Next-Generation Sequence Analysis of Cancer Xenograft Models

Abstract

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.

Full text

Next-Generation Sequence Analysis of Cancer Xenograft
Models
Fernando J. Rossello
1
, Richard W. Tothill
2,3
, Kara Britt
4,5
, Kieren D. Marini
1
, Jeanette Falzon
6
,
David M. Thomas
5,7
, Craig D. Peacock
8
, Luigi Marchionni
8
, Jason Li
9,10
, Samara Bennett
6
,
Erwin Tantoso
11
, Tracey Brown
6
, Philip Chan
12
, Luciano G. Martelotto
1,13
*, D. Neil Watkins
1
*
1Monash Institute of Medical Research, Monash University, Clayton, Victoria, Australia, 2Department of Pathology, University of Melbourne, Parkville, Victoria, Australia,
3Molecular Genomics Core Facility, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia, 4Department of Anatomy and Developmental Biology, Monash
University, Clayton, Victoria, Australia, 5Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia, 6Department of Biochemistry
and Molecular Biology, Monash University, Clayton, Victoria, Australia, 7Department of Cancer Medicine, Peter MacCallum Cancer Centre, East Melbourne, Victoria,
Australia, 8Department of Oncology, Sidney Kimmel Comprehensive Cancer Centre, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of
America, 9Bioinformatics Core Facility, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia, 10 Department of Mechanical Engineering, University of
Melbourne, Parkville, Victoria, Australia, 11 Partek SG Private Limited, Singapore, Republic of Singapore, 12 Monash eResearch Centre, Monash University, Clayton,
Victoria, Australia, 13 Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
Abstract
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.
Citation: Rossello FJ, Tothill RW, Britt K, Marini KD, Falzon J, et al. (2013) Next-Generation Sequence Analysis of Cancer Xenograft Models. PLoS ONE 8(9): e74432.
doi:10.1371/journal.pone.0074432
Editor: William B. Coleman, University of North Carolina School of Medicine, United States of America
Received April 29, 2013; Accepted August 1, 2013; Published September 26, 2013
Copyright: ß2013 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.
Funding: Funding for this work was provided by the National Health and Medical Research Council of Australia (Project Grant 546204), the Victorian Government
Operational Infrastructure Support Program, and the Victorian Cancer Agency. Funding for open access charge: Victorian Cancer Agency. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Mr. Erwin Tantoso is employed by Partek SG Pte. Ltd. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing
data and materials. The other authors disclosed no potential conflicts of interest.
* E-mail: martelol@mskcc.org (LGM); neil.watkins@monash.edu (DNW)
Introduction
Although the application of NGS technology to cancer research
has led to dramatic advances in the understanding of the genomic
basis of these diseases, the depth and complexity of sequencing
data is negatively correlated to the amount and quality of tumour
specimen used for analysis [1]. In addition, many common
tumours, such as pancreatic cancer, are characterized by extensive
infiltration of stromal elements, thereby reducing the detection
threshold for rare, cancer specific variants [2]. As a result,
common cancers diagnosed by small biopsies are vastly under-
represented in NGS studies, which rely predominantly on
surgically-resected tissue samples.
One approach to overcome this problem is the use of primary
xenograft models, in which small tissue samples can be directly
engrafted, expanded and passaged in immunodeficient mice
without exposure to conventional tissue culture conditions [3].
Although tumour cells are maintained in immunodeficient mice,
we [4], and others [5–7], have shown that they retain important
characteristics of the primary tumour that, importantly, are
irreversibly lost in cell culture [2,4]. Moreover, despite the fact
that the stromal component is mouse-derived, primary xenograft
models have been successfully used for the preclinical investigation
of a variety of cell autonomous and stromal derived signaling
systems of therapeutic relevance to cancer [7].
Based on these data, primary xenografts could represent a useful
platform for NGS analysis when cancer tissue is limiting. Ding et al.
[8], in a study that aimed to identify somatic mutations and
structural variants of basal-like breast cancer, estimated by
pathology techniques the tumor composition to then calculate
and adjust the tumour read number. Based on the pathology
estimates, the authors use a deterministic correction of contam-
ination of tumour by normal read counts, which affects the mutant
allele frequency, and applied it to the primary tumour and
metastasis samples only. It was assumed that due to the low
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mapping rate of host-specific reads to the graft genome, no read
depth correction was required to the xenograft sample.
In our view, the presence of contaminating mouse DNA and
RNA affects the sensitivity and specificity of NGS analysis in these
tumour models which should not be based on cellularity estimates,
but should be accurately and systematically addressed. Addition-
ally, since most current NGS techniques use shotgun-sequencing
methodology, resolution of any potential artifact could be
performed post-hoc during bioinformatic analyses, which unequiv-
ocally identify species-of-origin reads. This issue has been
previously discussed for ultra high-throughput cDNA sequencing
(RNA-Seq) by Conway et al. [9] and Raskatov et al. [10], who
found variable amounts of host-derived sequencing reads. Here,
we prospectively analyzed the capacity of an in silico workflow
designed to definitively assign species-of-origin to NGS reads in
several previously characterized primary and cell line-derived
xenograft models of SCLC, and compared these results with
published datasets.
