The pathogenic mutations in smokers with non-small cell lung cancer: a next-generation sequencing study

Introduction

Lung cancer is a common malignant tumor, and its morbidity and mortality are the first among all tumors (1). Lung cancer is generally at an advanced stage when it is diagnosed, and the 5-year survival rate is only 16.1% (2). Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancers (3). According to the World Health Organization, more than 80% of lung cancers in men and 45% in women are associated with smoking (4,5). Research shows that there are several differences between lung cancer in smokers and nonsmokers, for example in tissue structure, gene mutation, demographic profiles (6,7), and clinicopathological features (8,9). Smokers and not smokers are usually not distinguished in studies on mutational characteristics of NSCLC. However multiple features differ between the lung cancer in smokers and that in never-smokers, suggesting that they represent two different diseases; moreover, smokers have a higher frequency of lung cancer than do never-smokers.

Whole-genome sequencing has provided insights into the molecular landscape of lung cancer. Lung cancer is a molecularly heterogeneous disease, and the high rate of gene mutations found in whole-genome sequencing in lung cancer reflects the mutagenic role of smoking in the pathogenesis of lung cancer. The total number of identified point mutations in coding regions is about 10-fold higher in smokers than in nonsmokers (10,11). The molecular types of smoking patients are complex, and the individual variation of gene mutations in smoking patients is considerable. Therefore, there is a critical need to develop a means to selecting targeted therapy according to the gene mutation characteristics of smokers, as smoking is the primary pathogenic factor of lung cancer. Targeted therapy has changed the survival and quality of life of patients with advanced NSCLC. Previous studies (1,2) have analyzed smoking patients and nonsmoking patients as grouping factors, but there remains a lack of research specifically on the gene mutation characteristics of smoking patients. Therefore, to obtain a comprehensive understanding of the molecular characteristics of smoking patients, we analyzed the frequency and type of driver gene mutations, clinically relevant factors, concomitant mutations associated with immune response, copy number variation (CNV), and the impact of different sample types on gene mutation in smoking patients. It is hoped our findings can support the use of targeted therapy in regional populations. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-261/rc).

Methods

Data and procedures

Data from patients admitted to Jilin Cancer Hospital from June 2022 to July 2024 were collected. The inclusion criteria were stage IIIB–IV NSCLC, age ≥18 years old, histologically or cytologically proven adenocarcinoma, and basically normal organ function. Patients were evaluated for the first time 2 months after treatment and followed up by telephone every 1 month thereafter. According to the Response Evaluation Criteria in Solid Tumors (RECIST), the lesions were evaluated and divided into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Response rate (RR) was calculated as follows: RR = (CR + PR)/total cases × 100%. The clinical benefit rate (CBR) was calculated as follows: CBR = (CR + PR + SD)/total cases × 100%. Progression-free survival (PFS) was defined as the time interval from the first day of EGFR-tyrosine kinase inhibitor (TKI) treatment to the last follow-up, death, or the first discovery of disease progression. Data collection from patients with no progress or survival at the end of the data was terminated as of the date of their last follow-up. Written informed consent for sample collection and treatment was provided by all patients. This retrospective study was approved by Institutional Review Board of Jilin Province Cancer Hospital for Science and Technology Ethics (No. 202502-001-01) and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

A total of 302 smoking patients with NSCLC were included for next-generation sequencing (NGS) gene mutation detection. 152 patients had quit smoking (128 had abstained for more than 2 months and 24 for less than 2 months), 95 were still smoking, and 55 had unknown smoking cessation status. Among the cases, 135 (44.7%) cases underwent 1,021-gene panel, 36 (11.9%) cases underwent 520-gene panel, 30 (10.0%) cases underwent 168-gene panel, and 101 (33.4%) cases underwent 8-gene panel. According to the literature, when the sequencing depth is greater than 500×, the gene coverage can reach 99.9–100%, and the single-nucleotide polymorphism (SNP) detection rate is greater than 99% (12). For all gene panels in this study, the average sequencing depth was greater than 500×, which would not affect the accuracy of gene detection due to different panels. The sample types were pleural effusion (PE) in 10 (3.3%) cases, whole blood Plasma (PLA) in 55 (18.2%) cases, and formalin-fixed paraffin-embedded (FFPE) tissue in 237 (78.5%) cases. There were 92 patients aged ≤60 years and 210 patients aged >60 years, with 234 males and 68 females. The patients were divided into stage IIIB (85 cases) and stage IV (217 cases) groups. The performance status (PS) is a score that estimates the patient’s ability to perform certain activities of daily living (ADLs) without the help of others, the score was 0–1 in 252 cases and ≥2 in 50 cases. Among the patients, 60 had brain metastasis, while 242 cases had no brain metastasis. The flowchart of patient inclusion is shown in Figure 1, while the driver gene mutations and clinically relevant factors of the patients are summarized in Table 1.

