Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer
Menée à partir du séquençage de l'ADN libre circulant extrait d'échantillons sanguins provenant de 2 511 personnes, cette étude met en évidence l'intérêt d'une approche, reposant sur l'utilisation d'un algorithme d'apprentissage automatique intégrant des profils de mutations génétiques, pour détecter de manière non invasive un cancer
Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.
Nature Genetics , article en libre accès, 2023