Family History Of Lung Cancer Research Articles

Development and validation of a simple general population lung cancer risk model including AHRR-methylation

IntroductionScreening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk. MethodsUsing the 1991–94 examination of The Copenhagen City Heart Study in Denmark, 6,820 former or current smokers from the general population were followed for lung cancer within 10 years after examination. Logistic regression of baseline variables (age, sex, education, chronic obstructive pulmonary disease, family history of lung cancer, smoking status and cumulative smoking, secondhand smoking, occupational exposures to dust and fume, body mass index, lung function, plasma C-reactive protein, and AHRR(cg05575921) methylation) identified the best predictive model. The model was validated among 3,740 former or current smokers from the 2001-03 examination, also followed for 10 years. A simple risk chart was developed with Poisson regression. ResultsAge, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation identified 65 of 88 individuals who developed lung cancer in the validation cohort. The highest risk group, consisting of less educated men aged >65 with current smoking status and cumulative smoking >20 pack-years, had absolute 10-year risks varying from 4% to 16% by AHRR(cg05575921) methylation. ConclusionA simple risk chart including age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation, identifies individuals with 10-year lung cancer risk from below 1% to 16%. Including AHRR(cg05575921) methylation in the eligibility criteria for screening identifies smokers who would benefit the most from screening.

An Atypical Presentation of GABA-B Encephalitis (P5-5.023)

<h3>Objective:</h3> N/A <h3>Background:</h3> GABA-B receptor encephalitis is a cell surface mediated paraneoplastic disease secondary to antibody production resulting in neuronal cells loss, microglial proliferation, and preferentially targets the limbic system. Symptoms include seizures, memory loss, confusion, and altered behavior. The infrequency with which it is encountered can create a diagnostic challenge. <h3>Case Description:</h3> A 73-year-old female presented as a referral months post hospitalization and outpatient evaluation. Initial presentation began with subacute progressive disorientation, crying spells, ageusia, anosmia, and memory loss without a known precedent illness. On initial exam she was alert, oriented only to self, inattentive, and without other focality. <h3>Design/Methods:</h3> N/A <h3>Results:</h3> The results of the work up included contrasted MRI demonstrating T2 hyperintensities within the hippocampi. Lumbar puncture revealed antibodies against GABA-B receptors. High dose steroids were initiated with minimal improvement therefore IVIG was trialed in the outpatient setting resulting in stabilization and moderate improvement of symptoms. A PET scan was requested and revealed a mass within the left lower lobe of the lung. Biopsy identified features of small cell carcinoma confirming our suspicion of encephalitis secondary to paraneoplastic syndrome. Currently she is undergoing oncological evaluation and treatment. Symptoms remain stable, but emotional lability, poor short-term recall, ageusia and anosmia persist. No additional IVIG or long-term immunotherapy was initiated as treatment should be targeted toward the malignancy. She will remain under neurological surveillance with serial exams and MRIs. <h3>Conclusions:</h3> This case illustrates the importance of recognizing alternative causes of encephalitis. Localizing limbic symptoms were present although the lack of seizures and presence of ageusia and anosmia is highly unusual. We also discovered an extensive smoking history and family history of lung cancer. These red flags highlight the need for a complete history. Early consideration of a paraneoplastic process causing encephalitis will facilitate additional diagnostic testing, prompt initiation of immunotherapy and accurate diagnosis. <b>Disclosure:</b> Dr. DeHart-McCoyle has nothing to disclose. Dr. Chen has nothing to disclose.

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]v2, LLPv3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]M2012, PLCOM2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R2D in both sexes in the QResearch validation cohort and 59% of the R2D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLPv2 and PLCOM2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. Innovate UK (UK Research and Innovation). For the Chinese translation of the abstract see Supplementary Materials section.

