Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts?

• Published in: BMC genomics Vol. 15; no. 1; p. 1172
• Main Authors: Tso, Kai-Yuen, Lee, Sau Dan, Lo, Kwok-Wai, Yip, Kevin Y
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.12.2014; BioMed Central
• Subjects: Analysis; Animals; Cancer; Computational Biology - economics; Computational Biology - methods; Computational Biology - standards; Cost-Benefit Analysis; Deoxyribonucleic acid; DNA; DNA Contamination; Genomics; Genomics - methods; Heterografts; Humans; Information management; Methodology; Mice; Neoplasms - genetics; Reproducibility of Results; Sequence Alignment; Sequence Analysis, DNA - economics; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - standards
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-1172

Patient-derived tumor xenografts in mice are widely used in cancer research and have become important in developing personalized therapies. When these xenografts are subject to DNA sequencing, the samples could contain various amounts of mouse DNA. It has been unclear how the mouse reads would affect data analyses. We conducted comprehensive simulations to compare three alignment strategies at different mutation rates, read lengths, sequencing error rates, human-mouse mixing ratios and sequenced regions. We also sequenced a nasopharyngeal carcinoma xenograft and a cell line to test how the strategies work on real data. We found the "filtering" and "combined reference" strategies performed better than aligning reads directly to human reference in terms of alignment and variant calling accuracies. The combined reference strategy was particularly good at reducing false negative variants calls without significantly increasing the false positive rate. In some scenarios the performance gain of these two special handling strategies was too small for special handling to be cost-effective, but it was found crucial when false non-synonymous SNVs should be minimized, especially in exome sequencing. Our study systematically analyzes the effects of mouse contamination in the sequencing data of human-in-mouse xenografts. Our findings provide information for designing data analysis pipelines for these data.