Abstract
BackgroundMouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary.ResultsWe developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations.ConclusionsXenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR.
Highlights
Mouse xenografts from tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer
Similar challenges are observed when sequencing RNA: beside false positive single nucleotide variants (SNV), the gene expression levels are often obscured by reads that derive from mouse cells [19]
To investigate which genes and exons are likely to be affected by mouse reads, we mapped whole genome DNA sequence data (WGS) of three mouse strains (NOD/ ShiLtJ, BALB/cJ and C57BL/6NJ) [27, 28] to a human reference [29]
Summary
Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer Analyses of their RNA and DNA profiles are challenging, because they comprise reads from the grafted human cancer and from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. More advanced clinical cancer models are the cell line-derived xenograft and patient-derived xenografts (PDX) [2] With this approach, either a cancer cell line or a patient tumor sample is injected or transplanted into a host, generally immunodeficient mice. Either a cancer cell line or a patient tumor sample is injected or transplanted into a host, generally immunodeficient mice Despite mouse-derived sequence reads representing a potential source of bias in sequence analysis of tumor xenografts, the number of tools to solve this important issue is surprisingly limited
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
