摘要
The application of next-generation sequencing （NGS） technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft （PDX） models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.
The application of next-generation sequencing （NGS） technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft （PDX） models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.
基金
supported by the grants from the National Natural Science Foundation of China(Nos.,81672736 and 91529302)
the Shanghai Industrial Technology Institute(17CXXF008)
the Shanghai Sailing Program(16YF1408600)
the Shanghai Municipal Commission of Science and Technology(14DZ2252000)
the administrative committee of Shanghai Zhangjiang Hi-Teck Park(2016e08)
the Medical engineering cross fund of Shanghai Jiao Tong University(YG2015QN27)
