Next-Generation Sequencing in Early-Stage Multiple Primary Lung Cancer: The Prognostic Significance of Genomic Accumulation Status and BCL2L11 del

Next-Generation Sequencing in Early-Stage Multiple Primary Lung Cancer: The Prognostic Significance of Genomic Accumulation Status and BCL2L11 del | 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 Next-Generation Sequencing in Early-Stage Multiple Primary Lung Cancer: The Prognostic Significance of Genomic Accumulation Status and BCL2L11 del Mu-Ting Wang, Chen-Hui Ni, Yan-Qi Lu, Wei Zheng, Shu-Liang Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4423319/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 Objective : This study aimed to define the genomic features of tumors and to delineate the potential mutational pattern underlying the prognosis of patients using large-panel next-generation sequencing (NGS) assays. Methods : A total of 53 patients were enrolled, with a total of 130 malignant tumors. Clinical variables were collected, and the NGS sequencing of a large panel of 116 tumor-associated genes was performed. According to the gene mutation series and the number of mutation sites, the patients were divided into a series of groups. We investigated the relationship between the clinical–genetic features and the prognosis of MPLCs. Results : The patients exceeding the IA stage were associated with a significantly shorter DFS than those in the IA stage (mean time: 27.5 vs. 50.6 months, p = 0.044), and BCL2L11 del subsets were associated with a significantly worse DFS (31.9 vs. 50.2 months, p = 0.047). In the subgroups, the patients with a single gene mutation series with multiple gene mutation sites had a shorter DFS than those with a single mutation site (37.6 vs. 53.9 months, p = 0.047); and those with four gene series with over four mutation sites displayed a longer DFS than those with four sites (25.7 vs. 58 months, p = 0.034). In a Cox Multivariate analysis, exceeding the IA stage and a BCL2L11 del mutation were considered unfavorable independent prognostic factors (HR = 5.102, 95%CI: 1.526 to 17.054; p = 0.008, and HR = 6.010, 95%CI: 1.636 to 22.079; p = 0.007, respectively). A lower gene mutation series (≤2) was an independent factor for a longer DFS (HR = 0.276, 95%CI: 0.086 to 0.882; p = 0.03). Conclusions : The prognosis of patients with early-stage MPLC may potentially be related to the accumulation status of gene mutation series and sites; their driving powers may offset each other. Taken together, the application of genomic profiling may prove to be useful for subdividing and precisely managing patients with MPLC. multiple primary lung cancer next-generation sequencing prognosis gene mutation series early stage B-cell lymphoma 2-like 11 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Multiple primary lung cancers (MPLCs), defined as the presence of two or more primary lung cancers, including synchronous and metachronous cancers, have shown an increasing incidence due to the widespread use of computed tomography (CT) [1,2]. The diagnosis and treatment of MPLCs have been well developed in recent years [3,4]. However, the prognostic factors used to stratify the risk of patients are still limited. Previous studies have stated that the prognosis of patients with MPLC is related to age, gender, laterality, histological type, radiological features, TNM stage, differentiation, and lymphatic metastases; however, controversies remain [5–9]. Some studies have indicated that patients with select types of MPLCs would have a better long-term survival if analyzed according to their clinical characteristics [10,11]. With the wide implementation of next-generation sequencing (NGS) platforms, genomic features have played important roles in the diagnosis and molecular typing of MPLCs [12–14]. Comprehensive studies have focused on distinguishing malignant nodules and evaluating the progression status of MPLCs by combining the pathological–clinical features or radiological parameters with gene examination data [15–17]. The diverse genomic trajectories and prognoses of MPLCs have not been studied in detail, especially those in early-stage MPLCs. In this study, we performed next-generation sequencing (NGS) of a panel of 116 genes[Supplementary Materials] in 130 early-stage MPLCS surgical specimens from 53 patients who were diagnosed with stage I-II adenocarcinoma. We defined the genomic features of every tumor and delineated the potential mutational pattern underlying the prognosis of the patients. 2. Materials and Methods 2.1. Patient Selection and Data Collection A total of 1620 consecutive patients with primary lung cancer underwent surgical resection in our department between January 2016 and December 2018. They were pathologically confirmed as having at least two malignant tumors. Patients were excluded from this study if they had different histology types or carcinoma in situ, had N2/3 involvement, received neoadjuvant therapies, lacked surgical specimens, or were lost to follow-up. Finally, 53 patients were enrolled, with a total of 130 malignant tumors. Clinical variables were collected from the patients, including sex, age, tumor location, tumor number, TNM stage, and CT features. The tumor stage was classified according to the 8th edition of the tumor–node–metastasis (TNM) classification for lung cancer. Pathological diagnoses were classified as AAH, AIS, MIA, or IAC according to the 2015 WHO classification system. Two experienced radiologists evaluated CT imaging data independently. Written informed consent was obtained for tissue analysis before surgery. This study was approved by the Institutional Review Board of the Fujian Medical University Union Hospital. 2.2. Next-Generation Sequencing and Grouping Criteria Genomic profiling was performed in the laboratory at Amoydx (Shanghai, China). At least 100 ng of cancer tissue DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tumor samples using a DNA Extraction Kit (Amoy Diagnostics, Xiamen, China) according to the manufacturer’s protocols. All the coding exons of 116 key cancer-related genes were captured using a custom hybridization capture panel. The targeted library was analyzed using an Illumina Mid Output V2 Reagent kit and NextSeq CN500. A sequencing data analysis was performed by using AmoyDx NGS data analysis system software (ANDAS Data Analyzer) to obtain the related gene variant information. The variant filter criteria requiring absolute mutated allele read counts were set to a valid depth ≥ 180×, percentage of mutated copies ≥ 0.5%, and mutant copy number ≥ 2, which allowed for the detection of rarer variants at a read percentage of 1%. According to the gene mutation series or family, the patients were divided into two groups: ≤2, with 1 ~ 2 series gene mutations, and ≥ 3, with ≥ 3 series gene mutations. In addition, a mild mutation meant that the patient had no gene mutation sites or a single gene mutation site, and the severe group was defined as patients with ≥ 2 gene mutation sites. The following subgroups were defined: group 1, only one gene mutation series and one gene mutation site vs. a single series with multiple mutation sites; group 2, two gene mutation series and two gene mutation sites vs. two series with over two mutation sites; group 3, three gene mutation series with three gene mutation sites vs. three series over three mutation sites; group 4: four mutation series with four gene mutation sites vs. four series with over four mutation sites. 2.3. Statistical Methods All categorical variables were analyzed using SPSS Statistics version 23 (IBM, Chicago, IL, USA). A statistical analysis was performed with χ2 or Fisher’s exact test in nominal categorical variables and continuous variables with Student’s t test. The Kaplan–Meier method was used to calculate the cumulative survival rates. A multivariate analysis was conducted with the Cox proportional hazard ratio model. A two-sided p value < 0.05 was considered statistically significant. 3. Results 3.1. Clinical Characteristics and Gene Mutation Data In this study, 53 patients with early-stage multiple primary lung cancer were recruited, with a total of 130 tumors. No serious postoperative complications or perioperative deaths were observed in any of the patients. In total, there were 42 female (79.2%) and 11 male (20.7%) patients, and the median age was 59. Of the patients, 56.6 percent (30/53) had two tumors, and 43.4% had more than two pulmonary lesions. According to the locations of the tumors, the proportions of those in the same lobe, in different lobes ipsilaterally, and in different lobes bilaterally were 32% (17/53), 41.5% (22/53), and 22.4% (14/53), respectively. A patient was recorded as 0 (7.5%, 4/53) if they had tumors without malignant signs according to CT image features and as 1 (92.5%, 49/53) if they had at least one sign. Of these, 45 cases (85%) were assigned as being in the IA stage, and 8 (15%) were assigned as being in a stage higher than IA. In terms of the gene examination of the tumors (n = 130) using NGS on a panel of 116 genes (shown in Supplementary Materials), EGFR mutations were the most common (58.3%), followed by BCL2L11 (16.1%), ERBB2 (9.2%), PIK3CA (8.5%), and BRAF (7.7%) mutations. Among the patients, EGFR (L858R) mutations were observed in 47.2% (25/53), and BCL2L11 del mutations were found in 22.6% (12/53). According to the gene mutation series, the patients were divided into two groups: ≤2 (49%, 26/53), with 1 ~ 2 series gene mutations, and ≥ 3, with ≥ 3 series gene mutations (51%, 27/53). In addition, a mild mutation (15.1%, 8/53) meant that the patient had no gene mutation sites or a single gene mutation site, and the severe group (84.9%, 45/53) was defined as patients with ≥ 2 gene mutation sites. The clinical and genomic features of the cohort are summarized in Table 1 . Table 1 The correlation between mutation series and clinical characteristics in patients with multiple primary lung cancer. Variables Mutation Series a Chi-Square Value p * ≤ 2 ≥ 3 Age (years) ≤ 59 12 13 0.021 0.884 > 59 14 14 Gender male 5 6 0.072 0.788 female 21 21 Location same lobe 9 8 0.362 0.850 different lobe ipsilaterally 11 11 bilaterally 6 8 CT imaging without 2 2 0.002 0.969 with 24 25 Stage ≤IA 23 22 0.504 0.478 >IA 3 5 Mutation degree b mild 8 0 9.785 0.002 severe 18 27 BCL2L11 Del mutation no 25 16 10.294 0.001 yes 1 11 EGFR (L858R) mutation no 11 14 0.484 0.487 yes 15 13 Nodule number 2 15 15 0.025 0.875 > 2 11 12 * Chi-square test; p value 1 mutation sites. 