Materials and Methods
Ethics Statement
All experiments involving animals were approved in advance by
an Animal Ethics Committee at Monash University and were
carried out in accordance with ‘‘Australian Code of Practice for
the Care and Use of Animals for Scientific Purposes.’’
Cells
The SCLC primary xenograft lines LX22, LX33 and LX36
were passaged as previously described [4]. In brief, resected tissues
from chemo-naı
¨ve SCLC patients were used to generate primary
xenografts samples. Tumour samples were finely chopped with
sterile razor blades, triturated in 1 x PBS, filtered through a 60 mm
mesh filter, centrifuged and resuspended in 500 mL of Matrigel
(BD Biosciences) at 4 uC. Processed cells were then injected sub-
cutaneously in the flanks of non-obese diabetic/severe combined
immunodeficient mice. Once the P0 tumours reached a diameter
of 1 cm, the mouse was sacrificed and the resected tumour was
divided into sections for snap freezing or serial passage. Xenograft
tumours were prepared for serial passages in vivo as described
above and cells were injected into the flanks of athymic nude mice
in Matrigel. Passaged and snap frozen tumours samples were
routinely characterised for histopathologic and immunohistochem-
ical features of the parent tumour [4].
Authenticated NCI-H209 cell line was purchased from ATCC,
re-derived from a single cell clone using the single cell cloning by
serial dilution (Corning, Tewksbury, MA, USA) and then cultured
in vitro and in vivo as described in Watkins et al. [11]. DNA from
samples was extracted using DNAeasy Tissue and Blood Kit
(Qiagen, Santa Clara, CA, USA) according to manufacturer’s
instructions. RNA was purified using miRNeasy Mini Kit using
QIAzol (Qiagen, Santa Clara, CA, USA) following manufacturer’s
instructions.
Preparation of Sequencing Libraries
Exome and low-coverage whole-genome DNA re-sequencing:
Target DNA (3ug) was firstly sheared using a focal acoustic device
(Covaris, Woburn, MA, USA). DNA fragment libraries for exome
re-sequencing and low-coverage whole-genome sequencing were
constructed from sheared DNA by sequential steps of end-repair,
A-tailing and ligation of indexed lllumina compatible adapter
sequences (TruSeq DNA, Illumina, San Diego, CA, USA). For
exome re-sequencing, PCR amplified fragment libraries were
enriched for exonic DNA by long oligonucleotide hybridisation
capture according to the manufacturer’s protocol (SeqCap EZ
Exome Library v3.0, Roche Nimblegen, Madison, WI, USA). For
low-coverage whole-genome, PCR-amplified libraries were size
selected to capture DNA of 500–700nt length, using an automated
electrophoresis platform (Pippen Prep, Sage Science Inc., Beverly,
MA, USA). All sequencing libraries were quantified using real-
time PCR against a library of known concentration and then
processed for cluster generation and sequencing according to
standard protocols (HiSeq 2000, Illumina, San Diego, CA, USA).
RNA-Seq. total RNA was checked for quality and yield by
automated microfluidic electrophoresis (Bioanalyzer 2100, Agilent
Technologies, Santa Clara, CA, USA) and spectrophotometer
(NanoDrop, Thermo Scientific, Wilmington, DE, USA). Non-
directional RNA-Seq libraries were created according to the
manufacturers protocol (Truseq RNA-Seq Library Prep Kit v2,
Illumina, San Diego, CA, USA). Briefly this method involved
sequential steps of mRNA enrichment from 3ug total RNA, RNA
fragmentation by heating in the presence of divalent cations, a
randomly primed reverse transcription and second-strand cDNA
synthesis followed by preparation of DNA fragment libraries using
Illumina compatible adapters and PCR amplification as previously
described for DNA libraries.
All samples were assessed separately for overall read quality
using FASTQC (http://www.bioinformatics.bbsrc.ac.uk/
projects/fastqc) and low quality reads were filtered and were hard
trimmed using Trimmomatic (average minimum Phred score, 6
consecutive bases, of 20 and a minimum read length of 50nt,
Table S1) [12].
Raw deep sequencing datasets are publicly available in the
National Centre of Biotechnology Information Short Read
Archive (Accession number SRA082685).
Strategy to isolate and identify species-of-origin NGS
reads
The proposed strategy resembles that described by Conway et al.
[9], but differs in several important aspects. First, a primary
alignment to the graft genome, in this case the human genome, is
performed, where reads are divided into graft-mapped and graft-
unmapped reads; second, both graft-mapped and graft-unmapped
read-sets are realigned to the host genome, in this case the mouse
genome, to further identify common graft-host and host-specific
reads respectively; lastly, common graft-host reads are filtered
from the read set obtained in the primary alignment to obtain
graft-specific reads. In this study, the identification and classifica-
tion processes were performed via collecting and comparing the
read ids of the host/graft alignments, producing reads in FASTQ
format. As a result, identified graft-specific reads were re-aligned
to the graft genome.
