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Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts.

Published version
Peer-reviewed

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Authors

Batra, Ankita Sati 
Batra, Rajbir Nath 

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.

Description

Keywords

Animals, Humans, Mice, Breast Neoplasms, Xenograft Model Antitumor Assays, Gene Expression Profiling, Sequence Alignment, Sequence Analysis, DNA, Sequence Analysis, RNA, Genomics, Mutation, High-Throughput Nucleotide Sequencing

Journal Title

BMC genomics

Conference Name

Journal ISSN

1471-2164
1471-2164

Volume Title

19

Publisher

BioMed Central
Sponsorship
Cancer Research UK (60098573)
Cancer Research UK (unknown)
Cancer Research UK (CB4140)
European Commission FP7 Network of Excellence (NoE) (260791)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (660060)
EC FP7 NOE (260791)