Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts.
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ICR Authors
Authors
Callari, M
Batra, AS
Batra, RN
Sammut, S-J
Greenwood, W
Clifford, H
Hercus, C
Chin, S-F
Bruna, A
Rueda, OM
Caldas, C
Batra, AS
Batra, RN
Sammut, S-J
Greenwood, W
Clifford, H
Hercus, C
Chin, S-F
Bruna, A
Rueda, OM
Caldas, C
Document Type
Journal Article
Date
2018-01-05
Date Accepted
2017-12-22
Abstract
BACKGROUND: Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. RESULTS: In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time. CONCLUSIONS: The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species.
Citation
BMC Genomics, 2018, 19 (1), pp. 19 -
Source Title
BMC Genomics
Publisher
BMC
ISSN
1471-2164
eISSN
1471-2164
Collections
Research Team
Cancer Dynamics