Subsequent alignments produced three separate aligned data-
sets, i. e., reads that could only be mapped to the human genome,
reads that were exclusively mapped to the mouse genome and
reads that mapped to both genomes. In addition to analysing
RNA-Seq read sets, we further verify this strategy for low-coverage
whole-genome and exome-capture sequencing experiments. A
complete overview describing all the steps included in the
proposed strategy is shown in Figure 1. For each alignment,
mapped and unmapped reads contained in SAM/BAM formatted
files [13] were filtered based on their bitwise flag status using
Samtools [13], a customised Perl script that collected unique read
identities from the aligned/unaligned SAM formatted files and
filtered them from the raw fastq files, [Simon Andrews, 2010,
Seqanswers.com [14]. Available at: http://seqanswers.com/
forums/showpost.php?p = 25302&postcount = 3] and the
cmpfastq_pe software, that compared raw pair-end fastq files
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and reported common and unique reads (http://compbio.brc.iop.
kcl.ac.uk/software/cmpfastq_pe.php).
Mapping scores were used to assess the mapping quality of the
processed samples and to further discard multiple-hit reads. As a
general rule, it was assumed that a higher mapping quality means
a more ‘‘unique’’ aligned read and for most of the samples, a high
percentage of the read-pairs had a mapping quality above 20
(Table S2).
Transcriptome analysis
Whole transcriptome analysis of three SCLC primary xeno-
grafts was performed through RNA-Seq using the GAIIX and
HiSeq 2000 sequencing platforms (Illumina,San Diego, CA,
USA). The experiment was paired-end with 100nt read length
(300nt average insert size). The targeted minimum number of
reads per sample was 40 million reads (Table S1).
In order to identify and unequivocally separate graft (human)
and host (mouse) reads, processed sample reads were sequentially
aligned to both graft [complete hg19 human genome (UCSC
version, February 2009)] and host [complete mm9 mouse genome
(UCSC version, July 2007)] genomes using Bowtie-TopHat
[version 2.0.4, segment length 29nt, 1 mismatch in segment
permitted, for maximum sensitivity, coverage search performed
[15,16]. No de-duplication was performed for post-assembly
RNA-Seq analysis.
mRNA quantification for all annotated genes from the human
genome was performed using PartekHsoftware (Partek Inc. (1993)
PartekHGenomics Suite
TM
). Reads were normalized using the
reads per kilobase of exon model per million mapped reads
method [17].
A human-specific primary xenograft microarray expression
data-set (GSE15240) [4] was retrieved from the National Center
for Biotechnology Information (NCBI) Gene Expression Omnibus
(GEO) repository [18].
To compare the mouse-specific reads to previously published
cancer stromal gene signatures, a breast cancer associated
fibroblasts dataset [19] was retrieved from the GEO repository
(GSE10797).[18]
For all microarray analysis, gene probes were normalized using
quantile normalization (log base 2 and median polish for probeset
transformation and summarization respectively) and background
correction was performed using the robust multi-array average
method (RMA) [20].
Figure 1. Overview of the steps followed to identify and isolate common and species-specific sequence reads, including gene
identification and pathway analysis. The software components utilized in each step are also specified. Solid lines represent the principal
analytical path followed and dashed lines represent auxiliary steps.
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Comparison of microarray and RNA-Seq gene expression
results was performed using linear correlation (Spearman’s r)
between the log base 2 of the quantified gene arbitrary intensity
units and the log base 2 RPKM as described in Mortazavi et al
[17].
Exome resequencing analysis
Whole-exome analysis of samples obtained from peripheral
blood, NCI-H209 cell line and its derivative xenograft was
performed through whole exome ultra-high throughput sequence
using the HiSeq 2000 sequencing platform (llumina,San Diego,
CA, USA). The experiment was paired-end with 101nt read length
(200bp insert size). The average targeted depth of coverage was set
to 50x (see Table S1 for total number of reads sequenced).
Processed sample reads were sequentially aligned to both graft
[complete hg19 human genome (UCSC version, February 2009)]
and host [complete mm9 mouse genome (UCSC version, July
2007)] genomes using the Burrows-Wheeler Alignment tool
[(BWA), bwa aln algorithm used, seed length of 22nt; maximum
edit distance in the seed of 0 [21].
Single nucleotide variants (SNVs) discovery was performed
using a set of tools included in Picard (http://picard.sourceforge.
net) and GATK [22,23]. First, duplicate reads were removed from
the realigned BAM files using the MarkDuplicates command from
Picard (http://picard.sourceforge.net). Estimated duplication lev-
els are described in Table S3. Subsequently, de-duplicated BAM
files were locally realigned around novel and known indels using
the RealignerTargetCreator and the IndelRealigner walkers from
GATK [23]. Lastly, base quality scores were recalibrated using the
CountCovariates and TableRecalibration walkers from GATK
[23]. This procedure was performed for each of the three samples
analysed.
