Prospective evaluation of cell-free DNA fragmentation profiles for lung cancer detection.

Abstract

e20521 Background: Annual lung cancer screening can save lives, but fewer than 10% of eligible persons participate each year. More widespread screening is hindered by cost, inaccessibility, and uncertainty over individual-level benefit vs risk. Screening rates could be raised by a simple, inexpensive initial blood test, if it were sensitive for cancer detection. The DELFI (DNA evaluation of fragments for early interception) technology uses low-coverage, whole-genome sequencing and machine learning to identify patterns of cell-free DNA (cfDNA) fragmentation associated with cancer. Here we report preliminary cfDNA analysis results from DELFI-L101 (NCT04825834), a prospective, observational, multistate case-control study to train and test DELFI classifiers for lung cancer detection. Methods: Enrollees were ≥50 years old with current or previous smoking histories of ≥20 pack-years and recent or planned chest CT imaging. Medical history was recorded at enrollment, and blood samples were collected for DELFI analysis. Repeated 10-fold cross-validation was used to develop a classifier for lung cancer detection. A split study approach is planned for independent validation of the classifier. Results: At this time, 242 individuals with lung cancer and 652 without cancer have enrolled. Most participants were ≥65 years old, and the proportions of men and women were similar. Lung cancer risk factors were present among both cases and controls. Like the lung cancer screening population, approximately half of lung cancer cases were stage I. Median DELFI scores were higher among individuals with lung cancer than no cancer, overall and across groups stratified by age or body mass index (BMI). Cross-validated area under the receiver operator characteristic curve was 0.81 for lung cancer detection. Clinically meaningful sensitivity to detect lung cancer was attained across all disease stages, with sensitivity increasing stepwise with stage. Conclusions: We developed a classifier based on cfDNA fragmentome patterns analyzed using DELFI that could differentiate between lung cancer cases and controls with cross-validated performance across age groups, BMI categories, and cancer stages. A blood-based DELFI fragmentome test could serve as a low-cost, high-performance blood test with potential to improve lung cancer screening efficiency. Clinical trial information: NCT04825834 .