3.2. Clinical Features are linked with the Mutation Series Grouping of MPLCs The differences in gene mutation series (≤ 2 vs. ≥3) between the subsets categorized according to the thresholds of each parameter with relevant clinical implications were investigated. The BCL2L11 del mutation and more severe gene mutations frequently occurred in the patients with over three gene mutation series (1/53 vs. 11/53, p = 0.001, and 18/53 vs. 27/53, p = 0.002, respectively). There were no correlations with the other examined clinical features, such as gender, age, stage, tumor location, tumor number EGFR (L858R) mutation, or CT imaging features ( p > 0.05, Table 1 ). 3.3. Prognostic Analysis with Clinical Features and Gene Mutation Status of MPLCs Five-year disease-free survival (Five-year DFS) was defined as a prognostic parameter of the MPLCs. It revealed that the patients exceeding the IA stage were associated with a significantly shorter DFS than those in the IA stage (mean time: 27.5 vs. 50.6 months, p = 0.044) (Fig. 1 ). Other clinical characteristics were found to have no relationship in the DFS analysis ( p > 0.05) (Table 2 ). Among the gene examination data, only the BCL2L11 del subsets were found to have a significantly worse DFS (31.9 vs. 50.2 months, p = 0.047) (Fig. 2 ). The patients with over three gene mutation series or multiple mutation sites seemed to have a shorter DFS, but no statistical significance was found ( p > 0.05) (Figs. 3 and 4 ). Table 2 The clinical–genomic characteristics and 5-year progression-free survival (DFS) of patients with multiple primary lung cancer. Clinical and Clinical Indexes Case No. (%) 5-Year DFS (%) p * Age (year) ≤ 59 25 (47.2) 68 0.556 > 59 28 (52.8) 60.7 Gender male 11 (20.7) 54.5 0.651 female 42 (79.2) 66.7 Location same lobe 17 (32) 70.6 0.418 different lobe ipsilaterally 22 (41.5) 68.2 bilaterally 14 (26.4) 50 CT imaging with 49 (92.4) 63.3 0.706 without 4 (7.5) 75 Stage ≤IA 45 (85) 68.9 0.044 >IA 8 (15) 37.5 Mutation degree b mild 8 (15.1) 87.5 0.134 severe 45 (84.9) 60 BCL2L11 mutation no 41 (77.4) 68.3 0.047 yes 12 (22.6) 50 EGFR (L858R) mutation no 25 (47.2) 68 0.572 yes 28 (52.8) 60.7 Nodule number 2 30 (56.6) 66.7 0.734 > 2 23 (43.4) 60.9 Mutation series a ≤ 2 26 (49) 61.5 0.910 ≥ 3 27 (51) 66.7 * Log-rank test of Kaplan–Meier method; p 1 mutation sites. 3.4. Mutation Series Subgroups and 5-Year DFS By combining the gene mutation series and the number of mutation sites, the patients were categorized into four subgroups. In subgroup 1, the patients with a single gene mutation series that existed concurrently at multiple gene mutation sites had a shorter DFS than those with a single mutation site (42.8% (6/14) vs. 57.1% (8/14), median time of survival, mean time: 37.6 vs. 53.9 months, p = 0.047) (Fig. 5 ). In subgroup 2, the patients with two gene mutation series and more than two gene mutation sites had a worse DFS than those with two mutation sites, but no statistical significance was observed (25% (3/12) vs. 75% (9/12), median time of survival, mean time: 29 vs. 49 months, p = 0.132) (Fig. 6 ). In subgroup 3, the patients with 3 gene mutation series and over three mutation sites exhibited a better DFS than the patients with three gene mutation sets, although the difference was not statistically significant (61.5% (8/13) vs. 38.5% (5/13), median time of survival, mean time: 38.6 vs. 58.6 months, p = 0.381) (Fig. 7 ). However, in subgroup 4, the patients with four mutation series concurrently occurring at more than four mutation sites displayed a significantly longer DFS than those with four sites (50% (7/14) vs. 50% (7/14), median time of survival, mean time: 25.7 vs. 58 months, p = 0.034) (Fig. 8 ). 3.5. Multivariate Analysis In the Cox Multivariate analysis, the groups exceeding stage IA or with a BCL2L11 del mutation tended to have a poorer 5-year DFS, and these were considered as independent prognostic factors (HR = 5.102, 95%CI: 1.526 to 17.054; p = 0.008, and HR = 6.010, 95%CI: 1.636 to 22.079; p = 0.007, respectively). It was indicated that a lower gene series (≤ 2) mutation was a favorable independent factor for DFS (HR = 0.276, 95%CI :0.086 to 0.882; p = 0.03). There were no significant prognoses for the DFS of the other groups (Table 3 ). Table 3 Cox proportional hazards regression model for 5-year progression-free survival (DFS) in patients. Variables Multivariate Analysis 5-Year DFS HR (95%CI) p Location same lobe vs. different lobe ipsilaterally vs. bilaterally 1.500 (0.794 to 2.833) 0.212 CT imaging with vs. without 0.847 (0.104 to 6.880) 0.876 Stage ≤IA vs. >IA 5.102 (1.526 to 17.054) 0.008 Mutation degree Mild vs. severe 4.211 (0.523 to 33.919) 0.177 BCL2L11 del mutation no vs. yes 6.010 (1.636 to 22.079) 0.007 Mutation series ≤ 2 vs. ≥3 0.276 (0.086 to 0.882) 0.030 4. Discussion The present study was performed in the genomic landscape of early-stage MPLAs and evaluated the impact of prognosis by collecting data from a series of 53 patients with 130 tumors. It is widely accepted that genomic heterogeneity plays an important role in the treatment and prognosis of LUADs(Lung Adenocarcinomas), even in the early stages [18]. A high discordant mutation has been confirmed as being a common genomic event in MPLCs such as EGFR , KRAS , and TP53 [19,20]. It is supposed that multiple and accumulated genetic alterations may play critical roles in driving tumor progression and different prognoses in MPLCs. Our study found that EGFR mutations were the most common (58.3%), followed by BCL2L11 (16.1%), ERBB2 (9.2%), PIK3CA (8.5%), and BRAF (7.7%) mutations, in the 130 tumors. Among the patients, EGFR (L858R) mutations were observed in 47.2% (25/53), and BCL2L11 del mutations were found in 22.6% (12/53). These results are similar to those of previous studies [21–24]. As is well known, B-cell lymphoma 2-like 11 ( BCL2L11 / BIM ), identified as a critical modulator of cellular apoptosis, has been demonstrated to be a poor prognostic factor for Non-Small Cell Lung Cancer(NSCLC)patients, and it is always combined with EGFR/ALK/ROS1 mutations in NSCLC patients [25]. Consistent with this study, 50% (6/12) were found to have BCL2L11 del mutations with EGFR (L858R) mutations, indicating that BCL2L11 del , as the EGFR in lung cancers, may be an early genomic event in early-stage MPLCs. By comparing the clinical features of the subgroups, based on the number of gene mutation series in the MPLCs (≤ 2 vs. ≥3), we found that there were more mutation series and more mutation sites (≤ 1 vs. ≥2) and that BCL2L11 del was easily detected ( p < 0.05), but no relationships between the other parameters were observed (Table 1 ). It was accepted that accumulated genetic events occurred along with the increasing number and development of lesions in the patients. In the prognostic analysis, BCL2L11 del was associated with a poor 5-year DFS of early-stage MPLCs (68.3% vs. 50%, p = 0.047; Table 2 and Fig. 2 ). BCL2L11 del has always served as a poor prognostic factor in solitary NSCLC patients and is correlated with EGFR -TKI (EGFR-Tyrosine Kinase Inhibitor)resistance [26]. This implies that the BCL2L11 del mutation might occur early and helps to explain the different prognoses among patients with the same MPLC stage. These results were not reported previously. Meanwhile, patients in the stage IA or below had a favorable 5-year DFS (68.9 vs. 37.5, p = 0.044; Table 2 and Fig. 1 ). Staging serves as an important indicator of the prognosis in lung cancer, but there is no definitive method for MPLC [7,13,27]. The TNM stage of MPLC was considered to be the highest stage among the different lesions in our study [9]. This may be due to the fact that the included patients mainly had an IA stage diagnosis (85%) and were also diagnosed as having MIA or MIA with invasive adenocarcinoma. We found no significant difference in the prognosis of the other subsets of patients (Table 2 and Figs. 3 and 4 ). Moreover, in the subgroup analysis of the gene mutation series, we were able to delineate the potential genetic trajectories of the MPLCs. In the patients with a single gene mutation series, multiple mutation sites may have been the harmful mechanism in the prognosis of the MPLC (53.9 vs. 37.6 months, p = 0.047; Fig. 5 ). However, the order of the factors driving the progression of MPLCs changed gradually during the increase in the mutational series. A high number of gene mutation series (≥ 4) reflected a poor DFS only in combination with four mutation sites (29.7 vs. 58, p = 0.034; Fig. 8 ). We revealed that the prognosis of the patients with early-stage MPLC was potentially related to the accumulation status of gene mutation series and sites; their driving powers may offset each other. These results have not been previously demonstrated. In the multivariate analysis, we revealed that stage > IA and a BCL2L11 del mutation might be independent predictors of a worse DFS, and a lower mutation series (≤ 2) was associated with a favorable prognosis of MPLCs (Table 3 ). The speculation that potential genomic indexes exist in early-stage MPLCs may be used for the subdivision of patients, improving prognostic evaluations and individualized treatment strategies .Several limitations of the present study can be addressed, such as the use of a small sample from a single clinical center, only patients with stage IA-II and MIA or MIA with invasive adenocarcinoma were enrolled, and MPLC was diagnosed according to clinical and pathological features. In addition, there was a lack of research on the mutations in key genes in cancer-associated pathways. Thus, a larger cohort and more detailed genomic protein profiles are needed. 