Abstract 771: Mechanisms of genetic predisposition to multifocal lung cancer

Abstract Detection of multiple primary lung cancers is increasing in frequency, with up to 15% of all non-small cell lung cancer (NSCLC) patients presenting with two or more anatomically separate tumor nodules on CT scans. Increased detection is in part due to expanded lung cancer screening criteria in an aging population. Distinguishing multiple primary tumors from intrapulmonary metastases can be challenging, yet is critical for determining clinical management. Current models predict development of multiple primary tumors out of a cancerized field, such as occurs due to smoking. The occurrence of multiple primary tumors is unexplained in patients with EGFR-mutant lung cancer (~15% of all NSCLC), lacking environmental exposures. We identified patients with non-small cell lung cancer (NSCLC) who presented with multiple primary EGFR-mutant tumors, in the absence of family history of lung cancer or heavy smoking. We subjected the macrodissected tumors and surrounding normal tissues to whole exome sequencing as well as to analysis of hypermutable poly-guanine (poly-G) repeats, which are two orthogonal methods of lineage tracing. An additional familial case with a germline EGFR-T790M mutation was used to establish parameters for timing of somatic mutations, and functional properties of novel germline variants were modeled in vitro. Of eleven non-familial NSCLC cases with two or more geographically distinct EGFR-mutant lung cancers, two patients harbored a germline EGFR variant allele, which confers moderately enhanced signaling in vitro, followed by a somatically acquired EGFR mutation. In an additional four cases, both whole exome sequencing and poly-G repeat analyses indicate a distant shared somatic cell-of-origin, consistent with embryonic mosaicism. Three cases revealed clinically unappreciated metastasis and two cases remain unexplained. Together, our data suggest that multiple primary lung cancers with somatic EGFR driver mutations may result from genetic susceptibility, attributable either to attenuated EGFR germline variants or to embryonic mosaicism resulting in geographically disparate patches of predisposed lung tissue. Such predisposed cases should be surveilled for early detection of future tumors, and surgical resection in these cases should consider the life-long risk of additional cancers. Citation Format: Risa Burr, Ignaty Leshchiner, Christina L. Costantino, Martin Blohmer, Tilak Sundaresan, Justin Cha, Karsen Seeger, Sara Guay, Brian P. Danysh, Ira Gore, Raquel A. Jacobs, Kara Slowik, Filippo Utro, Kahn Rhrissorrakrai, Chaya Levovitz, Jaimie L. Barth, Taronish Dubash, Brian Chirn, Laxmi Parida, Lecia V. Sequist, Mari Mino-Kenudson, Kamila Naxerova, Shyamala Maheswaran, Gad Getz, Daniel A. Haber. Mechanisms of genetic predisposition to multifocal lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 771.

SCREENING, PROGNOSTIC EVALUATION AND SURGERY IN LUNG CANCER

Lung cancer is currently the neoplasm with the highest mortality among men and the second leading cause of mortality in women. OBJECTIVE : to review the importance of early screening and therapeutic possibilities in lung cancer DISCUSSION: The main risk factor for the development is smoking, however, other factors are also related, such as: presence of preexisting lung disease, occupational exposure (asbestos, uranium, chromium, alkylating agents), family history of lung cancer and previous lung cancer. In clinical practice, the prognosis is related to the staging of the disease and the histological type. In most cases, the diagnosis is late with the disease in an advanced stage, making curative treatment impossible. CONCLUSION: Due to the high mortality and the impact on treatment, it is necessary to recommend earlier screening of the population with risk factors, since treatment in the initial phase improves people’s survival and impacts on mortality indicators. METHODOLOGY: This is a literature review study. The research was guided by bibliographic articles from the Scielo, Lilacs; in addition to recommendations available on sites such as the National Cancer Institute and the American Cancer Society.

Lung cancer screening utilization rate varies based on patient, provider, and hospital factors

ObjectiveLow-dose computed tomography has been proven to reduce mortality, yet utilization remains low. The purpose of this study is to identify factors that impact the utilization of lung cancer screening. MethodsWe performed a retrospective review of our institution's primary care network from November 2012 to June 2022 to identify patients who were eligible for lung cancer screening. Eligible patients were 55 to 80 years of age and current or former smokers with at least a 30 pack-year history. Analyses were performed on the screened populations and patients who met eligibility criteria but were not screened. ResultsA total of 35,279 patients in our primary care network were current/former smokers aged 55 to 80 years. A total of 6731 patients (19%) had a 30 pack-year or more cigarette history, and 11,602 patients (33%) had an unknown pack-year history. A total of 1218 patients received low-dose computed tomography. The utilization rate of low-dose computed tomography was 18%. The utilization rate was significantly lower (9%) if patients with unknown pack-year history were included (P < .001). The utilization rates between primary care clinic locations were significantly different (range, 18% vs 41%, P < .05). Utilization of low-dose computed tomography on multivariate analysis was associated with Black race, former smoker, chronic obstructive pulmonary disease, bronchitis, family history of lung cancer, and number of primary care visits (all P < .05). ConclusionsLung cancer screening utilization rates are low and vary significantly on the basis of patient comorbidities, family history of lung cancer, primary care clinic location, and accurate documentation of pack-year cigarette history. The development of programs to address patient, provider, and hospital-level factors is needed to ensure appropriate lung cancer screening.

