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Research Project

Predictive model of lung cancer in Alpha 1 Antitrypsin Deficiency (CAN-DAAT study).

Principal Investigator:
Myriam Calle
Center:
Hospital Clínico San Carlos
City/Country:
Madrid, Spain
Start date:
November 2024
Status:
Ongoing
Contact E-mail:
mcallerubio@gmail.com
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Introduction

Alpha 1 antitrypsin deficiency (AATD) is the most common genetic cause COPD, and can lead to early age onset of emphysema. Increased rate of AAT deficient alleles carriers in the lung cancer (LC) patients have been observed compared with the general population. Several mechanisms have been described as the causes of this potential association between AATD and LC: 1)the decrease in the circulating plasma levels of AAT added to the molecular oxidation associated with exposure to tobacco smoke, acts increase in the risk of developing lung cancer.; 2) This oxidative stress, which is increased in AATD and emphysema, has also been implicated in the pathogenesis of LC.

Objectives

The purpose of this study is to characterize LC patients with AATD and analyze associated risk factors. Additionally, the study aims to develop a predictive algorithm for LC risk in the AATD population using artificial intelligence (machine learning) based on data from individuals with and without lung cancer. This research project seeks to establish more efficient screening strategies for LC and promote earlier and more proactive preventive actions regarding lifestyle habits and risk exposures.

Inclusion criteria

AAT deficient alleles carriers of EARCO registre, with age >18 years.

Brief summary

The objectives of this project align with the thematic priorities in Alpha-1 Antitrypsin Deficiency (AATD), such as generating knowledge about AATD risk factors and enhancing predictive measures in follow-up care. This study aims to develop a predictive model (composite score) that estimates the risk of lung cancer in the AATD population, identifying patients at higher risk of developing lung cancer. Additionally, demonstrating the relationship between a panel of biomarkers and lung cancer development, along with their consistency through a longitudinal pattern in the prospective follow-up cohort of patients registered in EARCO, could lead to improved strategies for the early diagnosis of lung cancer.