5. Conclusions To summarize, we investigated the clinical–genetic features and 5-year DFS of patients with early-stage MPLC using the NGS technique. Our study suggests that stage > IA and a BCL2L11 del mutation are unfavorable prognostic predictors and that a low-level gene mutation series (≤ 2) may be beneficial for 5-year DFS. Our data also shed light on the different prognoses according to the status of the gene mutation series and sites. Taken together, the application of genomic profiling may prove to be useful for subdividing and precisely managing patients with MPLC. Abbreviations MPLC = multiple primary lung cancer; DFS = disease-free survival; NGS = next-generation sequencing; MIA = microinvasive adenocarcinoma; TNM = tumor–node–metastasis; CT = computerized tomography; BCL2L11/BIM = B-cell lymphoma 2-like 11; EGFR = epidermal growth factor receptor. Declarations Author Contributions: Mu-Ting Wang: Conceptualization, Methodology, Data Curation, Software, Writing-Original Draft, Writing- Reviewing and Editing ChenHui Ni: Conceptualization, Methodology, WritingReviewing and Editing Shu-Liang Zhang: Writing- Reviewing and Editing Mao-Hui Chen:Data Curation, Investigation Yan-Qi Lu: Data Curation Wei Zheng: Supervision Bin Zheng: Conceptualization, Writing- Reviewing and Editing Chun Chen: Conceptualization, Writing- Reviewing and Editing. All authors read and approved the final manuscript. Mu-Ting Wang and Chen-Hui Ni had full access to the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. Chun Chen and Bin Zheng are guarantors of the article. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Funding: This work was supported by National Key Clinical Specialty OfThoracicSurgery, Fujian Union Hospital Minimally lnvasive Surgery Center, Startup Fundfor scientifc research, Fujian Medical University (Grant number: 20210H1036)Fujian Province University Construction Project(No.2019-67),TheMajor Project of Science And Technological Innovation In FujianProvince(2020Y9091), University-Industry-Academia Cooperation Program(2022Y4014). Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Fujian Medical University Union Hospital (IRB No. 2021QH029). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: No other data available. Conflicts of Interest: The authors declare no conflict of interest. References Vazquez, M.; Carter, D.; Brambilla, E.; Gazdar, A.; Noguchi, M.; Travis, W.D.; Huang, Y.; Zhang, L.; Yip, R.; Yankelevitz, D.F.; et al. Solitary and multiple resected adenocarcinomas after CT screening for lung cancer: Histopathologic features and their prognostic implications. Lung Cancer 2009 , 64 , 148–154. https://doi.org/10.1016/j.lungcan.2008.08.009. Chen, C.; Huang, X.; Peng, M.; Liu, W.; Yu, F.; Wang, X. Multiple primary lung cancer: A rising challenge. J. Thorac. Dis. 2019 , 11 (Suppl. S4), S523–S536. https://doi.org/10.21037/jtd.2019.01.56. Mansuet-Lupo, A.; Barritault, M.; Alifano, M.; Janet-Vendroux, A.; Zarmaev, M.; Biton, J.; Velut, Y.; Le Hay, C.; Cremer, I.; Régnard, J.-F.; et al. Proposal for a combined histomolecular algorithm to distinguish multiple primary adenocarcinomas from intrapulmonary metastasis in patients with multiple lung tumors. J. Thorac. Oncol. 2019 , 14 , 844–856. https://doi.org/10.1016/j.jtho.2019.01.017. Shintani, Y.; Okami, J.; Ito, H.; Ohtsuka, T.; Toyooka, S.; Mori, T.; Watanabe, S.; Asamura, H.; Chida, M.; Date, H.; et al. Clinical features and outcomes of patients with stage I multiple primary lung cancers. Cancer Sci. 2021 , 112 , 1924–1935. https://doi.org/10.1111/cas.14748. Lv, J.; Zhu, D.; Wang, X.; Shen, Q.; Rao, Q.; Zhou, X. The value of prognostic factors for survival in synchronous multifocal lung cancer: Aretrospective analysis of 164 patients. Ann. Thorac. Surg. 2018 , 105 , 930–936. Kocaturk, C.I.; Gunluoglu, M.Z.; Cansever, L.; Demir, A.; Cinar, U.; Dincer, S.I.; Bedirhan, M.A. Survival and prognostic factors in surgically resected synchronous multiple primary lung cancers. Eur. J. Cardio-Thorac. Surg. 2011 , 39 , 160–166. https://doi.org/10.1016/j.ejcts.2010.05.037. Zhang, Z.; Gao, S.; Mao, Y.; Mu, J.; Xue, Q.; Feng, X.; He, J. Surgical outcomes of synchronous multiple primary non-small cell lung cancers. Sci. Rep. 2016 , 6 , 23252. https://doi.org/10.1038/srep23252. Tanvetyanon, T.; Finley, D.J.; Fabian, T.; Riquet, M.; Voltolini, L.; Kocaturk, C.; Bryant, A.; Robinson, L. Prognostic nomogram to predict survival after surgery for synchronous multiple lung cancers in multiple lobes. J. Thorac. Oncol. 2015 , 10 , 338–345. https://doi.org/10.1097/jto.0000000000000400. Zheng, Y.; Han, X.; Wu, Y.; Jia, X.; Zhang, K.; Fan, J.; Shi, H. Prognostic Factors for Survival in Multiple Primary Lung Adenocarcinomas: A Ret-rospective Analysis of 283 Patients. Technol. Cancer Res. Treat. 2023 , 22 , 1–13. Ishikawa, Y.; Nakayama, H.; Ito, H.; Yokose, T.; Tsuboi, M.; Nishii, T.; Masuda, M. Surgical treatment for synchronous primary lung adeno-carcinomas. Ann. Thorac. Surg. 2014 , 98 , 1983–1988. Tie, H.; Luo, J.; Shi, R.; Li, Z.; Chen, D.; Wu, Q. Characteristics and prognosis of synchronous multiple primary lung cancer after surgical treatment: A systematic review and meta-analysis of current evidence. Cancer Med. 2021 , 10 , 507–520. https://doi.org/10.1002/cam4.3614. Li, Y.; Li, X.; Li, H.; Zhao, Y.; Liu, Z.; Sun, K.; Zhu, X.; Qi, Q.; An, B.; Shen, D.; et al. Genomic characterisation of pulmonary subsolid nodules: Mutational landscape and radiological features. Eur. Respir. J. 2020 , 55 , 1901409. https://doi.org/10.1183/13993003.01409-2019. Asmar, R.; Sonett, J.R.; Singh, G.; Mansukhani, M.M.; Borczuk, A.C. Use of Oncogenic Driver Mutations in Staging of Multiple Primary Lung Carcinomas: A Single-Center Experience. J. Thorac. Oncol. 2017 , 12 , 1524–1535. https://doi.org/10.1016/j.jtho.2017.06.012. Zhou, D.; Liu, Q.-X.; Li, M.-Y.; Hou, B.; Yang, G.-X.; Lu, X.; Zheng, H.; Jiang, L.; Dai, J.-G. Utility of whole exome sequencing analysis in differentiating intrapulmonary metastatic multiple ground-glass nodules (GGNs) from multiple primary GGNs. Int. J. Clin. Oncol. 2022 , 27 , 871–881. https://doi.org/10.1007/s10147-022-02134-8. Song, J.; Xu, Y.; Yang, Z.; Liu, Y.; Zhang, P.; Wang, X.; Sun, C.; Guo, Y.; Qiu, S.; Shao, G.; et al. Coexistence of atypical adenomatous hyperplasia, minimally invasive adenocarcinoma and invasive adenocarcinoma: Gene mutation analysis. Thorac. Cancer 2021 , 12 , 693–698. https://doi.org/10.1111/1759-7714.13798. Xiao, Z.; Cai, H.; Wang, Y.; Cui, R.; Huo, L.; Lee, E.Y.-P.; Liang, Y.; Li, X.; Hu, Z.; Chen, L.; et al. Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images. Quant. Imaging Med. Surg. 2023 , 13 , 1286–1299. https://doi.org/10.21037/qims-22-760. Teng, F.; Xu, J.; Wang, J.; Yang, B.; Wu, Y.-Z.; Jiang, Y.-Q.; Wang, Z.-Q. Correlation between gene mutation status and clinicopathologic features in early multiple primary lung cancer. Front. Oncol. 2023 , 13 , 1110259. https://doi.org/10.3389/fonc.2023.1110259. Zhang, C.; Zhang, J.; Xu, F.-P.; Wang, Y.-G.; Xie, Z.; Su, J.; Dong, S.; Nie, Q.; Shao, Y.; Zhou, Q.; et al. Genomic Landscape and Immune Microenvironment Features of Preinvasive and Early Invasive Lung Adenocarcinoma. J. Thorac. Oncol. 2019 , 14 , 1912–1923. https://doi.org/10.1016/j.jtho.2019.07.031. Wu, C.; Zhao, C.; Yang, Y.; He, Y.; Hou, L.; Li, X.; Gao, G.; Shi, J.; Ren, S.; Chu, H.; et al. High discrepancy of driver mutations in patients with NSCLC and synchronous multiple lung ground-glass nodules. J. Thorac. Oncol. 2015 , 10 , 778–783. https://doi.org/10.1097/jto.0000000000000487. Gazdar, A.F.; Minna, J.D. Multifocal lung cancers–clonality vs field cancerization and does it matter? J. Natl. Cancer Inst. 2009 , 101 , 541–3. Pei, G.; Li, M.; Min, X.; Liu, Q.; Li, D.; Yang, Y.; Wang, S.; Wang, X.; Wang, H.; Cheng, H.; et al. Molecular Identification and Genetic Characterization of Early-Stage Multiple Primary Lung Cancer by Large-Panel Next-Generation Sequencing Analysis. Front. Oncol. 2021 , 11 , 653988. https://doi.org/10.3389/fonc.2021.653988. Hu, C.; Zhao, L.; Liu, W.; Fan, S.; Liu, J.; Liu, Y.; Liu, X.; Shu, L.; Liu, X.; Liu, P.; et al. Genomic profiles and their associations with TMB, PD-L1 expression, and immune cell infiltration landscapes in synchronous multiple primary lung cancers. J. Immunother. Cancer 2021 , 9 , e003773. https://doi.org/10.1136/jitc-2021-003773. Liu, S.-Y.; Sun, H.; Zhou, J.-Y.; Jie, G.-L.; Xie, Z.; Shao, Y.; Zhang, X.; Ye, J.-Y.; Chen, C.-X.; Zhang, X.