Raw SNP calls were performed using the UnifiedGenotyper
walker from GATK [23] with a minimum base quality Phred
score of 20, a call confidence threshold of 50 (Phred-scaled) and an
emmition confidence threshold of 10 (Phred-scaled). Raw called
SNPs were filtered using the VariantFiltration walker with the
following parameters: SNP cluster size = 10; Coverage: $5;
Qual: $50; Strand bias: Fisher’s exact test, $60. Sample-specific
novel SNPs, i. e., those not present in the Database of Single
Nucleotide Polymorphisms (dbSNP) (Bethesda (MD): National
Center for Biotechnology Information, National Library of
Medicine. (dbSNP 137: 137; http://www.ncbi.nlm.nih.gov/
SNP/), were annotated and its effect predicted using SnpEff
[24] and the variantAnnotator walker from GATK [23].
Genome visualization was performed using the Integrative
Genome Browser (IGV) [25,26]. Multispecies local alignment
tracks were retrieved from IGV data server.
Whole-genome analysis
A low-coverage whole-genome sequencing of samples obtained
from peripheral blood, H209 cell line and its derived primary
xenograft was performed through shotgun whole genome ultra-
high throughput sequence using the HiSeq 2000 sequencing
platform (llumina,San Diego, CA, USA). The experiment was
paired-end with 101nt read length (200bp insert size). The average
targeted depth of coverage was set to 4x (see Table S1 for total
number of reads sequenced).
Processed sample reads were sequentially aligned to both graft
[complete hg19 human genome (UCSC version, February 2009)]
and host [complete mm9 mouse genome (UCSC version, July
2007)] genomes using the Burrows-Wheeler Alignment tool
[(BWA), bwa aln algorithm used, seed length of 22nt; maximum
edit distance in the seed of 0 [21]. Estimated duplication levels
were found to be marginal and are described in Table S3.
Intra- and inter-chromosomal rearrangements discovery of the
identified human specific reads was performed using FusionMap
[span and split read count threshold of 3 and split minimum
anchor of 4 reads [27]. Detected fusions were plotted against a
circular representation of the human genome (Circos plot) using
Circos [28].
Copy number variations (CNV) and allelic content in genomic
regions were detected using Control-Freec [29]. The peripheral
blood sample was used as a baseline control. Circos plots of the
detected CNV were built using Circos [28].
Results
As shown in Figure 2, the assessed NGS strategies revealed
different proportions of host-specific reads. Exome capture and
RNA-Seq produced the lowest proportion of mouse specific reads,
ranging from 4% to 7%. In contrast, shotgun whole genome
sequencing produced the highest number of reads that uniquely
aligned to the mouse genome, which corresponded to 20% of the
total number of reads (Figure 2). The homologous number of
reads, i.e., those reads that aligned to both the human and the
mouse genome, was found to be similar for all methods, ranging
from 4% (RNA-Seq) to 1.5% (Exome-capture). A complete
summary of the alignments performed is described in Table S2.
Whole-genome analysis
As expected, the sequence depth of coverage of the samples
subjected to low-coverage whole-genome sequencing was above 3
times for all analysed samples (Table S3 A). However, the depth of
coverage of the xenograft sample was severely affected by mouse
contamination and produced the lowest value of the 3 samples
both for mean depth of coverage (3.3 times) and percentage of
reads covered at least 3 times (Table S3 A).
Copy number variation analysis of both the cell line and
xenograft samples produced highly similar results when the
peripheral blood sample was used as control (Figure 3 A). A total
of 578 and 470 somatically acquired copy number alterations were
observed for the cell line and xenograft samples respectively.
These differences were mainly due to the subtle differences in the
depth of coverage of the genomic regions assessed and most of
them correspond to focal copy number gains or losses in the
middle of diploid regions (Figure 3 B). As observed in Figure S1,
both the cell line (Figure S1 A) and xenograft (Figure S1 B)
samples produced highly similar CNV profiles for all the analysed
chromosomes. A detailed CNV profile of both samples can be
found in Datasets S1 and S2. A similar pattern was observed for
beta allele frequency profiles for both sample types (Figure 3 C).
Comparable results could be observed for intra- and inter-
chromosomal rearrangements (Figure 3 A), where over 70
rearrangements for both samples were detected. An example of
inter-chromosomal rearrangements was found between BAGE4,a
candidate gene encoding tumour antigens, and MLL3, a member
of the myeloid/lymphoid or mixed-lineage leukemia (MLL)
family. A complete list of the intra- and inter-chromosomal
rearrangements common to both cell line and the xenograft
samples can be found in Dataset S3.
