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• W4362594867 abstract "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&lt;.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." @default.
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• W4362594867 date "2023-04-04" @default.
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• W4362594867 title "Abstract 5766: Prospective evaluation of cell-free DNA fragmentomes for lung cancer detection" @default.
• W4362594867 doi "https://doi.org/10.1158/1538-7445.am2023-5766" @default.
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