-C.; et al. Clinical characteristics and prognostic value of the KRAS G12C mutation in Chinese non-small cell lung cancer patients. Biomark. Res. 2020 , 8 , 22. https://doi.org/10.1186/s40364-020-00199-z. Tian, P.; Zeng, H.; Ji, L.; Ding, Z.; Ren, L.; Gao, W.; Fan, Z.; Li, L.; Le, X.; Li, P.; et al. Lung adenocarcinoma with ERBB2 exon 20 insertions: Comutations and immunogenomic features related to chemoimmunotherapy. Lung Cancer 2021 , 160 , 50–58. https://doi.org/10.1016/j.lungcan.2021.07.014. Li, X.; Zhang, D.; Li, B.; Zou, B.; Wang, S.; Fan, B.; Li, W.; Yu, J.; Wang, L. Clinical implications of germline BCL2L11 deletion polymorphism in pretreated advanced NSCLC patients with osimertinib therapy. Lung Cancer 2020 , 151 , 39–43. https://doi.org/10.1016/j.lungcan.2020.12.002. Isobe, K.; Hata, Y.; Tochigi, N.; Kaburaki, K.; Kobayashi, H.; Makino, T.; Otsuka, H.; Sato, F.; Ishida, F.; Kikuchi, N.; et al. Clinical significance of BIM deletion polymorphism in non-small-cell lung cancer with ep-idermal growth factor receptor mutation. J. Thorac. Oncol. 2014 , 9 , 483–487. Detterbeck, F.C.; Nicholson, A.G.; Franklin, W.A.; Marom, E.M.; Travis, W.D.; Girard, N.; Arenberg, D.A.; Bolejack, V.; Donington, J.S.; Mazzone, P.J.; et al. The IASLC lung cancer staging project: Summary of proposals for revisions of the classification of lung cancers with multiple pulmonary sites of involvement in the forthcoming eighth edition of the TNM classification. J. Thorac. Oncol. 2016 , 11 , 639–650. https://doi.org/10.1016/j.jtho.2016.01.024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4423319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304838825,"identity":"e42eccd5-ec7d-46bc-aa70-e5387b2fa8a4","order_by":0,"name":"Mu-Ting Wang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mu-Ting","middleName":"","lastName":"Wang","suffix":""},{"id":304838826,"identity":"82d2718d-2e34-4e11-80ba-194cb2410c65","order_by":1,"name":"Chen-Hui Ni","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen-Hui","middleName":"","lastName":"Ni","suffix":""},{"id":304838827,"identity":"7c96964c-3f95-4a09-bbaa-8e03f1bfab20","order_by":2,"name":"Yan-Qi Lu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan-Qi","middleName":"","lastName":"Lu","suffix":""},{"id":304838828,"identity":"275ac012-6688-4740-832c-041fa02cf4d2","order_by":3,"name":"Wei Zheng","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zheng","suffix":""},{"id":304838829,"identity":"839a3c5e-671d-46dd-87b3-dd1ce3198576","order_by":4,"name":"Shu-Liang Zhang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu-Liang","middleName":"","lastName":"Zhang","suffix":""},{"id":304838830,"identity":"a0aa9ee8-9363-46c5-85f5-e5e474a3086c","order_by":5,"name":"Mao-Hui Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mao-Hui","middleName":"","lastName":"Chen","suffix":""},{"id":304838831,"identity":"82ed7fdc-e49b-4404-a71f-8a59adabc1a8","order_by":6,"name":"Bin Zheng","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zheng","suffix":""},{"id":304838832,"identity":"0fab0e5e-a9c5-4009-b35d-c553293f2134","order_by":7,"name":"Chun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCSBmbGBgYGNvP/jgQ4UNDz9/A5Fa+HjOJBvOOJMmIznjAJFa5CQSzIQ52w7bGDQk4NfBP7v52IOfOw7nsTEkpDEznDnPY8BwgPHDxxw8ltw5lm7Ye+ZwMRvDwWOPCypu85gzNzBLztyGW4uBRI6ZNGPb4cQ2xoZ04xlnbvNYNhxgY+bFqyX/G0QLM4OZNG/bOR6DAwmEtOSwQbSwgbUcIKxF4kaamWRvW3piGw8PKJCTeSRnHGzG6xf+GcnPJH62WSfOn/8cFJV29vz8zQc/fMSjBRsARdMoGAWjYBSMAooAAEQKVQTj2Bd1AAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-15 07:26:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4423319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4423319/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57706571,"identity":"bb66f6ef-39c4-4ad8-b99c-8ae86b868f4b","added_by":"auto","created_at":"2024-06-04 14:59:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15622,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of the subsets of the staging of patients. A significant difference was found between the groups (below stage IA: 85% (45/53) vs. over stage IA: 15% (8/53), median time of survival, mean time: 50.6 vs. 27.5 months, \u003cem\u003ep\u003c/em\u003e= 0.044).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/ee2293b269d1a0314ac4e529.png"},{"id":57705933,"identity":"f39375fd-72a9-4e5b-8fca-deef3b1e33ff","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100263,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of the subsets of the \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation status of patients. A significant difference was found between the groups (without: 77.4% (41/53) vs. with: 22.6% (12/53), median time of survival, mean time: 50.2 vs. 31.9 months, \u003cem\u003ep\u003c/em\u003e = 0.047).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/8c2c78e63f26f27350e212e1.png"},{"id":57705936,"identity":"20fffb68-ad6b-46a7-8e10-c81be3b42597","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15709,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of the subsets of the mutation series status of patients. No significant difference was found between the groups (≤2 series: 49% (26/53) vs. ≥3 series: 51% (27/53), median time of survival, mean time: 47.6 vs. 46.9 months, \u003cem\u003ep\u003c/em\u003e = 0.91).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/88bb89ebe70057fd5ff3275d.png"},{"id":57706570,"identity":"bd9b1c02-7448-49e3-b097-23240a903cdd","added_by":"auto","created_at":"2024-06-04 14:59:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15600,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of the subsets of mutation degree (the number of mutation sites) of patients. No significant difference was found between the groups (mild: 15.1% (8/53) vs. severe: 84.9% (45/53), median time of survival, mean time: 54.3 vs. 45.4 months, \u003cem\u003ep\u003c/em\u003e = 0.134).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/400bbcd8b055cf6c1543ff6b.png"},{"id":57705935,"identity":"494d4807-3a11-48bf-b9aa-a9e7064d9938","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16893,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of subgroup 1 of patients. A significant difference was found between the groups (42.8% (6/14) vs. 57.1% (8/14), median time of survival, mean time: 37.6 vs. 53.9 months, \u003cem\u003ep\u003c/em\u003e = 0.047).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/e313f20370bb2bad9d0793cd.png"},{"id":57705940,"identity":"bcf2d1aa-a671-4b52-b871-a8cca8a112f4","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16791,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of subgroup 2 of patients. No significant difference was found between the groups (25% (3/12) vs. 75% (9/12), median time of survival, mean time: 29 vs. 49 months, \u003cem\u003ep \u003c/em\u003e=\u003cem\u003e \u003c/em\u003e0.132).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/f1d01bfbdf32c52740e19f7e.png"},{"id":57705938,"identity":"686e2ab6-1b88-4c4d-b961-d108d3bba6b2","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16491,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of subgroup 3 of patients. No significant difference was found between the groups (61.5% (8/13) vs. 38.5% (5/13), median time of survival, mean time: 38.6 vs. 58.6 months, \u003cem\u003ep \u003c/em\u003e=\u003cem\u003e \u003c/em\u003e0.381).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/4793894cc44f97b5a6eab148.png"},{"id":57705939,"identity":"3049bbf8-0f54-4072-acb9-d191df8d194f","added_by":"auto","created_at":"2024-06-04 14:51:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16737,"visible":true,"origin":"","legend":"\u003cp\u003eDFS of subgroup 4 of patients. A significant difference was found between the groups (50% (7/14) vs. 50% (7/14), median time of survival, mean time: 25.7 vs. 58 months, \u003cem\u003ep \u003c/em\u003e=\u003cem\u003e \u003c/em\u003e0.034).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/4332cc458e02b92a78c11815.png"},{"id":58048265,"identity":"f2b82f7a-cd2d-434d-b880-282a3d199d5f","added_by":"auto","created_at":"2024-06-10 12:05:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4423319/v1/c4e92db3-34da-4afa-ada2-1b4cb60282a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Next-Generation Sequencing in Early-Stage Multiple Primary Lung Cancer: The Prognostic Significance of Genomic Accumulation Status and BCL2L11 del","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMultiple primary lung cancers (MPLCs), defined as the presence of two or more primary lung cancers, including synchronous and metachronous cancers, have shown an increasing incidence due to the widespread use of computed tomography (CT) [1,2]. The diagnosis and treatment of MPLCs have been well developed in recent years [3,4]. However, the prognostic factors used to stratify the risk of patients are still limited.\u003c/p\u003e \u003cp\u003ePrevious studies have stated that the prognosis of patients with MPLC is related to age, gender, laterality, histological type, radiological features, TNM stage, differentiation, and lymphatic metastases; however, controversies remain [5\u0026ndash;9]. Some studies have indicated that patients with select types of MPLCs would have a better long-term survival if analyzed according to their clinical characteristics [10,11]. With the wide implementation of next-generation sequencing (NGS) platforms, genomic features have played important roles in the diagnosis and molecular typing of MPLCs [12\u0026ndash;14]. Comprehensive studies have focused on distinguishing malignant nodules and evaluating the progression status of MPLCs by combining the pathological\u0026ndash;clinical features or radiological parameters with gene examination data [15\u0026ndash;17]. The diverse genomic trajectories and prognoses of MPLCs have not been studied in detail, especially those in early-stage MPLCs. In this study, we performed next-generation sequencing (NGS) of a panel of 116 genes[Supplementary Materials] in 130 early-stage MPLCS surgical specimens from 53 patients who were diagnosed with stage I-II adenocarcinoma. We defined the genomic features of every tumor and delineated the potential mutational pattern underlying the prognosis of the patients.