Association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer

Introduction

Despite significant improvements in diagnosis and treatment, lung cancer remains one of the most common malignancies and the leading cause of cancer-related mortality worldwide (1,2). A large amount of evidence has demonstrated that cigarette smoking is a risk factor for lung cancer (3,4). Epidemiological and molecular genetic differences have been found between patients with non-smoking-associated lung cancer and tobacco-associated lung cancer (4-7). Never-smokers with non-small cell lung cancer (NSCLC) frequently have the following characteristics female gender, young age and a histological diagnosis of epidermal growth factor receptor (EGFR) mutated adenocarcinoma (ADC) (8,9). Moreover, this subgroup demonstrates a more favorable prognostic outlook when juxtaposed with their smoking counterparts. This clinical advantage is mainly determined by their elevated responsiveness to EGFR tyrosine kinase inhibitors (TKIs), such as osimertinib, afatinib, gefitinib, erlotinib and by the absence of smoking-related comorbidities (10,11). Lung ADC patients who had smoked more than 15 pack-years or quit less than 25 years ago have been shown to be less likely to have detectable EGFR mutations (12). In clinical practice, molecular detection of EGFR can be limited by invasive procedures, sampling errors, and long processing time (13). Liquid biopsy represents a non-invasive useful tool to identify EGFR alterations; nevertheless, it is worth noting that the sensitivity of this technique is around 65% (14). These data may help clinicians to assess the likelihood of EGFR mutations in patients with lung ADC when the results of EGFR mutation status are not available.

Positron emission tomography/computed tomography (PET/CT) with ¹⁸F-fluoro-2-deoxy-D-glucose (¹⁸F-FDG), a molecular imaging modality, has been widely used in clinical diagnosis, staging, response evaluation, and prognosis of lung cancer (15-17). Several metabolic parameters, including maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), could reflect the metabolic activity of ¹⁸F-FDG (18,19). In patients with advanced or metastatic NSCLC, high MTV and TLG usually represent a lower progression-free survival (PFS) and overall survival (OS) (20). In addition, we previously reviewed the correlation between EGFR mutation status and metabolic activity of ¹⁸F-FDG in NSCLC (21). It has been reported that primary lung cancers with lower SUVmax may represent a higher incidence of EGFR mutations (22,23), but opposite results have also been observed (24,25). Accordingly, further studies are needed to verify these findings, so that clinicians can accurately predict EGFR mutations and evaluate the prognosis.

The effect of cigarette smoking on ¹⁸F-FDG metabolic activity of primary lung cancer was investigated in our previous study. Our results suggested that the metabolic activity of ¹⁸F-FDG in early primary lung cancer may be influenced by cigarette smoking (26). Accordingly, we speculate that there may be a significant causal relationship between them, which may guide clinicians in risk stratification and treatment strategy selection for patients with NSCLC. Thus, we performed a retrospective analysis to investigate the association and potential causation between cigarette smoking, metabolic phenotypes, and EGFR mutation status in patients with NSCLC. We present this article in accordance with the STARD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1371/rc).

Methods

Patient selection

From September 2019 to March 2022, we performed a retrospective analysis of 1,104 consecutive patients with lung cancer who were initially diagnosed by ¹⁸F-FDG PET/CT imaging at Ningbo No.2 Hospital (Ningbo, China). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Institutional Review Board (IRB) of Ningbo No.2 Hospital (No. YJ-NBEY-KY202108401). The requirement for written informed consent was waived due to the anonymous nature of the retrospective data.

Patients who met the following criteria were further analyzed in our study: (I) confirmation of NSCLC, and classification into ADC, squamous cell carcinoma (SCC), and NSCLC-others, by histopathology; (II) ¹⁸F-FDG PET/CT scanning had been performed before any treatment; (III) the status of EGFR mutations was determined by tissue-based analysis (27); (IV) a detailed cigarette smoking history was obtained. Finally, a total of 198 patients met our criteria and were analyzed in our study (Figure 1). The clinical characteristics [including age at diagnosis, sex, smoking status, tumor-node-metastasis (TNM) stage, and histopathological type] and metabolic features of ¹⁸F-FDG [including SUVmax of the primary tumors (pSUVmax), metastatic lymph nodes (nSUVmax), and distant metastases (mSUVmax)] based on EGFR mutation status are summarized in Table 1.

Figure 1 Flowchart of patient selection. ¹⁸F-FDG, ¹⁸F-2-fluoro-2-deoxyglucose; PET/CT, positron emission tomography/computed tomography; EGFR, epidermal growth factor receptor.

Technique of PET/CT scan

All patients underwent PET/CT using a GE Discovery 710 PET scanner (GE Healthcare, Milwaukee, WI, USA). They were required to fast for more than 6 hours before PET/CT examination. The concentration of blood glucose was required to be less than 7.0 mmol/L before intravenous injection of ¹⁸F-FDG with 5.2–7.4 MBq/kg. We performed PET/CT scan 45–60 minutes after administration of ¹⁸F-FDG. Low-dose CT scan with 140 kV, 10 mA, 0.5 second rotation time, and 40 mm collimation was performed to evaluate the features of anatomy. In addition, CT scan data of an iterative algorithm was used for reconstruction. Subsequently, PET scanning in 3-dimensional (3D) mode was performed from the skull base to upper thigh at 2.5 minutes per bed position. Finally, the transverse, sagittal, and coronal PET, CT, and fused PET/CT images were obtained on a Xeleris workstation (GE Healthcare) for evaluation.

Analysis of PET/CT imaging

All PET and CT images were consistently evaluated by two senior nuclear physicians based on clinical data. The level of ¹⁸F-FDG uptake within the lesion was defined as abnormal when it was higher than the surrounding background. The SUVmax was used to quantify the uptake intensity of ¹⁸F-FDG. A 2-dimensional (2D) region of interest (ROI) was manually drawn at the edge of the tumor focus and placed in the tumor area with the highest ¹⁸F-FDG uptake. The peak SUV on the pixel with the highest count in the ROI was defined as SUVmax. The calculation formula was as follows: SUV = [radioactive concentration in the ROI (MBq/g)]/[injected dose (MBq)/patient’s total body weight (g)]. Based on visual qualitative analysis, metastatic lymph nodes were considered when the uptake of ¹⁸F-FDG was greater than the background mediastinal blood pool (28).

Assessment of cigarette smoking status

All patients underwent a face-to-face interview prior to the PET/CT scan, during which detailed information regarding their smoking history was obtained. The patients were classified into two groups based on cigarette smoking status: smokers and never-smokers. Patients were strictly defined as smokers if they had smoked ≥100 cigarettes in their lifetime, regardless of whether they had quit or not, and the remaining others were defined as never-smokers (8,12). The pack-year index is a parameter reflecting the cumulative smoking dose, which is calculated by multiplying the smoking period (year) by the number of cigarettes smoked every day (29). The number of pack-years is the number of packs smoked per day multiplied by the number of years smoked, with 1 pack-year being 1 day and 1 pack of accumulated smoking for 1 year, and so on.

Statistical analysis

We used descriptive statistics to express the demographic data of our included patients. The quantitative data were analyzed as mean ± standard deviation (SD). The clinical characteristics such as age, gender (male vs. female), histopathological subtypes (ADC, SCC, and NSCLC-others), clinical stage (I, II, III, and IV), and smoking status (never-smokers vs. smokers), were compared between patients with and without EGFR mutations by Fisher’s exact test analysis or chi-squared test. PET/CT parameters, including pSUVmax, nSUVmax, and mSUVmax, were compared between patients with or without EGFR mutations by Mann-Whitney test. Receiver operating characteristic (ROC) curves were constructed using factors that differed significantly between patients with and without EGFR mutations. The area under the ROC curve (AUC) was calculated to evaluate the predictive value. A two-sided P value <0.05 was considered statistically significant. All statistical analyses and graphic designs were performed using GraphPad Prism 9.0 software (GraphPad Software, San Diego, CA, USA).

Results

Patient characteristics

Patient characteristics stratified according to the status of EGFR mutations are summarized in Table 1. EGFR mutation was observed in 73 (60.3%) of 121 never-smokers and 18 (23.4%) of 77 smokers (P<0.001). Of the 198 patients, significant differences of sex (male vs. female), smoking status (smokers vs. never-smokers), and histopathological subtypes (ADC, SCC, and NSCLC-others) were observed between patients with and without EGFR mutations, but the age and clinical TNM stage showed no significant difference.

Association between EGFR mutation status and cigarette smoking history

In terms of cigarette smoking history, never-smokers had a higher rate of EGFR mutations than smokers [60.3% (73/121) vs. 23.4% (18/77), P<0.001]. With the increase of cigarette smoking dose, the incidence of EGFR mutations decreased significantly. EGFR mutation rates were 20% in patients who smoked >20 pack-years, 35% in patients who smoked 1–20 pack-years, and 60% in patients who never smoked (Table 2).

Association between metabolic parameters and cigarette smoking status

The correlations between metabolic parameters (pSUVmax, nSUVmax, and mSUVmax) and the status of cigarette smoking (smokers and never-smokers) are presented in Table 3. We found that smokers had a higher pSUVmax than never-smokers (12.5±6.4 vs. 9.9±5.9, P=0.004), but no significant differences were observed between them for nSUVmax (10.8±4.9 vs. 9.5±5.5, P=0.229) and mSUVmax (10.1±5.6 vs. 9.5±5.8, P=0.651). In addition, with the increase of cigarette smoking dose, the pSUVmax increased significantly (r=0.198, P=0.005, Figure 2).