The data presented above supports our hypothesis that a
thorough CNV and structural variant analysis can be performed
when both the cell line and xenograft samples were used. We
found that when correctly accounting for mouse-specific contam-
ination, the results obtained using uncontaminated cell lines can
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be accurately reproduced using xenograft samples, with the
additional benefits of the usage of an in vivo model.
Exome sequencing analysis
A mean sequence depth of coverage in the targeted captured
regions in all samples of over 100 times was achieved, with more
than 80% of the bases covered at least 30 times (Table S3 B). In
the cell line and the xenograft samples, 68.5 and 74.7 percent of
the targeted exome regions were covered at least 50 times, with a
mean sequence depth of coverage of 109 and 136 times
respectively. Sequence analysis across all three samples (i. e.,
peripheral blood, cell line and xenograft) detected a total of 53,186
(52,429 known and 757 novel) SNPs. Those variants that were
found in the peripheral blood were considered of germline origin,
and were no further processed for tertiary analysis.
A total of 946 somatic variants, 351 of these novel, were
common to both the cell line and xenograft samples (Figure 4 A).
Of these, 886 were single base substitutions, 28 were insertions and
32 were deletions (Figure 4 B). A complete list of the somatic
mutations detected is described in Dataset S4. Mutation class
analysis showed G.A/C.T transitions were the most common
(33%) followed by A.G/T.C transitions (23%) and G.T/C.A
transversions (20%) (Figure 4 C). Overall, this pattern was similar
to that reported by Pleasance et al [30].The previously described
TP53 splice acceptor disrupt and RB1 C706F point mutation,
characteristic of SCLC, [30], were detected both in the cell line
and xenograft samples.
For the 946 variants common to both cell line and xenograft,
the SnpEff effect predictor reported a total of 1806 (Figure 5 A &
B). For the purpose of this analysis, we reported the effect for all
possible gene transcripts, thus the total number of reported
variants differs from the total number of effects found. The most
represented effects categories, when classified by type, were those
corresponding to introns (721), non-synonymous coding (305) and
synonymous coding (170) (Figure 5 A). When the variant effects
were classified by region, intron and exon regions, as expected,
were the most significantly represented (Figure 5 B). A description
of moderate and high impact SNPs predicted effects for the first
affected transcript is described in Dataset S5.
Sixty-four somatic variants unique to the xenograft were
identified (Figure 4 B). Of these, only 15 were non-synonymous
coding variants. In all cases, the variants were heterozygous, and
Figure 2. Summary of the results produced by the proposed strategy to isolate and identify species-specific NGS reads in human
xenografts. For each read category, the proportion (%) of the total number of reads is specified.
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SnpEff predicted a moderate effect on protein function (Table S4
A). These variants affected gene transcripts of the following genes:
ESPN, KAZN, APEH, MUC20, MUC17, AQP7, ZNF808 and
LUZP4. In order to identify the cause of these discrepancies
between the variants detected in the cell line and the xenograft
samples, the genomic regions surrounding the variants detected
were examined. In order to exclude the possibility that these
variants arose from contaminating mouse sequence, we performed
the following analysis. First, we isolated the sequencing reads
adjacent to the region of interest within a range of 1,000bp (See
Figure S2 for detailed examples). Pairwise local alignments of these
regions between the human and mouse genomes showed that a
global alignment could not have been possible between the
analysed sequencing reads and the mouse genome (Figure S2).
Next, we attempted to align these reads to the mouse genome. No
alignments were produced. These data show that the coding-
region variants unique to the xenograft were of human origin.
Since genetic heterogeneity is now considered a cardinal feature
of many cancer types [31–33], we wondered whether these
xenograft-specific variants could be detected in the original cell
line dataset. Detailed inspection of the sequencing reads and
sequence depth-of-coverage of relevant regions revealed that the
great majority (9 out of 15) of these variants were detectable, but
were below the allele frequency threshold of 0.2 (Figure S3 &
Table S4 A). For variants not detected in the cell line, either the
sequence depth of coverage was below 10 times or the alternative
Figure 3. Copy number variations, inter and intra-chromosomal rearrangements and B allele frequencies of NCI-H209 cell line and
a xenograft tumour derived from it. (A) Circos plot representing copy number variations, inter and intra-chromosomal rearrangements of NCI-
H209 cell line and a xenograft tumour derived from it. Copy number variations (red, gain; green, loss) were calculated based on coverage using the
correspondent peripheral blood as control. Inter and intra-chromosomal rearrangements are represented in blue (inter-chromosomal) and dark blue
(intra-chromosomal). (B, C) Detailed profile of copy number variations and B-allele frequencies of chromosome 1 from the analysed cell line and
xenograft. As described above, the correspondent peripheral blood was used as control for both type of analysis. Copy number profiles are shown in
red (gain), green (loss) and grey (no change). LOH are shown light blue.
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allele nucleotide was not observed (Table S4 A). These data
support the conclusion that the variants unique to the xenograft
arose as a result of clonal expansion from a heterogeneous cell line
population, or new variants arising from spontaneous background
mutations.
