A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour <scp>DNA</scp> and machine learning to predict treatment outcome and progression‐free survival
Monitoring levels of circulating tumour-derived DNA (ctDNA) provides both a noninvasive snapshot of tumour burden and also potentially clonal evolution. Here, we describe how applying a novel statistical model to serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive predictive assessment of treatment response and progression-free survival. A cohort of 149 patients had DNA extracted from serial plasma samples (total 1013, mean samples per patient = 6.80). Plasma DNA was assessed using sWGS and the tumour fraction in total cell-free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA15-3 measurements. We identified a transition point of 7% estimated tumour fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox Proportional Hazards model and validated it across different breast cancer subtypes and treatments, outperforming the alternative methods. We used the longitudinal ichorCNA values to develop a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. In patients with metastatic breast cancer, a strategy of sWGS of ctDNA with longitudinal tracking of tumour fraction provides real-time information on treatment response. These results encourage a prospective large-scale clinical trial to evaluate the clinical benefit of early treatment changes based on ctDNA levels.
Authors
- Oscar M. Rueda (ORCID: https://orcid.org/0000-0003-0008-4884)
- Dana W.Y. Tsui (ORCID: https://orcid.org/0000-0002-0595-6664)
- Suet‐Feung Chin (ORCID: https://orcid.org/0000-0001-5697-1082)
- Fleur Tippin
- Solon Karapanagiotis (ORCID: https://orcid.org/0000-0003-4460-2073)
- Stephen‐John Sammut (ORCID: https://orcid.org/0000-0003-4472-904X)
- Maurizio Callari (ORCID: https://orcid.org/0000-0001-5239-0918)
- Justine M. Kane (ORCID: https://orcid.org/0000-0003-2688-4169)
- Alistair Martin (ORCID: https://orcid.org/0000-0001-9735-2149)
- Nitzan Rosenfeld (ORCID: https://orcid.org/0000-0002-2825-4788)
- Riccardo Masina (ORCID: https://orcid.org/0000-0002-9236-616X)
- Davina Gale (ORCID: https://orcid.org/0000-0002-4521-8199)
- Jonathan Lay
- Meiling Gao (ORCID: https://orcid.org/0000-0001-9835-906X)
- Ramona Woitek (ORCID: https://orcid.org/0000-0002-9146-9159)
- Emma Beddowes (ORCID: https://orcid.org/0000-0001-7649-2863)
- Carlos Caldas (ORCID: https://orcid.org/0000-0003-3547-1489)
- Sarah‐Jane Dawson (ORCID: https://orcid.org/0000-0002-8276-0374)
- Anja Brouwer (ORCID: https://orcid.org/0009-0008-9402-6792)
- Mario Ortega Duran
- James Tanner
Institutions
- St Thomas' Hospital (GB)
- Memorial Sloan Kettering Cancer Center (US)
- Institute of Cancer Research (GB)
- University of Antwerp (BE)
- University of Cambridge (GB)
- Breast Cancer Now (GB)
- Peter MacCallum Cancer Centre (AU)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- Cancer Research UK Cambridge Center (GB)
- Royal Marsden Hospital (GB)
- MRC Biostatistics Unit (GB)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Molecular Oncology
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1002/1878-0261.70015
- Citations
- 4
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 3.75
Funders
- Wellcome Trust
- Cancer Research UK
- National Institute for Health and Care Research
- Department of Health and Social Care
- Academy of Medical Sciences
- Austrian Science Fund
- Servier
- Medical Research Council
- NIHR Cambridge Biomedical Research Centre
- Cancer Research UK Cambridge Institute, University of Cambridge