Figure 2 Association between cumulative smoking dose and pSUVmax in patients with NSCLC. A significant correlation was observed between cumulative smoking dose (pack-years) and pSUVmax in patients with NSCLC (r=0.198, P=0.005). SUVmax, maximum standardized uptake value; pSUVmax, SUVmax of primary tumor; NSCLC, non-small cell lung cancer.

Association between metabolic phenotype, cigarette smoking, and EGFR mutation status

A significantly lower pSUVmax was observed in patients with EGFR mutant NSCLC than those with EGFR wild-type (8.9±4.5 vs. 12.7±6.9, P<0.001). There was no significant difference between nSUVmax and mSUVmax in patients with or without EGFR mutations (Table 1). In addition, we provided representative PET/CT images of two patients with EGFR mutations and EGFR wild-type NSCLC (Figure 3).

Figure 3 Representative ¹⁸F-FDG PET/CT images in patients with or without EGFR mutations. A 75-year-old woman with lung adenocarcinoma had never smoked. Axial chest CT (A), PET (B), and fusion PET/CT (C) images showed a nodule in the posterior upper lobe of right lung with a mild uptake of ¹⁸F-FDG (pSUVmax 3.61, thick arrows). A 69-year-old man with lung adenocarcinoma had a cumulative smoking dose of 100 pack-years. Axial chest CT (D), PET (E) and fusion PET/CT (F) images showed a nodule in the anterior upper lobe of right lung with markedly uptake of ¹⁸F-FDG [pSUVmax 19.69 (thick arrows) and nSUVmax 17.39 (thin arrows)]. ¹⁸F-FDG, ¹⁸F-fluoro-2-deoxy-D-glucose; PET/CT, positron emission tomography/computed tomography; pSUVmax, maximum standardized uptake value of primary tumor; nSUVmax, maximum standardized uptake value of metastatic lymph nodes.

Based on these results, we performed ROC curve analysis to assess the value of predicting EGFR mutations in patients with NSCLC. The AUC was 0.688 [95% confidence interval (CI): 0.614–0.762] for cumulative smoking dose with sensitivity of 60.7% and specificity of 77.6% (Figure 4A), and 0.673 (95% CI: 0.599–0.747) for pSUVmax with sensitivity of 59.1% and specificity of 64.6% (Figure 4B). A multivariate logistic regression analysis was performed which showed that the AUC was 0.753 (95% CI: 0.685–0.821) when we combined cumulative smoking dose and pSUVmax with sensitivity of 66.3% and specificity of 74.0% (Figure 4C).

Figure 4 The AUCs of cumulative smoking dose (A), metabolic phenotype (pSUVmax, B), and combined these 2 factors (C) to predict EGFR mutation status in patients with NSCLC. AUC, area under the curve; CI, confidence interval; pSUVmax, maximum standardized uptake value of primary tumor; EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer.

Discussion

In the present study, we observed a potential association between cigarette smoking and increased pSUVmax as well as decreased EGFR mutations in patients with NSCLC, thereby suggesting a plausible link between low pSUVmax and EGFR mutations. Based on cumulative smoking dose and pSUVmax, we obtained a moderate predictive value of EGFR mutations in patients with NSCLC.

The molecular genetic features of NSCLC have been evaluated by many studies, and patients with EGFR mutations usually benefit from targeted therapy with TKIs (30-32). However, almost all patients with NSCLC treated with EGFR-TKIs will inevitably develop drug resistance (33). Unfortunately, it is difficult to predict when resistance will occur during treatment. Thus, it is of great significance to evaluate EGFR mutations and prognosis in patients with lung cancer. Shigematsu et al. reported that EGFR mutations were found in 45% of never-smokers and only 7% of smokers (32); the frequency of EGFR mutations was negatively correlated with cigarette smoking exposure. Pham et al. (12) reported that among tobacco-related lung cancer patients with EGFR mutations, 39% were exposed for ≤15 pack-years and 7.48% were exposed for >15 pack-years. In our findings, EGFR mutations occurred in 60% of never smokers, compared with 35% of those who smoked 1–20 pack-years and 20% of those who smoked >20 pack-years. When we predicted EGFR mutations based on cumulative smoking dose, the AUC was 0.688 with a sensitivity of 60.7% and a specificity of 77.6%. These data may help clinicians to assess the likelihood of EGFR mutations in patients with NSCLC, but their predictive value is relatively low and further studies are needed.