Using Community Health Advisors to Increase Lung Cancer Screening Awareness in the Black Belt: a Pilot Study.

Disease stage at the time of diagnosis is the most important determinant of prognosis for lung cancer. Despite demonstrated effectiveness of lung cancer screening (LCS) in reducing lung cancer mortality, early detection continues to elude populations with the highest risk for lung cancer death. Consistent with the national rate, current screening rate in Alabama is dismal at 4.2%. While public awareness of LCS may be a likely cause, there are no studies that have thoroughly evaluated current knowledge of LCS within the Deep South. Therefore, we measured LCS knowledge before and after receiving education delivered by community health advisors (CHAs) among high-risk individuals living in medically underserved communities of Alabama and to determine impact of psychological, demographic, health status, and cognitive factors on rate of lung cancer screening participation. Participants were recruited from one urban county and six rural Black Belt counties (characterized by poverty, rurality, unemployment, low educational attainment, and disproportionate lack of access to health services). One hundred individuals (i) aged between 55 and 80years; (ii) currently smoke or have quit within the past 15years; and (iii) have at least a total of 30-pack-year smoking history were recruited. Knowledge scores to assess lung cancer knowledge were calculated. Paired t-test was used to assess pre- and post-knowledge score improvement. Screening for lung cancer was modeled as a function of predisposed factors (age, gender, insurance, education, fatalism, smoking status, and history of family lung cancer). Average age was 62.94 (SD = 6.28), mostly female (54%); mostly current smokers (53%). Most participants (80.85%) reported no family history of cancer. Fatalism was low, with a majority of the participants disagreeing that a cancer diagnosis is pre-destined (67.7%) and that there are no treatments for lung cancer (88.66%). Overall, lung cancer knowledge increased significantly from baseline of 4.64 (SD = 2.37) to 7.61 (SD = 2.26). Of the 100 participants, 23 underwent screening due to lack of access to primary care providers and reluctance of PCPs to provide referral to LCS. Sixty-five percent of those who were screened reported no family history of lung cancer. Regression analysis revealed no significant association between risk factors and the decision to get screened by participants. Our study demonstrates that while CHA delivered education initiatives increases lung cancer screening knowledge, there are significant structural barriers that prohibit effective utilization of LCS which needs to be addressed.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study.

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Active screening for lung cancer increases smoking abstinence in Australia.

Smoking cessation is an important screening component, but the evidence base to inform implementation is lacking. We report longitudinal smoking behavior in an Australian screening cohort and examine predictor variables associated with continued smoking. Healthy current or former smokers (quit less than 15 years and ≥30-pack year smoking history) aged 60-74 years underwent CT screening at baseline, year 1 and year 2. Participants received brief smoking cessation advice and generic Quitline materials. Smoking status was self-reported every 6 months for 5 years. Mediators of smoking behavior, adjusted for sociodemographic, health and scan variables were explored using logistic regression modeling. Two hundred thirty-five participants were analyzed. One hundred eight (46%) were current smokers at enrolment. At baseline, current smokers' mean Fagerström Test for Nicotine Dependence was 4.9, and they had higher levels of lung cancer-specific distress and passive smoke exposure than former smokers. At 36 months, 33% of baseline smokers achieved sustained (≥6 months) smoking abstinence. Five (4%) former smokers relapsed at any point during the study. Continued smoking was positively associated with greater nicotine dependence and smoking pack-years, and negatively associated with cardiovascular disease, stroke, and lung cancer family history. This study provides the first data on smoking cessation rates in Australian lung cancer screenees and supports screening as a teachable moment. We identify several factors that identify smokers who may require more intensive smoking cessation interventions and could be used to develop effective smoking cessation as part of lung cancer screening, tailored to individual risk profiles.

