Predicting high confidence ctDNA somatic variants with ensemble machine learning models
Abstract Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with cancer. Accurate somatic variant detection...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01326-2 |
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| author | Rugare Maruzani Liam Brierley Andrea Jorgensen Anna Fowler |
| author_facet | Rugare Maruzani Liam Brierley Andrea Jorgensen Anna Fowler |
| author_sort | Rugare Maruzani |
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| description | Abstract Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with cancer. Accurate somatic variant detection in circulating cell free DNA (cfDNA) NGS data requires filtering strategies to remove germline variants, and NGS artifacts. Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. We built two Random Forest (RF) models for predicting high confidence somatic ctDNA variants in low and high depth cfDNA NGS data. Low depth models were fitted and evaluated on whole exome sequencing (WES) cfDNA data at depths of approximately 10X while the high depth data was sequenced at approximately 500X. Both models utilise a set of 15 features from variants detected by bcftools, FreeBayes, LoFreq and Mutect2. High confidence ground truth sets were obtained from matched tissue biopsy samples. We benchmarked our models against rule-based filtering with a set of hard, medium, and soft thresholds. Precision-recall curves showed the high depth model outperformed rule-based filtering at all thresholds in Test Data (PR-AUC 0.71). Partial dependence plots showed membership in the COSMIC database, absence from the dbSNP common variants database, and increasing read depth increased mean probability of high confidence somatic variant prediction in both models. Our results demonstrate the utility of supervised ML models for filtering variants in cfDNA data. |
| format | Article |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-6ea40d04ef4d4f1bb92ed822b5bc39752025-08-20T02:03:35ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01326-2Predicting high confidence ctDNA somatic variants with ensemble machine learning modelsRugare Maruzani0Liam Brierley1Andrea Jorgensen2Anna Fowler3Department of Health Data Science, Institute of Population Health, Great Britain and Northern Ireland, University of LiverpoolSchool Of Infection & Immunity, of Great Britain and Northern IrelandDepartment of Health Data Science, Institute of Population Health, Great Britain and Northern Ireland, University of LiverpoolDepartment of Health Data Science, Institute of Population Health, Great Britain and Northern Ireland, University of LiverpoolAbstract Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with cancer. Accurate somatic variant detection in circulating cell free DNA (cfDNA) NGS data requires filtering strategies to remove germline variants, and NGS artifacts. Rule-based variant filtering methods either remove a substantial number of true positive ctDNA variants along with false variant calls or retain an implausibly large number of total variants. Machine Learning (ML) enables identification of complex patterns which may improve ability to distinguish between real somatic ctDNA variants and false positive calls. We built two Random Forest (RF) models for predicting high confidence somatic ctDNA variants in low and high depth cfDNA NGS data. Low depth models were fitted and evaluated on whole exome sequencing (WES) cfDNA data at depths of approximately 10X while the high depth data was sequenced at approximately 500X. Both models utilise a set of 15 features from variants detected by bcftools, FreeBayes, LoFreq and Mutect2. High confidence ground truth sets were obtained from matched tissue biopsy samples. We benchmarked our models against rule-based filtering with a set of hard, medium, and soft thresholds. Precision-recall curves showed the high depth model outperformed rule-based filtering at all thresholds in Test Data (PR-AUC 0.71). Partial dependence plots showed membership in the COSMIC database, absence from the dbSNP common variants database, and increasing read depth increased mean probability of high confidence somatic variant prediction in both models. Our results demonstrate the utility of supervised ML models for filtering variants in cfDNA data.https://doi.org/10.1038/s41598-025-01326-2 |
| spellingShingle | Rugare Maruzani Liam Brierley Andrea Jorgensen Anna Fowler Predicting high confidence ctDNA somatic variants with ensemble machine learning models Scientific Reports |
| title | Predicting high confidence ctDNA somatic variants with ensemble machine learning models |
| title_full | Predicting high confidence ctDNA somatic variants with ensemble machine learning models |
| title_fullStr | Predicting high confidence ctDNA somatic variants with ensemble machine learning models |
| title_full_unstemmed | Predicting high confidence ctDNA somatic variants with ensemble machine learning models |
| title_short | Predicting high confidence ctDNA somatic variants with ensemble machine learning models |
| title_sort | predicting high confidence ctdna somatic variants with ensemble machine learning models |
| url | https://doi.org/10.1038/s41598-025-01326-2 |
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