A comparison of next-generation sequencing analysis methods for cancer xenograft samples

Wentao Dai^{a, b, d},
Jixiang Liu^{a, b, d},
Quanxue Li^{a, c},
Wei Liu^{a, b, d},
Yi-Xue Li^{a, b, c, d
, ,},
Yuan-Yuan Li^{a, b, d
, ,}

a.
Shanghai Center for Bioinformation Technology, Shanghai 201203, China
b.
Shanghai Engineering Research Center of Pharmaceutical Translation & Shanghai Industrial Technology Institute, Shanghai 201203, China
c.
School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China
d.
Shanghai Industrial Technology Institute, Shanghai 201203, China

More Information

Corresponding author: E-mail address: yxli@scbit.org (Yi-Xue Li); E-mail address: yyli@scbit.org (Yuan-Yuan Li)

摘要

- /
- /
- /
Abstract: 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.
- Patient-derived xenograft /
- Next-generation sequencing /
- Host contamination control /
- Alignment

HTML全文

参考文献(64)

[1]	Ahdesmaki, M.J., Gray, S.R., Johnson, J.H. et al. Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples F1000Res., 5 (2016),p. 2741
[2]	Alizadeh, A.A., Aranda, V., Bardelli, A. et al. Toward understanding and exploiting tumor heterogeneity Nat. Med., 21 (2015),pp. 846-853
[3]	Aparicio, S., Hidalgo, M., Kung, A.L. Examining the utility of patient-derived xenograft mouse models Nat. Rev. Cancer, 15 (2015),pp. 311-316
[4]	Bawa, A., Anilakumar, K. Genetically modified foods: safety, risks and public concerns ‒ a review J. Food Sci. Technol., 50 (2013),pp. 1035-1046
[5]	Bernardo, C., Costa, C., Sousa, N. et al. Patient-derived bladder cancer xenografts: a systematic review Transl. Res., 166 (2015),pp. 324-331
[6]	Bruna, A., Rueda, O.M., Greenwood, W. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds Cell, 167 (2016),pp. 260-274
[7]	Buckingham, E.M., Carpenter, J.E., Jackson, W. et al. Autophagic flux without a block differentiates varicella-zoster virus infection from herpes simplex virus infection Proc. Natl. Acad. Sci. U. S. A., 112 (2015),pp. 256-261
[8]	Callari, M., Batra, A.S., Batra, R.N. et al. Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts BMC Genomics, 19 (2018),p. 19
[9]	Calles, A., Rubio-Viqueira, B., Hidalgo, M. Primary human non-small cell lung and pancreatic tumorgraft models ‒ utility and applications in drug discovery and tumor biology Curr. Protoc. Pharmacol. (2013)
[10]	Cassidy, J.W., Caldas, C., Bruna, A. Maintaining tumor heterogeneity in patient-derived tumor xenografts Cancer Res., 75 (2015),pp. 2963-2968
[11]	Cibulskis, K., Lawrence, M.S., Carter, S.L. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples Nat. Biotechnol., 31 (2013),pp. 213-219
[12]	Conway, T., Wazny, J., Bromage, A. et al. Xenome – a tool for classifying reads from xenograft samples Bioinformatics, 28 (2012),pp. i172-i178
[13]	Cook, J., Tyor, W. The pathogenesis of HIV-associated dementia: recent advances using a SCID mouse model of HIV-encephalitis Einstein J. Biol. Med., 22 (2006),pp. 32-40
[14]	Cusinato, R., Pacenti, M., Martello, T. et al. Effectiveness of hydrogen peroxide and electron-beam irradiation treatment for removal and inactivation of viruses in equine-derived xenografts J. Virol. Methods, 232 (2016),pp. 39-46
[15]	Cvetkovich, T.A., Lazar, E., Blumberg, B.M. et al. Human immunodeficiency virus type 1 infection of neural xenografts Proc. Natl. Acad. Sci. U. S. A., 89 (1992),pp. 5162-5166
[16]	Davis, L.A. Genetically engineered crops Engineering, 2 (2016),pp. 268-269
[17]	Day, C.P., Merlino, G., Van Dyke, T. Preclinical mouse cancer models: a maze of opportunities and challenges Cell, 163 (2015),pp. 39-53
