Akshaya V. Annapragada, Zachariah H. Foda, Hope Orjuela, Carter Norton, Shashikant Koul, Noushin Niknafs, Sarah Short, Keerti Boyapati, Adrianna L. Bartolomucci, Dimitrios Mathios, Michaël Noë, Chris Cherry, Jacob Carey, Alessandro Leal, Bryan Chesnick, Nicholas C. Dracopoli, Jamie E. Medina, Nicholas A. Vulpescu, Daniel C. Bruhm, Sarah Bacus, Vilmos Adleff, Amy K. Kim, Stephen B. Baylin, Gregory D. Kirk, Andrei Sorop, Răzvan Iacob, Speranta Iacob, Liana Gheorghe, Simona Dima, Manuel Ramírez-Zea, Katherine A. McGlynn, Claus Larsen Feltoft, J. D. Johansen, John D. Groopman, Jillian Phallen, Robert B. Scharpf, Victor E. Velculescu
Accessible liquid biopsies, including analyses of genome-wide cell-free DNA (cfDNA) fragmentation, are emerging for early detection of cancer but remain largely unexplored in other diseases. Here, we used whole-genome sequencing to examine cfDNA fragmentomes in 1576 individuals, including those with liver disease or with other morbidities such as vascular, autoimmune, and neurodegenerative conditions. As a prototype for disease-specific cfDNA fragmentomic biomarkers, we developed a machine learning classifier that detected early liver disease, advanced fibrosis, and cirrhosis with high sensitivity in separate discovery ( n = 423) and validation cohorts ( n = 221) and had limited cross-reactivity for other diseases. Genome-wide fragmentome and methylome analyses revealed liver-derived and immune-mediated changes in cfDNA in the circulation of individuals affected with liver disease. Fragmentomic changes were also observed across a range of other human morbidities and reflected disease-specific changes in the circulation. A machine learning model using cfDNA fragmentomes predicted overall survival in separate morbidity discovery ( n = 571) and validation cohorts ( n = 231). These analyses demonstrate the connection between cfDNA fragmentomes and an individual’s physiologic state and provide previously unrecognized possibilities for cfDNA liquid biopsies across human disease.