Next-generation sequence analysis of cancer xenograft models.
Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS s...
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2013
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oai:doaj.org-article:728a70be11b74d5e9bfec58d72aeeeed2021-11-18T08:53:35ZNext-generation sequence analysis of cancer xenograft models.1932-620310.1371/journal.pone.0074432https://doaj.org/article/728a70be11b74d5e9bfec58d72aeeeed2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24086345/?tool=EBIhttps://doaj.org/toc/1932-6203Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an in silico strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations.Fernando J RosselloRichard W TothillKara BrittKieren D MariniJeanette FalzonDavid M ThomasCraig D PeacockLuigi MarchionniJason LiSamara BennettErwin TantosoTracey BrownPhilip ChanLuciano G MartelottoD Neil WatkinsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e74432 (2013) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Medicine R Science Q |
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Medicine R Science Q Fernando J Rossello Richard W Tothill Kara Britt Kieren D Marini Jeanette Falzon David M Thomas Craig D Peacock Luigi Marchionni Jason Li Samara Bennett Erwin Tantoso Tracey Brown Philip Chan Luciano G Martelotto D Neil Watkins Next-generation sequence analysis of cancer xenograft models. |
| description |
Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an in silico strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations. |
| format |
article |
| author |
Fernando J Rossello Richard W Tothill Kara Britt Kieren D Marini Jeanette Falzon David M Thomas Craig D Peacock Luigi Marchionni Jason Li Samara Bennett Erwin Tantoso Tracey Brown Philip Chan Luciano G Martelotto D Neil Watkins |
| author_facet |
Fernando J Rossello Richard W Tothill Kara Britt Kieren D Marini Jeanette Falzon David M Thomas Craig D Peacock Luigi Marchionni Jason Li Samara Bennett Erwin Tantoso Tracey Brown Philip Chan Luciano G Martelotto D Neil Watkins |
| author_sort |
Fernando J Rossello |
| title |
Next-generation sequence analysis of cancer xenograft models. |
| title_short |
Next-generation sequence analysis of cancer xenograft models. |
| title_full |
Next-generation sequence analysis of cancer xenograft models. |
| title_fullStr |
Next-generation sequence analysis of cancer xenograft models. |
| title_full_unstemmed |
Next-generation sequence analysis of cancer xenograft models. |
| title_sort |
next-generation sequence analysis of cancer xenograft models. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/728a70be11b74d5e9bfec58d72aeeeed |
| work_keys_str_mv |
AT fernandojrossello nextgenerationsequenceanalysisofcancerxenograftmodels AT richardwtothill nextgenerationsequenceanalysisofcancerxenograftmodels AT karabritt nextgenerationsequenceanalysisofcancerxenograftmodels AT kierendmarini nextgenerationsequenceanalysisofcancerxenograftmodels AT jeanettefalzon nextgenerationsequenceanalysisofcancerxenograftmodels AT davidmthomas nextgenerationsequenceanalysisofcancerxenograftmodels AT craigdpeacock nextgenerationsequenceanalysisofcancerxenograftmodels AT luigimarchionni nextgenerationsequenceanalysisofcancerxenograftmodels AT jasonli nextgenerationsequenceanalysisofcancerxenograftmodels AT samarabennett nextgenerationsequenceanalysisofcancerxenograftmodels AT erwintantoso nextgenerationsequenceanalysisofcancerxenograftmodels AT traceybrown nextgenerationsequenceanalysisofcancerxenograftmodels AT philipchan nextgenerationsequenceanalysisofcancerxenograftmodels AT lucianogmartelotto nextgenerationsequenceanalysisofcancerxenograftmodels AT dneilwatkins nextgenerationsequenceanalysisofcancerxenograftmodels |
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1718421208176787456 |