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A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths

A deep-learning model can accurately quantify circulating tumour DNA from the density distribution of cell-free DNA-fragment lengths in plasma from patients with cancer and from healthy individuals.

Guanhua Zhu
Chowdhury Rafeed Rahman
Anders Jacobsen Skanderup
Research07 Mar 2025
Nature Biomedical Engineering

Volume: 9, P: 307-319
Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer

A single-cell transcriptomic analysis of 63 patients with colorectal cancer classifies tumor cells into two epithelial subtypes. An improved tumor classification based on epithelial subtype, microsatellite stability and fibrosis reveals differences in pathway activation and metastasis.

Ignasius Joanito
Pratyaksha Wirapati
Iain Beehuat Tan
ResearchOpen Access30 Jun 2022
Nature Genetics

Volume: 54, P: 963-975
Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden

Circulating tumour DNA (ctDNA) represents a non-invasive option to monitor cancer progression. Here, the authors perform deep sequencing of plasma cell-free DNA, and find that nucleosome-dependent cfDNA degradation at 6 specific regulatory regions is predictive of ctDNA burden.

Guanhua Zhu
Yu A. Guo
Anders J. Skanderup
ResearchOpen Access13 Apr 2021
Nature Communications

Volume: 12, P: 1-11

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A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer

Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden

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