Next-Generation Sequence Analysis of Cancer Xenograft Models

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dc.contributor.authorRossello, F.J.
dc.contributor.authorTothill, R.W.
dc.contributor.authorBritt, K.
dc.contributor.authorMarini, K.D.
dc.contributor.authorFalzon, J.
dc.contributor.authorThomas, D.M.
dc.contributor.authorPeacock, C.D.
dc.contributor.authorMarchionni, L.
dc.contributor.authorLi, J.
dc.contributor.authorBennett, S.
dc.contributor.authorTantoso, E.
dc.contributor.authorBrown, T.
dc.contributor.authorChan, P.
dc.contributor.authorMartelotto, L.G.
dc.contributor.authorWatkins, D.N.
dc.contributor.editorColeman, W.B.
dc.date.issued2013
dc.description.abstractNext-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 speciesspecific, 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.
dc.description.statementofresponsibilityFernando 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
dc.identifier.citationPLoS ONE, 2013; 8(9):e74432-1-e74432-12
dc.identifier.doi10.1371/journal.pone.0074432
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidMartelotto, L.G. [0000-0002-9625-1183]
dc.identifier.urihttps://hdl.handle.net/2440/134546
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/546204
dc.rights© 2013 Rossello et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pone.0074432
dc.subjectMammalian genomics; Human genomics; Breast cancer; Sequence alignment; Small cell lung cancer; Cancers and neoplasms; Copy number variation; RNA sequencing
dc.subject.meshCell Line, Tumor
dc.subject.meshAnimals
dc.subject.meshHumans
dc.subject.meshMice
dc.subject.meshMice, Nude
dc.subject.meshNeoplasms
dc.subject.meshDisease Models, Animal
dc.subject.meshOligonucleotide Array Sequence Analysis
dc.subject.meshXenograft Model Antitumor Assays
dc.subject.meshGene Expression Profiling
dc.subject.meshSpecies Specificity
dc.subject.meshGenome, Human
dc.subject.meshDNA Copy Number Variations
dc.subject.meshHigh-Throughput Nucleotide Sequencing
dc.subject.meshExome
dc.titleNext-Generation Sequence Analysis of Cancer Xenograft Models
dc.typeJournal article
pubs.publication-statusPublished

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