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Detection and characterization of lung cancer using cell-free DNA fragmentomes.

ABSTRACT: Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.

SUBMITTER: Mathios D

PROVIDER: S-EPMC8379179 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Detection and characterization of lung cancer using cell-free DNA fragmentomes.

Mathios Dimitrios D Johansen Jakob Sidenius JS Cristiano Stephen S Medina Jamie E JE Phallen Jillian J Larsen Klaus R KR Bruhm Daniel C DC Niknafs Noushin N Ferreira Leonardo L Adleff Vilmos V Chiao Jia Yuee JY Leal Alessandro A Noe Michael M White James R JR Arun Adith S AS Hruban Carolyn C Annapragada Akshaya V AV Jensen Sarah Østrup SØ Ørntoft Mai-Britt Worm MW Madsen Anders Husted AH Carvalho Beatriz B de Wit Meike M Carey Jacob J Dracopoli Nicholas C NC Maddala Tara T Fang Kenneth C KC Hartman Anne-Renee AR Forde Patrick M PM Anagnostou Valsamo V Brahmer Julie R JR Fijneman Remond J A RJA Nielsen Hans Jørgen HJ Meijer Gerrit A GA Andersen Claus Lindbjerg CL Mellemgaard Anders A Bojesen Stig E SE Scharpf Robert B RB Velculescu Victor E VE

Nature communications 20210820 1

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and ...[more]

PMID: 34417454

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Detection and characterization of lung cancer using cell-free DNA fragmentomes.

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Detection and characterization of lung cancer using cell-free DNA fragmentomes.

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