[18]	DeRose, Y.S., Wang, G., Lin, Y.-C. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes Nat. Med., 17 (2011),pp. 1514-1520
[19]	DeRose, Y.S., Wang, G., Lin, Y.C. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes Nat. Med., 17 (2011),pp. 1514-1520
[20]	Dobin, A., Davis, C.A., Schlesinger, F. et al. STAR: ultrafast universal RNA-seq aligner Bioinformatics, 29 (2013),pp. 15-21
[21]	Fidler, I.J. Rationale and methods for the use of nude mice to study the biology and therapy of human cancer metastasis Cancer Metastasis Rev., 5 (1986),pp. 29-49
[22]	Fishman, J.A., Scobie, L., Takeuchi, Y. Xenotransplantation – associated infectious risk: a WHO consultation Xenotransplantation, 19 (2012),pp. 72-81
[23]	Gao, H., Korn, J.M., Ferretti, S. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response Nat. Med., 21 (2015),pp. 1318-1325
[24]	Girotti, M.R., Gremel, G., Lee, R. et al. Application of sequencing, liquid biopsies, and patient-derived xenografts for personalized medicine in melanoma Cancer Discov., 6 (2016),pp. 286-299
[25]	Han, H., Peng, J., Han, Y. et al. PLoS One, 8 (2013)
[26]	Hidalgo, M., Amant, F., Biankin, A.V. et al. Patient-derived xenograft models: an emerging platform for translational cancer research Cancer Discov., 4 (2014),pp. 998-1013
[27]	Hodgkinson, C.L., Morrow, C.J., Li, Y. et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer Nat. Med., 20 (2014),pp. 897-903
[28]	Hu, Y., Sun, L., Yuan, Z. et al. Sci. Rep., 7 (2017),p. 11311
[29]	Julien, S., Merino-Trigo, A., Lacroix, L. et al. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer Clin. Cancer Res., 18 (2012),pp. 5314-5328
[30]	Khandelwal, G., Girotti, M.R., Smowton, C. et al. Next-generation sequencing analysis and algorithms for PDX and CDX models Mol. Cancer Res., 15 (2017),pp. 1012-1016
[31]	Kim, D., Langmead, B., Salzberg, S.L. HISAT: a fast spliced aligner with low memory requirements Nat. Methods, 12 (2015),pp. 357-360
[32]	Kim, D., Pertea, G., Trapnell, C. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions Genome Biol., 14 (2013),p. R36
[33]	Langmead, B., Salzberg, S.L. Fast gapped-read alignment with Bowtie 2 Nat. Methods, 9 (2012),pp. 357-359
[34]	Ledford, H. US cancer institute to overhaul tumour cell lines Nature, 530 (2016),p. 391
[35]	Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data Bioinformatics, 27 (2011),pp. 2987-2993
[36]	Li, H., Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform Bioinformatics, 25 (2009),pp. 1754-1760
[37]	Li, H., Ruan, J., Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores Genome Res., 18 (2008),pp. 1851-1858
[38]	Li, H., Zhu, Y., Tang, X. et al. Integrated analysis of transcriptome in cancer patient-derived xenografts PLoS One, 10 (2015)
[39]	Li, L., Wei, Y., To, C. et al. Integrated omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact Nat. Commun., 5 (2014),p. 5469
[40]	Li, R., Quan, S., Yan, X. et al. Molecular characterization of genetically-modified crops: challenges and strategies Biotechnol. Adv., 35 (2017),pp. 302-309
[41]	Li, R., Yu, C., Li, Y. et al. SOAP2: an improved ultrafast tool for short read alignment Bioinformatics, 25 (2009),pp. 1966-1967
[42]	Lin, M.-T., Tseng, L.-H., Kamiyama, H. et al. Quantifying the relative amount of mouse and human DNA in cancer xenografts using species-specific variation in gene length Biotechniques, 48 (2010),p. 211
[43]	Ling, S., Hu, Z., Yang, Z. et al. Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution Proc. Natl. Acad. Sci. U. S. A., 112 (2015),pp. E6496-E6505
[44]	Martincorena, I., Raine, K.M., Gerstung, M. et al. Universal patterns of selection in cancer and somatic tissues Cell, 171 (2017),pp. 1029-1041
