Detection and characterization of lung cancer using cell-free DNA fragmentomes
Dimitrios Mathios, Jakob Sidenius Johansen, Stephen Cristiano, Jamie E Medina, Jillian Phallen, Klaus R Larsen, Daniel C Bruhm, Noushin Niknafs, Leonardo Ferreira, Vilmos Adleff, Jia Yuee Chiao, Alessandro Leal, Michael Noe, James R White, Adith S Arun, Carolyn Hruban, Akshaya V Annapragada, Sarah Østrup Jensen, Mai-Britt Worm Ørntoft, Anders Husted Madsen, Beatriz Carvalho, Meike de Wit, Jacob Carey, Nicholas C Dracopoli, Tara Maddala, Kenneth C Fang, Anne-Renee Hartman, Patrick M Forde, Valsamo Anagnostou, Julie R Brahmer, Remond J A Fijneman, Hans Jørgen Nielsen, Gerrit A Meijer, Claus Lindbjerg Andersen, Anders Mellemgaard, Stig E Bojesen, Robert B Scharpf, Victor E Velculescu, Dimitrios Mathios, Jakob Sidenius Johansen, Stephen Cristiano, Jamie E Medina, Jillian Phallen, Klaus R Larsen, Daniel C Bruhm, Noushin Niknafs, Leonardo Ferreira, Vilmos Adleff, Jia Yuee Chiao, Alessandro Leal, Michael Noe, James R White, Adith S Arun, Carolyn Hruban, Akshaya V Annapragada, Sarah Østrup Jensen, Mai-Britt Worm Ørntoft, Anders Husted Madsen, Beatriz Carvalho, Meike de Wit, Jacob Carey, Nicholas C Dracopoli, Tara Maddala, Kenneth C Fang, Anne-Renee Hartman, Patrick M Forde, Valsamo Anagnostou, Julie R Brahmer, Remond J A Fijneman, Hans Jørgen Nielsen, Gerrit A Meijer, Claus Lindbjerg Andersen, Anders Mellemgaard, Stig E Bojesen, Robert B Scharpf, Victor E Velculescu
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.
Conflict of interest statement
D.M., S.C., J.P., A.L., V.A., R.B.S., and V.E.V. are inventors on patent applications submitted by Johns Hopkins University related to cell-free DNA for cancer detection. S.C., J.P., A.L., V.A., and R.B.S. are founders of Delfi Diagnostics, and V.A. and R.B.S are consultants for this organization. V.E.V. is a founder of Delfi Diagnostics and Personal Genome Diagnostics, serves on the Board of Directors and as a consultant for both organizations, and owns Delfi Diagnostics and Personal Genome Diagnostics stock, which are subject to certain restrictions under university policy. In addition, Johns Hopkins University owns equity in Delfi Diagnostics and Personal Genome Diagnostics. The technology used in the study described in this publication has been licensed to one or more entities. Under the terms of these license agreements, the University and inventors are entitled to fees and royalty distributions. V.E.V. is an advisor to Bristol-Myers Squibb, Genentech, and Takeda Pharmaceuticals. Within the last five years, V.E.V. has been an advisor to Merck and Ignyta. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies.
© 2021. The Author(s).
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Source: PubMed