Figure 1 Flowchart of patient inclusion. CNV, copy number variation; INDEL, insertion/deletion mutation; NGS, next-generation sequencing; NSCLC, non-small cell lung cancer; SNV, single nucleotide variation; SV, structural variation; TMB, tumor mutational burden.

Nucleic acid extraction reagents and quantitative instruments

Total DNA was extracted from human tissue specimens using FFPE DNA extraction kit (AmoyDx, Xiamen, China), in accordance with the manufacturer’s instructions. Extraction of ctDNA from 10 mL Whole Blood using QIAamp Circulating Nucleic Acid kit (Qiagen, Hilden, Germany), in accordance with the manufacturer’s instructions. The purity and quantity of DNA were evaluated using the NanoDrop 2000C Spectrophotometer (Thermo Fisher, Waltham, USA) and Qubit 3.0 (Waltham, MA, USA), with OD260/OD280 ratios of 1.8–2.0.

NGS

The NGS implemented in this study used the target region probe capture technology and NGS samples based on Gene+Seq-2000 sequencing platform for gene detection, with the Gene+Seq-2000 Reagent Kit. The tissue-sequencing depth was 500×, and blood-sequencing depth was 1,000×. After low-quality reads and terminal adapter sequences were removed from the sequencing data in FASTQ format, the Burrows-Wheeler Aligner (BWA; version 0.7.12-r1039) was used to compare clean reads with the reference human genome (hg19). The GATK (version 3.4-46-gbc02625; Broad Institute) was used to recalibrate the quality value of base sequencing. Somatic single-nucleotide variants (SNVs) and small-fragment insertion/deletion mutations (Indels) were via analyzed via GATK Mutect2 (version 4.1.4.1) was used to estimate CNV, and the ratio of normalized coverage depth (log₂ copy ratio ±0.1) of tumor and normal samples captured by the probe in each exon group was calculated. The amplification and deletion thresholds were 0.848 and 0.737, respectively. When the test sequence did not match the reference sequence on one or more bases, we considered that an SNV or an Indel mutation had occurred at that position. When the size of the affected DNA region was 30 or 50 bp or more compared with the reference genome, structural variation (SV) was considered present. For hot spots, the threshold of mutation abundance was 2%; for non-hot spots, the threshold of mutation abundance was 5%.

Statistical analysis

SPSS 18.0 statistical software (IBM Corp., Armonk, NY, USA) was used for data analysis. The Fisher exact probability test was used to compare the classification data of two groups with a theoretical frequency <5. The association between EGFR mutation status and PFS in NSCLC smokers was determined via Kaplan-Meier curves to evaluate treatment efficacy. P<0.05 indicated a statistically significant difference.

Results

Frequency of driver mutations and type of mutations in smoking patients

Among 302 smoking patients, the mutation rate of common driving genes was 62.9% (190/302). The specific mutation rates of common driving genes in smoking patients were as follows: EGFR, 34.8% (105/302); ALK, 3.3% (10/302); ROS1, 1.0% (3/302); KRAS, 15.9% (48/302); BRAF, 2.3% (7/302); MET, 2.3% (7/302); RET, 2.3% (7/302); and HER2, 1.0% (3/302); EGFR copy number amplification, 8.9% (27/302); MET copy number amplification, 3.3% (10/302); KRAS copy number amplification, 1.7% (5/302); HER2 copy number amplification, 5.0% (15/302); ROS1 copy number amplification, 0.3% (1/302); and RET copy number deletion, 0.3% (1/302) (Figure 2).

