Thoracic diseases, particularly lung cancer, pose a significant global health burden. These conditions are influenced by genetic factors, environmental exposures, and clinical comorbidities. However, current risk stratification tools often fail to capture the complex interactions between these domains. Over 70% of thoracic malignancies occur sporadically, emphasizing the need for a multidimensional approach to risk assessment in asymptomatic populations.
Objectives:
Uncover disease-specific genetic and proteomic signatures: Integrate plasma proteomics with UK Biobank genomic data to analyze rare variant burdens, polygenic risk scores, and protein expression profiles across thoracic diseases, focusing on lung cancer.
Quantify modifiable risk thresholds: Model interactions between lifestyle factors, clinical parameters, genetic susceptibility, and proteomic markers to identify actionable risk thresholds for intervention.
Develop adaptive risk prediction algorithms: Create algorithms that integrate real-time data from wearable sensors, imaging biomarkers, electronic health records, and proteomics to provide personalized risk assessments.
Explore targeted treatment strategies: Use identified genetic and proteomic signatures to explore therapeutic targets and personalized treatment options for thoracic diseases, especially lung cancer.
Scientific Rationale:
By combining UK Biobank’s longitudinal data (genomics, imaging, lifestyle surveys, and clinical information) with machine learning and plasma proteomics, we aim to identify critical points for preventive actions that can alter disease trajectories. This framework will enable clinicians to stratify patients into precision surveillance protocols, reducing overdiagnosis in low-risk groups while preventing adverse outcomes in high-risk populations. Integrating proteomics will enhance our understanding of disease mechanisms and provide novel biomarkers for early detection and targeted treatment.
