In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 1041-1041
Abstract:
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. However, analysis of their RNA and DNA profiles is challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin can result both in the generation of false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. Therefore, we developed the open-source R-package XenofilteR, which separates mouse from human sequence reads based on the number of discordant base pairs between each read and the reference genomes. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human whole genome and whole exome DNA sequence data. This analysis revealed that XenofilteR removes & gt;99.9% of sequence reads of mouse origin while retaining sequence reads of human origin. The filtering allowed for mutation analysis of PDX samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic mutations present in the original tumor. These findings were further validated in breast cancer and melanoma PDX samples, confirming the retrieval of accurate variant allele frequencies and somatic mutations. In conclusion, XenofilteR accurately dissects sequence reads from mouse and human origin in PDX sequence data, thereby outperforming currently available tools. Citation Format: Oscar Krijgsman, Roelof JC Kluin, Kristel Kemper, Thomas Kuilman, Julian R. de Ruiter, Vivek Iyer, Josep V. Forment, Paulien Cornelissen-Steijger, Iris de Rink, Petra ter Brugge, Ji-Ying Song, Sjoerd Klarenbeek, Ultan McDermott, Jos Jonkers, Arno Velds, David J. Adams, Daniel S. Peeper. XenofilteR: Computational dissection of mouse and human reads in PDX and xenograft sequence data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1041.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2018-1041
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2018
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
