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 .
Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to ...develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.
Abstract
Background: Less than 10% of eligible persons undergo annual lung cancer screening by low-dose computed tomography (LDCT). Greater uptake of LDCT is hampered in part by its cost, ...inaccessibility, and balance of benefit to risk. A blood-based, low-cost, widely available initial blood test could boost screening participation and improve the net benefit of screening, if it were sensitive for cancer detection and affordable. The DELFI (DNA evaluation of fragments for early interception) technology uses low-coverage, whole-genome sequencing and machine learning to identify patterns of circulating cell-free DNA (cfDNA) fragmentation indicative of cancer. We report initial results of the cfDNA analysis from DELFI-L101 (NCT04825834), a prospective, observational, national case-control study to train and test DELFI classifiers for lung cancer detection.
Methods: Eligible participants were adults ≥50 years old with current or previous smoking histories of ≥20 pack-years and recent or planned thoracic CT imaging. At enrollment, medical history was recorded and blood samples were collected for DELFI analysis. A classifier for lung cancer detection was developed using repeated 10-fold cross-validation. A split study approach for the purposes of independent validation of the classifier is forthcoming.
Results: The study cohort included 242 patients with lung cancer and 652 individuals without cancer. Study participants largely represented those of a lung cancer screening population, with 45% stage I/IA. Most participants were ≥65 years old with roughly equal proportions of men and women. There was broad representation across lung cancer risk factors among both cases and controls. The cross-validated area under the receiver operator characteristic curve (AUC) was 0.81 for lung cancer detection. AUCs for adenocarcinoma and squamous cell carcinoma were not significantly different, but the AUC for small cell lung cancer was significantly higher than that for adenocarcinoma (p<.001) and squamous cell carcinoma (p=.02). Clinically meaningful sensitivity to detect all stages of disease was achieved.
Conclusions: A classifier developed using samples collected prospectively distinguished between lung cancer cases and controls with robust cross-validated performance across all stages and lung cancer subtypes. A cfDNA DELFI fragmentome test could represent an affordable, high-performing blood test that may improve lung cancer screening.
Citation Format: Peter J. Mazzone, Kwok-Kin Wong, Jun-Chieh J. Tsay, Harvey I. Pass, Anil Vachani, Allison Ryan, Jacob Carey, Debbie Jakubowski, Tony Wu, Yuhua Zong, Carter Portwood, Keith Lumbard, Joseph Catallini, Nicholas C. Dracopoli, Tara Maddala, Peter B. Bach, Robert B. Scharpf, Victor E. Velculescu. Prospective evaluation of cell-free DNA fragmentomes for lung cancer detection. abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5766.
