Genome-wide cell-free DNA fragmentation in patients with cancer

By S. Cristiano, A. Leal, J. Phallen, J. Fiksel, V. Adleff, D. Bruhm, S. Jensen, J. Medina, C. Hruban, James Robert White and 26 more. Nature, 2019

1,195 citations recorded by the source corpus.

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer. Analyses of fragmentation patterns of cell-free DNA in the blood of patients with cancer and healthy individuals using a machine learning algorithm provide a proof-of principle approach for the early detection and screening of human cancer.

Subfields: Cancer Research, Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/380f83b0af32d7953964e460baf33e2039e6e2b6

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Citations in the corpus

Liquid biopsy: current technology and clinical applications (2022, methodology): “[93] evaluated fragmentation profiles across 236 patients with multiple cancer types and compared to healthy individuals with a machine learning model and found that these profiles could be used to identify the tissue of origin of cancers with 61% accuracy”
Tumor microenvironment complexity and therapeutic implications at a glance (2020, background): “Also, it can detect 91% of patients with cancer when combined with mutation-based cell-free DNA analyses [216] .”
ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: a report from the ESMO Precision Medicine Working Group. (2022, background): “32 Large efforts have been conducted in this ﬁ eld so far, with studies demonstrating high speci ﬁ city and encouraging sensitivity with error-corrected sequencing 116 that may be combined with protein biomarkers, 117 genome-wide fragmentation patterns 118 and methylation-based ctDNA assays.”
AI in health and medicine (2022, methodology): “When applied to data on circulating cell-free DNA, AI has enabled noninvasive cancer detection, prognosis and tumor origin identification 46–48 .”
Liquid Biopsy: From Discovery to Clinical Application. (2021, background): “Usually, targeted approaches have a higher analytic sensitivity than untargeted approaches, but strong efforts are ongoing to improve the detection limits of untargeted approaches (47, 48).”
Early detection of cancer (2022, positive): “cfDNA differ between people with and without cancer, and between different cancer types (72).”
Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA (2020, positive): “These studies combined mutation detection with serum protein biomarkers, 25 leveraged differences in cfDNA fragment lengths between cancer and non-cancer participants, 10 or utilized a methylation-based immunoprecipitation approach to avoid perceived issues with bisul ﬁ te sequencing degradation.”