Figure 2 Frequency of driver mutations and type of mutations in smoking patients.

The types of driving gene mutations in smoking patients included deletion, point mutation, and fusion mutation. There EGFR mutation types included 40 cases of exon 19 deletion (38.1%) and 34 cases of L858R (Leu858Arg) (32.4%). The KRAS mutation types included 24 cases of G12C (Gly12Cys) (50%), 7 cases of G12D (Gly12Asp) (14.6%), and 6 cases of G12V (Gly12Val) (12.5%). The ALK mutation types included 7 cases of EML4-ALK fusion (70.0%). The RET mutation types included 2 cases of KIF5B-RET fusion (28.6%) and 1 case CCDC88C-RET (14.3%) fusion. The MET mutation types included 5 cases of variable shear mutation in exon 14 (71.4%) and 2 cases of S940L (Ser940Leu) (28.6%). The ROS1 mutation types included CD74-ROS1 fusion (33.3%). The BRAF mutation types included 2 cases of V600E (Val600Glu) (28.6%). The HER2 mutation types included 2 cases of exon 2 frameshift mutation (66.7%) (Figure 3).

Figure 3 The type and proportion of driver gene mutations in smoking patients.

Analysis of the correlation between driver gene mutations and clinicopathologic factors in smoking patients

Among the common driver gene mutations, the CNV of patients aged ≤60 years was significantly higher than that of patients aged >60 years (P=0.008) (Table 1). The CNV of the EGFR gene mutation in patients aged 60 years or younger was significantly higher than that in patients aged 60 years or older (P=0.047) (Table 2). RET gene mutations (P=0.02) and BRAF gene mutations (P=0.02) were statistically different across patients with different PS scores (Tables 3,4). The common driver gene mutations did not significantly differ with the patient’s age, gender, specimen type, PS score, clinical stage, or presence of brain metastasis (P>0.05). However, the mutation rate of TP53 in males was higher than that in females (χ²=14.25; P<0.001).

Analysis of concomitant mutations associated with immunologic efficacy

A total of 201 smoking patients (135 cases with a 1,021-gene panel, 36 cases with a 520-gene panel, and 30 cases with a 168-gene panel) were statistically analyzed for immune associated mutations, including TP53, RB1, LRP1B, PTEN, STK11, BRCA2, JAK, KEAP1, POLD1, ATR, ATM, and BLM. The mutation frequencies were as follows: TP53, 66.7% (134/201) RB1, 13.4% (27/201); LRP1B, 20.4% (41/201); PTEN, 5.0% (10/201); STK11, 10.0% (20/201); BRCA1, 3.0% (6/201); BRCA2, 5.0% (10/201); JAK, 8.5% (17/201); KEAP1, 6.5% (13/201); POLD1, 3.0% (6/201); ATR, 6.0% (12/201); ATM, 4.5% (9/201); and BLM, 2.0% (4/201).

Smokers had a higher rate of driver mutations in pleural fluid samples than in other samples

The mutation rates of different genes in the paraffin tissue samples were as follows: EGFR, 34.2% (81/237); ALK, 3.4% (8/237); ROS1, 0.8% (2/237); KRAS, 16.5% (39/237); BRAF, 2.5% (6/237); MET, 2.1% (5/237); RET, 1.7% (5/237); and HER2, 1.3% (3/237). The mutation rates of different genes in the blood samples were as follows: EGFR, 29.1% (16/55); ALK, 3.6% (2/55); ROS1, 0% (0/26); KRAS, 14.5% (8/55); BRAF, 1.8% (1/55); MET, 3.6% (2/55); RET, 3.6% (2/55); and HER2, 0% (0/26). The mutation rates of different genes in the pleural fluid samples were as follows: EGFR, 80.0% (8/10); ALK, 0% (0/10); ROS1, 10% (1/10); KRAS, 10% (1/10); BRAF, 0% (0/10); MET, 0% (0/10); RET, 0% (0/10); and HER2, 0% (0/10). Among the common driver gene mutations, the gene mutation rate of pleural fluid sample was significantly higher than that of other samples (χ²=6.906; P=0.03), as shown in Table 1. The gene mutation rate of pleural fluid sample in the EGFR gene mutation was significantly higher than that of other samples (χ²=9.839; P=0.007), and the gene mutation rate of the ROS1 gene mutation in pleural fluid sample was significantly higher than that of other samples (χ²=8.854; P=0.01) (Table 5).

