Predicting tumour content of liquid biopsies from cell-free DNA Journal Article

   


Authors: Cardner, M.; Marass, F.; Gedvilaite, E.; Yang, J. L.; Tsui, D. W. Y.; Beerenwinkel, N.
Article Title: Predicting tumour content of liquid biopsies from cell-free DNA
Abstract: Background: Liquid 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. Results: We 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. Conclusions: This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies. © 2023, BioMed Central Ltd., part of Springer Nature.
Keywords: genetics; neoplasm; neoplasms; pathology; tumor marker; biopsy; dna; tumors; forecasting; body fluids; minimally invasive; diseases; dna fragment; procedures; cancer patients; wavelet transforms; humans; human; circulating tumor dna; liquid biopsy; biomarkers, tumor; cell-free dna; cell-free nucleic acids; circulating tumour dna; cell-free; cell free nucleic acid; fragmentomics; tumour content; fragmentomic; invasive methods; tumor content
Journal Title: BMC Bioinformatics
Volume: 24
ISSN: 1471-2105
Publisher: Biomed Central Ltd  
Date Published: 2023-09-30
Start Page: 368
Language: English
DOI: 10.1186/s12859-023-05478-8
PUBMED: 37777714
PROVIDER: scopus
PMCID: PMC10543881
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173561070&doi=10.1186%2fs12859-023-05478-8&partnerID=40&md5=3e5e683c8054206d3b8447c5818f720d
Notes: Article -- Source: Scopus
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MSK Authors
  1. Li Yang
    29 Yang
  2. Wai Yi Tsui
    50 Tsui
  3. Erika Gedvilaite
    38 Gedvilaite
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