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patient Selection and Data Collection\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 1620 consecutive patients with primary lung cancer underwent surgical resection in our department between January 2016 and December 2018. They were pathologically confirmed as having at least two malignant tumors. Patients were excluded from this study if they had different histology types or carcinoma in situ, had N2/3 involvement, received neoadjuvant therapies, lacked surgical specimens, or were lost to follow-up. Finally, 53 patients were enrolled, with a total of 130 malignant tumors. Clinical variables were collected from the patients, including sex, age, tumor location, tumor number, TNM stage, and CT features. The tumor stage was classified according to the 8th edition of the tumor\u0026ndash;node\u0026ndash;metastasis (TNM) classification for lung cancer. Pathological diagnoses were classified as AAH, AIS, MIA, or IAC according to the 2015 WHO classification system. Two experienced radiologists evaluated CT imaging data independently. Written informed consent was obtained for tissue analysis before surgery. This study was approved by the Institutional Review Board of the Fujian Medical University Union Hospital.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Next-Generation Sequencing and Grouping Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenomic profiling was performed in the laboratory at Amoydx (Shanghai, China). At least 100 ng of cancer tissue DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tumor samples using a DNA Extraction Kit (Amoy Diagnostics, Xiamen, China) according to the manufacturer\u0026rsquo;s protocols. All the coding exons of 116 key cancer-related genes were captured using a custom hybridization capture panel. The targeted library was analyzed using an Illumina Mid Output V2 Reagent kit and NextSeq CN500. A sequencing data analysis was performed by using AmoyDx NGS data analysis system software (ANDAS Data Analyzer) to obtain the related gene variant information. The variant filter criteria requiring absolute mutated allele read counts were set to a valid depth\u0026thinsp;\u0026ge;\u0026thinsp;180\u0026times;, percentage of mutated copies\u0026thinsp;\u0026ge;\u0026thinsp;0.5%, and mutant copy number\u0026thinsp;\u0026ge;\u0026thinsp;2, which allowed for the detection of rarer variants at a read percentage of 1%.\u003c/p\u003e \u003cp\u003eAccording to the gene mutation series or family, the patients were divided into two groups: \u0026le;2, with 1\u0026thinsp;~\u0026thinsp;2 series gene mutations, and \u0026ge;\u0026thinsp;3, with \u0026ge;\u0026thinsp;3 series gene mutations. In addition, a mild mutation meant that the patient had no gene mutation sites or a single gene mutation site, and the severe group was defined as patients with \u0026ge;\u0026thinsp;2 gene mutation sites. The following subgroups were defined: group 1, only one gene mutation series and one gene mutation site vs. a single series with multiple mutation sites; group 2, two gene mutation series and two gene mutation sites vs. two series with over two mutation sites; group 3, three gene mutation series with three gene mutation sites vs. three series over three mutation sites; group 4: four mutation series with four gene mutation sites vs. four series with over four mutation sites.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Methods\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll categorical variables were analyzed using SPSS Statistics version 23 (IBM, Chicago, IL, USA). A statistical analysis was performed with χ2 or Fisher\u0026rsquo;s exact test in nominal categorical variables and continuous variables with Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test. The Kaplan\u0026ndash;Meier method was used to calculate the cumulative survival rates. A multivariate analysis was conducted with the Cox proportional hazard ratio model. A two-sided \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Clinical Characteristics and Gene Mutation Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, 53 patients with early-stage multiple primary lung cancer were recruited, with a total of 130 tumors. No serious postoperative complications or perioperative deaths were observed in any of the patients. In total, there were 42 female (79.2%) and 11 male (20.7%) patients, and the median age was 59. Of the patients, 56.6 percent (30/53) had two tumors, and 43.4% had more than two pulmonary lesions. According to the locations of the tumors, the proportions of those in the same lobe, in different lobes ipsilaterally, and in different lobes bilaterally were 32% (17/53), 41.5% (22/53), and 22.4% (14/53), respectively. A patient was recorded as 0 (7.5%, 4/53) if they had tumors without malignant signs according to CT image features and as 1 (92.5%, 49/53) if they had at least one sign. Of these, 45 cases (85%) were assigned as being in the IA stage, and 8 (15%) were assigned as being in a stage higher than IA. In terms of the gene examination of the tumors (n\u0026thinsp;=\u0026thinsp;130) using NGS on a panel of 116 genes (shown in Supplementary Materials), \u003cem\u003eEGFR\u003c/em\u003e mutations were the most common (58.3%), followed by \u003cem\u003eBCL2L11\u003c/em\u003e (16.1%), \u003cem\u003eERBB2\u003c/em\u003e (9.2%), \u003cem\u003ePIK3CA\u003c/em\u003e (8.5%), and \u003cem\u003eBRAF\u003c/em\u003e (7.7%) mutations. Among the patients, \u003cem\u003eEGFR\u003c/em\u003e (L858R) mutations were observed in 47.2% (25/53), and \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutations were found in 22.6% (12/53). According to the gene mutation series, the patients were divided into two groups: \u0026le;2 (49%, 26/53), with 1\u0026thinsp;~\u0026thinsp;2 series gene mutations, and \u0026ge;\u0026thinsp;3, with \u0026ge;\u0026thinsp;3 series gene mutations (51%, 27/53). In addition, a mild mutation (15.1%, 8/53) meant that the patient had no gene mutation sites or a single gene mutation site, and the severe group (84.9%, 45/53) was defined as patients with \u0026ge;\u0026thinsp;2 gene mutation sites. The clinical and genomic features of the cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation between mutation series and clinical characteristics in patients with multiple primary lung cancer.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMutation Series \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep *\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \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\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \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\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \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\u003eLocation\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\u003esame lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edifferent lobe ipsilaterally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \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\u003ebilaterally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \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\u003eCT imaging\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\u003ewithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \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\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \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\u003eMutation degree \u003csup\u003eb\u003c/sup\u003e\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\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \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\u003eBCL2L11 Del 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 \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\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \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\u003eEGFR (L858R) 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 \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\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \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\u003eNodule number\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e* Chi-square test; \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. \u003csup\u003ea\u003c/sup\u003e \u0026le;2, 1\u0026thinsp;~\u0026thinsp;2 series gene mutations; \u0026ge;3, \u0026ge;\u0026thinsp;3 series gene mutations. \u003csup\u003eb\u003c/sup\u003e mild, \u0026le;\u0026thinsp;1 mutation site; severe, \u0026gt;\u0026thinsp;1 mutation sites.