EGFR mutation status and adaptive therapy were associated with the different responses in smoking patients

The clinical data of 122 patients with complete treatment data were collected in this study, including 48 patients with EGFR mutations and 74 patients with wild-type EGFR. In patients with EGFR mutations, 3 cases achieved CR, 16 cases achieved PR, 23 cases achieved SD, and 6 cases achieved PD. The overall RR was 39.6% (19/48), and the overall CBR was 87.5% (42/48). Among the patients with EGFR mutations, 29 patients received targeted therapy (8 with gefitinib, 5 with icotinib, 1 with afatinib, 10 with osimertinib, 2 with vemurafenib, 1 with amivantamab, and 2 with gefitinib and osimertinib), 12 received chemotherapy, and 7 received combination therapy (5 cases of chemotherapy combined with targeted therapy and 2 cases of chemotherapy combined with VEGF monoclonal antibody). The median PFS of the three treatment methods were 9, 9, and 11 months, respectively, and there was no statistically significant difference between the three groups (P=0.62) (Figure 4). For common EGFR mutations, there were 3 cases of CR, 13 cases of PR, 16 cases of SD, and 4 cases of PD, with an RR of 44.4% (16/36) and a median PFS of 10 months. For rare EGFR mutations, there were 5 cases of PR and 7 cases of SD, with an RR of 41.6% (5/12) and a median PFS of 7 months. Between common and rare EGFR mutations, there was no significant difference in RR or median PFS (RR: 44.4% vs. 41.6%; median PFS: 10 vs. 5.5 months; P=0.32) (Figure 5).

Figure 4 Differences in treatment modalities among smoking patients with EGFR mutations. PFS, progression-free survival.

Figure 5 Differences in efficacy between patients with common and rare EGFR mutations. CI, confidence interval; NA, not applicable; PFS, progression-free survival.

Among patients with EGFR wild type, 6 cases achieved CR, 24 cases achieved PR, 37 cases achieved SD, and 7 cases achieved PD. The overall RR was 40.5% (30/74), and the overall CBR was 90.5% (67/74). Among the patients with EGFR wild type, 7 patients received targeted therapy, 41 patients received chemotherapy, 23 patients received combined therapy (3 patients received chemotherapy combined with targeted therapy, 8 patients received chemotherapy combined with VEGF monoclonal antibody, 11 patients received chemotherapy combined with immunotherapy, and 1 patient received chemotherapy combined with VEGF monoclonal antibody and immunotherapy), and 3 patients received immunotherapy; the median PFS of these four treatment methods was 6, 7, 8, and 9 months, respectively, and there was no statistically significant difference between the four treatment groups (P=0.97) (Figure 6). There were 27 patients with EGFR and TP53 mutations, and there were 21 patients with EGFR and TP53 wild-type. The median PFS was 8 months and 13 months in patients with TP53 mutation and those with TP53 wild type, respectively (P=0.005; Figure 7). Further analysis indicated no difference in the PFS between patients with EGFR mutations treated with first- or second-generation TKIs and those treated with third-generation TKIs for first-line EGFR TKI therapy (P=0.12; Figure 8).

Figure 6 Differences in treatment modalities among smoking patients with wild-type EGFR. PFS, progression-free survival.

Figure 7 Differences in efficacy between patients with EGFR with or without TP53 mutations and patients with wild-type EGFR and TP53. PFS, progression-free survival.

Figure 8 Differences in efficacy between patients with EGFR mutations treated with first- or second-generation TKIs and those treated with third-generation TKIs. CI, confidence interval; PFS, progression-free survival; TKI, tyrosine kinase inhibitor.

