Title |
Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts
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Published in |
BMC Genomics, January 2018
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DOI | 10.1186/s12864-017-4414-y |
Pubmed ID | |
Authors |
Maurizio Callari, Ankita Sati Batra, Rajbir Nath Batra, Stephen-John Sammut, Wendy Greenwood, Harry Clifford, Colin Hercus, Suet-Feung Chin, Alejandra Bruna, Oscar M. Rueda, Carlos Caldas |
Abstract |
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. 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. 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. |
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