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Clinical Features are linked with the Mutation Series Grouping of MPLCs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe differences in gene mutation series (\u0026le;\u0026thinsp;2 vs. \u0026ge;3) between the subsets categorized according to the thresholds of each parameter with relevant clinical implications were investigated. The \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation and more severe gene mutations frequently occurred in the patients with over three gene mutation series (1/53 vs. 11/53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, and 18/53 vs. 27/53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, respectively). There were no correlations with the other examined clinical features, such as gender, age, stage, tumor location, tumor number \u003cem\u003eEGFR\u003c/em\u003e (L858R) mutation, or CT imaging features (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Prognostic Analysis with Clinical Features and Gene Mutation Status of MPLCs\u003c/h2\u003e \u003cp\u003eFive-year disease-free survival (Five-year DFS) was defined as a prognostic parameter of the MPLCs. It revealed that the patients exceeding the IA stage were associated with a significantly shorter DFS than those in the IA stage (mean time: 27.5 vs. 50.6 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Other clinical characteristics were found to have no relationship in the DFS analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the gene examination data, only the \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e subsets were found to have a significantly worse DFS (31.9 vs. 50.2 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The patients with over three gene mutation series or multiple mutation sites seemed to have a shorter DFS, but no statistical significance was found (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical\u0026ndash;genomic characteristics and 5-year progression-free survival (DFS) of patients with multiple primary lung cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical and Clinical Indexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5-Year DFS (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e *\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esame lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edifferent lobe ipsilaterally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebilaterally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation degree \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL2L11 mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR (L858R) mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation series \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e* Log-rank test of Kaplan\u0026ndash;Meier method; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. \u003csup\u003ea\u003c/sup\u003e \u0026le;2, 1\u0026thinsp;~\u0026thinsp;2 series gene mutations; \u0026ge;3, \u0026ge;\u0026thinsp;3 series gene mutations. \u003csup\u003eb\u003c/sup\u003e mild, \u0026le;\u0026thinsp;1 mutation site; severe, \u0026gt;\u0026thinsp;1 mutation sites.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Mutation Series Subgroups and 5-Year DFS\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy combining the gene mutation series and the number of mutation sites, the patients were categorized into four subgroups. In subgroup 1, the patients with a single gene mutation series that existed concurrently at multiple gene mutation sites had a shorter DFS than those with a single mutation site (42.8% (6/14) vs. 57.1% (8/14), median time of survival, mean time: 37.6 vs. 53.9 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In subgroup 2, the patients with two gene mutation series and more than two gene mutation sites had a worse DFS than those with two mutation sites, but no statistical significance was observed (25% (3/12) vs. 75% (9/12), median time of survival, mean time: 29 vs. 49 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.132) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In subgroup 3, the patients with 3 gene mutation series and over three mutation sites exhibited a better DFS than the patients with three gene mutation sets, although the difference was not statistically significant (61.5% (8/13) vs. 38.5% (5/13), median time of survival, mean time: 38.6 vs. 58.6 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.381) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, in subgroup 4, the patients with four mutation series concurrently occurring at more than four mutation sites displayed a significantly longer DFS than those with four sites (50% (7/14) vs. 50% (7/14), median time of survival, mean time: 25.7 vs. 58 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Multivariate Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the Cox Multivariate analysis, the groups exceeding stage IA or with a \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation tended to have a poorer 5-year DFS, and these were considered as independent prognostic factors (HR\u0026thinsp;=\u0026thinsp;5.102, 95%CI: 1.526 to 17.054; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, and HR\u0026thinsp;=\u0026thinsp;6.010, 95%CI: 1.636 to 22.079; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, respectively). It was indicated that a lower gene series (\u0026le;\u0026thinsp;2) mutation was a favorable independent factor for DFS (HR\u0026thinsp;=\u0026thinsp;0.276, 95%CI :0.086 to 0.882; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). There were no significant prognoses for the DFS of the other groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards regression model for 5-year progression-free survival (DFS) in patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-Year DFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003cp\u003esame lobe vs. different lobe\u003c/p\u003e \u003cp\u003eipsilaterally vs. bilaterally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.500 (0.794 to 2.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT imaging\u003c/p\u003e \u003cp\u003ewith vs. without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847 (0.104 to 6.880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003e\u0026le;IA vs. \u0026gt;IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.102 (1.526 to 17.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation degree\u003c/p\u003e \u003cp\u003eMild vs. severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.211 (0.523 to 33.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation\u003c/p\u003e \u003cp\u003eno vs. yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.010 (1.636 to 22.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutation series\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;2 vs. \u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.276 (0.086 to 0.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe present study was performed in the genomic landscape of early-stage MPLAs and evaluated the impact of prognosis by collecting data from a series of 53 patients with 130 tumors. It is widely accepted that genomic heterogeneity plays an important role in the treatment and prognosis of LUADs(Lung Adenocarcinomas), even in the early stages [18]. A high discordant mutation has been confirmed as being a common genomic event in MPLCs such as \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e [19,20]. It is supposed that multiple and accumulated genetic alterations may play critical roles in driving tumor progression and different prognoses in MPLCs. Our study found that \u003cem\u003eEGFR\u003c/em\u003e mutations were the most common (58.3%), followed by \u003cem\u003eBCL2L11\u003c/em\u003e (16.1%), \u003cem\u003eERBB2\u003c/em\u003e (9.2%), \u003cem\u003ePIK3CA\u003c/em\u003e (8.5%), and \u003cem\u003eBRAF\u003c/em\u003e (7.7%) mutations, in the 130 tumors. Among the patients, \u003cem\u003eEGFR\u003c/em\u003e (L858R) mutations were observed in 47.2% (25/53), and \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutations were found in 22.6% (12/53). These results are similar to those of previous studies [21\u0026ndash;24]. As is well known, B-cell lymphoma 2-like 11 (\u003cem\u003eBCL2L11\u003c/em\u003e/\u003cem\u003eBIM\u003c/em\u003e), identified as a critical modulator of cellular apoptosis, has been demonstrated to be a poor prognostic factor for Non-Small Cell Lung Cancer(NSCLC)patients, and it is always combined with \u003cem\u003eEGFR/ALK/ROS1\u003c/em\u003e mutations in NSCLC patients [25]. Consistent with this study, 50% (6/12) were found to have \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutations with \u003cem\u003eEGFR\u003c/em\u003e (L858R) mutations, indicating that \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e, as the \u003cem\u003eEGFR\u003c/em\u003e in lung cancers, may be an early genomic event in early-stage MPLCs. By comparing the clinical features of the subgroups, based on the number of gene mutation series in the MPLCs (\u0026le;\u0026thinsp;2 vs. \u0026ge;3), we found that there were more mutation series and more mutation sites (\u0026le;\u0026thinsp;1 vs. \u0026ge;2) and that \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e was easily detected (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but no relationships between the other parameters were observed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It was accepted that accumulated genetic events occurred along with the increasing number and development of lesions in the patients.\u003c/p\u003e \u003cp\u003eIn the prognostic analysis, \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e was associated with a poor 5-year DFS of early-stage MPLCs (68.3% vs. 50%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e has always served as a poor prognostic factor in solitary NSCLC patients and is correlated with \u003cem\u003eEGFR\u003c/em\u003e-TKI (EGFR-Tyrosine Kinase Inhibitor)resistance [26]. This implies that the \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation might occur early and helps to explain the different prognoses among patients with the same MPLC stage. These results were not reported previously. Meanwhile, patients in the stage IA or below had a favorable 5-year DFS (68.9 vs. 37.