Discussion

Whole-genome sequencing allows for lung cancer to be understood from the molecular level. Studies (10,13) have found that the molecular genetic characteristics of nonsmoking patients lung cancer are different from those of smoking patients lung cancer. The majority of nonsmoking patients with lung cancer are female, have adenocarcinoma, and are at the advanced stage (8). The molecular types of smoking patients are complex, and the mutations vary greatly from person to person. The innovation of this study lies in the separate analysis of smoking patients in a large sample, which may offer greater clarity regarding the molecular characteristics of smoking patients and provide data support for their diagnosis and treatment.

In our study, the mutation rates of the driver genes EGFR, ALK, and KRAS in smoking patients were 32.9%, 3.3%, 15.9%, respectively. Compared with studies that did not consider smoking status (14,15), the mutation rate of EGFR and ALK was low, while the mutation rate of KRAS was high. It has been suggested that EGFR and KRAS are mutually exclusive (16,17), which explain why in our study, KRAS mutations were significantly more common in smokers (15%) than in nonsmokers (8%). Liu et al. (18) conducted EGFR gene detection on 490 NSCLC samples and found that the mutation rate of the EGFR gene in nonsmoking patients was higher than in smokers, suggesting that EGFR gene mutation in patients with NSCLC is related to smoking. In another study (19), the probability of ALK gene rearrangement in 20,541 patients with NSCLC was between 0% and 19.44% in smokers and between 0% and 41.67% in nonsmokers, suggesting that ALK gene mutations are more likely to be present in nonsmokers (as well as in in male patients with lung adenocarcinoma), but the difference was not statistically significant. Another study reported that Asian women had a higher prevalence of EGFR mutations than did White/mixed-race women. Moreover, with the increase in the prevalence of smoking, the probability of EGFR mutation has decreased, while the probability of KRAS mutation among smokers in White/mixed-race and Asian descent has increased. Dogan et al. (20) indicated that the majority of KRAS gene mutations were due to the permanent damage to DNA caused by tobacco carcinogens from smoking. In their study, the mutation rate of KRAS gene in smokers was significantly higher than in nonsmokers, with the most common KRAS mutation subtypes being Gly12Cys and Gly12Asp. In another study (21), it was found that the Gly12Asp mutation was more likely to be present in nonsmokers or patients with less tobacco exposure than in smokers.

Researches (22-24) have shown that the RR to first-generation EGFR-TKIs is 50–80%, the median PFS is 8–12 months, and the median overall survival (OS) is 19–35 months. In our study, the RR, CBR and median PFS of smoking patients were lower than those of EGFR-sensitive non-smokers, suggesting that smoking status is one of the key factors affecting the prognosis associated with EGFR TKI treatment. Moreover, smoking patients with EGFR mutation received first-line targeted therapy, chemotherapy, and combination therapy, and the results showed that combination therapy had the highest median PFS, which was consistent with several other studies as followed, the FLAURA2 trial (25) evaluated the efficacy of osimertinib plus chemotherapy and that of monotherapy, with PFS as the primary endpoint. The results showed a significant improvement in median PFS and a higher objective response rate (ORR) after combination chemotherapy. In the NEJ009 study (26), patients from 47 Japanese institutions were randomized to receive chemotherapy plus targeted therapy or single-target therapy, and the combination therapy improved the ORR, PFS, and OS. In addition, in the phase 3 NEJ026 trial (27), a first-generation EGFR TKI combined with VEGF monoclonal antibody was found to significantly improve PFS. These results indicated that combination therapy is the preferred treatment for smoking patients with EGFR mutations. The Keynote010, POPLAR, and OAK trials showed that smoking patients with EGFR wild-type received first-line targeted therapy, chemotherapy, combination therapy, and immunotherapy. The results showed that immunotherapy yielded the highest median PFS, with the median survival time of patients with wild-type EGFR being significantly prolonged after immunotherapy (28-30). The above results suggest that immunotherapy may be the preferred treatment for smoking patients with wild-type EGFR. In the study by Roeper et al. (31), TP53 mutations influenced the efficacy of EGFR TKIs in patients with EGFR mutations and TP53 mutations, exerting a significantly negative impact on all clinical endpoints of TKI therapy. This finding is consistent with our findings, which suggest that novel treatment modalities are needed to increase clinical benefit for patients with TP53 mutations. In addition, our results showed that the mutation rate of TP53 in males was higher than that in females, which was consistent with the work by Haupt et al. who speculated that this may be related to the sex chromosome, as there may be a group of genes on the X sex chromosome that can regulate the p53 protein (32).

