Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples

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Abstract

Grafting of cell lines and primary tumours is a crucial step in the drug development process between cell line studies and clinical trials. Disambiguate is a program for computationally separating the sequencing reads of two species derived from grafted samples. Disambiguate operates on alignments to the two species and separates the components at very high sensitivity and specificity as illustrated in artificially mixed human-mouse samples. This allows for maximum recovery of data from target tumours for more accurate variant calling and gene expression quantification. Given that no general use open source algorithm accessible to the bioinformatics community exists for the purposes of separating the two species data, the proposed Disambiguate tool presents a novel approach and improvement to performing sequence analysis of grafted samples. Both Python and C++ implementations are available and they are integrated into several open and closed source pipelines. Disambiguate is open source and is freely available at https://github.com/AstraZeneca-NGS/disambiguate.

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Xenome—a tool for classifying reads from xenograft samples

Thomas Conway, Jeremy Wazny, Andrew Bromage … (2012)

Motivation: Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. Results: We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets. Availability: Xenome is available for non-commercial use from http://www.nicta.com.au/bioinformatics Contact: tom.conway@nicta.com.au

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RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib

James R. Bradford, Matthew Farren, Steve Powell … (2013)

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers.

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Next-Generation Sequence Analysis of Cancer Xenograft Models

Fernando Rossello, Richard W. Tothill, Kara Britt … (2013)

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.

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Author and article information

Journal

Journal ID (nlm-ta): F1000Res

Journal ID (iso-abbrev): F1000Res

Journal ID (pmc): F1000Research

Title: F1000Research

Publisher: F1000Research (London, UK )

ISSN (Electronic): 2046-1402

Publication date (Electronic): 22 November 2016

Publication date Collection: 2016

Volume: 5

Electronic Location Identifier: 2741

Affiliations

[1 ]AstraZeneca Oncology iMed, Cambridge, UK

[2 ]AstraZeneca R&D Information, Cambridge, UK

[3 ]AstraZeneca Oncology iMed, Waltham, UK

[1 ]Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

[1 ]Orion Corporation Orion Pharma, Espoo, Finland

Author notes

[a ] miika.ahdesmaki@ 123456astrazeneca.com

MA authored the bwa and rna-star disambiguation algorithms, co-authored the manuscript and implemented the algorithms in Python. SG wrote the C++ implementation of the algorithms. JJ co-authored the manuscript. ZL designed and implemented the original Tophat (and Hisat2) disambiguation algorithm and co-authored the manuscript.

Competing interests: All authors are employees of AstraZeneca.

Competing interests: No competing interests were disclosed.

Article

DOI: 10.12688/f1000research.10082.1

PMC ID: 5130069

PubMed ID: 27990269

SO-VID: 1a687195-972b-4fe6-b0ca-86a933c67296

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This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Date accepted : 17 November 2016

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The author(s) declared that no grants were involved in supporting this work.

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Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples

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Most cited references 3

Xenome—a tool for classifying reads from xenograft samples

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib

Next-Generation Sequence Analysis of Cancer Xenograft Models

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