A further 74 variants were identified in the cell line, but not in
the xenograft sample (Figure 4 B). Of these, 9 (RHOA, MUC17,
TRIM22, UNC93B1, MAML2, HIF1A, FAM18B2 and GPR64)
resulted in non-synonymous coding region changes with a
predicted moderate impact on protein function (Table S4 B). All
of these discrepant variants were found to be heterozygous (Table
S4 B). A comparison of the sequencing reads and sequence depth
of coverage of these regions revealed similar coverage in both cell
line and xenograft sample (Table S4 B & Figure S4). Using a
similar approach to that taken for the xenograft-specific variants,
we determined that in all but one case, the cell line-specific variant
could be readily detected in the xenograft, but once again were
below the same allele frequency threshold. Since these reads were
identified in a pure human cell line population, we conclude that
cells containing these discrepant variants are represented at lower
frequency in the xenograft, rather than as a result of mouse
contamination or variation in sequencing depth.
The number of discordant variants detected for each sample –
64 xenograft specific versus 74 cell line specific variants – may have
biased the known-to-novel ratio observed in the xenograft (Figure
4 B). This sample ratio is close to 1:1, higher than the observed for
the cell line specific and common cell line - xenograft variants
which is below 1 (Figure 4 B).
The data set from the xenograft sample produced the highest
mean sequence depth of coverage and 75% of the sequenced bases
were covered at least 50 times. The great majority of somatic
variants were detected in both cell line and xenograft, whereas
variants that were uniquely detected to either in the cell line or the
xenograft represented a minor proportion with no significant effect
on translation of mRNA splicing. Taken together, these data show
that exome-capture sequencing in xenograft models yields highly
accurate and reproducible detection of significant coding-region
variants.
Transcriptome analysis
Human-specific transcriptome analysis of three SCLC primary
xenograft models (LX22, LX33 and LX36) showed a strong
correlation (Spearman correlation = 0.75, P,0.001) with a
previously published gene-expression array data set in the same
Figure 4. Somatic variants profile of the NCI-H209 cell line and a xenograft tumour derived from it. Number of known and novel
variants (A) and variant types (B) found to be common to both the cell line and xenograft and those detected only in the cell line and xenograft. (C)
Quantification of the six possible mutation classes.
doi:10.1371/journal.pone.0074432.g004
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tumor models using human-specific cDNA probesets [4] (Figure 6
A), thus independently validating our species-specific strategy.
A gene expression correlation analysis between a recently
published SCLC primary tumors RNA-Seq experiment [34] and
the human-specific RNA-Seq reads of SCLC primary xenograft
models, showed positive correlation between both datasets
(Spearman correlation = 0.68, P ,0.001) (Figure 6 B). Isolated
mouse-specific reads from the RNA-Seq experiment were
compared with a laser micro-dissected human breast cancer
stroma-specific gene expression array dataset [19]. As shown in
Figure 6 C, a positive correlation between mouse-specific RNA-
Seq expression data and the stroma-specific cancer gene signature,
Figure 5. Variants classification by type of predicted effect (A) and genomic region affected (B).
doi:10.1371/journal.pone.0074432.g005
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determined by the expression array analysis of laser micro-
dissected human breast cancer tissue, was observed. As previously
reported [19], genes highly expressed in cancer stroma compared
to normal stroma, such as BGN,COL1A1,COL1A2,COL5A1,FN1,
NID2,COL10A,COL11A1,COL3A1,MMP2,POSTN,SPARC,DST
and THBS2, produced high number of RPKM and positively
correlated with the gene expression array data (Figure 6 C). At the
same time, genes found to be down-regulated for the same
comparison, namely FGF1,IGJ,PTN,MGP,CHI3L1,DMD,
MMP7 and EFEMP1, were found to have low expression levels
(Figure 6 C). Expression levels of FBLN1,FBN1,CFDP1 and NID2
were found to be low, in contrast to what reported previously [19].
The analyses described above convey and support two main
hypotheses. First, that there was a high cross-platform correlation,
microarray versus RNA-Seq, when expression was analyzed in a
previously described model, i. e., a primary xenograft model of
SCLC [4]. Secondly, the positive correlation found between the
previously published SCLC primary tumours [34] and the human-
specific reads plus the positive correlation between breast cancer
stroma-specific gene expression array dataset [19] and the mouse-
specific reads, verifies the main hypothesis of this work where host
Figure 6. Comprehensive correlation analysis between the RNA-Seq and Affymetrix expression array platforms. (A) Comparison of
gene expression detected by RNA-Seq and Affymetrix expression array platforms for identical SCLC samples (mean, n =3, P,0.01). (B) Comparison of
the gene expression between SCLC primary tumours [34] (Y axis, mean, n = 15) and primary xenografts (X axis, mean, n = 3) (P,0.01). (C) Comparison
of gene expression detected by Affymetrix array of micro-dissected human cancer stroma [19] (Y axis, mean, n = 28) and mouse-specific RNA-Seq
expression data in the SCLC xenograft models (X axis, mean, n = 3) (P,0.01).
doi:10.1371/journal.pone.0074432.g006
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and graft reads can be unequivocally identified and separately
analyzed.