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Staging serves as an important indicator of the prognosis in lung cancer, but there is no definitive method for MPLC [7,13,27]. The TNM stage of MPLC was considered to be the highest stage among the different lesions in our study [9]. This may be due to the fact that the included patients mainly had an IA stage diagnosis (85%) and were also diagnosed as having MIA or MIA with invasive adenocarcinoma. We found no significant difference in the prognosis of the other subsets of patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, in the subgroup analysis of the gene mutation series, we were able to delineate the potential genetic trajectories of the MPLCs. In the patients with a single gene mutation series, multiple mutation sites may have been the harmful mechanism in the prognosis of the MPLC (53.9 vs. 37.6 months, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the order of the factors driving the progression of MPLCs changed gradually during the increase in the mutational series. A high number of gene mutation series (\u0026ge;\u0026thinsp;4) reflected a poor DFS only in combination with four mutation sites (29.7 vs. 58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). We revealed that the prognosis of the patients with early-stage MPLC was potentially related to the accumulation status of gene mutation series and sites; their driving powers may offset each other. These results have not been previously demonstrated. In the multivariate analysis, we revealed that stage\u0026thinsp;\u0026gt;\u0026thinsp;IA and a \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation might be independent predictors of a worse DFS, and a lower mutation series (\u0026le;\u0026thinsp;2) was associated with a favorable prognosis of MPLCs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The speculation that potential genomic indexes exist in early-stage MPLCs may be used for the subdivision of patients, improving prognostic evaluations and individualized treatment strategies\u003c/p\u003e \u003cp\u003e.Several limitations of the present study can be addressed, such as the use of a small sample from a single clinical center, only patients with stage IA-II and MIA or MIA with invasive adenocarcinoma were enrolled, and MPLC was diagnosed according to clinical and pathological features. In addition, there was a lack of research on the mutations in key genes in cancer-associated pathways. Thus, a larger cohort and more detailed genomic protein profiles are needed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo summarize, we investigated the clinical\u0026ndash;genetic features and 5-year DFS of patients with early-stage MPLC using the NGS technique. Our study suggests that stage\u0026thinsp;\u0026gt;\u0026thinsp;IA and a \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation are unfavorable prognostic predictors and that a low-level gene mutation series (\u0026le;\u0026thinsp;2) may be beneficial for 5-year DFS. Our data also shed light on the different prognoses according to the status of the gene mutation series and sites. Taken together, the application of genomic profiling may prove to be useful for subdividing and precisely managing patients with MPLC.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMPLC = multiple primary lung cancer; DFS = disease-free survival; NGS = next-generation sequencing; MIA = microinvasive adenocarcinoma; TNM = tumor\u0026ndash;node\u0026ndash;metastasis; CT = computerized tomography; BCL2L11/BIM = B-cell lymphoma 2-like 11; EGFR = epidermal growth factor receptor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Mu-Ting Wang: Conceptualization, Methodology, Data Curation, Software, Writing-Original Draft, Writing- Reviewing and Editing ChenHui Ni: Conceptualization, Methodology, WritingReviewing and Editing Shu-Liang Zhang: Writing- Reviewing and Editing Mao-Hui Chen:Data Curation, Investigation Yan-Qi Lu: Data Curation Wei Zheng: Supervision Bin Zheng: Conceptualization, Writing- Reviewing and Editing Chun Chen: Conceptualization, Writing- Reviewing and Editing. All authors read and approved the final manuscript. Mu-Ting Wang and Chen-Hui Ni had full access to the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. Chun Chen and Bin Zheng are guarantors of the article. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by National Key Clinical Specialty OfThoracicSurgery, Fujian Union Hospital Minimally lnvasive Surgery Center, Startup Fundfor scientifc research, Fujian Medical University (Grant number: 20210H1036)Fujian Province University Construction Project(No.2019-67),TheMajor Project of Science And Technological Innovation In FujianProvince(2020Y9091), University-Industry-Academia Cooperation Program(2022Y4014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Fujian Medical University Union Hospital (IRB No. 2021QH029).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e No other data available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVazquez, M.; Carter, D.; Brambilla, E.; Gazdar, A.; Noguchi, M.; Travis, W.D.; Huang, Y.; Zhang, L.; Yip, R.; Yankelevitz, D.F.; et al. Solitary and multiple resected adenocarcinomas after CT screening for lung cancer: Histopathologic features and their prognostic implications. \u003cem\u003eLung Cancer\u003c/em\u003e \u003cb\u003e2009\u003c/b\u003e, \u003cem\u003e64\u003c/em\u003e, 148\u0026ndash;154. https://doi.org/10.1016/j.lungcan.2008.08.009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C.; Huang, X.; Peng, M.; Liu, W.; Yu, F.; Wang, X. Multiple primary lung cancer: A rising challenge. \u003cem\u003eJ. Thorac. Dis.\u003c/em\u003e \u003cb\u003e2019\u003c/b\u003e, \u003cem\u003e11\u003c/em\u003e (Suppl. S4), S523\u0026ndash;S536. https://doi.org/10.21037/jtd.2019.01.56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansuet-Lupo, A.; Barritault, M.; Alifano, M.; Janet-Vendroux, A.; Zarmaev, M.; Biton, J.; Velut, Y.; Le Hay, C.; Cremer, I.; R\u0026eacute;gnard, J.-F.; et al. Proposal for a combined histomolecular algorithm to distinguish multiple primary adenocarcinomas from intrapulmonary metastasis in patients with multiple lung tumors. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2019\u003c/b\u003e, \u003cem\u003e14\u003c/em\u003e, 844\u0026ndash;856. https://doi.org/10.1016/j.jtho.2019.01.017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShintani, Y.; Okami, J.; Ito, H.; Ohtsuka, T.; Toyooka, S.; Mori, T.; Watanabe, S.; Asamura, H.; Chida, M.; Date, H.; et al. Clinical features and outcomes of patients with stage I multiple primary lung cancers. \u003cem\u003eCancer Sci.\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e112\u003c/em\u003e, 1924\u0026ndash;1935. https://doi.org/10.1111/cas.14748.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv, J.; Zhu, D.; Wang, X.; Shen, Q.; Rao, Q.; Zhou, X. The value of prognostic factors for survival in synchronous multifocal lung cancer: Aretrospective analysis of 164 patients. \u003cem\u003eAnn. Thorac. Surg.\u003c/em\u003e \u003cb\u003e2018\u003c/b\u003e, \u003cem\u003e105\u003c/em\u003e, 930\u0026ndash;936.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKocaturk, C.I.; Gunluoglu, M.Z.; Cansever, L.; Demir, A.; Cinar, U.; Dincer, S.I.; Bedirhan, M.A. Survival and prognostic factors in surgically resected synchronous multiple primary lung cancers. \u003cem\u003eEur. J. Cardio-Thorac. Surg.\u003c/em\u003e \u003cb\u003e2011\u003c/b\u003e, \u003cem\u003e39\u003c/em\u003e, 160\u0026ndash;166. https://doi.org/10.1016/j.ejcts.2010.05.037.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Z.; Gao, S.; Mao, Y.; Mu, J.; Xue, Q.; Feng, X.; He, J. Surgical outcomes of synchronous multiple primary non-small cell lung cancers. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e2016\u003c/b\u003e, \u003cem\u003e6\u003c/em\u003e, 23252. https://doi.org/10.1038/srep23252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanvetyanon, T.; Finley, D.J.; Fabian, T.; Riquet, M.; Voltolini, L.; Kocaturk, C.; Bryant, A.; Robinson, L. Prognostic nomogram to predict survival after surgery for synchronous multiple lung cancers in multiple lobes. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2015\u003c/b\u003e, \u003cem\u003e10\u003c/em\u003e, 338\u0026ndash;345. https://doi.org/10.1097/jto.0000000000000400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y.; Han, X.; Wu, Y.; Jia, X.; Zhang, K.; Fan, J.; Shi, H. Prognostic Factors for Survival in Multiple Primary Lung Adenocarcinomas: A Ret-rospective Analysis of 283 Patients. \u003cem\u003eTechnol. Cancer Res. Treat.\u003c/em\u003e \u003cb\u003e2023\u003c/b\u003e, \u003cem\u003e22\u003c/em\u003e, 1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshikawa, Y.; Nakayama, H.; Ito, H.; Yokose, T.; Tsuboi, M.; Nishii, T.; Masuda, M. Surgical treatment for synchronous primary lung adeno-carcinomas. \u003cem\u003eAnn. Thorac. Surg.\u003c/em\u003e \u003cb\u003e2014\u003c/b\u003e, \u003cem\u003e98\u003c/em\u003e, 1983\u0026ndash;1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTie, H.; Luo, J.; Shi, R.; Li, Z.; Chen, D.; Wu, Q. Characteristics and prognosis of synchronous multiple primary lung cancer after surgical treatment: A systematic review and meta-analysis of current evidence. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e10\u003c/em\u003e, 507\u0026ndash;520. https://doi.org/10.1002/cam4.3614.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y.; Li, X.; Li, H.; Zhao, Y.; Liu, Z.; Sun, K.; Zhu, X.; Qi, Q.; An, B.; Shen, D.; et al. Genomic characterisation of pulmonary subsolid nodules: Mutational landscape and radiological features. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cb\u003e2020\u003c/b\u003e, \u003cem\u003e55\u003c/em\u003e, 1901409. https://doi.org/10.1183/13993003.01409-2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsmar, R.; Sonett, J.R.; Singh, G.; Mansukhani, M.M.; Borczuk, A.C. Use of Oncogenic Driver Mutations in Staging of Multiple Primary Lung Carcinomas: A Single-Center Experience. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2017\u003c/b\u003e, \u003cem\u003e12\u003c/em\u003e, 1524\u0026ndash;1535. https://doi.org/10.1016/j.jtho.2017.06.012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, D.; Liu, Q.-X.; Li, M.-Y.; Hou, B.; Yang, G.-X.; Lu, X.; Zheng, H.; Jiang, L.; Dai, J.-G. Utility of whole exome sequencing analysis in differentiating intrapulmonary metastatic multiple ground-glass nodules (GGNs) from multiple primary GGNs. \u003cem\u003eInt. J. Clin. Oncol.\u003c/em\u003e \u003cb\u003e2022\u003c/b\u003e, \u003cem\u003e27\u003c/em\u003e, 871\u0026ndash;881. https://doi.org/10.1007/s10147-022-02134-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, J.; Xu, Y.; Yang, Z.; Liu, Y.; Zhang, P.; Wang, X.; Sun, C.; Guo, Y.; Qiu, S.; Shao, G.; et al. Coexistence of atypical adenomatous hyperplasia, minimally invasive adenocarcinoma and invasive adenocarcinoma: Gene mutation analysis. \u003cem\u003eThorac. Cancer\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e12\u003c/em\u003e, 693\u0026ndash;698. https://doi.org/10.1111/1759-7714.13798.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, Z.; Cai, H.; Wang, Y.; Cui, R.; Huo, L.; Lee, E.Y.-P.; Liang, Y.; Li, X.; Hu, Z.; Chen, L.; et al. Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images. \u003cem\u003eQuant. Imaging Med. Surg.\u003c/em\u003e \u003cb\u003e2023\u003c/b\u003e, \u003cem\u003e13\u003c/em\u003e, 1286\u0026ndash;1299. https://doi.org/10.21037/qims-22-760.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeng, F.; Xu, J.; Wang, J.; Yang, B.; Wu, Y.-Z.; Jiang, Y.-Q.; Wang, Z.-Q. Correlation between gene mutation status and clinicopathologic features in early multiple primary lung cancer. \u003cem\u003eFront. Oncol.\u003c/em\u003e \u003cb\u003e2023\u003c/b\u003e, \u003cem\u003e13\u003c/em\u003e, 1110259. https://doi.org/10.3389/fonc.2023.1110259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C.; Zhang, J.; Xu, F.-P.; Wang, Y.-G.; Xie, Z.; Su, J.; Dong, S.; Nie, Q.; Shao, Y.; Zhou, Q.; et al. Genomic Landscape and Immune Microenvironment Features of Preinvasive and Early Invasive Lung Adenocarcinoma. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2019\u003c/b\u003e, \u003cem\u003e14\u003c/em\u003e, 1912\u0026ndash;1923. https://doi.org/10.1016/j.jtho.2019.07.031.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, C.; Zhao, C.; Yang, Y.; He, Y.; Hou, L.; Li, X.; Gao, G.; Shi, J.; Ren, S.; Chu, H.; et al. High discrepancy of driver mutations in patients with NSCLC and synchronous multiple lung ground-glass nodules. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2015\u003c/b\u003e, \u003cem\u003e10\u003c/em\u003e, 778\u0026ndash;783. https://doi.org/10.1097/jto.0000000000000487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGazdar, A.F.; Minna, J.D. Multifocal lung cancers\u0026ndash;clonality vs field cancerization and does it matter? \u003cem\u003eJ. Natl. Cancer Inst.\u003c/em\u003e \u003cb\u003e2009\u003c/b\u003e, \u003cem\u003e101\u003c/em\u003e, 541\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePei, G.; Li, M.; Min, X.; Liu, Q.; Li, D.; Yang, Y.; Wang, S.; Wang, X.; Wang, H.; Cheng, H.; et al. Molecular Identification and Genetic Characterization of Early-Stage Multiple Primary Lung Cancer by Large-Panel Next-Generation Sequencing Analysis. \u003cem\u003eFront. Oncol.\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e11\u003c/em\u003e, 653988. https://doi.org/10.3389/fonc.2021.653988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, C.; Zhao, L.; Liu, W.; Fan, S.; Liu, J.; Liu, Y.; Liu, X.; Shu, L.; Liu, X.; Liu, P.; et al. Genomic profiles and their associations with TMB, PD-L1 expression, and immune cell infiltration landscapes in synchronous multiple primary lung cancers. \u003cem\u003eJ. Immunother. Cancer\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e9\u003c/em\u003e, e003773. https://doi.org/10.1136/jitc-2021-003773.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S.-Y.; Sun, H.; Zhou, J.-Y.; Jie, G.-L.; Xie, Z.; Shao, Y.; Zhang, X.; Ye, J.-Y.; Chen, C.-X.; Zhang, X.-C.; et al. Clinical characteristics and prognostic value of the KRAS G12C mutation in Chinese non-small cell lung cancer patients. \u003cem\u003eBiomark. Res.\u003c/em\u003e \u003cb\u003e2020\u003c/b\u003e, \u003cem\u003e8\u003c/em\u003e, 22. https://doi.org/10.1186/s40364-020-00199-z.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, P.; Zeng, H.; Ji, L.; Ding, Z.; Ren, L.; Gao, W.; Fan, Z.; Li, L.; Le, X.; Li, P.; et al. Lung adenocarcinoma with ERBB2 exon 20 insertions: Comutations and immunogenomic features related to chemoimmunotherapy. \u003cem\u003eLung Cancer\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, \u003cem\u003e160\u003c/em\u003e, 50\u0026ndash;58. https://doi.org/10.1016/j.lungcan.2021.07.014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X.; Zhang, D.; Li, B.; Zou, B.; Wang, S.; Fan, B.; Li, W.; Yu, J.; Wang, L. Clinical implications of germline BCL2L11 deletion polymorphism in pretreated advanced NSCLC patients with osimertinib therapy. \u003cem\u003eLung Cancer\u003c/em\u003e \u003cb\u003e2020\u003c/b\u003e, \u003cem\u003e151\u003c/em\u003e, 39\u0026ndash;43. https://doi.org/10.1016/j.lungcan.2020.12.002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsobe, K.; Hata, Y.; Tochigi, N.; Kaburaki, K.; Kobayashi, H.; Makino, T.; Otsuka, H.; Sato, F.; Ishida, F.; Kikuchi, N.; et al. Clinical significance of BIM deletion polymorphism in non-small-cell lung cancer with ep-idermal growth factor receptor mutation. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2014\u003c/b\u003e, \u003cem\u003e9\u003c/em\u003e, 483\u0026ndash;487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDetterbeck, F.C.; Nicholson, A.G.; Franklin, W.A.; Marom, E.M.; Travis, W.D.; Girard, N.; Arenberg, D.A.; Bolejack, V.; Donington, J.S.; Mazzone, P.J.; et al. The IASLC lung cancer staging project: Summary of proposals for revisions of the classification of lung cancers with multiple pulmonary sites of involvement in the forthcoming eighth edition of the TNM classification. \u003cem\u003eJ. Thorac. Oncol.\u003c/em\u003e \u003cb\u003e2016\u003c/b\u003e, \u003cem\u003e11\u003c/em\u003e, 639\u0026ndash;650. https://doi.org/10.1016/j.jtho.2016.01.024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multiple primary lung cancer, next-generation sequencing, prognosis, gene mutation series, early stage, B-cell lymphoma 2-like 11","lastPublishedDoi":"10.21203/rs.3.rs-4423319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4423319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to define the genomic features of tumors and to delineate the potential mutational pattern underlying the prognosis of patients using large-panel next-generation sequencing (NGS) assays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 53 patients were enrolled, with a total of 130 malignant tumors. Clinical variables were collected, and the NGS sequencing of a large panel of 116 tumor-associated genes was performed. According to the gene mutation series and the number of mutation sites, the patients were divided into a series of groups. We investigated the relationship between the clinical–genetic features and the prognosis of MPLCs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The patients exceeding the IA stage were associated with a significantly shorter DFS than those in the IA stage (mean time: 27.5 vs. 50.6 months, \u003cem\u003ep\u003c/em\u003e = 0.044), and \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e subsets were associated with a significantly worse DFS (31.9 vs. 50.2 months, \u003cem\u003ep\u003c/em\u003e= 0.047). In the subgroups, the patients with a single gene mutation series with multiple gene mutation sites had a shorter DFS than those with a single mutation site (37.6 vs. 53.9 months, \u003cem\u003ep\u003c/em\u003e = 0.047); and those with four gene series with over four mutation sites displayed a longer DFS than those with four sites (25.7 vs. 58 months, \u003cem\u003ep\u003c/em\u003e = 0.034). In a Cox Multivariate analysis, exceeding the IA stage and a \u003cem\u003eBCL2L11\u003c/em\u003e\u003csup\u003edel\u003c/sup\u003e mutation were considered unfavorable independent prognostic factors (HR = 5.102, 95%CI: 1.526 to 17.054; \u003cem\u003ep\u003c/em\u003e = 0.008, and HR = 6.010, 95%CI: 1.636 to 22.079; \u003cem\u003ep\u003c/em\u003e = 0.007, respectively). A lower gene mutation series (≤2) was an independent factor for a longer DFS (HR = 0.276, 95%CI: 0.086 to 0.882; \u003cem\u003ep\u003c/em\u003e = 0.03).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The prognosis of patients with early-stage MPLC may potentially be related to the accumulation status of gene mutation series and sites; their driving powers may offset each other. Taken together, the application of genomic profiling may prove to be useful for subdividing and precisely managing patients with MPLC.\u003c/p\u003e","manuscriptTitle":"Next-Generation Sequencing in Early-Stage Multiple Primary Lung Cancer: The Prognostic Significance of Genomic Accumulation Status and BCL2L11 del","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 14:51:50","doi":"10.21203/rs.3.rs-4423319/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":"cd489dd5-4162-46e2-b4c6-0099dfc20580","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-19T11:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 14:51:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4423319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4423319","identity":"rs-4423319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}