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Integration of omics data and lifestyle factors to assess lung cancer risk among former smokers

Principal Investigator

Name
Xiao Ou Shu

Degrees
MD, Ph.D.

Institution
Vanderbilt University Medical Center

Position Title
Professor

Email
xiao-ou.shu@vumc.org

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
2025-0054

Initial CDAS Request Approval
Sep 10, 2026

Title
Integration of omics data and lifestyle factors to assess lung cancer risk among former smokers

Summary
Lung cancer (LC) is the leading cause of cancer-related deaths in the U.S. and worldwide. Smoking is the most important risk factor for lung cancer, accounting for 80% of LC cases. Quitting smoking dramatically reduces lung cancer risk. However, the risk of lung cancer remained elevated, nearly three-fold in former heavy smokers, even after they quit smoking for more than 25 years, compared to never smokers. Four out of every 10 lung cancers occur in former smokers who quit at least 15 years,
Early diagnosis is the key to reduce lung cancer mortality. The low-dose computed tomography (LDCT) screening is associated with up to 20% reduction of lung cancer mortality. However, only 16-20% of eligible Americans have been screened for lung cancer. In addition, concerns were raised on the benefit-to-harm ratio for lung cancer screening given that a false positive result may lead to unnecessary invasive procedures and complications. Thus, LDCT screening could be more acceptable, effective and less harmful if screening eligibility is determined based on a personalized lung cancer risk assessment incorporating more detailed smoking, potential risk factors and predictive biomarkers.
We propose to use resources from the PLCO and multiple other prospective cohort studies to build and validate a lung cancer prediction model for former smokers. We will further investigate whether incorporating genetic and metabolic biomarkers improves performance of the prediction model. Specifically, we will comprehensively evaluate risk factors for lung cancer among former smokers, including but not restrict to smoking history, family cancer history, weight and lifestyle factors (e.g., physical activities, diet) and build a risk prediction model using the PLCO resources and then validate in the Lung Cancer Cohort Consortium (LC3) (Aim 1). Near half million former smokers will be included in the Aim 1 study. We will then identify and valid metabolomic lung cancer risk biomarkers for former smokers by conducting a case-control study of approximate 1,800 pairs of former smoking lung cancer cases and controls nested in 6 prospective cohort studies using pre-diagnostic plasma/serum samples (Aim 2). We will evaluate the added value for inclusion of metabolomic and genetic susceptibility biomarkers in predicting lung cancer risk among the former smokers included in Aim 2 of the study (Aim 3). We will derive and apply the compositional metabolomic and genetic risk scores in risk prediction evaluation.
This will be the first study focusing on building a lung cancer prediction model exclusively for former smokers, and the first to incorporating genetic and metabolic biomarkers in risk prediction. Building upon on tremendous resources from the PLCO, LC3 and other ongoing cohorts, highly promising preliminary data, and a strong collaborative team, our study holds great potential to make a significant impact on the secondary prevention of lung cancer among former smokers.

Aims

We propose to use resources from the PLCO and multiple other prospective cohort studies to build and validate a lung cancer prediction model for former smokers. We will further investigate whether incorporating genetic, methylation and metabolic biomarkers improves performance of the prediction model. Specific aims of our study are:
• Aim 1: to comprehensively evaluate risk factors for lung cancer among former smokers, build a risk prediction model using the PLCO resources and then validate in the Lung Cancer Cohort Consortium (LC3). The LC3 included 24 prospective cohorts , 424,483 former smokers, of whom 3,282 developed incident lung cancer.
• Aim 2: to identify and valid metabolomic lung cancer risk biomarkers for former smokers by conducting a case-control study of approximate 1,800 pairs of former smoking lung cancer cases and controls nested in 6 prospective cohort studies using pre-diagnostic plasma/serum samples. The discovery phase will use a global non-targeted metabolomic panel. We will use a quantitative metabolomics panel in the validation phase to enhance the translational potential of validated biomarkers. Validated metabolites will be quantitatively measured for all participants of the discovery phase for inclusion in Aim 3 of the study.
• Aim 3: to evaluate the added value for inclusion of metabolomic, methylation and genetic susceptibility biomarkers in predicting lung cancer risk among the former smokers included in Aim 2 of the study. We will derive and apply the compositional metabolomic and genetic risk scores in risk prediction evaluation.

Collaborators

Xiao Ou Shu (Vanderbilt University Medical Center)
Qiuyin Cai (Vanderbilt University Medical Center)
Hui Cai (Vanderbilt University Medical Center)
Jirong Long (Vanderbilt University Medical Center)
Duc Huy Le (Vanderbilt University)