We analyzed the mutation frequency of immune-efficacy-related genes TP53, RB1, LRP1B, and PTEN in 66 smoking patients. We found that the mutation rate of TP53, RB1, LRP1B, and PTEN in smoking patients were higher than those in previous studies (33,34). Smoking status has been repeatedly confirmed to be related to the efficacy of immune checkpoint inhibitors (ICIs) among patients with NSCLC, but the specific mechanism remains unclear. A study (35) has analyzed the clinical NSCLC cohort of Memorial Sloan Kettering Cancer Center (MSKCC), The Cancer Genome Atlas (TCGA) pan-lung cancer cohort, and the Gene Expression Omnibus (GEO) GSE41271 lung cancer cohort not receiving ICI treatment, for survival prediction, gene mutations, CNV, immunogenicity, and the immune microenvironment, along with influence of smoking status on the prognosis of patients with NSCLC receiving ICI treatment. Their results indicated that smokers with NSCLC receiving ICI treatment had a longer PFS [hazard ratio =0.69, 95% confidence interval (CI): 0.49–0.97; P=0.031]. Meanwhile, other studies (28,36) have found that the combination of tumor mutation burden and smoking status may be a potential predictor of the efficacy of combined immunotherapy for advanced NSCLC. The above-mentioned research suggests that smoking patients have distinct molecular typing characteristics, and it is necessary to select appropriate gene detection according to the clinical characteristics these patients in clinical practice.

This study also found that the mutation rate of ALK, ROS1, BRAF, MET, RET, and HER2 genes in PLA and PE samples was lower than that in FFPE samples, suggesting that in clinical practice, FFPE samples should be used among smoking patients for detecting the above-mentioned genes. The mutation rate of EGFR gene in PE samples was higher than that of FFPE and PLA, a finding in line with previous research (37). Pleural effusion samples provide a promising alternative for identifying clinically relevant mutations via NGS. Although tissue-based genetic testing remains the standard in routine clinical practice, some studies support the integration of effusion as a reliable tool for molecular diagnosis.

The limitations of our study include its retrospective, single-center design. In addition, due to the low mutation rate of ALK, ROS1, KRAS, BRAF, MET, RET, and HER2 genes, further research with a larger sample size is needed to clarify the molecular typing characteristics of smoking patients.

Conclusions

The gene mutation rate of smoking patients with NSCLC was lower than that of nonsmoking patients. In clinical practice, patients should be screened for gene detection according to their clinical factors, pathology, smoking status, and sample types. This may provide suitable detection panels for smoking patients and help generate a body of data to provide support for the use of targeted treatment in local populations.

Acknowledgments

We would like to thank Clinical Research Big Data Center of Jilin Cancer Hospital for their assistance in data searching extraction, and processing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-261/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-261/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-261/prf