[45]	McCarthy, D.J., Chen, Y., Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation Nucleic Acids Res., 40 (2012),pp. 4288-4297
[46]	Moro, M., Bertolini, G., Tortoreto, M. et al. Patient-derived xenografts of non small cell lung cancer: resurgence of an old model for investigation of modern concepts of tailored therapy and cancer stem cells J. Biomed. Biotechnol., 2012 (2012),p. 568567
[47]	Morton, C.L., Houghton, P.J. Establishment of human tumor xenografts in immunodeficient mice Nat. Protoc., 2 (2007),pp. 247-250
[48]	Ni, X., Zhuo, M., Su, Z. et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients Proc. Natl. Acad. Sci. U. S. A., 110 (2013),pp. 21083-21088
[49]	Niemann, H., Petersen, B. The production of multi-transgenic pigs: update and perspectives for xenotransplantation Transgenic Res., 25 (2016),pp. 361-374
[50]	Niu, D., Wei, H.-J., Lin, L. et al. Inactivation of porcine endogenous retrovirus in pigs using CRISPR-Cas9 Science, 357 (2017),pp. 1303-1307
[51]	Nunes, M., Vrignaud, P., Vacher, S. et al. Evaluating patient-derived colorectal cancer xenografts as preclinical models by comparison with patient clinical data Cancer Res., 75 (2015),pp. 1560-1566
[52]	Pauli, C., Hopkins, B.D., Prandi, D. et al. Cancer Discov., 7 (2017),pp. 462-477
[53]	Pearson, A.T., Finkel, K.A., Warner, K.A. et al. Patient-derived xenograft (PDX) tumors increase growth rate with time Oncotarget, 7 (2016),pp. 7993-8005
[54]	Rossello, F.J., Tothill, R.W., Britt, K. et al. Next-generation sequence analysis of cancer xenograft models PLoS One, 8 (2013)
[55]	Salm, M., Schelhorn, S.-E., Lancashire, L. et al. pdxBlacklist: identifying artefactual variants in patient-derived xenograft samples bioRxiv (2017)
[56]	Siolas, D., Hannon, G.J. Patient-derived tumor xenografts: transforming clinical samples into mouse models Cancer Res., 73 (2013),pp. 5315-5319
[57]	Stewart, E., Federico, S.M., Chen, X. et al. Orthotopic patient-derived xenografts of paediatric solid tumours Nature, 549 (2017),pp. 96-100
[58]	Teicher, B.A.
[59]	Tentler, J.J., Tan, A.C., Weekes, C.D. et al. Patient-derived tumour xenografts as models for oncology drug development Nat. Rev. Clin. Oncol., 9 (2012),pp. 338-350
[60]	Tso, K.-Y., Lee, S.D., Lo, K.-W. et al. Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts? BMC Genomics, 15 (2014),p. 1172
[61]	Venditti, J.M., Wesley, R.A., Plowman, J. Adv. Pharmacol. Chemother., 20 (1984),pp. 1-20
[62]	Wang, D.C., Wang, W., Zhu, B. et al. Lung cancer heterogeneity and new strategies for drug therapy Annu. Rev. Pharmacol. Toxicol., 58 (2017),pp. 531-546
[63]	Wang, K., Singh, D., Zeng, Z. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery Nucleic Acids Res., 38 (2010),p. e178
[64]	Wei, Q., Ye, Z., Zhong, X. et al. Multiregion whole-exome sequencing of matched primary and metastatic tumors revealed genomic heterogeneity and suggested polyclonal seeding in colorectal cancer metastasis Ann. Oncol., 28 (2017),pp. 2135-2141

施引文献

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 101
HTML全文浏览量: 31
PDF下载量: 2
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

doi: 10.1016/j.jgg.2018.07.001

计量

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

Corresponding author: E-mail address: yxli@scbit.org (Yi-Xue Li); E-mail address: yyli@scbit.org (Yuan-Yuan Li)

计量

目录

留言板

doi: 10.1016/j.jgg.2018.07.001

计量

出版历程

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

Corresponding author: E-mail address: yxli@scbit.org (Yi-Xue Li); E-mail address: yyli@scbit.org (Yuan-Yuan Li)

计量

出版历程

目录