XenoMix: Estimating mouse content from DNA methylation data of Patient-Derived Xenograft models

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ID: 333311
2026
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Abstract
Abstract Summary DNA methylation microarrays enable cost-effective, high-throughput profiling of human and mouse samples and are widely used to study epigenetic alterations in cancer. Patient-Derived Xenograft (PDX) models complement this approach by preserving primary tumor characteristics while enabling in vivo studies of drug response and therapy resistance. However, it remains difficult to quantify mouse DNA contamination in PDX DNA methylation data generated using microarrays. We present XenoMix, a light-weight computational tool for quantifying mouse DNA contamination directly from DNA methylation data in PDX models. XenoMix leverages the high sequence homology between human and mouse genomes to detect and quantify unexpected signal contributions originating from mouse DNA. Using ground truth mixtures, XenoMix achieved Pearson’s correlation coefficients higher than 0.99. When applied to PDX data, we identified varying levels of mouse contamination originating from biological and technological variability. XenoMix establishes a critical quality control step in DNA methylation analysis of PDX models, and we show that the estimated contamination can be incorporated as a confounding variable in downstream analyses. Availability and Implementation XenoMix is available as a standalone R package from https://git.dkfz.de/b370/scepigen/XenoMix and as a module in the RnBeads software package. Supplementary information Supplementary data are available at Bioinformatics online.
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Authors Nima Esmaeelpour, Oliver Mücke, Gizem Altun, Joseph Cornick, Maria Capone, Rocı́o Sotillo, Maria Llamazares Prada, Christoph Plass, Michael Scherer
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag791
URL
https://doi.org/10.1093/bioinformatics/btag791
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