026ndash;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u0026ndash;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u0026ndash;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53 mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation vs. Wild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 4/7 vs. others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u0026ndash;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55\u0026ndash;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExon 4/7 vs. wild type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026ndash;3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: UA\u0026thinsp;=\u0026thinsp;Univariate analysis; PFS\u0026thinsp;=\u0026thinsp;Progression-free survival; OS\u0026thinsp;=\u0026thinsp;Overall survival; MA\u0026thinsp;=\u0026thinsp;Multivariate analysis; HR\u0026thinsp;=\u0026thinsp;hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Adjusted confounding covariates: Age, Histology, Brain Metastasis, Liver Metastasis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEGFR/TP53 co-mutation patients treatment outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExon4 or 7\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (63.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (68.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (33.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (28.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (63.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (68.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (90.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (96.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (97.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe TP53 gene encodes the P53 protein, which is a highly effective tumor suppressor that surveils the development of cancer cells. Upon sensing DNA damage, the p53 pathway activates and induces either a temporary halt in the cell cycle to facilitate repair or, in case of irreparable damage, initiates apoptosis\u003csup\u003e[32]\u003c/sup\u003e. TP53 mutation can lead to the loss of normal function of the p53 protein, cause a dominant negative effect, cause genome instability, and down-regulated apoptosis. There is an ongoing conversation on the prognostic significance of TP53 mutation for TKI therapy in EGFR mutant non-small cell lung cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA prior meta-analysis\u003csup\u003e[19]\u003c/sup\u003e, which included several studies, found that patients with NSCLCs carrying concurrent TP53 mutation have a significantly worse prognosis than those without TP53 mutation when treated with EGFR TKIs. The meta-analysis examined the relationship between concurrent TP53 mutation and the clinical outcomes of patients with EGFR mutant NSCLC treated with TKIs. Shorter PFS and OS were discovered to be related to concurrent TP53 mutation.\u0026nbsp;However, the study included mostly retrospective clinical studies, which may have some bias. Seven prospective clinical studies with 1319 patients were included in our meta-analysis to examine the relationship between concurrent TP53 mutation and clinical outcomes. Patients without TP53 mutation had significantly longer PFS (HR=0.66; 95% CI=0.57 0.76; P0.0001), while patients with concurrent TP53 mutation who were treated with EGFR TKIs had significantly shorter OS (HR=0.61; 95% CI=0.51 0.74; P P0.0001). This outcome was in line with other meta-analyses, which had found that the prognosis was poorer for TP53 co-mutation.\u003c/p\u003e\n\u003cp\u003eWe simultaneously gathered a relevant cohort from our institution and conducted a retrospective study on it in order to further determine the influence of the TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR sensitizing mutations.\u0026nbsp;The prevalence of TP53 gene mutation in our group was 57% (94/164), which was consistent with other reports\u003csup\u003e[33]–[34]\u003c/sup\u003e. We categorized exons according to how frequently they underwent mutation and named \"hot spot\" exons that underwent a lot of mutation. Exons 5-8 in TP53 are often mutated, according to a number of studies\u003csup\u003e[18][35]\u003c/sup\u003e. Exon 7 (14%) and exon 5 (14%), followed by exon 8, exon 6, exon 4, and exon 10, were the most often found mutation in our investigation, which was in line with other findings.