Discussion
To date, complex NGS analyses of cancer have relied on
conventional cell line models, or fresh-frozen surgical specimens.
Although cell lines provide high-quality, tumour-specific RNA and
DNA, the process of adapting to adherent, serum-dependent
culture conditions results in irreversible transcriptional, epigenetic
and genomic changes [4–7], which can limit the interpretation of
NGS studies. Suitable freshly isolated surgical material is limited
by stromal contamination and tissue quality [1], and the fact that
resected cancers become over-represented in NGS studies. In
addition, although complex mathematical modeling can be used to
correct for sample purity and allele frequency in exome
sequencing [2,35], complex tissue samples remain a major
challenge for whole-genome analysis. Our results also suggest that
much larger and more complex stroma-specific gene expression
studies can be undertaken to further validate primary xenograft
models in tumours such as SCLC.
Using an in-silico workflow based on short-read aligners, such as
Tophat-Bowtie and BWA, we have shown that xenograft models
can be used for highly accurate, sensitive and specific NGS at a
whole genome, exome and transcriptome level. Despite significant
mouse contamination in both DNA and RNA analysis, we were
able to derive highly accurate sequence data that was internally
consistent when comparing xenografts to matched cell lines, and
also replicated NGS and array-based analyses of identical cell and
xenograft lines generated independently.
Conway et al. [9] recently described a classification technique
called Xenome. This tool allows a pre-processing and further
classification of high throughput sequencing reads using a k-mer
decomposition of the host and graft reference genome sequences.
Once processed, reads are classified into four categories: reads
originated from the host tissue, reads originated from the graft,
reads that could have been originated from both type of tissues and
reads which origin could not be attributed to either of them [9].
They also described an alternative classification strategy based on
the Tophat splice junction mapper [15]. Firstly, human, mouse
and xenograft RNA-Seq read sets were aligned to the host
genome, then these were subsequently and independently aligned
to the graft genome to finally post-process and classify the aligned
reads into four types: host, graft, both and neither [9]. A method
that simultaneously maps RNA-Seq sequencing reads to a merged
reference combining both the host and graft transcriptomes has
been described by Raskatov et al [10]. The authors argue that a
minimum of 40% of the sequencing reads could be attributed to
host-specific reads. The methodology described in our work could
not identify such a high proportion of host-specific reads and
agrees with what is been previously reported by the studies
described in Conway et al. [9].
Our strategy expands the short-read aligner method [9] and
uses widely used mapping tools, such as Bowtie-Tophat and BWA
for classification purposes. By utilising a short-read aligner
methodology, we successfully validated our strategy using the
three main NGS techniques: RNA-Seq, exome-capture and low-
coverage whole-genome sequence. In spite of being a more
conservative approach than Xenome, i. e., the number of reads
which fall into the both category is higher, our strategy is more
robust when the certainty of the detections made is prioritized.
Additionally, the customized modifications that can be made to
the aligner parameters, such as seed length, number of mismatches
in the seed and minimum mapping quality, could become an
additional advantage supporting the robustness of the methodol-
ogy. Although this approach was previously reported for RNA-Seq
[9,10], the authors did not describe a detailed workflow for the
deep sequencing technologies mentioned above. Our strategy is
based on short-read aligners with the advantage of flexible and
customizable stringency. In addition, our classification/filtering
process can be performed at the aligning stage, avoiding extra
computing and storage requirements.
Rather than being a disadvantage, our data support the idea
that the species-specific tumour-stromal interface innate to
xenograft models allow us to more sensitively and specifically
detect tumour-specific variants without the need for extra depth or
complex algorithms needed to account for human stroma. The use
of primary xenograft models derived from the overwhelming
numbers of patients with inoperable solid tumours may therefore
represent a useful platform for complex and informative NGS
research.
Supporting Information
Figure S1 Copy number variation analysis. Complete
human chromosome profile of the of CLH209 (A) cell
line and a xenograft tumour derived from it (B).
(PDF)
Figure S2 Analysis of xenograft-specific variants. Local
pairwise alignment of the human (hg19) and mouse
(mm9) genomes compared against the sequencing read
alignments of genomic regions surrounding the variants
detected. Representative detected variants are shown for AQP7
(A), APEH (B), MUC17 (C) and MUC20 (D) genes. Genomic
locations for the variants shown are described in supplementary
Table 4 A. In the local pairwise alignments, vertical lines, and the
number above them, represent sequence gaps and its length
respectively; dots represent conserved human-mouse sequences.
Variant position is highlighted by black parallel bars. Nucleotide
residues are shown in red (thymine), blue (cytosine), green
(adenine) and yellow (guanine). Heterozygous variants are
indicated in the depth of coverage track and show both reference
and alternative alleles. Forward and reverse sequencing reads are
shown in pink and blue respectively. Sequence base mismatches
are highlighted with its corresponding nucleotide colour.