Metabolic phenotypes, namely, pSUVmax, nSUVmax, and mSUVmax, have been used to predict EGFR mutation status in patients with NSCLC (34-36). Ko et al. retrospectively analyzed the predictive value of ¹⁸F-FDG PET/CT for EGFR mutation status, and the results showed that patients with SUVmax ≥6 were more likely to have EGFR mutations (35). However, Lee et al. found that ¹⁸F-FDG uptake in NSCLC had no significant clinical value in predicting EGFR mutation status (36). Conversely, Lv et al. demonstrated that low pSUVmax <7.0 was associated with mutant EGFR status in patients with NSCLC (34). In addition, the association between SUVmax and EGFR mutation status in lung ADC may be influenced by smoking status, particularly among individuals with a positive smoking history (37). These conflicting findings prompted us to further evaluate the association between metabolic activity and EGFR mutation status. In our results, we found that patients with EGFR mutations had a lower pSUVmax than those without EGFR mutation. In addition, there were no significant differences of nSUVmax and mSUVmax between patients with EGFR mutant NSCLC and EGFR wild-type. The AUC was 0.673 when we used pSUVmax to predict EGFR mutation status with sensitivity of 59.1% and specificity of 64.6%.

It has been shown that patients with lung cancer who have smoked and have high pSUVmax generally have lower survival rates than those who have never smoked and have low pSUVmax (16,20,38). EGFR mutations in lung cancer patients are commonly found in never-smokers (35). To our knowledge, a paradoxical association between EGFR mutation status and metabolic phenotype has been observed in NSCLC patients (22,24,25), but this situation still needs further research and verification. Moreover, the direct relationship between cigarette smoking, metabolic phenotypes, and EGFR mutation status has not been reported in NSCLC patients. In this study, we found that smoking patients had a higher pSUVmax and a lower rate of EGFR mutations than never-smokers, and patients with EGFR mutations had a lower pSUVmax than those with EGFR wild-type. Thus, we performed a multivariate logistic regression analysis using cumulative smoking dose and pSUVmax to predict EGFR mutation status. Our results showed a moderate predictive value, and the AUC was 0.753 with a sensitivity of 66.3% and a specificity of 74.0%. Based on these results, we suggest that cigarette smoking may be one of the inducements for the increase of pSUVmax and the decrease of EGFR mutation, and further confirm that EGFR mutation is related to the low pSUVmax, which may provide additional information to guide clinicians in risk stratification and treatment strategy selection for patients with NSCLC.

Although we elucidated the association between cigarette smoking, metabolic phenotypes, and EGFR mutation status in patients with NSCLC, our study had some limitations. First, the number of patients we retrospectively analyzed was relatively small, and the results need to be confirmed by extensive prospective analysis. Second, not all patients were tested for EGFR mutations, and there may have been bias in the selection of patients. Third, it would be of great significance if we could monitor the therapeutic response of patients with NSCLC and assess their prognosis based on the results of cigarette smoking and metabolic phenotypes. Overall, further studies are needed to verify these findings.

Conclusions

Our study comprehensively analyzed the association between cigarette smoking, metabolic phenotype, and the status of EGFR mutations in patients with NSCLC. Compared to never-smokers and with the increase of cumulative smoking dose, the metabolic activity of ¹⁸F-FDG in primary tumors increased significantly and the incidence of EGFR mutations decreased notably. We also observed that the status of EGFR mutations correlates with low pSUVmax. A moderate predictive value of EGFR mutations was found based on cumulative smoking dose and pSUVmax. These observations may guide clinicians to stratify the risk of NSCLC patients and select treatment strategies. Further prospective and retrospective studies are needed to confirm our findings and extend our study.

Acknowledgments

The authors are grateful to Dr. Haibo Shen, Gang Hua, Jie Li, Yiting Shi, Zhenyue Ye, and Junyong Zou in Ningbo No.2 Hospital for providing detailed clinical and molecular genetic information regarding our cases.

Funding: This work was supported by the Ningbo Public Service Technology Foundation, China (No. 2021S176), the Exploration Project of Zhejiang Provincial Natural Science Foundation of China (No. LTGY23H180004), the Hwa Mei Research Foundation of Ningbo No.2 Hospital, China (No. 2022HMKY27), the Ningbo Clinical Research Center of Medical Imaging (No. 2021L003), and the Provincial and Municipal Co-Construction Key Discipline of Medical Imaging (No. 2022-S02).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1371/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1371/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1371/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1371/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 the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of Ningbo No.2 Hospital (No. YJ-NBEY-KY202108401). Due to the retrospective nature of this study, the need for written informed consent was waived.

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/.

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