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<p>The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named ‘Fragle’, by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method’s versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility.</p>

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

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Guanhua Zhu,
  • Chowdhury Rafeed Rahman,
  • Victor Getty,
  • Denis Odinokov,
  • Probhonjon Baruah,
  • Hanaé Carrié,
  • Avril Joy Lim,
  • Yu Amanda Guo,
  • Zhong Wee Poh,
  • Ngak Leng Sim,
  • Ahmed Abdelmoneim,
  • Yutong Cai,
  • Lakshmi Narayanan Lakshmanan,
  • Danliang Ho,
  • Saranya Thangaraju,
  • Polly Poon,
  • Yi Ting Lau,
  • Anna Gan,
  • Sarah Ng,
  • Si-Lin Koo,
  • Dawn Q. Chong,
  • Brenda Tay,
  • Tira J. Tan,
  • Yoon Sim Yap,
  • Aik Yong Chok,
  • Matthew Chau Hsien Ng,
  • Patrick Tan,
  • Daniel Tan,
  • Limsoon Wong,
  • Pui Mun Wong,
  • Iain Beehuat Tan,
  • Anders Jacobsen Skanderup

摘要

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named ‘Fragle’, by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method’s versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility.