Can smoking duration alone replace pack-years to predict the risk of smoking-related oncogenic mutations in non-small cell lung cancer? A cross-sectional study in Japan

Tobacco Patterns and Risk of Chronic Obstructive Pulmonary Disease: Results From a Cross-Sectional Study

INTRODUCTION

There is still uncertainty about which aspects of cigarette smoking influence the risk of Chronic Obstructive Pulmonary Disease (COPD). The aim of this study was to estimate the COPD risk as related to duration of use, intensity of use, lifetime tobacco consumption, age of smoking initiation and years of abstinence.

METHODS

We conducted an analytical cross-sectional study based on data from the EPISCAN-II study (n=9092). All participants underwent a face-to-face interview and post-bronchodilator spirometry was performed. COPD was defined as post-bronchodilator FEV1/FVC<70%. Parametric and nonparametric logistic regression models with generalized additive models were used.

RESULTS

8819 persons were included; 858 with COPD and 7961 without COPD. The COPD risk increased with smoking duration up to ≥50 years [OR 3.5 (95% CI: 2.3-5.4)], with smoking intensity up to ≥39cig/day [OR 10.1 (95% CI: 5.3-18.4)] and with lifetime tobacco consumption up to >29 pack-years [OR 3.8 (95% CI: 3.1-4.8)]. The COPD risk for those who started smoking at 22 or later was 0.9 (95% CI: 0.6-1.4). The risk of COPD decreased with increasing years of cessation. In comparison with both never smokers and current smokers, the lowest risk of COPD was found after 15-25 years of abstinence.

CONCLUSION

COPD risk increases with duration, intensity, and lifetime tobacco consumption and decreases importantly with years of abstinence. Age at smoking initiation shows no effect. After 15-25 years of cessation, COPD risk could be equal to that of a never smoker. This work suggests that the time it takes to develop COPD in a smoker is about 30 years.

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

OBJECTIVE

Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning.

METHOD

Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from ¹⁸F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model.

RESULTS

One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively).

CONCLUSIONS

Our model combining ¹⁸F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.

Differential properties of KRAS transversion and transition mutations in non-small cell lung cancer: associations with environmental factors and clinical outcomes

Background

KRAS-mutated non-small cell lung cancer (NSCLC) accounts for 23–35% and 13–20% of all NSCLCs in white patients and East Asians, respectively, and is therefore regarded as a major therapeutic target. However, its epidemiology and clinical characteristics have not been fully elucidated because of its wide variety of mutational subtypes. Here, we focused on two distinct base substitution types: transversion mutations and transition mutations, as well as their association with environmental factors and clinical outcome.

Methods

Dataset from the Japan Molecular Epidemiology Study, which is a prospective, multicenter, and molecular study epidemiology cohort study involving 957 NSCLC patients who underwent surgery, was used for this study. Questionnaire-based detailed information on clinical background and lifestyles was also used to assess their association with mutational subtypes. Somatic mutations in 72 cancer-related genes were analyzed by next-generation sequencing, and KRAS mutations were classified into three categories: transversions (G > C or G > T; G12A, G12C, G12R, G12V), transitions (G > A; G12D, G12S, G13D), and wild-type (WT). Clinical correlations between these subtypes have been investigated, and recurrence-free survival (RFS) and overall survival (OS) were evaluated.

Results

Of the 957 patients, KRAS mutations were detected in 80 (8.4%). Of these, 61 were transversions and 19 were transitions mutations. Both pack-years of smoking and smoking duration had significant positive correlation with the occurrence of transversion mutations (p = 0.03 and < 0.01, respectively). Notably, transitions showed an inverse correlation with vegetable intake (p = 0.01). Patients with KRAS transitions had the shortest RFS and OS compared to KRAS transversions and WT. Multivariate analysis revealed that KRAS transitions, along with age and stage, were significant predictors of shorter RFS and OS (HR 2.15, p = 0.01; and HR 2.84, p < 0.01, respectively).

Conclusions

Smoking exposure positively correlated with transversions occurrence in a dose-dependent manner. However, vegetable intake negatively correlated with transitions. Overall, KRAS transition mutations are significantly poor prognostic factors among resected NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10246-7.