Print Email Facebook Twitter Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels Title Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels Author Jongbloed, Elisabeth M. (Erasmus MC) Jansen, Maurice P.H.M. (Erasmus MC) de Weerd, Vanja (Erasmus MC) Helmijr, Jean A. (Erasmus MC) Beaufort, Corine M. (Erasmus MC) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics) van Marion, Ronald (Erasmus MC) van IJcken, Wilfred F.J. (Erasmus MC) Makrodimitris, S. (TU Delft Pattern Recognition and Bioinformatics; Erasmus MC) Date 2023 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. To reference this document use: http://resolver.tudelft.nl/uuid:ffe91987-e882-4f85-9a3e-1535689da90f DOI https://doi.org/10.1038/s41598-023-37409-1 ISSN 2045-2322 Source Scientific Reports, 13 (1) Part of collection Institutional Repository Document type journal article Rights © 2023 Elisabeth M. Jongbloed, Maurice P.H.M. Jansen, Vanja de Weerd, Jean A. Helmijr, Corine M. Beaufort, M.J.T. Reinders, Ronald van Marion, Wilfred F.J. van IJcken, S. Makrodimitris Files PDF s41598_023_37409_1.pdf 2.19 MB Close viewer /islandora/object/uuid:ffe91987-e882-4f85-9a3e-1535689da90f/datastream/OBJ/view