Eligibility to lung cancer screening among staffs of a university hospital: A large cross-sectional survey

IntroductionImplementation of Lung cancer screening (LCS) programs is challenging. The ILYAD study objectives is to evaluate communication methods to improve participation rate among the Lyon University Hospital employees. In this first part of the study, we aimed to determinate the number of eligible individuals among our population of employees. MethodIn November 2020, we conducted a questionnaire based cross sectional survey among the Lyon University Hospital employees (N = 26,954). We evaluated the PLCO m2012 risk prediction model and the eligibility criteria recommended by French guidelines. We assessed the proportion of eligible individuals among the responders and calculated the total eligible individuals in our hospital. ResultsOverall, 4,526 questionnaires were available for analysis. 16.0% were current smokers, and 28.2% were former smokers. Among the 50–75yo ever-smoker employees, 27% were eligible according to the French guidelines, 2.7% of all eversmokers according to a PLCO m2012 score ≥ 1.51%, and thus, 3.8% of the surveyed population were eligible to the combined criteria. The factors associated with higher eligibility among 50–75yo ever-smokers were educational level, feeling symptoms related to tobacco smoking, personal history of COPD and family history of lung cancer. Using the French guidelines criteria only, we estimated the total number of eligible individuals in the hospital at 838. ConclusionIn this study, we determined a theoretical number of eligible employees to LCS in our institution and the factors associated to eligibility. Secondly, we will propose LCS to all eligible employees of Lyon University Hospital with incremented information actions.

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

Contribution of germline PALB2 variants to an unselected and prospectively registered pancreatic cancer patient cohort in Pakistan

BackgroundPartner and localizer of BRCA2 (PALB2) is a pancreatic cancer (PC) susceptibility gene reported in Caucasians. However, limited data are available among Asians. We investigated the contribution of PALB2 germline variants to Pakistani PC patients. Methods150 unselected and prospectively enrolled PC patients were comprehensively screened for PALB2 variants, using denaturing high-performance liquid chromatography and DNA sequencing. Novel variants were investigated for their pathogenic effect using in-silico tools. Potentially functional variants were screened in 200 controls. ResultsTwenty-two different PALB2 variants were identified. A missense variant (p.Arg37His) was identified in a 48-years-old male patient with a family history of breast cancer. Another missense variant (p.Trp898Arg) was identified in a 48-years-old male patient with a family history of esophageal cancer. A novel 3′ downstream variant (c.∗480A>G) was detected in a 34-years-old female patient with family history of lung cancer. Another novel 3′ downstream variant (c.∗417A>C) was identified in a 41-years-old male patient. All these variants were absent in 200 controls. p.Arg37His and p.Trp898Arg were predicted as likely pathogenic. c.∗417A>C and c.∗480A>G were classified as variants of uncertain significance. ConclusionThis is the first study that suggests a minimal contribution of PALB2 variants to PC risk in Pakistani population.

CN16 Lung cancer risk prediction nomogram in Chinese female non-smokers

About 53% of lung cancers in females 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 female non-smokers in China. A large-sample size, population-based study was conducted under the framework of a population-based 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. A total of 151,834 eligible subjects were included, with a mean age of 55.34 years. Subjects were randomly divided into the training (75,917) and validation (75,917) sets. Elder age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease were the independent risk factors for lung cancer. Using these five variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.762, 0.718, and 0.703 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a good predictive discrimination, with the AUC was 0.646, 0.658, and 0.650 for the 1-, 3- and 5-year lung cancer risk. We developed and validated a simple and non-invasive lung cancer risk model in female non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in female non-smokers.

Abstract 2254: Risk models for lung cancer in never-smoking women in Shanghai with implications for screening