(PDF)
Figure S3 Analysis of xenograft-specific variants. Se-
quence depth of coverage and allele frequency compar-
isons between the xenograft and cell line samples. Both
samples were aligned to the human reference genome hg19.
Representative detected variants are shown for AQP7 (A), APEH
(B), MUC17 (C) and MUC20 (D) genes. Genomic locations for the
variants shown are described in supplementary Table 4 A.
Variants position is highlighted by black parallel bars. Nucleotide
residues are shown in red (thymine), blue (cytosine), green
(adenine) and yellow (guanine). Heterozygous variants are
indicated in the depth of coverage track and show both reference
and alternative alleles. Forward and reverse sequencing reads are
shown in pink and blue respectively. Sequence base mismatches
are highlighted with its corresponding nucleotide colour.
(PDF)
Figure S4 Analysis of cell line-specific variants. Se-
quence depth of coverage and allele frequency compar-
isons between the cell line and xenograft samples. Both
samples were aligned to the human reference genome hg19.
Representative detected variants are shown for HIF1A (A),
TRIM22 (B), GPR64 (C) and MAML2 (D) genes. Genomic
Next-Generation Sequencing and Xenograft Models
PLOS ONE | www.plosone.org 10 September 2013 | Volume 8 | Issue 9 | e74432

locations for the variants shown are described in supplementary
Table 4 A. Variants position is highlighted by black parallel bars.
Nucleotide residues are shown in red (thymine), blue (cytosine),
green (adenine) and yellow (guanine). Heterozygous variants are
indicated in the depth of coverage track and show both reference
and alternative alleles. Forward and reverse sequencing reads are
shown in pink and blue respectively. Sequence base mismatches
are highlighted with its corresponding nucleotide color.
(PDF)
Table S1 Summary of the total and post QA/QC
number of sequenced reads from each NGS experiment
performed. Control: peripheral blood BL209, Cell line: NCI-
H209; Xenograft: xenograft sample derived from the NCI-H209
cell line. LX22, LX33 and LX33: SCLC primary xenograft lines
LX22, LX33 and LX36. The number of mapped reads for the
xenograft samples are human-specific only. PE: pair-ends.
(PDF)
Table S2 Summary of the alignments statistics of each
NGS experiment performed. LX22, LX33 and LX33: SCLC
primary xenograft lines LX22, LX33 and LX36.Control:
peripheral blood BL209, Cell line: NCI-H209; Xenograft:
xenograft sample derived from the NCI-H209 cell line. The
number of mapped reads for the xenograft samples are human-
specific only. For the exome capture and low-coverage whole
genome analyses of the xenograft sample concordantly paired
reads were analyzed.
(PDF)
Table S3 Summary of the depth of coverage obtained
for the exome capture (A) and low pass whole genome
(B) experiments. Control: peripheral blood BL209, Cell line:
NCI-H209; Xenograft: xenograft sample derived from the NCI-
H209 cell line. The results of the xenograft sample only represent
those reads that were human specific. Depth of coverage was
estimated on whole reads.
(PDF)
Table S4 Table describing sample-specific single nu-
cleotide variants. (A) Xenograft-specific non-synonymous
coding variants. (B) Cell line specific non-synonymous coding
variants. For sample-specific variants, approximate read depth is
shown (reads with MQ =255 or with bad mates were filtered).
Read and allelic depth of the sample were the variant was not
identified were calculated on de-duplicated reads. NSC: non-
synonymous coding variant.
(PDF)
Dataset S1 Detected copy number variations in the
NCI-H209 cell line sample.
(PDF)
Dataset S2 Detected copy number variations in the
xenograft sample derived from the NCI-H209 cell line.
(PDF)
Dataset S3 Inter- and intrachromosomal rearrange-
ments common to both the NCI-H209 cell line and its
derived xenograft sample.
(PDF)
Dataset S4 Somatic variants detected in both the cell
line and xenograft samples.
(PDF)
Dataset S5 SNPs effects common to both the cell line
and xenograft samples (only one transcript of the
moderate and high impact effects are reported.
(PDF)
Acknowledgments
We thank the Australian Genome Research Facility, the Australian Cancer
Research Foundation - Centre for Cancer Genomic, the Medicine Monash
Health Translation Precinct - Medical Genomics Facility and Peter
MacCallum Cancer Centre Molecular Genomics Facility for technical
support.
Author Contributions
Conceived and designed the experiments: FJR RWT LGM DNW.
Performed the experiments: KDM JF SB TB LGM. Analyzed the data:
FJR KB JL ET LGM DNW. Contributed reagents/materials/analysis
tools: PC. Wrote the paper: FJR RWT LGM DNW. Experimental advice:
DMT CDP LM. Manuscript proof-reading: DMT CDP LM.
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