Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

Journal

Nature medicine

ISSN: 1546-170X

Titre abrégé: Nat Med

Pays: United States

ID NLM: 9502015

Informations de publication

Date de publication:
14 Jun 2024

Historique:

received: 21 12 2023

accepted: 30 04 2024

medline: 15 6 2024

pubmed: 15 6 2024

entrez: 14 6 2024

Statut: aheadofprint

Résumé

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE

Identifiants

DOI: 10.1038/s41591-024-03040-4 PMID: 38877116

pubmed: 38877116

doi: 10.1038/s41591-024-03040-4

pii: 10.1038/s41591-024-03040-4

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Subventions

Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

ID : CA263301-01A1

Organisme : Melanoma Research Alliance (MRA)

ID : 1039927

Informations de copyright

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