Abstract Background: The majority of female lung cancer cases in Asia are never-smokers with distinct risk factor profiles. Given the high burden of disease in this population, there is an increasing need to improve the understanding of lung cancer. Current risk models for lung cancer focus on active smokers and individuals of European ancestry. Therefore, we developed statistical models by integrating genetic and environmental risk factors to estimate absolute and population attribute risk of lung cancer among never-smoking women in Asia. Methods: We built absolute risk models for lung cancer among never-smoking women using data from 71,300 women (760 incident cases) in the Shanghai Women’s Health Study (SWHS), a population-based prospective cohort study. Relative risks were estimated using a multivariable Cox regression model with questionnaire-based risk factors. To account for missing genetic data for some subjects, we simulated genotypes for 10 common single nucleotide polymorphisms (SNP) using information on minor allele frequencies (MAF) and odds ratio estimates from previous genome-wide association studies (GWAS), conditional on family history of lung cancer. We used the iCARE tool to build two models for predicting lifetime (40 years) and 6-year absolute risk of lung cancer using age-specific lung cancer incidence rates, age-specific competing mortality rates, and risk factor distribution with: 1) questionnaire-based risk factors only and 2) questionnaire and genetic data. We then used the full absolute risk model to estimate the population attributable risk (PAR) due to modifiable risk factors, namely coal use and exposure to environmental tobacco smoke (ETS). Results: The questionnaire-based only model included family history of lung cancer, coal use, exposure to ETS, and body mass index (BMI). The full model also included data on 10 lung cancer related SNPs from our previous GWAS and had a wider spread in distribution of absolute lifetime risk (median=2.41%; range=0.43-12.36) compared to the questionnaire-based only model (median=2.72%; range=1.93-4.87). We used the full model to estimate the PAR and found that 1.74% and 6.33% of lung cancer cases could be prevented if never-smoking women in Shanghai did not use coal and were not exposed to ETS, respectively. Furthermore, we found that the full model estimated that 2.5% of the study population had a 6-year absolute risk of lung cancer higher than 1.51%, which is the suggested risk threshold for screening by existing risk models. Conclusion: We built risk models for never-smoking Asian women and estimated the contribution of coal use and ETS to the burden of lung cancer in Shanghai. This initial work shows promise for expanding and validating risk models in this population with potential translational implications, such as providing insight to identifying high risk individuals that may be eligible for lung cancer screening and primary prevention efforts. Citation Format: Batel Blechter, Parichoy Pal Choudhury, Xiao-ou Shu, Wei Zheng, Qiuyin Cai, Gong Yang, Jason Y.Y. Wong, Bu-Tian Ji, Wei Hu, Anne Rositch, Nilanjan Chatterjee, Nathaniel Rothman, Qing Lan. Risk models for lung cancer in never-smoking women in Shanghai with implications for screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2254.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations.

To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting. The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs. A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs. Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

Lung cancer risk prediction nomogram in Chinese female non-smokers.

10530 Background: About 53% of lung cancers in females 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 female non-smokers in China. Methods: A large-sample size, population-based study was conducted under the framework of a population-based 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. Results: A total of 151,834 eligible subjects were included, with a mean age of 55.34 years. Subjects were randomly divided into the training (75,917) and validation (75,917) sets. Elder age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease were the independent risk factors for lung cancer. Using these five variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.762, 0.718, and 0.703 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a good predictive discrimination, with the AUC was 0.646, 0.658, and 0.650 for the 1-, 3- and 5-year lung cancer risk. Conclusions: We developed and validated a simple and non-invasive lung cancer risk model in female non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in female non-smokers.

Association Between Diabetes Risk Reduction Diet and Lung Cancer Risk in 98,159 Participants: Results From a Prospective Study.

PurposeTo evaluate the association between diabetes risk reduction diet (DRRD) score and the risk of lung cancer in a large population.MethodsData of participants in this study were collected from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated in the Cox proportional hazards regression model for the association of DRRD score and lung cancer incidence in all included participants. Prespecified subgroup analyses were performed to evaluate whether the observed association was modified by age, sex, BMI, race/ethnicity, family history of lung cancer, smoking status and history of diabetes.ResultsA total of 98,159 participants were included in this study. The mean (SD) age of the study participants cohort at baseline was 65.5 (5.73) years old. The mean (SD) follow-up time was 8.83 (1.96) years. The mean (SD) score of DRRD was 26.82 (5.19), and ranged from 20.47 (2.3) to 33.65 (2.42) from the lowest quartile to the highest quartile of the DRRD score, inferring the possibility of highest through the lowest risk of type 2 diabetes. The calculated HRs showed there was a trend that higher quartile indicated lower risk of lung cancer after adjusted for covariates (HRQ4vsQ1: 0.85; 95% CI:0.73,0.98; p for trend =0.036). The inverse trend between higher DRRD score and the risk of squamous cell carcinoma was more evident (HRQ4vsQ1: 0.50; 95% CI:0.34,0.73; p for trend =0.002). The inverse association between DRRD score and the incidence of lung cancer was more pronounced in participants who had a clear family history of lung cancer (p for interaction=0.016).ConclusionA protective association between DRRD score and risk of lung cancer is obtained. People are encouraged to adhere to higher DRRD score in their daily diet. Further studies should be conducted to confirm the result and explore the mechanism.