\u003c/p\u003e\n\u003cp\u003eSeveral studies have endeavored to evaluate the predictive implications of distinct TP53 mutation. Jiao et al. disclosed that patients with TP53 mutation in exons 4 and 6, multiple exonic mutations, or mutations of unknown type, exhibited the poorest prognosis in patients harboring common sensitizing EGFR mutation\u003csup\u003e[36]\u003c/sup\u003e. Hou et al. identified TP53 non-missense mutation, non-destructive mutation, mutation in exons 6, 7, and non-DBD regions as unfavorable prognosticators in NSCLC patients who underwent first-generation EGFR-TKI treatment\u003csup\u003e[37]\u003c/sup\u003e. Conversely, Labbe et al. observed that NSCLC patients with TP53 missense mutation, instead of not non-missense mutation, were associated with significantly decreased PFS following EGFR-TKI treatment\u003csup\u003e[38]\u003c/sup\u003e. Two studies found exon 8 can acted as an indicator for predicting prognosis\u003csup\u003e[28][39]\u003c/sup\u003e. In addition, we identified that TP53 exon 4 or 7 mutation emerged as independent predictors of poor prognosis after treatment (mPFS = 9.37months; HR = 2.49, 95% CI: 1.56-3.97,P\u0026lt;0.001). Due to a diverse range of genetic mutations in cancer cells, it is not uncommon for different studies to produce slightly different results, especially when studying specific genetic mutations and their impact on patient prognosis. Differences in patient populations, variations in gene detection techniques, and treatment regimens can also contribute to inconsistencies. On the other hand, further investigation on TP53 mutation impacting on patient’s prognosis should be highly encouraged.\u003c/p\u003e\n\u003cp\u003eFor patients with TP53 co-mutation, using a combination strategy to improve the efficacy seems to be promising and attractive.\u0026nbsp;A prospective clinical trial, NEJ009\u003csup\u003e[40]\u003c/sup\u003e, revealed that gefitinib combined with chemotherapy regimen significantly improved PFS and PFS2 with an acceptable safety profile compared with gefitinib-alone. Wu et al. conducted a meta-analysis that included eight randomized trials with a total of 1349 patients, showing that compared to EGFR-TKI monotherapy, EGFR-TKI combined with chemotherapy improved OS, and PFS\u003csup\u003e[41]\u003c/sup\u003e.\u0026nbsp;Regretfully, patients with TP53 co-mutation were not included in any of the two earlier studies' evaluations. A growing number of clinical research have reported on the efficacy of TP53 co-mutation as a result of increased scholarly attention to the condition. The majority of these studies have found that combination treatment is an effective means of improving the prognosis of patients with TP53 co-mutation.\u0026nbsp;Nishio et al. published the RELAY study's findings\u003csup\u003e[31]\u003c/sup\u003e. The analysis of TP53 co-mutation data in the article revealed that patients with concurrent TP53 mutation had a shorter PFS than individuals whose tumors were TP53 wild-type. The combination of ramucirumab and erlotinib showed a longer PFS of 15.2 months and 10.6 months, respectively, when compared to erlotinib monotherapy. In order to determine whether EGFR-TKI with chemotherapy or monotherapy is more successful as the first-line treatment for patients with advanced EGFR-mutant lung adenocarcinomas who also have TP53 mutation, Yang et al. performed a retrospective study\u003csup\u003e[42]\u003c/sup\u003e. The findings indicated that those individuals might benefit better from EGFR-TKIs in addition to chemotherapy. Our data revealed that regardless of whether patients received monotherapy or combination therapy, patients were numerically very close to one another in terms of mPFS (10.57 months,95%CI:9.37-11.9 vs. 10.73 months,95%CI:9.37-15.9,P=0.68) and mOS (29.37 months,95%CI:23.23-35.13 vs. 29.30 months,95%CI:22.57-58.83,P=0.87). This treatment data from our study needs to be analyzed with additional caution because, in real-world clinical practice, the simultaneous use of both TKI and chemotherapy modalities cannot completely rule out the possibility of additive toxic effects, which could hasten patient deterioration and affect treatment outcomes. There was less data from prospective clinical studies on the use of combination therapy to treat patients with TP53 co-mutation, and there was still debate as to whether combination therapy was the optimal treatment modality for those patients. A large number of clinical studies are underway, and we look forward to data from future clinical studies that will give us a definitive answer.\u003c/p\u003e\n\u003cp\u003eTo sum up, our research offered several benefits: First and foremost, the fact that prospective clinical studies were included in our meta-analysis lends greater credibility to the results. Second, in order to guarantee the stability and dependability of the outcomes, we thoroughly statistically analyze the data. Not to mention, we did a preliminary comparison between the combination therapy model and the monotherapy model, as well as an examination of actual data from our facility to support that finding. The results are useful to doctors because they may be applied to develop more personalized treatment plans for different patients in a clinical setting.\u003c/p\u003e\n\u003cp\u003eThe current study was not without its flaws. Firstly, the meta-analysis had very few studies, and the studies were not dispersed equally across the population, therefore the findings drawn were not necessarily applicable to a larger population. Furthermore, because the research was conducted retrospectively and the cases were collected from a single site, it was simple to introduce selection bias and distort the associations that were discovered. Third, some inadequate statistical conclusions may have been drawn from the study's small sample size. To get more accurate results, further large-scale clinical trials from various populations in the prospective setting and more meta-analysis are required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current updated meta-analysis suggested that individuals with advanced non-small cell lung cancer who also had TP53 co-mutation had worse shorter PFS and OS. Particularly, TP53 exon 4 or exon 7 mutations suggest a poorer prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with the supports of Beijing Xisike Clinical Oncology Research Foundation (No. Y-2019AZQN-04532) and Guangxi Medical and health key discipline construction project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data's statistical analysis was carried out by Wenshu Huang, who also prepared the Chinese text. Huiting Wei and Jiang Wei\u0026nbsp;contributed to the translation of the text, analysis, and creation of several of the illustrations. Cuiyun Su, Yun Zhao, Jianbo He and Liping Tan got the patients' fully informed permission before treating them, giving them care, and giving them their clinical information. Shubin Chen provided some helpful feedback and made changes to the manuscript. Shaozhang Zhou directed and conceptualized the research procedure;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eApproval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Guangxi Medical University Cancer Hospital 's ethical committee gave its approval for this study. All processes and information collection for this study followed the ethical standards of the Research Committee of the Guangxi Medical University Cancer Hospital\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to publish\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur manuscripts do not contain any personal data of any kind (including any personal details, images or videos)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal J, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Front Oncol. 11: 681429. https://doi.org/10.3389/fonc.2021.681429\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"advanced non-small cell lung cancer, epidermal growth factor receptor, TP53, coexistence of mutation, meta-analysis, retrospective cohort study","lastPublishedDoi":"10.21203/rs.3.rs-5354638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5354638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe currently updated research was conducted in order to determine the effect of TP53 co-mutation on the clinical outcomes of patients with advanced non-small cell lung cancer who also had EGFR-sensitizing mutation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used the MINORS Methodological items for non-comparative single-arm clinical research, and for eligible randomized clinical trials (RCTs), we used the Cochrane risk of bias instrument. Outcomes including progression-free survival (PFS) and overall survival (OS) were extracted for further analysis. A total of 164 advanced non-small cell lung cancer (NSCLC) patients were enrolled in the study. The prognostic value of TP53 mutation status for PFS and OS was evaluated using Kaplan-Meier survival analysis and Cox's proportional hazards regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA pooled incidence of TP53 co-mutation was 49.6%. With regard to survival analysis, patients with no TP53 mutation showed a significantly longer PFS (HR\u0026thinsp;=\u0026thinsp;0.66; 95% CI\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.76; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), in the meantime, meta-analysis demonstrated a significantly shorter OS in patients harboring concurrent TP53 mutation treated with EGFRTKIs (HR\u0026thinsp;=\u0026thinsp;0.61; 95% CI\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.74; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Mutations in exon 4 or 7 of TP53 served as independent worse prognostic factors for disease progression compared with no TP53 co-mutation (HR\u0026thinsp;=\u0026thinsp;2.49, 95% CI: 1.56\u0026ndash;3.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the others TP53 mutation (HR\u0026thinsp;=\u0026thinsp;2.38, 95% CI: 1.46\u0026ndash;3.86, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIndividuals with advanced NSCLC who also had TP53 co-mutation had worse shorter PFS and OS. Particularly, TP53 exon 4 or exon 7 mutations suggest a poorer prognosis.\u003c/p\u003e","manuscriptTitle":"The impact of TP53 co-mutation on the clinical outcomes of advanced non-small cell lung cancer patients with EGFR sensitizing mutation: An updated systematic review and meta-analysis of prospective clinical trials and a single-center retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:21:20","doi":"10.21203/rs.3.rs-5354638/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb587fef-be2d-4790-b661-14f878f064de","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-27T17:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:21:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5354638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5354638","identity":"rs-5354638","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}