XenoMix: estimating mouse content from DNA methylation data of Patient-Derived Xenograft models
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.
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BibTeX
@article{esmaeelpour2026xenomix,
title = {{XenoMix: estimating mouse content from DNA methylation data of Patient-Derived Xenograft models}},
author = {Nima Esmaeelpour and Oliver Mücke and Gizem Altun and Joseph Cornick and Maria Capone and Rocio Sotillo and Maria Llamazares-Prada and Christoph Plass and Michael Scherer},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag791},
url = {https://doi.org/10.1093/bioinformatics/btag791},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - XenoMix: estimating mouse content from DNA methylation data of Patient-Derived Xenograft models
AU - Nima Esmaeelpour
AU - Oliver Mücke
AU - Gizem Altun
AU - Joseph Cornick
AU - Maria Capone
AU - Rocio Sotillo
AU - Maria Llamazares-Prada
AU - Christoph Plass
AU - Michael Scherer
PY - 2026
DA - 2026/10/21
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag791
UR - https://doi.org/10.1093/bioinformatics/btag791
AB - 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.
SN - 1367-4803
SN - 1367-4811
ER -