Risk perception and disease knowledge in attendees of a community-based lung cancer screening programme

ObjectivesIn England, a risk-based approach is used to determine eligibility for lung cancer screening. Ensuring effective communication and counselling of risk is therefore increasingly important. In this study, we explore the perception of lung cancer risk in attendees of a community-based screening service, located in socio-economically deprived areas of Manchester. We analyse responses based on demographic variables, calculated risk score and screening eligibility. Materials and MethodsThe Manchester Lung Health Check (LHC) programme invited ever smokers, age 55–80, to a lung cancer risk assessment in which their 6-year risk was calculated (using the PLCOM2012 model). Those at high risk (PLCOM2012 score ≥ 1.51%) were eligible for low dose CT (LDCT) screening. Prior to their assessment, attendees were invited to complete the study questionnaire, which assessed absolute and comparative risk perception, disease knowledge (incidence, survival, and risk factors), lung cancer specific worry, and mental health. Results371 participants completed the questionnaire; 66% (n = 243) had linked clinical data. Perceived absolute risk was markedly higher than calculated risk (median: 20% vs. 1%; p < 0.001) and higher in women than men (25% vs. 15%; p = 0.001). There was no correlation between perceived absolute and calculated risk. Overall, 30% classified themselves at higher, and 21% at lower, lung cancer risk compared to others their age. Median PLCOM2012 score increased with perceived comparative risk (p = 0.004). Those eligible for screening were more likely to: classify themselves at higher comparative risk (41% vs. 21%; p < 0.0001), report lung cancer-specific worry (27% vs. 10%; p = 0.001) and have indications of depression (20% vs. 10%; p = 0.05). Family history of lung cancer was significantly associated with higher comparative risk (adjOR 4.03, 95%CI 1.74–9.3; p = 0.001). ConclusionEmploying comparative rather than absolute risk may assist risk counselling. Further research is required to determine the optimal approach to risk communication in this setting.

170P Clonal hematopoiesis in asymptomatic individuals enrolled in a lung cancer screening program

Clonal hematopoiesis of indeterminate potential (CHIP) is defined as a clonal accumulation of somatic mutations in hematopoietic stem cells in the absence of hematologic malignancy or other clonal disorders. CHIP has been associated with a higher risk of developing hematologic malignancies. Recently, CHIP has been linked to a family history of lung cancer (FHLC) in lung cancer patients. In the present study, we evaluated the association between clinical factors and CHIP in individuals enrolled in a lung cancer screening program.

Identification of heritable rare variants associated with early-stage lung adenocarcinoma risk.

BackgroundIn East Asia, the number of patients with adenocarcinoma, especially those presenting with ground-glass nodules (GGNs), is gradually increasing. Family aggregation of pulmonary GGNs is not uncommon; however, genetic predisposition in these patients remains poorly understood and identification of genes involved in the cause of these early-stage lung cancers might contribute to understanding of the underlying mechanisms and potential prevention strategies.MethodsFifty patients with early-stage lung adenocarcinoma (LUAD) presenting as GGNs and a first-degree family history of lung cancer (FHLC) from 34 independent families were enrolled into this study. Germline mutations of these patients were analyzed with whole exome sequencing (WES) and compared with age- and sex-matched 39 patients with sporadic lung cancer and 689 local healthy people. We used a stepwise variant filtering strategy, gene-based burden testing, and enrichment analysis to investigate rare but potentially pathogenic heritable mutations. Somatic tumor mutations were analyzed to consolidate germline findings.ResultsIn total, 1,571 single nucleotide variants (SNVs) and 238 frameshifts with a minor allele frequency (MAF) <0.01, which were rare, recurrent, and potentially damaging candidates, were finally identified through the filtering in the GGN cohort. Pathway analysis showed the extracellular matrix to be the top dysregulated pathway. Gene-based burden testing of these highly disruptive risk-conferring heritable variants showed that MSH5 [odds ratio (OR), 9.28, 95% confidence interval (CI): 2.49–35.87], MMP9 (OR, 8.11, 95% CI: 2.22–28.43), and CYP2D6 (OR, 8.09, 95% CI: 2.68–24.92) were significantly enriched in our cohort (P<0.05). The number of rare damaging germline variants in non-smoking patients was significantly higher than that of smoking-affected patients (Spearman’s ρ=−0.39, P=0.02).ConclusionsHeritable, potentially deleterious, and rare candidate variants of MSH5, MMP9 and CYP2D6 were significantly associated with early-stage LUAD presenting with GGNs. Nonsmoking patients likely have a higher genetic predisposition to this type of cancer than smoking-affected patients. These results have extended our understanding of the underlying mechanisms of early-stage LUAD.