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Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels

Authors :
Elisabeth M. Jongbloed
Maurice P. H. M. Jansen
Vanja de Weerd
Jean A. Helmijr
Corine M. Beaufort
Marcel J. T. Reinders
Ronald van Marion
Wilfred F. J. van IJcken
Gabe S. Sonke
Inge R. Konings
Agnes Jager
John W. M. Martens
Saskia M. Wilting
Stavros Makrodimitris
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract Next generation sequencing of cell-free DNA (cfDNA) is a promising method for treatment monitoring and therapy selection in metastatic breast cancer (MBC). However, distinguishing tumor-specific variants from sequencing artefacts and germline variation with low false discovery rate is challenging when using large targeted sequencing panels covering many tumor suppressor genes. To address this, we built a machine learning model to remove false positive variant calls and augmented it with additional filters to ensure selection of tumor-derived variants. We used cfDNA of 70 MBC patients profiled with both the small targeted Oncomine breast panel (Thermofisher) and the much larger Qiaseq Human Breast Cancer Panel (Qiagen). The model was trained on the panels’ common regions using Oncomine hotspot mutations as ground truth. Applied to Qiaseq data, it achieved 35% sensitivity and 36% precision, outperforming basic filtering. For 20 patients we used germline DNA to filter for somatic variants and obtained 245 variants in total, while our model found seven variants, of which six were also detected using the germline strategy. In ten tumor-free individuals, our method detected in total one (potentially germline) variant, in contrast to 521 variants detected without our model. These results indicate that our model largely detects somatic variants.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.25950c21f0c9448e94b13a8b82eb4ebd
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-37409-1