Funding: This study was supported by Science and Technology Agency of Jilin Provincial Project (grant No. YDZJ202401431ZYTS).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-261/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with Declaration of Helsinki and its subsequent amendments. This retrospective study was approved by Institutional Review Board of Jilin Province Cancer Hospital for Science and Technology Ethics (No. 202502-001-01). Written informed consent for sample collection and treatment was provided by all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Chen WQ, Li H, Sun KX, et al. Report of Cancer Incidence and Mortality in China, 2014. Zhonghua Zhong Liu Za Zhi 2018;40:5-13. [Crossref] [PubMed]
2. Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer 2015;136:1921-30. [Crossref] [PubMed]
3. Chen Z, Fillmore CM, Hammerman PS, et al. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer 2014;14:535-46. [Crossref] [PubMed]
4. Hung RJ, McKay JD, Gaborieau V, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature 2008;452:633-7. [Crossref] [PubMed]
5. Thorgeirsson TE, Geller F, Sulem P, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 2008;452:638-42. [Crossref] [PubMed]
6. Au JS, Cho WC, Yip TT, et al. Proteomic approach to biomarker discovery in cancer tissue from lung adenocarcinoma among nonsmoking Chinese women in Hong Kong. Cancer Invest 2008;26:128-35. [Crossref] [PubMed]
7. Potter AL, Xu NN, Senthil P, et al. Pack-Year Smoking History: An Inadequate and Biased Measure to Determine Lung Cancer Screening Eligibility. J Clin Oncol 2024;42:2026-37. [Crossref] [PubMed]
8. Kang H, Park CW, Kim W, et al. Never-smoker lung cancer is increasing. J Lung Cancer 2012;11:89-93.
9. Luo W, Zeng Z, Jin Y, et al. Distinct immune microenvironment of lung adenocarcinoma in never-smokers from smokers. Cell Rep Med 2023;4:101078. [Crossref] [PubMed]
10. Crispo A, Brennan P, Jöckel KH, et al. The cumulative risk of lung cancer among current, ex- and never-smokers in European men. Br J Cancer 2004;91:1280-6. [Crossref] [PubMed]
11. Adler N, Bahcheli AT, Cheng KCL, et al. Mutational processes of tobacco smoking and APOBEC activity generate protein-truncating mutations in cancer genomes. Sci Adv 2023;9:eadh3083. [Crossref] [PubMed]
12. Feliubadaló L, Tonda R, Gausachs M, et al. Benchmarking of Whole Exome Sequencing and Ad Hoc Designed Panels for Genetic Testing of Hereditary Cancer. Sci Rep 2017;7:37984. [Crossref] [PubMed]
13. Govindan R, Ding L, Griffith M, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012;150:1121-34. [Crossref] [PubMed]
14. Zhuang X, Zhao C, Li J, et al. Clinical features and therapeutic options in non-small cell lung cancer patients with concomitant mutations of EGFR, ALK, ROS1, KRAS or BRAF. Cancer Med 2019;8:2858-66. [Crossref] [PubMed]
15. Allemann AT, Gautschi O. Divide and conquer: towards isoform-specific diagnosis and therapy of KRAS-mutant lung cancer. Transl Lung Cancer Res 2023;12:1328-31. [Crossref] [PubMed]
16. Gainor JF, Varghese AM, Ou SH, et al. ALK rearrangements are mutually exclusive with mutations in EGFR or KRAS: an analysis of 1,683 patients with non-small cell lung cancer. Clin Cancer Res 2013;19:4273-81. [Crossref] [PubMed]
17. Unni AM, Lockwood WW, Zejnullahu K, et al. Evidence that synthetic lethality underlies the mutual exclusivity of oncogenic KRAS and EGFR mutations in lung adenocarcinoma. Elife 2015;4:e06907. [Crossref] [PubMed]
18. Liu J, Shen J, Yang C, et al. High incidence of EGFR mutations in pneumonic-type non-small cell lung cancer. Medicine (Baltimore) 2015;94:e540. [Crossref] [PubMed]
19. Hoang T, Myung SK, Pham TT, et al. Efficacy of Crizotinib, Ceritinib, and Alectinib in ALK-Positive Non-Small Cell Lung Cancer Treatment: A Meta-Analysis of Clinical Trials. Cancers (Basel) 2020;12:526. [Crossref] [PubMed]
20. Dogan S, Shen R, Ang DC, et al. Molecular epidemiology of EGFR and KRAS mutations in 3,026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers. Clin Cancer Res 2012;18:6169-77. [Crossref] [PubMed]
21. Ricciuti B, Alessi JV, Elkrief A, et al. Dissecting the clinicopathologic, genomic, and immunophenotypic correlates of KRAS(G12D)-mutated non-small-cell lung cancer. Ann Oncol 2022;33:1029-40. [Crossref] [PubMed]
22. Lu S, Dong X, Jian H, et al. AENEAS: A Randomized Phase III Trial of Aumolertinib Versus Gefitinib as First-Line Therapy for Locally Advanced or MetastaticNon-Small-Cell Lung Cancer With EGFR Exon 19 Deletion or L858R Mutations. J Clin Oncol 2022;40:3162-71. [Crossref] [PubMed]
23. Fukuoka M, Wu YL, Thongprasert S, et al. Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J Clin Oncol 2011;29:2866-74. [Crossref] [PubMed]
24. Shi YK, Wang L, Han BH, et al. First-line icotinib versus cisplatin/pemetrexed plus pemetrexed maintenance therapy for patients with advanced EGFR mutation-positive lung adenocarcinoma (CONVINCE): a phase 3, open-label, randomized study. Ann Oncol 2017;28:2443-50. [Crossref] [PubMed]
25. Planchard D, Feng PH, Karaseva N, et al. Osimertinib plus platinum-pemetrexed in newly diagnosed epidermal growth factor receptor mutation-positive advanced/metastatic non-small-cell lung cancer: safety run-in results from the FLAURA2 study. ESMO Open 2021;6:100271. [Crossref] [PubMed]
26. Miyauchi E, Morita S, Nakamura A, et al. Updated Analysis of NEJ009: Gefitinib-Alone Versus Gefitinib Plus Chemotherapy for Non-Small-Cell Lung Cancer With Mutated EGFR. J Clin Oncol 2022;40:3587-92. [Crossref] [PubMed]
27. Kawashima Y, Fukuhara T, Saito H, et al. Bevacizumab plus erlotinib versus erlotinib alone in Japanese patients with advanced, metastatic, EGFR-mutant non-small-cell lung cancer (NEJ026): overall survival analysis of an open-label, randomised, multicentre, phase 3 trial. Lancet Respir Med 2022;10:72-82. [Crossref] [PubMed]
28. Herbst RS, Baas P, Kim DW, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 2016;387:1540-50. [Crossref] [PubMed]
29. Fehrenbacher L, Spira A, Ballinger M, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet 2016;387:1837-46. [Crossref] [PubMed]
30. Rittmeyer A, Barlesi F, Waterkamp D, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 2017;389:255-65. [Crossref] [PubMed]
31. Roeper J, Falk M, Chalaris-Rißmann A, et al. TP53 co-mutations in EGFR mutated patients in NSCLC stage IV: A strong predictive factor of ORR, PFS and OS in EGFR mt+ NSCLC. Oncotarget 2020;11:250-64. [Crossref] [PubMed]
32. Haupt S, Caramia F, Herschtal A, et al. Identification of cancer sex-disparity in the functional integrity of p53 and its X chromosome network. Nat Commun 2019;10:5385. [Crossref] [PubMed]
33. Smardova J, Liskova K, Ravcukova B, et al. Complex analysis of the p53 tumor suppressor in lung carcinoma. Oncol Rep 2016;35:1859-67. [Crossref] [PubMed]
34. Quan X, Lu S. Prognostic value of TP53 and RB1 gene mutation status on the efficacy of immunotherapy in patients with extensive-stage small cell lung cancer: a retrospective cohort study. Tumor 2024;44:683-92.
35. Sun Y, Yang Q, Shen J, et al. The Effect of Smoking on the Immune Microenvironment and Immunogenicity and Its Relationship With the Prognosis of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer. Front Cell Dev Biol 2021;9:745859. [Crossref] [PubMed]
36. Singal G, Miller PG, Agarwala V, et al. Association of Patient Characteristics and Tumor Genomics With Clinical Outcomes Among Patients With Non-Small Cell Lung Cancer Using a Clinicogenomic Database. JAMA 2019;321:1391-9. [Crossref] [PubMed]
37. Li H, Yan S, Liu Y. Comparison of EGFR gene mutation status between supernatant and cell sediment of malignant pleural effusion in patients with advanced non-small cell lung cancer. Int J Oncol 2018;45:10-5.

(English Language Editor: J. Gray)