Genetic Associations Between High-Risk Pulmonary Nodules and Risk of Lung Adenocarcinoma and Squamous Cell Carcinoma

Principal Investigator

Name
Douglas Easton

Degrees
Ph.D

Institution
University of Cambridge

Position Title
Professor

Email
dfe20@cam.ac.uk

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-1987

Initial CDAS Request Approval
Sep 22, 2025

Title
Genetic Associations Between High-Risk Pulmonary Nodules and Risk of Lung Adenocarcinoma and Squamous Cell Carcinoma

Summary
Lung cancer screening has emerged as an effective tool for early detection, with promising results from various trials. With the adoption of low-dose computed tomography, the detection of pulmonary nodules has risen significantly. This project will systematically investigate the genetic associations between high-risk pulmonary nodules and lung cancer, with a focus on adenocarcinoma and squamous cell carcinoma. Specifically, it will assess how genetic predisposition to benign pulmonary conditions relates to subsequent cancer development. By examining germline genetic links between benign diseases and cancer risk, this study aims to advance understanding of the biological mechanisms underlying cancer initiation and progression, while also contributing to the development of more precise risk prediction models.

Aims

This proposal aims to systematically investigate the genetic association between high-risk pulmonary nodules and lung cancer. The ultimate goal is to use this information to develop refined risk prediction models that incorporate genetic and lifestyle factors with data on pre-cancerous phenotypes for more accurate cancer risk stratification, balancing early detection with the need to minimise unnecessary medical interventions.
1. Conduct genome-wide association analyses of high-risk pulmonary nodules using a two-step whole-genome regression model implemented in Regenie.
2. Use genetic correlation analyses to identify genome-wide and regional pleiotropy between benign conditions and lung cancer.
3. Assess the impact of genetic predisposition to benign conditions on cancer development through Cox regression models, leveraging longitudinal data.

Collaborators

Xiaomeng Zhang University of Cambridge
Doug Easton University of Cambridge