ArticleBMC bioinformatics2023
Predicting tumour content of liquid biopsies from cell-free DNA.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Frequency-Domain Transformation of cfDNA End-Motif Profiles Enhances Robust Cancer Detection.Genes · 2026Article
- DeepFRAG: a method for cancer detection based on DNA fragmentomics and deep learning.Bioinformatics advances · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundLiquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample's tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy.
resultsWe used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma.
conclusionsThis proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.
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