Lung cancer risk from radon in Ontario, Canada: how many lung cancers can we prevent?

e21155 Background: Over 50% of new lung cancers occur in former smokers, who often are seeking strategies to reduce their lung cancer risk. However, recruitment and retention of participants in chemoprevention trials continues to be costly and presents unique challenges. Evaluation of feasibility and knowledge of challenges are critical to inform design and ensure accrual in chemoprevention trials.The study assessed interest and willingness of former heavy smokers to participate in a chemoprevention clinical trial using a botanical agent to prevent lung cancer. Methods: An introductory letter and survey instrument that included the goal of the survey, epidemiological and smoking history, acceptability of trial procedures, perception of lung cancer risk and interest in participating in this trial were mailed to 500 consecutive, former heavy smokers with no cancer from a database of 826 subjects at the Moffitt Cancer Center. Results: 202 (40.4%) men and women returned completed surveys. 98% of respondents were over age 60 and 56% had an undergraduate education or higher. The average years smoked was 40.7 (SD 11.9) pack years. 76% believed there was a 50% chance or greater of developing lung cancer. In response to interest and motivation to participate, 92-96% reported interest in receiving free lung exams, health status monitoring and knowing their lung cancer risk. 88% were interested in being a part of a trial to evaluate a botanical agent for lung cancer prevention. Over 92% of subjects reported a willingness to comply with study requirements, multiple blood draws and trips to the Center, spiral CTs and chest x-rays. Subjects were relatively less enthusiastic (73-79%) about undergoing bronchoscopy, taking multiple study agents and possible assignment to a placebo arm. Conclusions: Our study strongly suggests feasibility, highlights potential challenges and the significant interest and willingness of former smokers to participate in chemoprevention trials.

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Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk

10.1101/2021.11.24.21266740 ◽

2021 ◽

Author(s):

Maria Stella de Biase ◽

Florian Massip ◽

Tzu-Ting Wei ◽

Federico Manuel Giorgi ◽

Rory Stark ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Lung Cancer Risk ◽

Lung Cancer Mortality ◽

Lung Cancers ◽

Transcriptomic Data ◽

Incidence And Mortality ◽

Immune Alterations ◽

Former Smokers ◽

Lifestyle Risk

Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to lifestyle risk in the form of cigarette smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, often many years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk are unclear. CT screening of current and former smokers has been shown to reduce lung cancer mortality by up to 26%. To examine whether clinical risk stratification can be improved upon by the addition of genetic data, and to explore the mechanisms of the persisting risk in former smokers, we have analyzed transcriptomic data from accessible airway tissues of 487 subjects. We developed a model to assess smoking associated gene expression changes and their reversibility after smoking is stopped, in both healthy subjects and clinic patients. We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune and interferon related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Our results provide initial evidence for germline-mediated personalised smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

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Liverpool Lung Project lung cancer risk stratification model: calibration and prospective validation

Thorax ◽

10.1136/thoraxjnl-2020-215158 ◽

2020 ◽

pp. thoraxjnl-2020-215158

Author(s):

John K Field ◽

Daniel Vulkan ◽

Michael P A Davies ◽

Stephen W Duffy ◽

Rhian Gabe

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Lung Cancer Risk ◽

Absolute Risk ◽

Lung Cancers ◽

Risk Stratification Model ◽

Implementation Studies ◽

The Uk ◽

Lung Screening ◽

Optimal Implementation

BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.MethodsVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.ResultsThere were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was −58.3% (95% CI −61.6% to −54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was −22.0% (95% CI −28.1% to −15.5%).ConclusionsWhile LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.

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Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

Frontiers in Oncology ◽

10.3389/fonc.2021.766939 ◽

2022 ◽

Vol 11 ◽

Author(s):

Lan-Wei Guo ◽

Zhang-Yan Lyu ◽

Qing-Cheng Meng ◽

Li-Yang Zheng ◽

Qiong Chen ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Cancer Risk ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Training Set ◽

Lung Cancers ◽

Non Invasive ◽

History Of ◽

Validation Set

BackgroundAbout 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.MethodsA large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.ResultsA total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.ConclusionsWe developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

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